Scheduling manufacturing systems in an agile environment

Abstract Producing customized products to respond to changing markets in a short time and at a low cost is one of the goals in agile manufacturing. To achieve this goal customized products can be produced using an assembly-driven product differentiation strategy. The successful implementation of this strategy lies in efficient scheduling of the system. However, little research has been done in addressing the scheduling issues related to assembly-driven product differentiation strategies in agile manufacturing. In this paper, scheduling problems associated with the assembly-driven product differentiation strategy in a general flexible manufacturing system are defined, formulated, and solved. The manufacturing system consists of two stages: machining and assembly. At the machining stage, multiple identical machines produce parts. These parts are then assembled at the assembly stage to form customized products. The products to be produced in the system are characterized by their assembly sequences that are represented by different digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of machines at the machining stage. The scheduling problems discussed in this paper have not been solved in the literature. The originality of the paper lies in defining and formulating the problems in the context of agile manufacturing and developing optimal and near-optimal for solving them. The heuristic algorithm solves the scheduling problem in two steps. First, an optimal aggregate schedule is determined by solving a two-machine flowshop problem. Next, the optimal aggregate schedule is decomposed by solving a simple integer programming formulation model. The computational experiment shows that the heuristics provide optimal and near-optimal solutions to the scheduling problems.

[1]  Chung-Yee Lee,et al.  Minimizing the makespan in the 3-machine assembly-type flowshop scheduling problem , 1993 .

[2]  Robert J. Wittrock,et al.  An Adaptable Scheduling Algorithm for Flexible Flow Lines , 1988, Oper. Res..

[3]  Krishna R. Pattipati,et al.  A practical approach to job-shop scheduling problems , 1993, IEEE Trans. Robotics Autom..

[4]  D. Santos,et al.  Global lower bounds for flow shops with multiple processors , 1995 .

[5]  Moshe Dror,et al.  Mathematical programming formulations for machine scheduling: A survey , 1991 .

[6]  Z.-P. Lo,et al.  Scheduling with neural networks for flexible manufacturing systems , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[7]  Denis Pellerin,et al.  Scheduling with neural networks: Application to timetable construction , 1994, Neurocomputing.

[8]  John L. Hunsucker,et al.  Comparative performance analysis of priority rules in a constrained flow shop with multiple processors environment , 1994 .

[9]  Tadeusz Sawik,et al.  A scheduling algorithm for flexible flow lines with limited intermediate buffers , 1993 .

[10]  M. Sacramento Quintanilla,et al.  A tabu search approach to machine scheduling , 1998, Eur. J. Oper. Res..

[11]  D.J. Hoitomt,et al.  Scheduling jobs with simple precedence constraints on parallel machines , 1990, IEEE Control Systems Magazine.

[12]  Jan Karel Lenstra,et al.  Complexity of Scheduling under Precedence Constraints , 1978, Oper. Res..

[13]  Mark D. Johnston,et al.  Scheduling with neural networks - the case of the hubble space telescope , 1992, Comput. Oper. Res..

[14]  Andrew Kusiak Aggregate scheduling of a flexible machining and assembly system , 1989, IEEE Trans. Robotics Autom..

[15]  Michael Kolonko,et al.  Some new results on simulated annealing applied to the job shop scheduling problem , 1999, Eur. J. Oper. Res..

[16]  Ronald L. Graham,et al.  Bounds on Multiprocessing Timing Anomalies , 1969, SIAM Journal of Applied Mathematics.

[17]  Michael A. Langston,et al.  Evaluation of a MULTIFIT-Based Scheduling Algorithm , 1986, J. Algorithms.

[18]  Chelliah Sriskandarajah,et al.  Scheduling algorithms for flexible flowshops: Worst and average case performance , 1988 .

[19]  Akio Yamamoto,et al.  Scheduling for minimizing total actual flow time by neural networks , 1992 .

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

[21]  Jatinder N. D. Gupta,et al.  Two-Stage, Hybrid Flowshop Scheduling Problem , 1988 .

[22]  Krishna R. Pattipati,et al.  Schedule generation and reconfiguration for parallel machines , 1990, IEEE Trans. Robotics Autom..

[23]  J. Hunsucker,et al.  BRANCH AND BOUND ALGORITHM FOR THE FLOW SHOP WITH MULTIPLE PROCESSORS , 1991 .

[24]  Yuehwern Yih,et al.  A competitive neural network approach to multi-objective FMS scheduling , 1998 .

[25]  R. GareyM.,et al.  `` Strong '' NP-Completeness Results , 1978 .

[26]  Eugeniusz Nowicki,et al.  The flow shop with parallel machines: A tabu search approach , 1998, Eur. J. Oper. Res..

[27]  James P. Ignizio,et al.  A stochastic neural network for resource constrained scheduling , 1992, Comput. Oper. Res..

[28]  David S. Johnson,et al.  `` Strong '' NP-Completeness Results: Motivation, Examples, and Implications , 1978, JACM.

[29]  Peter B. Luh,et al.  Lagrangian relaxation neural networks for job shop scheduling , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[30]  John L. Hunsucker,et al.  Mathematical Modeling of Scheduling Problems , 1991 .

[31]  B. J. Lageweg,et al.  Surrogate duality relaxation for job shop scheduling , 1983, Discret. Appl. Math..

[32]  James P. Ignizio,et al.  Neural networks and operations research: An overview , 1992, Comput. Oper. Res..

[33]  Shaukat A. Brah,et al.  Heuristics for scheduling in a flow shop with multiple processors , 1999, Eur. J. Oper. Res..

[34]  SahniSartaj,et al.  Flowshop and Jobshop Schedules , 1978 .

[35]  Bo Chen Analysis of Classes of Heuristics for Scheduling a Two-Stage Flow Shop with Parallel Machines at One Stage , 1995 .

[36]  T.C.E. Cheng,et al.  A state-of-the-art review of parallel-machine scheduling research , 1990 .

[37]  J. Gupta,et al.  Schedules for a two-stage hybrid flowshop with parallel machines at the second stage , 1991 .

[38]  Edward G. Coffman,et al.  An Application of Bin-Packing to Multiprocessor Scheduling , 1978, SIAM J. Comput..

[39]  Marie-Claude Portmann,et al.  Branch and bound crossed with GA to solve hybrid flowshops , 1998, Eur. J. Oper. Res..

[40]  Yuehwern Yih,et al.  ROBOT SCHEDULING IN A CIRCUIT BOARD PRODUCTION LINE:A HYBRID OR/ANN APPROACH , 1993 .

[41]  George Chryssolouris,et al.  The use of neural networks in determining operational policies for manufacturing systems , 1991 .

[42]  Teofilo F. Gonzalez,et al.  Flowshop and Jobshop Schedules: Complexity and Approximation , 1978, Oper. Res..

[43]  S. M. Johnson,et al.  Optimal two- and three-stage production schedules with setup times included , 1954 .

[44]  Martin Read The Structured Programming Aid: An Interactive Fortran Program Generator , 1983 .

[45]  Tao Li,et al.  Design of Competition-Based Neural Networks for Combinatorial Optimization , 1990, Int. J. Neural Syst..