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An effective hybrid goal programming approach for multi-objective straight assembly line balancing problem with stochastic parameters

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Abstract

A new multi-objective straight assembly line balancing problem is focused in this study. The problem happens in a stochastic environment where the task times and the task performing quality levels are distributed normally. The objectives like equipment purchasing cost, worker time dependent wage, and average task performing quality of the assembly line are to be optimized simultaneously. A mixed integer non-linear formulation is proposed for the problem. Applying a chance-constrained modeling approach and some linearization techniques the model is converted to a crisp multi-objective mixed integer linear formulation. To tackle such problem, a hybrid fuzzy programming approach is proposed and combined with a typical goal programming method to construct a new hybrid goal programming approach. The computational experiments of the study results in a superior performance of the proposed approach comparing to the literature.

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[source: Heydari et al. (2016)]

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References

  • Abd El-Wahed WF, Lee SM (2006) Interactive fuzzy goal programming for multi-objective transportation problems. Omega 34:158–166

    Google Scholar 

  • Agpak K, Gokcen H (2007) A chance-constrained approach to stochastic line balancing problem. Eur J Oper Res 180:1098–1115

    Google Scholar 

  • Alavidoost MH, Tarimoradi M, Fazel Zarandi MH (2015) Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Appl Soft Comput 34:655–677

    Google Scholar 

  • Alavidoost MH, Babazadeh H, Sayyari ST (2016) An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Appl Soft Comput 40:221–235

    Google Scholar 

  • Amen M (2001) Heuristic methods for cost-oriented assembly line balancing: a comparison on solution quality and computing time. Int J Prod Econ 69(3):255–264

    Google Scholar 

  • Amen M (2006) Cost-oriented assembly line balancing: model formulations, solution difficulty, upper and lower bounds. Eur J Oper Res 168(3):747–770

    Google Scholar 

  • Aneja YP, Nair KPK (1979) Bicriteria transportation problems. Manage Sci 1979(25):73–80

    Google Scholar 

  • Battini D, Delorme X, Dolgui A, Sgarbossa S (2015) Assembly line balancing with ergonomics paradigms: two alternative methods. IFAC-PapersOnLine 48(3):586–591

    Google Scholar 

  • Baybars I (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manage Sci 32:909–932

    Google Scholar 

  • Buyukozkan K, Kucukkoc I, Satoglu SI, Zhang DZ (2016) Lexicographic bottleneck mixed-model assembly line balancing problem: artificial bee colony and tabu search approaches with optimised parameters. Expert Syst Appl 50:151–166

    Google Scholar 

  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Naval Res Logist 9(3–4):181–186

    Google Scholar 

  • Climaco JN, Antunes CH, Alves MJ (1993) Interactive decision support for multiobjective transportation problems. Eur J Oper Res 65:58–67

    Google Scholar 

  • Colapinto C, Jayaraman R, Marsiglio S (2017) Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review. Ann Oper Res 251(1–2):7–40

    Google Scholar 

  • Demirli K, Yimer AD (2008) Fuzzy scheduling of a build-to-order supply chain. Int J Prod Res 46:3931–3958

    Google Scholar 

  • Fattahi A, Turkay M (2015) On the MILP model for the U-shaped assembly line balancing problems. Eur J Oper Res 242(1):343–346

    Google Scholar 

  • Hazır Ö, Dolgui A (2015) A decomposition based solution algorithm for U-type assembly line balancing with interval data. Comput Oper Res 59:126–131

    Google Scholar 

  • Heydari A, Mahmoodirad A, Niroomand S (2016) An entropy-based mathematical formulation for straight assembly line balancing problem. Int J Strateg Decis Sci 7(2):57–68

    Google Scholar 

  • Jablonsky J (2007) Measuring the efficiency of production units by AHP models. Math Comput Model 46(7–8):1091–1098

    Google Scholar 

  • Jablonsky J (2014) MS Excel based software support tools for decision problems with multiple criteria. Proc Econ Finance 12:251–258

    Google Scholar 

  • Kasana HS, Kumar KD (2000) An efficient algorithm for multiobjective transportation problems. Asia-Pacific Oper Res 17:27–40

    Google Scholar 

  • Khanjani Shiraz R, Tavana M, Fukuyama H, Di Caprio D (2015) Fuzzy chance-constrained geometric programming: the possibility, necessity and credibility approaches. Oper Res Int J. https://doi.org/10.1007/s12351-015-0216-7

    Article  Google Scholar 

  • Kucukkoc I, Zhang DZ (2014) Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines. Int J Prod Econ 158:314–333

    Google Scholar 

  • Kucukkoc I, Zhang DZ (2016) Mixed-model parallel two-sided assembly line balancing problem: a flexible agent-based ant colony optimization approach. Comput Ind Eng 97:58–72

    Google Scholar 

  • Lei D, Guo X (2016) Variable neighborhood search for the second type of two-sided assembly line balancing problem. Comput Oper Res 72:183–188

    Google Scholar 

  • Mahmoodirad A, Niroomand S, Mirzaei N, Shoja A (2017) Fuzzy fractional minimal cost flow problem. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-017-0293-2

    Article  Google Scholar 

  • Mosallaeipour S, Mahmoodirad A, Niroomand S, Vizvari B (2017) Simultaneous selection of material and supplier under uncertainty in carton box industries: a fuzzy possibilistic multi-criteria approach. Soft Comput. https://doi.org/10.1007/s00500-017-2542-6

    Article  Google Scholar 

  • Niroomand S, Mahmoodirad A, Heydari A, Kardani F, Hadi-Vencheh A (2016a) An extension principle based solution approach for shortest path problem with fuzzy arc lengths. Oper Res Int J. https://doi.org/10.1007/s12351-016-0230-4

    Article  Google Scholar 

  • Niroomand S, Hadi-Vencheh A, Mirzaei N, Molla-Alizadeh-Zavardehi S (2016b) Hybrid greedy algorithms for fuzzy tardiness/earliness minimization in a special single machine scheduling problem: case study and generalization. Int J Comput Integr Manuf 29(8):870–888

    Google Scholar 

  • Ogan D, Azizoglu M (2015) A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. J Manuf Syst 36:46–54

    Google Scholar 

  • Oksuz MK, Buyukozkan K, Satoglu SI (2017) U-shaped assembly line worker assignment and balancing problem: a mathematical model and two meta-heuristics. Comput Ind Eng 112:246–263

    Google Scholar 

  • Ramezanian R, Ezzatpanah A (2015) Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Comput Ind Eng 87:74–80

    Google Scholar 

  • Romero C (1991) Handbook of critical issues in goal programming. Pergamon Press, Oxford

    Google Scholar 

  • Salehi M, Maleki HR, Niroomand S (2017) A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment. Appl Intell. https://doi.org/10.1007/s10489-017-1065-2

    Article  Google Scholar 

  • Samouei P, Fattahi P, Ashayeri J, Ghazinoory S (2016) Bottleneck easing-based assignment of work and product mixture determination: fuzzy assembly line balancing approach. Appl Math Model 40(7–8):4323–4340

    Google Scholar 

  • Selim H, Ozkarahan I (2008) A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int J Adv Manuf Technol 36:401–418

    Google Scholar 

  • Sepahi A, Jalali Naini SJ (2016) Two-sided assembly line balancing problem with parallel performance capacity. Appl Math Model 40(13–14):6280–6292

    Google Scholar 

  • Tamiz M, Jones DF, Romero C (1998) Goal programming for decision making: an overview of the current state-of-the-art. Eur J Oper Res 111:569–581

    Google Scholar 

  • Tavana M, Abtahi AR, Khalili-Damghani K (2014a) A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems. Expert Syst Appl 41(4):1830–1846

    Google Scholar 

  • Tavana M, Fazlollahtabar H, Hassanzade R (2014b) A bi-objective stochastic programming model for optimising automated material handling systems with reliability considerations. Int J Prod Res 52(19):5597–5610

    Google Scholar 

  • Tavana M, Li Z, Mobin M, Komaki GM, Teymourian E (2016) Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Syst Appl 50:17–39

    Google Scholar 

  • Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159:193–214

    Google Scholar 

  • Tuncel G, Aydin D (2014) Two-sided assembly line balancing using teaching–learning based optimization algorithm. Comput Ind Eng 74:291–299

    Google Scholar 

  • Yang C, Gao J (2016) Balancing mixed-model assembly lines using adjacent cross-training in a demand variation environment. Comput Oper Res 65:139–148

    Google Scholar 

  • Yuguang Z, Bo A, Yong Z (2016) A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy. Comput Ind Eng 98:53–62

    Google Scholar 

  • Zacharia PTh, Nearchou AC (2016) A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem. Eng Appl Artif Intell 49:1–9

    Google Scholar 

  • Zimmermann HJ (1996) Fuzzy set theory and its applications. Kluwer-Nijhoff, Boston

    Google Scholar 

Download references

Acknowledgements

The authors are grateful of the editors and reviewers of the journal for their helpful and constructive comments that improved the quality of the paper. This study was supported by Firouzabad Institute of Higher Education (Research Project No. 1396.003). The authors are grateful of this financial support.

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Correspondence to Sadegh Niroomand.

Appendix

Appendix

The data set for the benchmarks with 16, 21, and 35 tasks which are named benchmarks 2, 3, and 4 respectively, are presented by the tables (from Tables 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24) of this section.

Table 13 The precedence relationships of the tasks of Benchmark 2 (precedence value of 1 shows that task i is the precedence of task j)
Table 14 The precedence relationships of the tasks of Benchmark 3 (precedence value of 1 shows that task i is the precedence of task j)
Table 15 The precedence relationships of the tasks of Benchmark 4 (precedence value of 1 shows that task i is the precedence of task j)
Table 16 Some basic parameters of the benchmarks
Table 17 Cost of the tools of the benchmarks
Table 18 Task times, cost for performing the tasks, and required tools of the tasks for the benchmarks
Table 19 Mean dis-quality levels (\(\mu_{dq,ik}\)) of Benchmark 2
Table 20 Standard deviation of dis-quality levels (\(\sigma_{dq,ik}\)) of Benchmark 2
Table 21 Mean dis-quality levels (\(\mu_{dq,ik}\)) of Benchmark 3
Table 22 Standard deviation of dis-quality levels (\(\sigma_{dq,ik}\)) of Benchmark 3
Table 23 Mean dis-quality levels (\(\mu_{dq,ik}\)) of Benchmark 4
Table 24 Standard deviation of dis-quality levels (\(\sigma_{dq,ik}\)) of Benchmark 4

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Mardani-Fard, H.A., Hadi-Vencheh, A., Mahmoodirad, A. et al. An effective hybrid goal programming approach for multi-objective straight assembly line balancing problem with stochastic parameters. Oper Res Int J 20, 1939–1976 (2020). https://doi.org/10.1007/s12351-018-0428-8

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