Abstract
The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.
Similar content being viewed by others
References
Allahverdi A, Al-Anzi FS (2006) A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput Oper Res 33:1056–1080
Bard JF, Dar-El EM, Shtub A (1992) An analytic framework for sequencing mixed model. Int J Prod Res 30:35–48
Bard JF, Shtub A, Joshi SB (1994) Sequencing mixed-model assembly lines to level parts usage and minimize the length. Int J Prod Res 32:2431–2454
Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169:426–449
Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proceeding ICEC, Washington, DC 1951–1957
Coello Coello CA, Toscano Pulido G (2001) A micro-genetic algorithm for multiobjective optimization. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization. Lecture Notes in Computer Sciences, No. 1993, Springer, Heidelberg pp 126–140
Coello Coello CA, Lechuga MS (2002) MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation, vol 2, pp 1051–1056
Coello Coello CA, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE, Transactions on Evolutionary Computation 8(3):256–279
Collette Y, Siarry P (2003) Multiobjective optimization: principles and case studies. Springer, Heidelberg
Deb K (1999) Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evol Comput J 7(3):205–230
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Fieldsend JE, Singh S (2002) A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence. In: Proceedings of the 2002 UK Workshop on Computational Intelligence 37–44
Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, University of Illinois at Urbana- Morgan Kaufman Publishers, Champaign pp 416–423
Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multi-objective optimization. Proceeding Of 1st IEEE-ICEC Conference pp 82–87
Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation 2:1677–1681
Hu X, Eberhart R, Shi Y (2003) Particle swarm with extended memory for multiobjective optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis 193–197
Hyun CJ, Kim Y, Kim YK (1998) A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Comput Oper Res 25(7–8):675–690
Inman PR, Bulfin RL (1991) Note on sequencing JIT mixed-model assembly lines. Manage Sci 37(7):910–904
Jaszkiewicz A (1999) Genetic local search for multiple objective combinatorial optimization, Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology: Technical Report
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks 4:1942–1948
Knowles JD, Corne DW (1999) The Pareto archieved evolution strategy: A new baseline algorithm for multiobjective optimization. In: Congress on evolutionary computation. IEEE Service Center Washington, DC, pp 98–105
Korkmazel T, Meral S (2001) Bicriteria sequencing methods for the mixed-model assembly line in Just-in-time Production systems. Eur J Oper Res 131:188–207
Mansouri SAA (2005) Multi-objective Genetic Algorithm for mixed-model sequencing on JIT assembly Lines. Eur J Oper Res (in press)
McMullen PR (1998) JIT sequencing for mixed-model assembly lines with setups using tabu search Production Planning& Control 9 (5):504–510
McMullen PR (2001a) An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics. Comput Ind Eng 41:335–353
McMullen PR (2001b) A Kohonen self-organizing map approach to addressing a multiple objective, mixed-model JIT sequencing problem. Int J Prod Econ 72:59–71
McMullen PR (2001c) An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artif Intell Eng 15:309–317
McMullen PR, Frazier GV (2000) A simulated annealing approach to mixed-model sequencing with multiple objectives on a JIT line .IIE Trans 32(8):679–686
Miltenburg J (1989) Level schedules for mixed-model assembly lines in just-in-time production systems. Manage Sci 35(2): 192–207
Miltenburg J, Goldstein GT (1991) Developing production scheduling which balance part usage and smooth production loads in just-in-time production systems. Naval Res Logistics 38: 893–910
Miltenburg J, Steiner G, Yeomans S (1990) A dynamic programming algorithm for scheduling mixed-model just-in-time production systems. Mathematical Computation Modeling 13: 57–66
Monden Y (1983) Toyota production system. Institute if industrial engineers Press. Atlanta, GA
Moore J, Chapman R (1999) Application of particle swarm to multiobjective optimization, Department of Computer Science and Software Engineering, Auburn University
Okamura K, Yamshina H (1979) A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor. Int J Prod Res 17:233–247
Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing 603–607
Parsopoulos KE, Tasoulis DK, Vrahatis MN (2004) Multiobjective optimization using parallel vector evaluated particle swarm optimization. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications 2:823–828
Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Schaffer JD (eds) Genetic algorithms and their applications: proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum, Hillsdale, New Jersey, pp 93–100
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In Evolutionary Programming VIZ: Proceeding EP98, Springer New York pp 591–600
Srinivas N, Deb K (1994) Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol Comput 2(2):221–248
Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proceedings of the IEEE congress on evolutionary computation, Oregon, Portland 2:1412–9
Tavakkoli-Moghaddam R, Rahimi-Vahed AR (2006) Multi- criteria sequencing problem for a mixed-model assembly line in a JIT production system. Appl Math Comput (in press)
Veldhuizen DV, Lamont G (1999) In: Carroll J, Haddad H, Oppenheim D, Bryant B, Lamont G (eds) Multi-objective evolutionary algorithm test suites 351–357
Yano CA, Rachamadugu R (1991) Sequencing to minimize work overload in assembly lines with product options. Manage Sci 37:572–586
Zitzler E, Laumanns M, Thiele L (2001a) SPEA2: Improving the strength Pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis D, Periaux J, Papailou P, Fogarty T (eds) EUROGEN 2001, Evolutionary methods for design, optimization and control with applications to industrial problems, Athens, Greece: September pp 95–100
Zitzler E, Laumanns M, Thiele L (2001b) SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK) -Report 103. Sept
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rahimi-Vahed, A.R., Mirghorbani, S.M. & Rabbani, M. A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem. Soft Comput 11, 997–1012 (2007). https://doi.org/10.1007/s00500-007-0149-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-007-0149-z