Simulation of workflow balancing in assembly shopfloor operations
Tóm tắt
To provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.
The assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.
The relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.
Non‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.
The experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.
The allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.
Từ khóa
Tài liệu tham khảo
Araujo, E.G., Dakin, G.A., Huber, M. and Grupen, R.A. (1995), “Hierarchical scheduling of robotic assembly operations in a flexible manufacturing system”, International Journal of Flexible Automation and Integrated Manufacturing, Vol. 3 No. 3/4, pp. 301‐16.
Brucker, P., Drexl, A., Möhring, R., Neumann, K. and Pesch, E. (1999), “Resource‐constrained project scheduling: notation, classification, models, and methods”, European Journal of Operational Research, Vol. 112, pp. 3‐41.
Chow, W.‐M. (1988), “A dynamic job‐assignment policy”, International Journal of Production Research, Vol. 26 No. 6, pp. 1073‐87.
Chudak, F. and Shmoys, D.B. (1999), “Approximation algorithms for precedence‐constrained scheduling problems on parallel machines that run at different speeds”, Journal of Algorithms, Vol. 30 No. 2, pp. 323‐43.
Dubois, D., Fargier, H. and Prade, H. (1995), “Fuzzy constraints in job shop scheduling”, Journal of Intelligent Manufacturing, Vol. 6, pp. 215‐34.
Farkas, A., Koltai, T. and Stecke, K.E. (1999), “Workload balancing using the concept of operation types”, Working Paper, No. 99‐002, Faculty Research, The University of Michigan Business School, Ann Arbor, MI, pp. 1‐23.
French, S. (1982), An Introduction to the Mathematics of the Job‐Shop, Wiley, New York, NY.
Guerrero, F., Lozano, S., Koltai, T. and Larraneta, J. (1999), “Machine loading and part type selection in flexible manufacturing systems”, International Journal of Production Research, Vol. 37 No. 6, pp. 1303‐17.
Gung, R.R. and Steudel, H.J. (1999), “A workload balancing model for determining set‐up time and batch size reductions in GT flowline workcells”, International Journal of Production Research, Vol. 37 No. 4, pp. 769‐91.
Hall, L.A., Schulz, A.S., Shmoys, D.B. and Wein, J. (1997), “Scheduling to minimize average completion time: off‐ and on‐line algorithms”, Mathematics of Operation Research, Vol. 22, pp. 513‐44.
Häyrinen, T., Johnson, M., Johtela, T., Smed, J. and Nevalainen, O. (1998), “Scheduling algorithms for computer‐aided line balancing in printed circuit board assembly”, TUCS Technical Report, No. 212, Turku Centre for Computer Science, Turku, pp. 1‐34.
Herrmann, J.W. and Lee, C.‐Y. (1994), “On parallel machine scheduling with operator constrained setup”, Technical Research Report, TR 94‐85, Institute for Systems Research, The University of Maryland, College Park, MD, pp. 1‐13.
Hunag, P.V. (1984), “A comparative study of priority dispatching rules in a hybrid assembly/jobshop”, International Journal of Production Research, Vol. 22 No. 3, pp. 375‐87.
Karger, D., Stein, C. and Wein, J. (1999), “Scheduling algorithms”, in Atallah, M.J. (Ed.), Algorithms and Theory of Computation Handbook, CRC Press, Boca Raton, FL.
Kumar, N. and Shankar, K. (2001), “Comparing the effectiveness of workload balancing objectives in FMS loading”, International Journal of Production Research, Vol. 39 No. 5, pp. 843‐71.
Land, M. and Gaalman, G. (1996), “Workload control concepts in job shops: a critical assessment”, International Journal of Production Economics, Vol. 46/47 No. 1‐3, pp. 535‐48.
Mokotoff, E. (2001), “Parallel machine scheduling problems: a survey”, Asia‐Pacific Journal of Operation Research, Vol. 18 No. 2, pp. 193‐242.
Mönch, L., Prause, M. and Schmalfuss, V. (2001), “Simulation‐based solution of load‐balancing problems in the photolithography area of a semiconductor wafer fabrication facility”, Proceedings of the 2001 Winter Simulation Conference, Arlington, VA, pp. 1170‐7.
Muhlemann, A.P., Lockett, A.G. and Farn, C.K. (1982), “Job shop scheduling heuristics and frequency of scheduling”, International Journal of Production Research, Vol. 20 No. 2, pp. 227‐41.
Roser, C., Nakano, M. and Tanaka, M. (2001), “A practical bottle‐neck detection method”, Proceedings of the 2001 Winter Simulation Conference, Arlington, VA, pp. 949‐53.
Skutella, M. and Uetz, M. (2001), “Scheduling precedence‐constrained jobs with stochastic processing times on parallel machines”, Proceedings of the 12th Annual ACM‐SIAM Symposium on Discrete Algorithms (SODA'01), Washington, DC, pp. 589‐90.
Vidyarthi, N.K. and Tiwari, M.K. (2001), “Machine loading problems of FMS: a fuzzy‐based heuristic approach”, International Journal of Production Research, Vol. 39 No. 5, pp. 953‐79.