Conflict resolution by simulation and optimization

As has been described in the previous section, many alternatives exist for planning orders on the basis of specified strategies. The number of varia-tions rises drastically when manual intervenvaria-tions are included.

By applying different strategies and making an objective appraisal of the results, various simulations can be carried out.

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Fig. 7.8. Components of a stored plan

Due to this objective evaluation, the planning situations which emerge can be compared and better plans thus obtained.

In what follows we shall describe the basic functionalities required for carrying out useful simulations. We show the general procedure.

 It is possible to save a loading plan as a simulation. Here, in addition to the plan itself, the underlying basic data, such as the planning strategy, the capacity available and the starting situation, are also stored.

 When several simulations are run they are all produced using a single selectable starting situation as otherwise it would not be possible to compare them.

 A simulation can in turn also be used as a starting situation for other simulations.

 It is possible, on the basis of one starting situation, to automatically start several planning runs using different parameters or strategies.

 To be able to ensure objectivity in comparisons, the different plans or simulations can be evaluated objectively by means of key data and then saved in turn together with a plan, that is, a simulation.

 Once several simulations have been compared and a variant selected, that planning situation can be accepted as a binding loading plan and saved. Since in this case the starting situation will usually have changed in the meantime, this acceptance will need to take into account the cur-rent situation. Any conflicts or deviations which may occur will either be

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corrected immediately or, if this is not clearly possible, will be marked for processing.

 For simulations dealing with the future – that is, beyond the normal de-tailed planning horizon – so-called planning or capacity orders are sup-ported so that realistic loadings can even be simulated when not all de-tailed requirements are yet present.

As has already been mentioned in the overview, it is necessary to evalu-ate different simulations on the basis of objective key data so as to be able to compare several situations. Some typical key data used in plan evalua-tion include:

 Total delays

 Idle times

 Compliance with deadlines

 Setting-up time

In addition to the usual criteria, specific variables exist in the companies which take special circumstances into account and thus frequently allow the quality of a particular loading to be assessed very clearly.

To enable coverage of specific factors of this kind, user-specific evalua-tion criteria can be set flexibly to match the circumstances in quesevalua-tion. As is well known, one dilemma in production planning is that the different objec-tives compete with each other. For example, an optimum setting-up situation

Fig. 7.9. Plan evaluation

7.2 Use of MES for detailed planning and control 159 may mean that loading or compliance with deadlines is neglected or, in the reverse case, complying with all date requests or even date promises may be at the expense of setting-up time or of the work in progress inventory.

This permanent conflict of objectives can be very clearly illustrated by the following diagram in which the corners of the pyramid represent the competing objectives and the ball in the middle the individual objective or the compromise.

By giving suitable weightings to the individual objectives they can be brought together and summarized in a combined objective – provided cer-tain compromises are accepted. Various plans can now be generated and judged on the basis of this combined objective and thus compared with each other.

The various simulations can be generated by selecting and applying dif-ferent planning strategies. If, for example, an overload situation has to be compensated for, the capacity available can easily be varied by adding an additional shift or even by simulating new installations.

If the situation as regards the individual objective can be improved by varying a criterion, the results can be further optimized by repeating the simulation on the basis of the “best” situation and by varying different parameters.

To sum up, the overall situation can be continually improved by finding

“better” plans. What is of decisive importance to the quality and the success of this procedure is the coherence of the individual objective. The manufac-turers of MES systems offer different strategies for system-supported optimization. Research has been going on for years into the optimization of

Fig. 7.10. Conflicting objectives in production planning

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planning problems in general and its application in production planning in particular. Many years ago, for example, so-called optimal planning strate-gies were modern but due to long running times and the extreme effects on the result which arose from minor discrepancies in modeling they did not gain widespread acceptance. Current research work is concerned with algo-rithms which look to the natural world and which adapt like evolutionary processes. The disadvantage of such approaches is that they are extremely solution-specific and thus in their unchanged form only to a limited extent suitable for use in standard systems.

Some MES vendors have recognized this tendency and thanks to early cooperation and collaboration with institutes, universities and customers have been able to utilize their findings and incorporate them in their own product as specific functionalities.