Use of Genetic algorithm based
approaches in scheduling of FMS: A
Review.
Hameshbabu Nanvala
Babasaheb Naik College of Engineering, Pusad, Maharashtra, Pincode-445215, India.
Abstract:
Scheduling in an FMS environment is more complex and difficult than in a conventional manufacturing environment. Therefore, determining an optimal schedule and controlling an FMS is considered a difficult task. To achieve high performance for an FMS, a good scheduling system should make a right decision at a right time according to system conditions. In recent years, the use of Meta heuristic based approaches sch as Tabu Search, Simulated Annealing and genetic algorithms increased for scheduling in a flexible manufacturing system (FMS). These ,do not guarantee to find an optimal schedule, but have the ability to find near to optimum solutions in a short time. This work presents a review on use of genetic algorithms based approaches in scheduling of flexible manufacturing systems.
Keywords: genetic algorithms; Scheduling; FMS.
Introduction:
A flexible manufacturing system (FMS) provides the efficiency of automated high-volume mass production while retaining the flexibility of low-volume job shop production. Scheduling in an FMS environment is more complex and difficult than in a conventional manufacturing environment. Scheduling of FMS is NP-hard scheduling problems. Therefore, determining an optimal schedule and controlling an FMS is considered a difficult task. Since the invention of the flexible manufacturing systems, many researchers are working on the topic to find out the solution to scheduling of flexible manufacturing systems and developed number of solution methods for scheduling FMS. The most general OR-optimization methods are mathematical programming formulations. Several mathematical programming models have been developed for solving FMS scheduling problems. However, the computational effort required makes such as an approach impractical for real-time control in most applications. Therefore, mathematical programming formulations may be used as a basis for the development of scheduling heuristics. Recently, several heuristic approaches based on iterative improvement procedures have been applied to the FMS scheduling problem as the computation power of available computers has rapidly improved. Several heuristic procedures such as dispatching rules, local search and meta-heuristics such as tabu search, simulated annealing and GAs have been developed in recent years for FJSP.
Genetic Algorithm (GA), proposed by J. Holland in 1975 [Goldberg D.E et al. [1], Wang L [2]]. Genetic Algorithms are randomized search and optimization techniques guided by the principles of evolution and natural genetics, having a large amount of implicit parallelism. GAs perform search in complex, large and multimodal landscapes, and provide near-optimal solutions for objective or fitness function of an optimization problem[Goldberg D.E.[3], Filho J.L.R[4], Ujjwal Maulik [5]]. According to Chan F.T.S, Chan H.K[6], GA is a permutation approach that systematically permutes an initial pool of randomly generated solution based on predefine attributes to return the best solution. Some of the advantages of a GA are [Haupt R.L., [7]]:
• Optimizes with continuous or discrete variables, • Doesn’t require derivative information,
• Simultaneously searches from a wide sampling of the cost surface, • Deals with a large number of variables,
• Is well suited for parallel computers,
• Optimizes variables with extremely complex cost surfaces.(they can jump out of a local minimum), • Provides a list of optimum variables, not just a single solution,
Literature review:
In recent days, Genetic algorithms are one of widely used approach for scheduling of flexible manufacturing system.Chen, H.et al[8], presented a new genetic algorithm to solve the flexible job-shop scheduling problem with makespan criterion. The representation of solutions for the problem by chromosomes consists of two parts. The first part defines the routing policy and the second part the sequence of the operations on each machine. Genetic operators are introduced and used in the reproduction process of the algorithm. Numerical experiments show that our algorithm can find out high-quality schedules. Yang [9] presented a genetic algorithm (GA)-based discrete dynamic programming approach. Zhang H.P, Gen M [10], proposed a new multistage operation-based representation of GA (moGA) approach to solve flexible job shop problem(FJSP). The proposed algorithm is designed for optimal the 3 objectives including the makespan , total workloads of all machines , and maximum of workloads for all machines . By using some numerical example of related works, they demonstrate the efficiency of moGA. The optimal result is better than the other related approaches .Jie Gao, Linyan Sun, Mitsuo Gen[11],developed a new approach hybridizing genetic algorithm with variable neighborhood descent to exploit the “global search ability” of genetic algorithm and “the local search ability” of variable neighborhood descent for solving multiobjective flexible job shop scheduling problem.Chen et al[12], used genetic algorithm with new chromosome representation to solve FJSP.
Guohui Zhang, Yang Shi, Liang Gao [13],the two traditional algorithms of GA and TS are combined to solve the FJSP. TS is executed for local search according to different probability. And the tabu list length is variable according to different solutions. The experiments prove that the proposed algorithm could be used to solve the FJSP effectively. Kacem et al. [14,15] proposed a genetic algorithm controlled by the assigned model, which is generated by the approach of localization, for the single-objective and multi-objective FJSSP. In the work of Jerald,J et al [16], simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). Taghavifard,M.Tet al[17],presented a genetic algorithm-based technique to schedule machines and Automated Guided Vehicle (AGV), simultaneously. Choudhury B.B. et al[18],used two different approaches viz.GA and SA on the same set of problems to determine the optimized schedule in an FMS.. They used both these methods to find out the appropriate one for the purpose. The coding of the methods is designed in a manner that yields global optima for the solution. The results show that GA scores over SA in dealing with the FMS scheduling under constraint condition and such behavior of GA against that of SA may be attributed to the fact that, the intricacies of the problem are well taken care of by the coding methods of GA.
Nasr Al-Hinai ,ElMekkawy T.Y.[28] proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature. Jahangirian, Conroy [29] develop a learning mechanism which is driven by a genetic algorithm to determine the best multi-criteria scheduling option in an FMS. Fanti et al.[30] combine fuzzy logic and a genetic algorithm to solve a multi-criteria scheduling problem. Saravana Sankar S, Ponnanbalam S.G.,Rajendran C.A,[31]proposed an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented. Erkmen, A.M, et al.[32],focused on the development and implementation of a genetically tuned fuzzy scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on time criticality in deadline and machine need, taking into account machine availability, uniformity, process time and select ability.Luis Rabelo et al. [33],A scheme for the scheduling of Flexible Manufacturing Systems (FMS) has been developed which integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms and machine learning. Modular neural networks are used to generate a small set of attractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo Simulation predicts the impact of each on the future evolution of the manufacturing system. Genetic algorithms are utilized to combine attractive alternatives into a single "best" decision. Induction mechanisms are used for learning and simplify the decision process in future performances. Sankar, S.S.; Ponnambalam, S.G. [34] Proposed a genetic algorithm based iterative procedure to approximately solve the integrated scheduling problem, which accommodates the simultaneous scheduling of incoming jobs, machines, and vehicle dispatching in a flexible manufacturing system (FMS) having a single device, an automated guided vehicle (AGV). The objective is to find an optimal sequence of incoming parts, which will reduce the waiting times due to blocking and starving of resources and deadheading times, resulting in overall minimization of makespan. The procedure is evaluated through different benchmark problems.
scheduling problem, which involves routing selection, machine selection, and processing sequence selection.Chan F.T.S et al. [41], proposed a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem. Jie Gao, Linyan Sun, Mitsuo Gen[42], developed a hybrid genetic algorithm (GA) for the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload.. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. they developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. Zhang H.P, Gen M [43] proposed a multistage operation-based GA to deal with the flexible job-shop scheduling problem from a point view of dynamic programming. Mesghouni, K., Hammadi, S, Borne, P[44], presented an article with an objective to improve performance of the genetic algorithms based approach to jobshop scheduling problems by developing effective genetic operators, such as a parallel representation of the chromosome, on the one hand, and genetic operators associated with this original representation. In this article they deal with the problem of flexible job-shop scheduling which presents two difficulties : the first one is the assignment of each operation to a machine, and the second one is scheduling this set of operations in order to minimize the makespan criterion .Tay, J. C.,Wibowo, D. [45] proposed a new chromosome representation and a design of related parameters to solve the FJSP efficiently. The results of applying the new chromosome representation for solving the 10 jobs x 10 machines FJSP are compared with three other chromosome representations. Empirical experiments show that the proposed chromosome representation obtains better results than the others in both quality and processing time requiredGuohui Zhang, Liang Gao, Yang Shi, .[46] proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.
Jensen, M.T[47] considered the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases. Haipeng Zhang, Mitsuo Gen[48], proposed a new multistage operation-based representation of GA (moGA) approach to solve fJSP. The proposed algorithm is designed for optimal the 3 objectives including the makespan , total workloads of all machines , and maximum of workloads for all machines . By using some numerical example of related works, they demonstrate the efficiency of moGA. The optimal result is better than the other related approaches. Guohui Zhang, Yang Shi, Liang Gao[49], combined the two traditional algorithms of GA and TS to solve the FJSP. TS is executed for local search according to different probability. And the tabu list length is variable according to different solutions. The experiments prove that the proposed algorithm could be used to solve the FJSP effectively.
Conclusion:
References:
[1] Goldberg D.E., Holland J.H.: (1988) . Genetic Algorithms and Machine Learning, Machine Learning, Vol. 3, No. 2-3, pp.95-99.
[2] Wang L. (2001). Intelligent Optimization Algorithms with Applications. Tsinghua University Press, Beijing.
[3] Goldberg D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York.
[4] Filho J.L.R. et al. (1994). Genetic algorithm programming environments, IEEE Comput. 27, pp.28-43.
[5] Ujjwal Maulik, Sanghamitra Bandyopadhyay, (2000) .Genetic algorithm-based clustering technique, Pattern Recognition 33,
pp.1455-1465
[6] Chan F.T.S, Chan H.K.(2004)A comprehensive survey and future trends of simulation study on FMS scheduling,Journal of intelligent
manufacturing,15,pp.87-102
[7] Haupt R.L., Haupt S.E.(2004). Practical Genetic Algorithms, A JOHN WILEY & SONS, INC., Hoboken, New Jersey, 2ed ,pp. 22-23
[8] Chen, H.et al.(1999) A genetic algorithm for flexible job-shop scheduling, 1999 IEEE International Conference on IEEE International
Conference on Robotics and Automation, 1999. Proceedings. 1999 , vol-2, pp- 1120 – 1125
[9] Yang J.B.(2001) GA-based discrete dynamic programming approach for scheduling in FMSenvironments. IEEE Transactions on
Systems, Man, and Cybernetics—Part B ,31(5),pp.824–35.
[10] Zhang H.P, Gen M.(2005). Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity
International,11.pp.223–232.
[11] Jie Gao, Linyan Sun, Mitsuo Gen. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop
scheduling problems, Computers & Operations Research 35. Pp.2892 – 2907
[12] Chen H, Ihlow J, Lehmann C.(1999) A genetic algorithm for flexible job-shop scheduling. IEEE International conferenceon Robotics
and Automation, Detroit, 2: pp.1120-1125.
[13] Guohui Zhang, Yang Shi, Liang Gao.(2008) A Genetic Algorithm and Tabu Search for Solving Flexible Job Shop Schedules, ISCID
'08 Proceedings of the 2008, International Symposium on Computational Intelligence and Design, Volume 01, pp-369-372
[14] Kacem I, Hammadi S, Borne P.(2002). Approach by Localization and Multi-Objective Evolutionary Optimization for Flexible
Job-Shop Scheduling Problems, IEEE Transactions on Systems, Man, and Cybernetics, Part C, 32, pp.1-13.
[15] Kacem I, Hammadi S, Borne P.(2002). Pareto-Optimality Approach for Flexible JobShop Scheduling Problems: Hybridization of
Evolutionary Algorithms and Fuzzy Logic, Mathematics and Computers in Simulation, 60, pp.245-276.
[16] Jerald J, Asokan P, Saravanan R, Rania A.D.C(2006) Simultaneous Scheduling of Parts and Automated Guided Vehicles in an FMS
Environment using Adaptive Genetic Algorithm. Int. Adv. Manuf. Technol. 29, pp.584-589.
[17] Taghavifard M.T, Heydar M, Mousavi S.S. (2009). A Genetic Algorithm for Scheduling Flexible Manufacturing Cells. Journal of
Applied Sciences 9(1), pp.97-104,
[18] Choudhury B B et al.(2009) Appropriate Evolutionary Algorithm for Scheduling in FMS, 2009 World Congress on Nature &
Biologically Inspired Computing (NaBIC 2009),pp 1139-1144.
[19] Pezzella F, Morganti G, Ciaschetti G. (2008),.A genetic algorithm for the flexible job-shop scheduling problem. Computers &
Operations Research Vol, 35 pp. 3202-3212.
[20] Chryssolouris, G., Subramaniam, V. (2001). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of
Intelligent Manufacturing, 12(3),pp.281–293.
[21] Leon, V. J., Wu, S. D., Storer, R. H. (1994). Robustness measures and robust scheduling for job shops. IIE Transactions, 26(5), pp.32–
41.
[22] Jensen, M. T. (2001). Improving robustness and flexibility of tardiness and total flow-time job shops using robustness measures.
Applied Soft Computing, 1(1), pp.35–52
[23] Wu, S. D., Storer, R. H.,Chang, P. C. (1991). A rescheduling procedure for manufacturing systems under random disruptions. In
Proceedings joint USA/German conference on new directions for operations research in manufacturing .pp. 292–306.
[24] Wu, S. D., Storer, R. H., Chang, P. C. (1993). One machine rescheduling heuristics with efficiency and stability as criteria.Computers
Operations Research, 20(1), pp.1–14.
[25] Bierwirth, C.,Mattfeld, D. C. (1999). Production scheduling and rescheduling with genetic algorithms. Evolutionary Computation,7(1),
pp.1–17
[26] Ho, N. B., Tay, J. C. (2004). GENACE: An efficient cultural algorithm for solving the flexible job-shop problem. In Proceedings of
the congress on evolutionary computation CEC2004 .pp. 1759–1766.
[27] Libo Song, Xuejun Xu.(2010). Flexible job shop scheduling problem solving based on genetic algorithm with chaotic local search,
Sixth International Conference on Natural Computation (ICNC),pp. 2356 – 2360
[28] Nasr Al-Hinai, ElMekkawy T. Y.(2010), An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling
problem, Flexible Services and Manufacturing Journal,December 2010.
[29] Jahangirian M. and Conroy G. V. (2000). Intelligent dynamic scheduling system: the application of genetic algorithms, Integrated
Manufacturing Systems, Vol. 11, No. 4, pp.247-257
[30] Fanti M. P., Maione B., Naso D. Turchiano B. (1998). Genetic multi-criteriaapproach to flexible line scheduling, International Journal
of Approximate Reasoning,Vol. 19, No. 1-2, pp. 5-21
[31] Saravana Sankar S, Ponnanbalam S.G.,Rajendran C.A multiobjective genetic algorithm for scheduling a flexible manufacturing
system, The International Journal of Advanced Manufacturing Technology ,Volume 22, Numbers 3-4, pp. 229-236.
[32] Erkmen, A.M, et al.(1997). Genetically tuned fuzzy scheduling for flexible manufacturing systems, IEEE International Conference on
Robotics and Automation, 1997. Proceedings., 1997 , pp.951-956
[33] Luis Rabelo et al.(1993),INTELLIGENT SCHEDULING FOR FLEXIBLE MANUFACTURING SYSTEMS 1993 IEEE
International Conference on Robotics and Automation. Proceedings., vol.3, pp-810 - 815
[34] Sankar, S.S.,Ponnambalam.(2004) S.G.; An intelligent integrated scheduling model for Flexible Manufacturing System, IEEE
Conference on Robotics, Automation and Mechatronics,vol.2,pp- 1095 - 1100
[35] Hou, E.S.H., Li, H.-Y.(1991) Task scheduling for flexible manufacturing systems based on genetic algorithms, IEEE International
Conference on Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings.,vol-1,pp- 397 – 402.
[36] Haipeng Zhang, Mitsuo Gen.(2006) Effective genetic approach for optimizing advanced planning and scheduling in flexible
manufacturing system, Genetic and Evolutionary Computation Conference - GECCO , pp. 1841-1848.
[37] António Ferrolho,
Manuel Crisóstomo.(2007). Optimization of Genetic Operators for Scheduling Problems, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.11, No.9 pp. 1092-1098.
[38] Kumar, A., Prakash, Tiwari, M., Shankar, R.,Baveja, A. (2006). Solving machine-loading problem of a flexible manufacturing system
[39] Chen, J, Ho, S.(2005) A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm, International Journal of Machine Tools & Manufacture, 45. Pp. 949-957
[40] Zhao, C, Wu, Z. (2001). A genetic algorithm approach to the scheduling of FMSs with multiple routes, International Journal of
Flexible Manufacturing Systems, 13, 1, pp.71-88.
[41] Chan F.T.S et al.(2006),Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach,Robotics
and Computer-Integrated Manufacturing, Volume 22, Issues 5-6, , pp. 493-504
[42] Jie Gao, Linyan Sun, Mitsuo Gen,(2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop
scheduling problems, Computers & Operations Research 35,pp.2892 – 2907
[43] Zhang H-P, Gen M.(2005) Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity
International,11,pp.223–32
[44] Mesghouni K, Hammadi S. Borne P.(1998) On Modeling Genetic Algorithms for Flexible Job-shop Scheduling Problems, Stud. Inf.
Control (Romania).
[45] Tay, J. C, Wibowo, D. (2004). An effective chromosome representation for evolving flexible job-shop schedules. In Proceedings of the
genetic and evolutionary computation GECCO2004,pp. 210–221.
[46] Guohui Zhang,
Liang Gao,
Yang Shi.(2011) An effective genetic algorithm for the flexible job-shop scheduling problem, Expert Systems with Applications Volume 38, Issue 4, Pages 3563-3573
[47] Jensen, M.T(2003). Generating robust and flexible job shop schedules using genetic algorithms, IEEE Transactions on Evolutionary
Computation,vol-7 issue-3, pp. 275 – 288
[48] Haipeng Zhang, Mitsuo Gen.(2009). Multistage-based genetic algorithm for flexible job-shop scheduling problem, Intelligent and
Evolutionary Systems Studies in Computational Intelligence, 2009, Volume 187/2009, pp.183-196
[49] Guohui Zhang, Yang Shi, Liang Gao.(2008)., A Genetic Algorithm and Tabu Search for Solving Flexible Job Shop Schedules, 2008
