A COMPARATIVE STUDY OF THE USABILITY OF DIFFERENT PRODUCTION SCHEDULING ALGORITHMS AND RULES
Keywords:logistics, production scheduling, optimization algorithms, flow shop, job shop, open shop, mixed shop
Production scheduling optimization is a very important part of a production process. There are production systems with one service object and systems with multiple service objects. When using several service objects, there are systems with service objects arranged in a parallel or in a serial manner. We also distinguish between systems such as flow shop, job shop, open shop and mixed shop. Throughout the history of production planning, a number of algorithms and rules have been developed to calculate optimal production plans. These algorithms and rules differ from each other in the possibilities and conditions of their application. Since there are too many possible algorithms and rules it is not easy to select the proper algorithm or rule for solving a specific scheduling problem. In this article we analyzed the usability of 33 different algorithms and rules in total. Each algorithm or rule is suitable for a specific type of problem. The result of our analysis is a set of comparison tables that can serve as a basis for making the right decision in the production process decision-making process in order to select the proper algorithm or rule for solving a specific problem. We believe that these tables can be used for a quick and easy selection of the proper algorithm or rule for solving some of the typical production scheduling problems.
Brezina, I. (2003). Kvantitatívne metódy v logistike. Bratislava, Slovak Republic: Vydavateľstvo EKONÓM.
Brucker, P., Jurisch, B., & Sievers, B. (1994). A branch and bound algorithm for the job-shop scheduling problem. Discrete Applied Mathematics, 49, 107-127.
Campbell, H. G., Dudek, R. A., & Smith, M. L. (1970). A heuristic algorithm for the `n’ job `m’ machine sequencing problem. Management Science, 16, B630-B637.
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394. http://dx.doi.org/10.1016/j.ijpe.2018.08.019.
Dannenbring, D. G. (1977). An evaluation of flow shop sequencing heuristics. Management Science, 23(11), 1174-1182.
Fast Technologies. Welcome to Fast Technologies. Retrieved March 15, 2021, from https://fasttechnologies.com.
Ghazanfari, M., & Yaghoobi, Z. (2002). A Hybrid GA Model to Solve Regular and Blow Flow-Shop Problems. Scientia Iranica, 9(3), 276-282.
Gonzales, T., & Sahni, S. (1976). Open Shop Scheduling To Minimize Finish Time. Journal of the ACM, 23(4), 665-679. doi:10.1145/321978.321985.
Gowrishankar, K., Rajendran, C., & Srinivasan, G. (2001). Flow Shop Scheduling Algorithms for Minimizing the Completion Time Variance and the Sum of Squares of Completion Time Deviations from a Common Due Date. European Journal of Operational Research, 132, 643-665.
Gupta, J. (1971). A functional heuristic algorithm for the flow shop scheduling problem. Operations Research Quarterly, 22, 39-47.
Hax, A. C., & Candea, D. (1984). Production and Inventory Management. Englewood Cliffs, New Jersey: Prentice-Hall.
Hu, T. C. (1961). Parallel Sequencing and Assembly Line Problems. Operations Research, 9(6), 841-848.
Liu, S. Q., Ong H. L. (2004). Metaheuristics for the Mixed Shop Scheduling Problem. Asia-Pacific Journal of Operational Research, 21(4), 97-115.
Lawler, E. L. (1973). Optimal sequencing of a single machine subject to precedence constraints. Management Science, 19, 544-546.
Masuda, T., Ishii, H., & Nishida, T. (1985). The Mixed Shop Scheduling Problem. Discrete Applied Mathematics, 11, 175-186.
Malik, A., & Dhingra, A. K. (2013). Comparative Analysis of Heuristics for Makespan Minimising in Flow Shop Scheduling. International Journal of Innovations in Engineering and Technology, 2(4), 263-269.
Modrak, V. Semanco, P. & Kulpa, W. (2009). Performance measurement of selected heuristics algorithm for solving scheduling problems. European Journal of Operational Research, 34, 158–183. doi: 10.1109/SAMI.2013.6480977.
Moore, J. M. (1968). A n job, one machine sequencing algorithm for minimizing the number of late jobs. Management Science, 15, 102-109.
Nawaz, M. Enscore, E. & Ham, I. (1983). A heuristic algorithm for the m machine, n job flow shop sequence problem. OMEGA, 11, 91-95.
Palmer, D. S. (1965). Sequencing jobs through a multi stage process in the minimum total time - a quick method of obtaining a near optimum. Operational Research Quarterly, 16, 101-107.
Pan, J. H., Chen, J. S., & Chao, C. M. (2002). Minimizing Tardiness in a Two-Machine Flow-Shop. Computers & Operations Research, 29, 869-885.
Parveen, S., & Ullah, H. (2010). Review On Job-Shop And Flow-Shop Scheduling Using Multi Criteria Decision Making. Journal of Mechanical Engineering, ME41(2), 130-146.
Pinedo, M. (1995). Scheduling: Theory, Algorithms, and Systems. New Jersey: Prentice Hall.
Rothkopf, M. (1966). Scheduling with random service times. Management Science, 12 (9), 707-713. doi: 10.1287/mnsc.12.9.707.
Semančo, P., & Modrák, V. (2012). A Comparison of Constructive Heuristics with the Objective of Minimizing Makespan in the Flow-Shop Scheduling Problem. Acta Polytechnica Hungarica, 9(5), 177-190.
Shakhlevich, N. V., Sotsko Y. N. & Werner, F. (2000). Complexity of mixed shop scheduling problems: a survey. European Journal of Operational Research, 120, 343–351.
Smith, W. E. (1956). Various optimizers for single-stage production. Naval Research Logistics Quarterly, 3.
Strusevich, VA (1991). Two machine super shop scheduling problem. Journal of the Operational Research Society, 42(6), 479–492.
Šeda, M. (2006). Mathematical Models of Flow Shop and Job Shop Scheduling Problems. International Journal of Applied Mathematics and Computer Sciences, 4(4), 241-246.
Unčovský, L. (1991). Modely sieťovej analýzy. Bratislava, Slovak Republic: Alfa.
Waschneck, B., Bauernhansl, T., Altenmüller, T., & Kyek, A. (2016). Production Scheduling in Complex Job Shops from an Industrie 4.0 Perspective: A Review and Challenges in the Semiconductor Industry. 1st International Workshop on Science, Application and Methods in Industry 4.0, SAMI 2016. Proceedings.
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