Evolutionary production planning and scheduling
vorgelegt von Dipl.-Ing. Andreas Schöpperl
aus Berlin
von der Fakultät VII – Wirtschaft und Management der Technischen Universität Berlin
zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften
- Dr.-Ing.- genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. H. Hirth Berichter: Prof. Dr. H.-O. Günther Berichter: Prof. Dr. C. Bierwirth
Tag der wissenschaftlichen Aussprache: 26. August 2013
Berlin 2013 D83
and scheduling
eingereicht von:
Dipl.-Ing. Andreas Schöpperl
Dissertation
zur Erlangung des akademischen Grades
Doktor-Ingenieur (Dr.-Ing.)
Doktor der Ingenieurwissenschaften
Fakultät VII
Wirtschaft und Management Technische Universität Berlin
To my son. To my mother.
I wish to express my gratitude to Prof. Dr. Hans-Otto Günther for his support, guidance and for providing valuable insights and advice.
Additionally, I would like to thank the PhD committee for the assessment of this dissertation.
Furthermore, I offer my sincere thanks to Anna Barkhoff for her friendship, support and precious advice.
Finally, I wish to thank my family and friends for their on-going support and encouragement.
I. Concept 11
1. Introduction 12
1.1. Motivation . . . 12
1.2. Object of study . . . 15
1.3. Way of proceeding . . . 16
2. Production planning in a dynamic environment 17 2.1. Production planning environments . . . 17
2.2. Uncertainty sources in production planning . . . 18
2.3. Common production planning approaches . . . 20
2.3.1. Static and flexible planning . . . 20
2.3.2. Rolling horizon planning . . . 21
2.3.3. Robust planning . . . 23
2.3.4. Reactive planning . . . 25
2.4. Production plan evaluation . . . 26
2.4.1. Plan variation impacts . . . 27
2.4.2. Measuring plan variations . . . 28
2.4.3. Combining multiple measures . . . 29
2.5. Planning policies . . . 31
2.5.1. Periodical planning policies . . . 31
2.5.2. Event-based planning policies . . . 32
2.5.3. Hybrid planning policies . . . 32
2.6. Classification of an evolutionary production planning . . . 33
3. Evolutionary production planning concept 36 3.1. Characteristics . . . 36
3.1.1. Main characteristics . . . 36
3.1.2. Balancing evolutionary production planning goals . . . 40
3.1.2.1. Plan efficiency & variation trade-off . . . 40
3.1.2.2. Multi-step techniques . . . 44
3.1.3. Responsive evolutionary production planning . . . 46
3.1.4. Further characteristics . . . 50
3.1.5. Classification of evolutionary production planning applications . . 52
3.2. Evolutionary production planning system development . . . 54
II. Case studies 60
4. Case 1 - Evolutionary scheduling of a beverages bottling facility 61
4.1. Introduction . . . 61
4.2. Literature . . . 63
4.3. Model formulations . . . 65
4.3.1. Representation of time . . . 66
4.3.2. Scheduling model . . . 66
4.3.3. Schedule efficiency & variation objectives . . . 75
4.3.4. Compact scheduling model . . . 79
4.3.5. Model extension . . . 80
4.4. Experimental design . . . 81
4.5. Numerical study results . . . 87
4.5.1. Preliminary considerations and simulation excerpts . . . 88
4.5.2. Main results — strategy comparison . . . 92
4.5.3. Result details & parameter impacts . . . 98
4.5.3.1. Schedule variation & production cost trade-off . . . 98
4.5.3.2. Fixation of schedule elements . . . 99
4.5.3.3. Two-step strategies with production cost bounds . . . 103
4.5.3.4. Limited number of planning periods with schedule varia-tion consideravaria-tions . . . 105
4.5.3.5. Number of planning periods . . . 106
4.5.3.6. Scheduling policies . . . 107
4.5.3.7. Demand characteristics . . . 108
4.6. Case summary . . . 112
5. Case 2 - Evolutionary scheduling of chemical commodity products 115 5.1. Introduction . . . 115
5.2. Literature . . . 118
5.3. Model formulations . . . 119
5.3.1. Scheduling model . . . 119
5.3.2. Schedule efficiency & variation objectives . . . 129
5.3.3. Compact scheduling model . . . 131
5.3.4. Model extensions . . . 133
5.3.4.1. Inverse production sequences . . . 133
5.3.4.2. Variable production speed . . . 136
5.4. Experimental design . . . 138
5.5. Numerical study results . . . 145
5.5.1. Preliminary considerations . . . 145
5.5.2. Main results — strategy comparison . . . 147
5.5.3. Detailed results & parameter impacts . . . 152
5.5.3.1. Schedule variation & production cost trade-off . . . 153
5.5.3.4. Limited number of planning periods with schedule
varia-tion consideravaria-tions . . . 157
5.5.3.5. Inverse production sequences . . . 158
5.5.3.6. Number of planning periods . . . 159
5.5.3.7. Scheduling policies . . . 160
5.5.3.8. Demand characteristics . . . 161
5.6. Case summary . . . 165
6. Concluding remarks and outlook 167
1.1. Dissimilarity of successive plans . . . 13
1.2. Additional coordination and planning efforts and costs . . . 14
1.3. Case-specific planning system implementation . . . 15
2.1. Categorization of production planning environments . . . 18
2.2. Uncertainty causes . . . 19
2.3. Rigid planning . . . 20
2.4. Rolling horizon planning . . . 22
2.5. Robust planning for a worst case scenario . . . 23
2.6. Robust planning for estimated plan realizations . . . 24
2.7. Robust planning for estimated disruptions . . . 24
2.8. Reactive planning for a machine break-down . . . 26
2.9. Hierarchical grouping of plan variation measures . . . 28
2.10. Plan variation measure example . . . 29
2.11. Planning policy categories . . . 31
2.12. Periodical planning policy . . . 31
2.13. Event-based planning policy . . . 32
2.14. Hybrid planning policy . . . 33
2.15. Common production planning approaches & typical characteristics . . . . 34
3.1. Evolutionary production planning logic . . . 38
3.2. Evolutionary production system planning information overview . . . 40
3.3. High vs. low schedule variation . . . 41
3.4. One dominant goal type . . . 42
3.5. One quantity unit for all objectives . . . 43
3.6. Example trade-off curve . . . 43
3.7. Example trade-off curves for different data sets . . . 44
3.8. Time-based goal balance . . . 44
3.9. Time dependent goal dominance . . . 45
3.10. Goal as constraint example . . . 47
3.11. Normalization of goal elements . . . 48
3.12. Periodical planning policies . . . 49
3.13. Event-based planning policies . . . 50
3.14. Hybrid planning policies . . . 50
3.15. Evolutionary planning system development . . . 55
3.18. EPPSF-Planner example 2 . . . 57
3.19. General EPPSF simulation logic . . . 59
4.1. Example of a beverage bottling production cycle . . . 62
4.2. Time representation . . . 66
4.3. EPPSF-Planner - beverages bottling case . . . 82
4.4. Generation of demand orders . . . 85
4.5. Generation of order cancellations . . . 86
4.6. Simulation run excerpt — base demand . . . 88
4.7. Simulation run excerpt — demand change . . . 89
4.8. Simulation run excerpt — production costs . . . 90
4.9. Simulation run excerpt — lot starting time variation costs . . . 90
4.10. Simulation run excerpt — schedule variation measures . . . 91
4.11. Average solution time per scheduling iteration . . . 91
4.12. Cost strategy & deterministic planning comparison . . . 92
4.13. Strategy comparison — total production & schedule variation costs . . . 94
4.14. Strategy comparison — lot variation costs . . . 95
4.15. Strategy comparison — sub-lot variation costs . . . 95
4.16. Strategy comparison — production costs . . . 96
4.17. Schedule variation & production cost objective weighting . . . 96
4.18. Schedule variation & production cost trade-off (SlStSiCost strategy) . . . 99
4.19. Schedule variation & production cost trade-off (SlSiCost strategy) . . . . 100
4.20. Schedule variation & production cost trade-off (SlStCost strategy) . . . . 100
4.21. Schedule variation & production cost trade-off (LStCost strategy) . . . . 101
4.22. Schedule variation & production cost trade-off (SlSeCost strategy) . . . . 101
4.23. Schedule variation & production cost trade-off (LSeCost strategy) . . . . 102
4.24. Sub-lot starting time & size variation cost trade-off (SlStSi strategy) . . . 102
4.25. Schedule fixation strategies . . . 103
4.26. Production cost bounds — schedule variation costs . . . 104
4.27. Production cost bounds — production costs . . . 104
4.28. Production cost bounds — SlStCb strategy . . . 105
4.29. Limited number of planning periods with schedule variation consideration 106 4.30. Planning horizon length & production costs . . . 107
4.31. Planning horizon length & lot starting time variation costs . . . 108
4.32. Scheduling policy & cost measures . . . 109
4.33. Demand level & production costs . . . 110
4.34. Demand level & lot starting time variation costs . . . 110
4.35. Demand change offset impact . . . 111
4.36. Demand change level impact . . . 112
4.37. Demand granularity impact . . . 113
5.1. Exemplary changeover matrix . . . 117
5.2. Changeover matrix excerpt — product cluster with sub-clusters . . . 117
5.4. Generation of demand orders . . . 142
5.5. Generation of order cancellations . . . 143
5.6. Simulation run excerpt - schedule variation measures . . . 146
5.7. Average solution time per scheduling iteration . . . 146
5.8. Cost strategy & deterministic planning comparison . . . 147
5.9. Strategy comparison — total production & schedule variation costs . . . . 149
5.10. Strategy comparison — schedule variation costs . . . 150
5.11. Strategy comparison — production costs . . . 150
5.12. Schedule variation & production cost objective weighting . . . 151
5.13. Schedule variation & production cost trade-off (LStSiCost strategy) . . . 154
5.14. Schedule variation & production cost trade-off (LSiCost strategy) . . . . 154
5.15. Schedule variation & production cost trade-off (LStCost strategy) . . . . 155
5.16. Schedule variation & production cost trade-off (LSeCost strategy) . . . . 155
5.17. Schedule fixation strategies . . . 156
5.18. Production cost bounds — schedule variation costs . . . 157
5.19. Production cost bounds — production costs . . . 158
5.20. Limited number of planning periods with schedule variation consideration 159 5.21. Product sequence impact . . . 160
5.22. Planning horizon length & production costs . . . 161
5.23. Scheduling policy & cost measures . . . 162
5.24. Demand level impact . . . 163
5.25. Demand granularity impact . . . 164
5.26. Impact of demand change occurrence . . . 164
3.1. Case study classification . . . 53
4.1. Model parameterization examples . . . 76
4.2. Production system parameters . . . 83
4.3. Demand data overview . . . 93
5.1. Model parameterization examples . . . 130
5.2. Production system parameters . . . 140
5.3. Production system parameters — Product changeover . . . 141
This work arose from research activities investigating the possibilities of a more contin-uous development of plans in modern production planning and scheduling applications. The following section 1.1 states the underlying motivation for this research while section 1.2 isolates the object of study and defines research goals. Finally, the last section 1.3 of this chapter describes the way of proceeding and the contents of the remaining chapters of this work.
1.1. Motivation
Nowadays, companies often encounter highly competitive markets in very dynamic en-vironments. Indeed, companies in many industries are faced with an increased product variety and complex, fast changing and ever more sophisticated environments, increas-ing demand variability as well as decreasincreas-ing order timeframes, while the need to remain competitive results in an increased cost pressure.
Furthermore, many companies are shifting from Stock (MTS) to Make-To-Order systems (cf. Kaminsky and Kaya (2009)), in order to reduce inventory holding and associated costs. Production is thus based on actual customer demands instead of demand forecast. In consequence, a constant change in consumer behavior and demand variations (often on short notice) result in a requirement of quick responses and frequent planning decisions, while still remaining competitive. An on-going challenge is e.g. the need to quickly respond to demand-related influences, such as short-term customer or-ders and order modifications. A further characteristic of these competitive markets and the sophisticated customer side (cf. Adebanjo and Mann (2000)) is the need to quote short, reliable lead times (cf. Kaminsky and Kaya (2009)). Examples of markets with characteristics as described above may be found e.g. in the fast-moving consumer goods industry. It is a competitive industry, characteristic are low margins for relatively high volumes, a high product variety, small order sizes, short lead times, cost pressure and high demand variability. Quick responses to changing consumer behavior are required, in terms of demand, quality, flexibility, service and price (cf. e.g. van Dam et al. (1993), Keh and Park (1997)).
Classically, if cost (or time, respectively) efficient planning is pursued, in order to produce with minimal costs, no attention is payed to previously released production plans. Respective planning processes typically result in a complete regeneration of production plans without consideration of former plans. Furthermore, the outcome of corresponding planning methods will usually react rather sensitive to even small data changes. In conjunction with a high demand variability, necessitating frequent planning decisions,
Arrival of a new order
Modification of an order
...
...
... Scheduled production lots (colors indicate specific products)
Successive Production schedules
Figure 1.1.: Dissimilarity of successive plans
this leads to an increased variation in resulting planning decisions and rather dissimilar or unrelated seeming successive production plans (cf. figure 1.1).
On the other hand, alterations between two successively released production plans do not only concern the production system which is executing these plans but usually also influence further planning activities (e.g. material sourcing, personnel planning or financ-ing) within a company which rely on a released production plan. Thus, if alterations to an already released production plan are made, further coordination will usually arise (e.g. with other company departments) when an adjusted plan is verified and released. Furthermore, an altered plan then may necessitate the execution of other dependent planning activities to incorporate the plan alterations, which in turn strains correspond-ing personnel and planncorrespond-ing capacities (cf. figure 1.2). In consequence, additional costs arise, due to the occupation of planning capacities but also as a result of alterations to the respective plans made by dependent planning activities (e.g. additional costs for ma-terial delivery on short notice). In turn, alterations to these plans of dependent planning activities influence further dependent planning activities as well, resulting in even more additional planning, coordination and planning efforts and costs. In fact, some of these plan alterations may again affect the validity of released production plans and require further coordination and potentially renewed production planning activities.
For these reasons the described interdependencies have to be considered in produc-tion planning consideraproduc-tions as well. Ideally an integrated planning might encompass all relevant coherences in order to accomplish an integrated efficient planning of all respec-tive planning activities within a company. However, such integrated planning models are usually far too complex to allow for an integrated approach. Furthermore, not all of the required information regarding these interdependencies and resulting costs will be available or even ascertainable in a specific planning case. Thus, it may not even be possible to completely assess realistic costs as a result of specific plan adjustments. Com-monly, this information deficit is countered by the estimation and application of penalty costs to plan alterations, by the fixation of plan components or by focusing solely on the minimization of certain plan alterations or costs. The estimation quality of penalty costs is important in the first type of approaches, as respective planning methods may react rather sensitive to penalty cost variations. A fixation of plan components restricts respective plan alterations without the need for cost estimations, though the time period in which plan components may be fixed is limited by the due dates and occurrence of
ůƚĞƌĂƚŝŽŶƐ ƚŽ ƉƌĞǀŝŽƵƐůLJ ƌĞůĞĂƐĞĚ ƉƌŽĚƵĐƚŝŽŶ ƉůĂŶƐ ĂĚĚŝƚŝŽŶĂů ŽŽƌĚŝŶĂƚŝŽŶ ĞĨĨŽƌƚƐ ^ĞĐŽŶĚĂƌLJ ƉůĂŶŶŝŶŐ ĂĐƚŝǀŝƚŝĞƐ ŽƐƚƐ WůĂŶ ĂůƚĞƌĂƚŝŽŶƐ WƌŽĚƵĐƚŝŽŶ ƉůĂŶŶŝŶŐ
Figure 1.2.: Additional coordination and planning efforts and costs
(short-term) demand variations to be planned. While a focus on cost minimization does not consider described interdependencies in planning and perhaps implicitly assumes a handling of occurring demand variation events during plan execution, the exclusive min-imization of plan alterations focuses on plan repairs and may not be very cost efficient if frequent plan adaptions become necessary.
In practical applications, it is e.g. not uncommon to focus solely on the reduction of plan alterations, as reliable plans are desired in order to reduce coordination efforts. This repair of production plans is then usually coupled with a periodical complete regeneration of new production plans. However, frequent occurrence of new demand information, such as new orders or order modifications, induces the need for an on-going inclusion of respec-tive demand information into a continuously developed production plan. On the other hand, due to cost pressure in highly competitive markets, cost considerations cannot be neglected in the development of production plans. Hence a requirement in the considered highly dynamic environments and competitive markets is the continuous adaptation of production plans in a cost-efficient but also reliable way under constant consideration of new demand-related information. The way such a planning goal is pursued is, of course, specific to each individual planning application, including implemented planning meth-ods, consideration of specific costs and plan alterations as well as the desired balance of cost-efficiency and plan reliability (cf. figure 1.3).
While research work exists which considers the inclusion of new demand informa-tion into an existing producinforma-tion plan (usually producinforma-tion scheduling problems) for some planning applications, there is still considerable demand for research on further planning
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Figure 1.3.: Case-specific planning system implementation
applications and case studies intent on a more continuous plan development, in respect to plan efficiency and reliability, in challenging markets. Furthermore, while a specific planning system implementation is very dependent on the individual planning applica-tion in focus, the formulaapplica-tion of a general concept summarizing and classifying common characteristics of such planning applications is important in order to support a general overview and categorization (cf. Vieira et al. (2003) for a framework for the related research field of rescheduling problems).
1.2. Object of study
This work aims at the formulation of a general concept describing the continuous de-velopment of production plans under consideration of frequent demand variations in competitive markets. This planning field is called “Evolutionary production planning” in the remainder of this work. Considered evolutionary production planning problems will usually reside on the operative planning level, with short-term (or short- to mid-term) planning timeframes, though depending on each specific planning application, the defi-nition of what is considered as a short timeframe may vary considerably (e.g. a number of shifts, days, weeks etc.).
Beside the formulation of a general concept, in the core part of this work specific production planning applications are addressed, planning methods developed and nu-merical case studies conducted. A variety of planning strategies is compared as well as the sensitivity of planning results to environmental influences and planning param-eters. A framework supporting the design, implementation and evaluation of specific evolutionary planning systems is developed and applied in the investigated case studies. As applications of the evolutionary production planning concept, two case studies are
been considered in separate planning steps, lately increased attention has been given to integrated planning models in order to allow for a more efficient planning. The dis-cussed case studies are concerned with integrated lot-sizing and scheduling of beverages and chemical commodity products, respectively. Specifically, the block planning princi-ple as a practical tool for lot-sizing and scheduling product variants in a predetermined sequence is adopted for the modeling of the two cases. In conjunction with the second discussed application, characteristics such as series dependent as well as limited product changeovers are considered.
1.3. Way of proceeding
In chapter 2 existing concepts and planning approaches for a production planning in dynamic environments, found in the literature or in practice, are discussed. The chapter closes with a discussion of the relation as well as overlaps of these existing planning approaches with an evolutionary production planning and indicates the demand for a tailored concept focusing only on the specific aspects of evolutionary production planning applications. Chapter 3 then presents the general concept and planning framework for an evolutionary production planning.
In part II, the core of this work, two case studies, implementing the evolutionary planning concept for a beverages production (cf. chapter 4) and a chemical commodities production system (cf. chapter 5), are presented. In numerical studies, various planning strategies are evaluated and compared. Finally, in chapter 6 the insights gained during these research activities are summarized and future research possibilities are indicated.
environment
In practical applications, companies usually encounter a dynamic environment. When production activities are being planned, it is rarely the case that a once determined production plan remains unchanged until the end of the last production activity, specified by the plan. Instead companies are faced with the necessity to adapt their production plans to cope with new information regarding the dynamic environment, which becomes available as time progresses. Updated demand information, such as new, changed or even cancelled production orders, create the necessity to replan production activities. Other changes to the planning environment may be aspects of the production site, e.g. disturbances, such as machine break-downs, variable processing times etc.
In this chapter, important concepts and existing planning approaches concerned with production planning in a dynamic environments are discussed. The chapter then closes with an assessment of planning approaches with respect to a suitability to accomplish evolutionary planning goals.
2.1. Production planning environments
Production planning environments can be of static or dynamic nature. Static environ-ments have a finite set of demand eleenviron-ments and planning periods to be considered. In dynamic environments the set of demand elements and future planning time is infinite and available information is changing as time progresses. In addition, if the environment is static and deterministic, all relevant planning information is available and certain at the time of planning. If some information is uncertain, such as variable processing times for certain production tasks, the environment is called stochastic.
In a dynamic environment, demand related information is variable, albeit the kind of variability may differ in dependence on a specific planning problem considered (e.g. order arrival time, amount, due date etc.). Demand variability can be further distinguished by the possibility or impossibility of alterations to already available demand information (e.g. order cancellations, due date changes, order amount changes etc.). Again, in addition to demand related information, other information may be uncertain as well. For more information, the reader may confer Vieira et al. (2003) or Pfeiffer et al. (2007).
The type of environments that is the focus of this work is highlighted in figure 2.1. Note that the term “no alterations” in figure 2.1 means that while new planning information becomes available as time progresses, it is not subject to later alterations once it is known (e.g. incoming demand orders are not subject to later modification or cancellation).
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͙
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2.2. Uncertainty sources in production planning
As stated in 2.1, dynamic environments are characterized by uncertainties regarding future planning relevant information. These uncertainties can be demand related but may also have other sources. As a simple categorization, these uncertainty sources are distinguishable as being internal or external. A typical example of external sources are demand related influences on the environment. Internal sources refer to the production system itself (e.g. variable processing times, available production capacity etc.).
According to Aytug et al. (2005), uncertainties can be further categorized by intro-ducing the three dimensions cause, context and impact. Causes for uncertainties are attributed to objects, such as processes, machines, demand etc. and variability in re-spective states in which these objects may be in the future (e.g. normal production, machine break down, new demand order etc.). Furthermore, interdependencies between objects may evoke consecutive reactions of other objects if an objects changes its state.
Typical objects of uncertainty can be grouped into the categories of being demand-related, material-related or production resource/process-related (cf. figure 2.2). As dis-cussed before, demand-related uncertainty causes are within the main focus of this work. Examples for these are deviations from expected due dates and demand amounts due to order modifications by customers or realizations of predicted demands which differ from the predictions, order cancellations, new urgent orders or even changes in order priori-ties. Material-related uncertainty causes include variations concerning the quality (e.g.
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Figure 2.2.: Uncertainty causes
amount of rejects) and availability (e.g. material shortage, delivery delay) of required raw materials and other production input materials. Production resource/process-related uncertainty causes comprise processing variances (e.g. processing time and quality), function (e.g. machine failures) and capacity (e.g. personnel shortage) of production resources, among others (cf. Vieira et al. (2003); Neuhaus (2008); Gebhard and Kuhn (2009, p.29ff.)).
Furthermore, the impact of uncertainties is not only related to a specific object and state changes but also its context — the specific situation of the production system and environment at the time of a state change of an uncertainty object. Machine failures which occur during night shifts may be more serious then during day shifts, e.g. due to less personnel being available for repair. Processing times and quality may depend on the expertise of assigned personnel. Short-term demand changes are more serious to adjust to than demand changes which affect orders with due dates lying further in the future. Impacts of uncertainties comprise finishing time, quality and availability of products as well as availability and processing time of production facilities.
According to Aytug et al. (2005), uncertainty should be explicitly considered during problem modeling and execution of planning activities, including sources, impacts and interdependencies.
dŝŵĞ dŝŵĞ dŝŵĞ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ ϭ͗ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ Ϯ͗ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ ϯ͗ WůĂŶŶŝŶŐ ƚŝŵĞǁŝŶĚŽǁ ϭ WůĂŶŶŝŶŐ ƚŝŵĞǁŝŶĚŽǁ Ϯ WůĂŶŶŝŶŐ ƚŝŵĞǁŝŶĚŽǁ ϯ dŝŵĞ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ ϰ͗ WůĂŶŶŝŶŐ ƚŝŵĞǁŝŶĚŽǁ ϰ
Figure 2.3.: Rigid planning
2.3. Common production planning approaches
This section presents common planning approaches found in the literature, concerned with various aspects of production planning in a dynamic environment.
2.3.1. Static and flexible planning
Static planning assumes a static, deterministic environment — all relevant information is known in advance and not subject to changes. When performing a static planning, a plan is regarded as fixed once it is created. It is assumed that a plan is carried out as planned, until the end of the planned time period is reached. This approach is also called predictive planning in the literature, especially in the area of reactive planning. Schneeweiß (1992) also distinguishes between static and rigid planning approaches — a rigid planning being a static planning that is repeatedly performed in a dynamic environment, without attention to its dynamic nature (and the possibility of further alterations). Instead, the infinite set of planning periods is partitioned into distinct finite subsets for which a static planning is then executed (cf. figure 2.3).
While carrying out a static plan, it may of course become necessary to make ad-justments, but these are not considered in an explicit planning process, but implicitly assumed to being performed during plan execution. This approach has the advantage of simplicity and apparent reliability once a plan is created. Depending planning activities can rely on the apparent constancy of a released plan. On the other hand, the approach remains only realistic as long as hardly any changes to the planning information occur.
In contrast to a static planning approach, flexible planning techniques try to account for the dynamic nature of environments, incorporating changes to the available planning information by revising created plans or taking pro-active measures during plan creation.
In the following sub-sections, major planning concepts which apply — to one degree or another — flexible planning techniques, are discussed.
2.3.2. Rolling horizon planning
A typical planning technique is the classical rolling horizon planning approach. It is found in many planning areas, in the literature as well as in practice. It accounts for the dynamic nature of planning environments by the use of moving, overlapping planning time windows. As time progresses, production activities are repeatedly planned. Over-lapping planning periods account for the fact that planning information for overlapped periods may have changed since the last planning iteration and already created plans are in need of a revision. Figure 2.4 illustrates a rolling horizon planning.
Sethi and Sorger (1991) list the quality of demand forecasts, often declining with the distance in time of future planning periods, as one reason for requiring a gliding planning technique with overlapping planning time periods and frequent plan revisions. Rolling horizon planning is also used in other planning fields, such as financing. Rolling horizon approaches are most common in mid- or long-term planning application (cf. e.g. Liu et al. (2009)), but occasionally also in short-term planning applications (cf. e.g. Gomes et al. (2010)) If mid- or long term planning is performed, planned activities are typically only released and executed for the first planned period. If short term planning is performed, usually more than one planned period is released and may induce further replanning and coordination activities, if revised at a later time. Thus, when plans with overlapping periods are subsequently revised, these depending planning and coordination activities will have to be performed repeatedly.
In rolling horizon planning applications, usually a discrete time frame is used, dividing the planning time into a series of planning periods. Planning activities are then per-formed periodically, each time moving (rolling) forward the planning time window (or the planning horizon, respectively) by a specific number of planning periods, discarding expired periods and including an equal number of new periods at the end of the planning time window. The planning time windows, considered in each planning iteration, over-lap by a specific number of planning periods, defining the number of repeatedly revised periods. Thus, planning decisions in earlier planning periods are not included in the set of overlapping periods and binding, while decisions in later periods are preliminary and going to be revised during subsequent planning iterations. A classical argument support-ing this approach is that it enables the periodical update and correction of production plans by incorporation of new planning information. In consequence, plans for planning periods which have been planned before are discarded and completely new plans are cre-ated instead. For more information on rolling horizon planning confer Stadtler (1988); Steven (1994); Sethi and Sorger (1991); Kurbel (2005); Kistner and Steven (2001); Kurbel (2011); Schneeweiß (1992).
The length and number of planning periods varies depending on the planning area and level and of course the specific planning problem under consideration. Furthermore the length of planning periods can be either constant throughout the planning time window,
dŝŵĞ dŝŵĞ dŝŵĞ WůĂŶŶŝŶŐ ŚŽƌŝnjŽŶ WůĂŶŶŝŶŐ ƚŝŵĞǁŝŶĚŽǁ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ ϭ͗ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ Ϯ͗ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ ϯ͗ ZĞͲƉůĂŶŶŝŶŐ ŝŶƚĞƌǀĂů KǀĞƌůĂƉ ͬZĞǀŝƐŝŽŶ
Figure 2.4.: Rolling horizon planning
or the aggregation level of planning periods may vary, allowing for a detailed planning in earlier periods and coarser planning for later composite periods.
Typically, a new predictive plan is created at each planning iteration, as for a static planning, assuming all information to be known and not subject to changes — it may be viewed as a mix between a rigid and flexible planning. Created production plans for planning periods which have already been planned in previous planning iterations are usually not considered during a planning iteration — instead a complete replanning is performed.
The application of rolling horizons and rolling horizon decision making has been ad-dressed intensively in the past (cf. Sethi and Sorger (1991)). Rolling horizon planning is used in many applications in practice as well as in the literature (cf. e.g. Clark (2005b); Li and Ierapetritou (2010); Millar (1998); Clark and Clark (2000); Balakrishnan and Cheng (2009); Stauffer and Liebling (1997)). A lot of works in the literature are also concerned with the related problem of examining the impact of and finding an efficient length for the planning horizon parameter. For an overview, confer Chand et al. (2002). Another topic that has received considerable attention is concerned with the effect that planning methods tend to react rather sensitive to even slight changes in planning data, resulting in rather dissimilar plans because of slight data alterations. Consequently, frequent replan-ning due to rolling horizon techniques (especially frequent in short-term planreplan-ning) then often leads to a lot of changes to plans for planning periods which have been revised. This effect of plan variations is called nervousness (cf. Inderfurth and Jensen (1996); Kurbel (2011)) and has been studied extensively in the literature, especially in the area of material requirement planning (MRP) systems, but other areas as well (cf. Pujawan
dŝŵĞ ^ĐĞŶĂƌŝŽϭ͗ ϭ WƌŽĚƵĐƚŝŽŶŽƌĚĞƌƐ WƌŽĐĞƐƐŝŶŐƚŝŵĞ dŝŵĞ ^ĐĞŶĂƌŝŽϮ͗ ϭ Ϯ Ϯ dŝŵĞ ϭ Ϯ ^ĐĞŶĂƌŝŽϯ͗ ϯ dŝŵĞ ϭ Ϯ &ŝŶĂůƉůĂŶ͗ ϯ
Figure 2.5.: Robust planning for a worst case scenario
(2004); Kimms (1998); Kropp et al. (1983); Sridharan et al. (1987); Carlson et al. (1979); Blackburn et al. (1986); Federgruen and Tzur (1994); Ho and Ireland (1993, 1998); Kaipia et al. (2006)). Measures against planning nervousness include the application of penalty costs for alterations to the original plan (cf. e.g. Kazan et al. (2000)) as well as fixation planning periods or fixations of plan components (cf. e.g. Gomes et al. (2010)).
2.3.3. Robust planning
Robust planning approaches assume a non-deterministic (stochastic or dynamic) envi-ronment and try to anticipate disturbances by taking proactive measures to counter the impact of planning uncertainties. A production plan is created to be robust with the goal of minimizing the effect of major disturbances (usually due to resource-related disrup-tions) and simple process variances (e.g. variable processing times) on the plan validity in respect to performance measures in terms of efficiency or predictability. Ideally, a ro-bust plan should need none or only minor adjustments during execution, if a disturbance occurs.
A lot of literature on robust planning is focused on the area of machine scheduling and the minimization of disruptions to machine availability. For more information on robust planning, confer Aytug et al. (2005); Samsatli et al. (1998); Scholl (2001); Herroelen and Leus (2005); Gebhard and Kuhn (2009).
Several groups of solution strategies, dealing with robust planning, can be found in the literature. The first group of strategies considers a set of planning scenarios which differ in the realization of disturbances and tries to create a valid plan under the assumption of a worst-case scenario. Individual solutions of this worst-case scenario are rated by their performance over the whole set of considered scenarios (cf. e.g. Kouvelis et al. (2000)).
Figure 2.5 shows a simple planning example considering 3 scenarios which differ in the assumed processing time of order 1 and the occurrence of an urgent order 3. The final plan includes the assumptions of the worst case — the longer processing time assumed
dŝŵĞ ZĞĂůŝnjĂƚŝŽŶϭ͗ ϭ WƌŽĚƵĐƚŝŽŶŽƌĚĞƌƐ WƌŽĐĞƐƐŝŶŐƚŝŵĞ dŝŵĞ ZĞĂůŝnjĂƚŝŽŶϮ͗ ϭ dŝŵĞ ϭ Ϯ &ŝŶĂůƉůĂŶ͗ ͘͘͘
Figure 2.6.: Robust planning for estimated plan realizations
ϭ dŝŵĞ WĂƐƚďƌĞĂŬͲĚŽǁŶϭ͗ ϭ WƌŽĚƵĐƚŝŽŶŽƌĚĞƌƐ dŝŵĞ WĂƐƚďƌĞĂŬͲĚŽǁŶϮ͗ dŝŵĞ ϭ Ϯ &ŝŶĂůƉůĂŶ͗ Z DĂƐĐŚŝŶĞƌĞƉĂŝƌ ϭ ϭ Z ͘͘͘
Figure 2.7.: Robust planning for estimated disruptions
A second group of strategies tries to determine expected plan realizations and to min-imize the difference between predicted and realized plans in respect to a defined perfor-mance measure (cf. e.g. Wu et al. (1999)). Figure 2.6 shows a simple planning example considering 2 possible realizations for the processing time of a production order 1. The final plan includes an estimate based on those 2 realizations for the processing time.
The third group of strategies tries to estimate the effect of certain disruptions. The production plan is then created in a way that, if the regarded disruptions occur, the plan does not have to be adjusted during execution (cf. e.g. Mehta and Uzsoy (1998)). The impact estimation of certain disruption will usually be based on the past performance of regarded production resources. As an example, a planning strategy could try to estimate the impact of machine failures on the prolongation of process completion times, include this information into the planning method creating the predictive plan and thus ensure good estimates of realized completion times (e.g. by inclusion of buffer times). These strategies try to ensure good estimates of the realized production flow by lowering the resource capacity. Figure 2.7 shows a simple example considering 2 past machine break-downs with different repair times. The final plan includes a buffer time which is estimated from these past repair times.
Note that in approaches of the second and third group, the modeling of some planning parameters no longer treats these as deterministic but instead as random variables. Often, empirically determined statistical distribution functions are used in planning models.
The characteristics of robust planning described in this sub-section lead to the conclu-sion that such strategies are only applicable as long as actual disruptions and realized variances from estimated effects do not exceed a certain level. If disturbances occur, which have effects that are much stronger than estimated, or which are completely un-expected, the created production plan cannot be carried out as planned. Furthermore, there are often many different uncertainties effecting a production system, making it difficult to create plans which are robust in respect to all, or at least to a set containing the most important uncertainties and effected system parameters (cf. Neuhaus (2008)).
2.3.4. Reactive planning
Reactive planning assumes a production environment which is dynamic but production planning does not take proactive measures as in robust planning approaches. In case of a disturbance (e.g. a machine break-down), of an amount which makes a plan adjust-ment necessary, the current production plan is adjusted, incorporating the changes to the planning information. The main goal of reactive planning is the restoration of plan feasibility in case of occurring disturbances, albeit usually the retention of plan perfor-mance (in respect to defined planning goals) is desired as well. A replanning is initiated on a periodical basis or tied to specific events. Sometimes a mixture of both is used (cf. 2.5 for more information on planning policies).
Reactive planning approaches can be divided into dynamic planning and predictive-reactive planning approaches. In the case of a pure dynamic (also called “online” or “completely reactive”) planning, no predictive plan is created. Instead always only the next decision is planned and executed. Further information about the environment is not taken into account. Often, rule-based strategies are used for decision making. This lessens the computational burden and increases solution speed of planning methods (cf. Holthaus and Rajendran (2000)).
In the case of predictive-reactive planning approaches, first a predictive plan is created and subsequently executed. Note that predictive planning is sometimes also referred to as “offline” planning (in contrast to online planning). Usually, all relevant information is included in the planning process with the goal to create an optimal plan in respect to the efficiency goals defined. The computational burden is higher but such methods can significantly outperform rule-based approaches by including much more information in the planning process and realizing existing optimization potential (cf. Ovacik and Uzsoy (1997)). However, if uncertainty increases, the performance-advantage of optimal methods may decline in specific cases (e.g. high processing time variances, cf. Lawrence and Sewell (1997)). Due to the occurrence of a disturbance, the plan is then adjusted as required. The applied methods for plan adjustments may again be simple rules, heuristics or optimal methods. The plan adjustment may be partial, meaning that it is restricted to a part of the plan, or comprise the complete plan. If a complete replanning is performed,
Ϯ dŝŵĞ WƌĞĚŝĐƚŝǀĞƉůĂŶ͗ ϭ WƌŽĚƵĐƚŝŽŶŽƌĚĞƌƐ dŝŵĞ DĂĐŚŝŶĞďƌĞĂŬͲĚŽǁŶ͗ dŝŵĞ Ϯ ĚũƵƐƚĞĚƉůĂŶ͗ DĂƐĐŚŝŶĞƌĞƉĂŝƌ Z ϯ Ϯ ϰ ϰ ϯ
Figure 2.8.: Reactive planning for a machine break-down
reactive plan adjustment in reaction to a machine break-down. Also confer Neuhaus and Günther (2006) for an example of a reactive scheduling system for applications in the process industry.
The majority of reactive planning papers in the literature is concerned with scheduling problems. Most of these, according to Aytug et al. (2005), focus on resource-related uncertainties, such as variable processing times or major disruptions, namely mean times for machine failures and repair operations. However some works also include or specifi-cally address the inclusion of new orders into a predetermined productions schedule (cf. e.g. Vin and Ierapetritou (2000); Artigues and Roubellat (2002); Roslöf et al. (2002); Mendez and Cerda (2003); Janak et al. (2006); Ferrer-Nadal et al. (2007); Caricato and Grieco (2008); Gomes et al. (2010)). For more information on reactive planning, confer Aytug et al. (2005); Neuhaus (2008); Pfeiffer et al. (2007); Sabuncuoglu and Bayiz (2000); Herroelen and Leus (2005). Also note that robust and reactive planning approaches may be combined, resulting in so-called robust-reactive planning methods.
2.4. Production plan evaluation
Given a decision problem in the area of production planning, a typical goal in the creation of a production plan is to not only find a valid plan under given conditions, but to also find the best possible plan in respect to defined planning goals. In order to be able to compare different plans for a specific planning problem, evaluation criteria are required, in order to evaluate plan performances (cf. Neuhaus (2008), p.47 et sqq.).
In many cases, in the literature as well as in practice, classical efficiency criteria are used, usually requiring the calculation of cost- or time-related efficiency measures (e.g. inventory holding costs or production makespans). A listing of efficiency measures can be found in Blömer (1999).
As described in 2.3.3 robust planning aims at creating plans which are insensitive to environmental influences. In order to evaluate the robustness of a plan, respective ro-bustness measures are required. As discussed before, robust planning is either focused on preserving the feasibility of a plan (e.g. by insertion of buffer times) or on the re-duction of performance measure deterioration. Thus, robustness measures may roughly
be grouped into measuring robustness of feasibility or robustness against performance deterioration, respectively (for a listing of robustness measures, confer e.g. Mignon et al. (1995)).
Another criterion for the evaluation of a production plan is its flexibility. Flexibility describes the ability of a plan to be adjustable in reaction to occurring events. A flexible plan is easily adjustable to changing environmental influences (cf. Jensen (2001)).
A third criterion, plan stability, describes the similarity between an original plan and a resulting plan after adjustments have been made. A stable plan is very similar to its original plan. Plan variations may be due to replanning activities, resulting in differing plans or ad hoc adjustments during execution, in order to retain plan feasibility. A contrary defined criterion is the aforementioned nervousness of a plan. An adjusted plan which is very dissimilar to its original plan has a high nervousness. To express the amount of plan variations, appropriate (stability or nervousness) measures may be calculated. For a listing of stability measures, confer e.g. Neuhaus (2008) (p.50 et sqq.). The following sub-section will focus on effects of plan variations, while 2.4.2 will present a short overview on plan variation measures.
2.4.1. Plan variation impacts
As discussed before, variations from an original released plan may arise due to variances during execution (e.g. processing time variances) or plan adjustments in reaction to occurring events. This sub-section will focus on the impacts of such plan variations. Typically, after a production plan has been created, a revision phase follows during which the plan is verified and altered if required. After this revision phase, the plan is released and thus made available to the production system as well as other depending planning activities (e.g. material sourcing, personnel disposition or financing activities) within the company (or within the supply chain, respectively). If an already released plan is adjusted, another revision phase follows, verifying the altered plan. The type and amount of variations between the original and altered plan determine the verification efforts. A higher dissimilarity usually induces more verification efforts. In addition to a verification, the plan alterations have to be coordinated with other depending planning activities. This generates additional coordination efforts (as well as associated costs) between the involved planning authorities as well as with the executing production system. Beside coordination efforts, a revision of depending plans may induce further costs. These occur for a wide range of reasons, e.g. higher material costs for deliveries on short notice, penalty costs for plan alterations from external peers in the supply chain etc. Additionally, depending planning activities may also have to be repeated, again inducing further planning efforts for depending planning activities. The revised plans of other planning activities of course may themselves lead to additional coordination and planning efforts and additional costs. Furthermore, released plans are also used for the communication of delivery dates to customers. Frequent changes to these will likely degrade the customer service quality or add further penalty costs. In conclusion, in practical applications a low plan variation is usually desired to mitigate negative effects, such as additional coordination and planning
WůĂŶǀĂƌŝĂƚŝŽŶ ŵĞĂƐƵƌĞƐ dŝŵĞ WŽƐŝƚŝŽŶ ^ƚĂƌƚ ŶĚ ƵƌĂƚŝŽŶ ƐƐŝŐŶŵĞŶƚ &ĂĐŝůŝƚLJ džĞĐƵƚŝŽŶ ;ƐĞƚƵƉͿ ŽŶĨŝŐƵƌĂƚŝŽŶ ^ŝnjĞ ^ĞƋƵĞŶĐĞ EƵŵďĞƌ ŽĨ ĐŚĂŶŐĞƐ
Figure 2.9.: Hierarchical grouping of plan variation measures
In the area of rolling horizon planning, planning nervousness induced by repeated replanning of complete planning periods due to the overlapping planning time windows has been studied extensively in the literature. Research focused mostly on the effect of planning horizon length on planning nervousness and efficiency measures as well as the ascertainment of an optimal planning horizon length. Further research examined techniques for a nervousness reduction by introducing penalty costs or fixation periods.
In the areas of reactive and robust planning the majority of literature focuses on clas-sical efficiency measures, ignoring additional coordination efforts induced by plan recon-figurations (cf. Aytug et al. (2005)). However, more recent literature is now addressing this perspective and is also systematically modeling plan variations or replanning costs (cf. Neuhaus (2008)).
2.4.2. Measuring plan variations
In order to measure the amount of plan variations, a variety of stability and nervous-ness measures has been introduced in the literature. These are often case-specific but may be grouped into several categories — Neuhaus (2008) describes three groups: the finishing time of a production order, its assignment to a specific production facility and planned production sequences. Further categories can be found, namely process dura-tions, production sizes, process execution (e.g. setup), production facility configurations and simply the number of required plan alterations. Combinations of several categories are also possible, of course. Figure 2.9 shows a hierarchical categorization of plan varia-tion measures.
Note that according to Neuhaus (2008), these measure may be further divided into local and global measures. Local measures are based one one replanning iteration while global measures refer to a whole sequence of replanning iterations over a certain period
dŝŵĞ WƌŽĚƵĐƚŝŽŶ ůŝŶĞ ϭ͗ WƌŽĚƵĐƚŝŽŶ ůŽƚƐ ŶĚƚŝŵĞ ^ƚĂƌƚƚŝŵĞ ƵƌĂƚŝŽŶΘƐŝnjĞ >ŽƚƐĞƚƵƉ dŝŵĞ >ŝŶĞĂƐƐŝŐŶŵĞŶƚ WƌŽĚƵĐƚŝŽŶ ůŝŶĞ Ϯ͗ ^ĞƋƵĞŶĐĞ dŝŵĞ WƌŽĚƵĐƚŝŽŶ ůŝŶĞ ϯ͗ ŽŶĨŝŐƵƌĂƚŝŽŶ ;ĐŚĂŶŐĞ ŽĨ ƉƌŽĚƵĐƚŝŽŶ ƐƉĞĞĚͿ
Figure 2.10.: Plan variation measure example
of study. Some of these measures may thus only be calculated ex post. Mixed measures may be defined of course, referring to more than one iteration (e.g. by calculating mean values). Figure 2.10 shows an example of different plan variation measures. As can be seen, plan variation measures are not independent of each other but are often interrelated in a way that if one measure changes, other measures change as well.
2.4.3. Combining multiple measures
If a single measure is calculated in order to evaluate the performance of a production plan the comparison of different plans and selection of the best solution can be simply executed with respect to this single measure. If multiple measures are required the difficulty of the decision process depends on the respective measures and the way how these are combined in a planning method.
If the different measures can be expressed in the same unit these may be combined to form a single measure then used for comparison. The is, for example, usually the case when several monetary measures are summarized into a single one (e.g. cost or profit measure).
As discussed in 2.4.1, plan variation may induce further costs. When e.g. both cost and plan variation measures are considered in the evaluation of a production plan and if enough information about the resulting costs of regarded plan variations are available, the considered cost and plan variation measures can be easily summarized into a single
costs of plan variations sufficient information allowing for a sensible estimate of these costs is usually not available. Alternatively, a set of penalty costs on plan variations are often applied instead (cf. e.g. Vin and Ierapetritou (2000)). The scale of penalty costs in relation to the calculated economic costs then reflects a chosen weighting of these goal components which influences the solution process. Thus, ideally a chosen weighting should reflect the desired balance of planning goals in order to create adequate production plans. This selection of a sufficient weighting may not be an easy accomplishment, though, depending on the specific planning application. The sensitivity of the solution process to small weighting alterations can further complicate this matter.
Instead of summarizing different measures directly into a resulting single measure other ways for the consideration of measures with different units have been explored. Gomes et al. (2010) e.g. presented a reactive scheduling algorithm for the inclusion of new orders into an existing schedule, which outputs not one final schedule solution but presents several possible schedules (each generated by using a different set of fixated orders in the original schedule) to the planner, each having separate cost and stability values. It is then up to the user to select the most appropriate schedule, thus using an ex post calculation of stability measures. Amorim et al. (2011) developed a multi-objective genetic algorithm for a lot sizing and scheduling of perishable goods (yoghurt in this case) using a random weighting for each individual of the population in the calculation of a single objective function value.
In general, Loukil et al. (2005) lists five types of approaches, dealing with multi-objective scheduling problems, in the literature:
• A hierarchical ranking of goals which is used in the selection of favorable planning solutions
• The calculation of a single objective function value as a weighted sum of the goal components
• The inclusion of goals as constraints in planning models, favoring solutions with good approximations of desired objective function values
• Interactive strategies, requiring the user of the respective planning software to make certain decisions
• The calculation of the complete pareto front for a multi-objective planning problem, allowing for a selection of a planning solution according to an appropriate trade-off of goal achievement
Arguably, as every approach finally aims at the release of a single production plan to be executed, in the end, the combination of different objectives into a single preference for a specific solution, to be selected and eventually released as new or adjusted production plan, is inevitable. What distinguishes different approaches, is the procedure in which this combination of goals is performed. For more information on multi-objective scheduling research, please confer Lei (2009).
WůĂŶŶŝŶŐ ƉŽůŝĐŝĞƐ
WĞƌŝŽĚŝĐĂů ,LJďƌŝĚ ǀĞŶƚͲ
ďĂƐĞĚ
Figure 2.11.: Planning policy categories
dŝŵĞ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ
Figure 2.12.: Periodical planning policy
2.5. Planning policies
Planning policies determine the point in time when planning activities are to be per-formed, as well as the planning method to be used for a specific planning operation. In general, planning policies can be grouped into the three categories of periodical, event-based and hybrid planning policies (cf. figure 2.11). For more information on planning policies, confer Church and Uzsoy (1992), Vieira et al. (2003) or Pfeiffer et al. (2007).
2.5.1. Periodical planning policies
If planning is executed periodically, the time frame is divided into intervals of equal length. At the beginning (or end, respectively) of each interval, planning activities are performed (cf. figure 2.12). The rolling horizon planning approach, described in 2.3.2, uses a periodical planning policy, for example. Confer also Kurbel (2005, 2011); Pfeiffer et al. (2007). Depending on the planning problem it may be reasonable to use sev-eral periodicities, e.g. a weekly interval for major replanning activities and daily minor adjustments of plans.
Periodical planning has the effect that changes to the planning information, which occur during a specific time interval, are always only considered at the end of that time interval, namely at the time of the next planning process. If those changes to the planning data were urgent it might then be too late at the next planning point in time. On the other hand, shorter intervals result in a more timely consideration of events, but also in even more frequent planning activities and consequential coordination efforts and costs.
dŝŵĞ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ
Figure 2.13.: Event-based planning policy
2.5.2. Event-based planning policies
Event-based planning does not divide time into planning points of equal distance at which planning activities are executed. Instead it considers predefined events as triggers of planning activities (cf. figure 2.13). Confer also Kurbel (2005, 2011); Pfeiffer et al. (2007).
Events may be any changes to the environment which affect the relevant planning data. What is considered as an event in a specific case varies and has to be determined individually for each planning environment. In an extreme example, each single planning information change could trigger planning activities to incorporate those data changes immediately. This continuous triggering would then result in very frequent replanning, increasing the overall planning efforts drastically. A more sensible approach is the trig-gering of planning activities only if the occurring planning information changes reach or exceed a certain level, individually or accumulated, since the last planning iteration. On the other hand, the time between individual planning iterations may then become undesirably long if the defined triggering levels are not reached.
In general, if several different types of events exist, which have to be considered, it is reasonable to formulate different triggering rules for different event types. The kind of planning activities which are triggered may then also depend on the specific rule, e.g. triggering only minor adjustments or a major replanning. Note that a further difficulty arises if it is not practical or even possible to create and monitor a complete set of triggering rules. This is not unlikely if a high variety of event types has to be treated, resulting in a rather large and complex rule set.
2.5.3. Hybrid planning policies
Hybrid planning approaches combine periodical and event-based planning techniques. They aim at the utilization of the strengths of both periodical and event-based policies, while diminishing the impact of the described weaknesses. Typically, planning activities are performed periodically, ideally using a periodicity which avoids unnecessary frequent planning iterations. Additionally, specific events and corresponding triggering rules are defined for which further aperiodic planning activities are initiated. This way, important events may be treated in a timely manner, while all other information of planning data changes is collected and incorporated at the next periodically occurring planning point in time (cf. figure 2.14). Confer also Kurbel (2005, 2011); Pfeiffer et al. (2007).
dŝŵĞ WůĂŶŶŝŶŐ ŝƚĞƌĂƚŝŽŶ
Figure 2.14.: Hybrid planning policy
2.6. Classification of an evolutionary production planning
This chapter provided an overview of import concepts regarding production planning ap-plications in dynamic environments. Several common production planning approaches, found in the literature as well as in practical applications, were discussed. In this last section, the main characteristics of these approaches are summarized in relation to simi-larities and overlappings with an evolutionary production planning.
Rigid planning and rolling horizon planning are classical production planning ap-proaches, usually dealing with the dynamic nature of an external company environment, such as demand fluctuations (often based on demand forecasts). While rigid plans for a given time period are assumed to being not altered after plan creation, rolling horizon planning accounts for changing planning information by periodical revision, classically in terms of a complete regeneration of respective plans. This general planning approach occurs in a variety of manifestations and is originally used in mid-term to long-term planning tasks. However some works in the literature also address short-term plan-ning problems. Plan variations, which occur naturally by overlapping of planplan-ning time windows, are sometimes reduced by applying fixations on plan components or by an assessment and application of replanning penalty costs.
Robust planning introduces proactive measures, aiming at the generation of produc-tion plans which are preferably insensitive to environmental influences. Occurring distur-bances and resulting required plan alterations are implicitly assumed to being considered during the execution of respective production plans. The applicability of this type of approaches naturally depends on sufficient estimates for important planning parameters (such as processing times) and a preferably low level of environmental influences and associated disturbances.
Reactive planning on the other hand reacts on environmental influences as these oc-cur, either by a simple rule-based online planning (dynamic planning) or by repairing a previously created predictive plan (predictive-reactive planning). A multitude of reactive planning literature is located in the area of reactive scheduling problems.
Both robust and predictive-reactive planning literature usually focus more on the inter-nal environment and measures against associated disturbances, such as machine failures or variable processing times. However, some literature (on scheduling problems) specifi-cally addresses the inclusion of new orders into a predetermined production schedule.
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ning approaches. It categorizes them in accordance to their typical appearance. Note that of course overlappings between these approaches exist, but have been omitted for the sake of clarity.
These approaches also include work which is related to (and shares characteristics with) an evolutionary production planning, in terms of an on-going and frequent inclusion of new demand information into existing production plans. However they are of course not tailored specifically to suit the evolutionary production planning approach and typ-ically exhibit a different focus, therefore are defined broader in some regards and tighter in others. Thus, when categorizing evolutionary production planning applications and research, instead of assigning it to these existing approaches in conjunction with an enu-meration of relevant characteristics, it appears sensible to define evolutionary production planning as a distinct planning approach in order to allow for a fitting categorization of respective applications and research.
In the next chapter a general evolutionary production planning concept and planning framework specifically designed to describe planning application in this planning area are presented. Furthermore, while some work has been done already in the area of rescheduling problems, there still is demand for research, addressing specific planning problems and case studies, in different industries and other planning areas. In part II of this work, two case studies addressing integrated lot-sizing and scheduling problems as evolutionary production planning applications are presented.
concept
In this chapter a concept for an evolutionary production planning will be presented. As discussed in the previous chapter, several research areas exist, which are concerned with production planning in dynamic environments. Currently missing though is a general concept which is specifically concerned with a continuously progressing plan development under frequent inclusion of new demand-related planning information, as it is for exam-ple important in planning situations in which companies are faced with a fast changing environment and short planning time periods. In the following sections important char-acteristics of an evolutionary planning system are presented. Following these sections, a framework supporting the development of evolutionary production planning systems as well as an evolutionary production planning simulation framework for implementation and study of these planning systems are presented.
3.1. Characteristics
This section discusses characteristics of evolutionary production planning systems.
3.1.1. Main characteristics
Evolutionary production planning is proposed as a general concept for a continuous de-velopment of production plans. Evolutionary production planning may be summarized as being concerned with the following aspects.
The field of application for an evolutionary production planning is the short-term (or short- to medium-term) production planning in fast-changing dynamic environments. Company environments in which such a planning approach is suitable usually exhibit a number of the following characteristics:
• Competitive markets
• Cost pressure
• High product variety
• Complex and constantly changing consumer behavior
• Make-to-order prevalent
• Requirement for short and reliable lead time quotations and order acceptance The environmental focus of an evolutionary production planning lies on demand-related dynamics, such as new orders, order modifications or cancellations. Th goal of an evolu-tionary production planning is a continuously progressing plan development under con-stant inclusion of new demand-related information. In competitive markets, efficient planning is required, e.g. in reaction to increasing cost pressure or the need for prefer-ably short lead time quotations. On the other hand, in practice it is desired that a production plan is preferably not altered after release. A requirement for reliable lead time quotations also provides a further external reason for low planning variations. Thus, a continuous plan development requires a balance of plan reliability (low planning varia-tion) and efficiency. The definition of this balance is specific to each case of application and requires explicit consideration in planning methods (cf. 3.1.2).
The requirement for a balance of plan efficiency and reliability is also related to a balance of the reduction of direct cost due to the economic plan performance and indirect cost due to additional coordination and planning efforts as a result of plan alterations (cf. 3.1.2). Note that in the following the economic performance of a plan is also referred to as plan (or cost-) efficiency, while plan variation and associated costs are also simply referred to as plan variation.
Figure 3.1 shows the planning logic for a generic evolutionary production planning system. Considered environmental event types are new orders, modifications of already accepted orders as well as order cancellations. The actual event segmentation and thus number of different events to be considered depends on the individual planning appli-cation. The occurring events are handled by the planning policy which is defined for a specific evolutionary planning application. The planning policy decides if a planning iteration is necessary to make adjustments to the current production plan in order to incorporate the new information related to the occurred event. Depending on the spe-cific planning application it may be preferable to design a hierarchical structure to the planning policy with each planning policy handling specific event types of the segmenta-tion and superordinated policies delegating events to an appropriate handling policy. If, according to the planning policy, no production plan adjustment is necessary, the event is collected in the event stack. Events waiting in the event stack may be included in the decision of planning policies on the necessity of plan adjustments.
Beside these demand-related event types periodical planning intervals may be defined. Again, the number of different periodical planning intervals which are defined is specific to each planning application. If an interval timer has expired a corresponding period-ical event is triggered. Planning policies decide if a periodperiod-ical timer is to be reset. A planning policy handling a periodical interval will usually reset the corresponding timer, for example. Policies handling demand-related events may or may not reset periodical timers. If these are not reset, periodical planning events occur with fixed periodic
