Jobshop scheduling with imprecise durations: a fuzzy approach

Jobshop scheduling problems are NP-hard problems. The durations in the reality of manufacturing are often imprecise and the imprecision in data is very critical for the scheduling procedures. Therefore, the fuzzy approach, in the framework of the Dempster-Shafer theory, commands attention. The fuzzy numbers are considered as sets of possible probabilistic distributions. After a review of some issues concerning fuzzy numbers, we discuss the determination of a unique optimal solution of the problem and then we cast a meta-heuristic (simulated annealing-SA) to this particular framework for optimization. It should be stressed that the obtained schedule remains feasible for all realizations of the operations durations.

[1]  Ibrahim H. Osman,et al.  Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem , 1993, Ann. Oper. Res..

[2]  Hideo Tanaka,et al.  Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems , 1994 .

[3]  Takeshi Yamada,et al.  A Genetic Algorithm Applicable to Large-Scale Job-Shop Problems , 1992, PPSN.

[4]  Zuliang Shen,et al.  R.R. Yager, S. Ovchinnikov, R.M. Tong and H.T. Nguyen, eds., Fuzzy Sets and Applications: Selected Papers by L. A. Zadeh , 1993, Artif. Intell..

[5]  E. L. Ulungu,et al.  Multi‐objective combinatorial optimization problems: A survey , 1994 .

[6]  Yoshikazu Nishikawa,et al.  A Parallel Genetic Algorithm based on a Neighborhood Model and Its Application to Jobshop Scheduling , 1993, PPSN.

[7]  L. Zadeh,et al.  Fuzzy sets and applications : selected papers , 1987 .

[8]  Paolo Brandimarte,et al.  Routing and scheduling in a flexible job shop by tabu search , 1993, Ann. Oper. Res..

[9]  Andrzej Jaszkiewicz,et al.  Fuzzy project scheduling system for software development , 1994 .

[10]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[11]  Yufei Yuan Criteria for evaluating fuzzy ranking methods , 1991 .

[12]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[13]  Didier Dubois,et al.  Readings in Fuzzy Sets for Intelligent Systems , 1993 .

[14]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[15]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[16]  Hiroaki Ishii,et al.  Two scheduling problems with fuzzy due-dates , 1992 .

[17]  H. Ishibuchi,et al.  Local search algorithms for flow shop scheduling with fuzzy due-dates☆ , 1994 .

[18]  Marc Roubens,et al.  Ranking and defuzzification methods based on area compensation , 1996, Fuzzy Sets Syst..

[19]  D. Dubois,et al.  The mean value of a fuzzy number , 1987 .

[20]  Mauro Dell'Amico,et al.  Applying tabu search to the job-shop scheduling problem , 1993, Ann. Oper. Res..

[21]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[22]  Marc Roubens,et al.  Inequality Constraints between Fuzzy Numbers and Their Use in Mathematical Programming , 1990 .

[23]  H. Rommelfanger Fulpal — An Interactive Method for Solving (Multiobjective) Fuzzy Linear Programming Problems , 1990 .

[24]  Sergei Ovchinnikov,et al.  Fuzzy sets and applications , 1987 .

[25]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[26]  Jan Karel Lenstra,et al.  Recent developments in deterministic sequencing and scheduling: a survey : (preprint) , 1981 .

[27]  E.Stanley Lee,et al.  Fuzzy job sequencing for a flow shop , 1992 .

[28]  Oscar H. IBARm Information and Control , 1957, Nature.

[29]  R. Słowiński,et al.  Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty , 1990, Theory and Decision Library.