Modelling and heuristics of FMS scheduling with multiple objectives

The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, we make use of one of the multiple objective decision-making methods, a global criterion approach, to develop a multi-objective model for solving FMS scheduling problems with consideration of three performance measures, namely minimum mean job flow time, mean job tardiness, and minimum mean machine idle time, simultaneously. In addition, hybrid heuristics, which are a combination of two common local search methods, simulated annealing and tabu search, are also proposed for solving the addressed FMS scheduling problems. The feasibility and adaptability of the proposed heuristics are investigated through experimental results.

[1]  FEDERICO DELLA CROCE,et al.  A genetic algorithm for the job shop problem , 1995, Comput. Oper. Res..

[2]  Richard L. Daniels,et al.  Incorporating preference information into multi-objective scheduling , 1994 .

[3]  Chinyao Low,et al.  Mathematical modelling and heuristic approaches to operation scheduling problems in an FMS environment , 2001 .

[4]  Masatoshi Sakawa,et al.  Flexible scheduling in a machining center through genetic algorithms , 1996 .

[5]  Egon Balas,et al.  The Shifting Bottleneck Procedure for Job Shop Scheduling , 1988 .

[6]  J. Barnes,et al.  Solving the job shop scheduling problem with tabu search , 1995 .

[7]  Jr. Mikell P. Groover.  Automation, production systems, and computer-aided manufacturing , 1980 .

[8]  E. Aanen,et al.  A scheduling approach for a flexible manufacturing system , 1993 .

[9]  Xi-Ren Cao.  Perturbation analysis of discrete event systems: Concepts, algorithms, and applications , 1996 .

[10]  Hirofumi Matsuo,et al.  A bottleneck-based beam search for job scheduling in a flexible manufacturing system , 1989 .

[11]  M. Widmer,et al.  Job Shop Scheduling with Tooling Constraints: a Tabu Search Approach , 1991 .

[12]  Belarmino Adenso-Díaz,et al.  An SA/TS mixture algorithm for the scheduling tardiness problem , 1996 .

[13]  Taeyong Yang,et al.  An exchange heuristic imbedded with simulated annealing for due-dates job-shop scheduling , 1996 .

[14]  H. Ishibuchi,et al.  Multi-objective genetic algorithm and its applications to flowshop scheduling , 1996 .

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

[16]  J. Carlier,et al.  An algorithm for solving the job-shop problem , 1989 .

[17]  C. Hwang,et al.  Fuzzy Multiple Objective Decision Making: Methods And Applications , 1996 .

[18]  Sang M. Lee,et al.  A multi-objective production planning model in a flexible manufacturing environment , 1989 .

[19]  Joseph J. Talavage,et al.  Intelligent dispatching for flexible manufacturing , 1991 .

[20]  Jiyin Liu,et al.  The classification of FMS scheduling problems , 1996 .

[21]  S. Engell,et al.  A survey of priority rules for FMS scheduling and their performance for the benchmark problem , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[22]  Jim Hutchison,et al.  Scheduling approaches for random job shop flexible manufacturing systems , 1991 .

[23]  Varghese S. Jacob,et al.  A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts , 1993, IEEE Trans. Syst. Man Cybern..

[24]  T. Sawik.  Modelling and scheduling of a Flexible Manufacturing System , 1990 .

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

[26]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[27]  Richard Bellman,et al.  The Job-shop Scheduling Problem , 1982 .

[28]  John E. Biegel,et al.  Genetic algorithms and job shop scheduling , 1990 .

[29]  Chen-Hua Chung,et al.  Effects of loading and routeing decisions on performance of flexible manufacturing systems , 1991 .

[30]  P. Brucker,et al.  A new lower bound for the job-shop scheduling problem , 1993 .

[31]  Mario Calderini,et al.  A hierarchical bicriterion approach to integrated process plan selection and job shop scheduling , 1995 .

[32]  D. M. Deighton,et al.  Computers in Operations Research , 1977, Aust. Comput. J..

[33]  Stefka P. Stoeva.  A due date-based dispatching rule for flexible manufacturing systems , 1990 .

[34]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[35]  William J. Cook,et al.  A Computational Study of the Job-Shop Scheduling Problem , 1991, INFORMS Journal on Computing.