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An Effective Hybrid Fuzzy Programming Approach for an Entropy-Based Multi-Objective Assembly Line Balancing Problem
Volume 30, Issue 3 (2019), pp. 503–528
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 March 2018
Accepted
1 March 2019
Published
1 January 2019

Abstract

In cases where the balance problem of an assembly line with the aim to distribute the work loads among the stations as equal as possible, the concept of entropy function can be used. In this paper, a typical assembly line balancing problem with different objective functions such as entropy-based objective function plus two more objective functions like equipment purchasing cost and worker time-dependent wage is formulated. The non-linear entropy-based objective function is approximated as a linear function using the bounded variable method of linear programming. A new hybrid fuzzy programming approach is proposed to solve the proposed multi-objective formulation efficiently. The extensive computational experiments on some test problems proves the efficiency of the proposed solution approach comparing to the available approaches of the literature.

References

 
Alavidoost, M.H., Tarimoradi, M., Fazel Zarandi, M.H. (2015). Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Applied Soft Computing, 34, 655–677.
 
Alavidoost, M.H., Babazadeh, H., Sayyari, S.T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied Soft Computing, 40, 221–235.
 
Amen, M. (2001). Heuristic methods for cost-oriented assembly line balancing: A comparison on solution quality and computing time. International Journal of Production Economics, 69(3), 255–264.
 
Amen, M. (2006). Cost-oriented assembly line balancing: Model formulations, solution difficulty, upper and lower bounds. European Journal of Operational Research, 168(3), 747–770.
 
Battini, D., Delorme, X., Dolgui, A., Sgarbossa, S. (2015). Assembly line balancing with ergonomics paradigms: two alternative methods. IFAC-PapersOnLine, 48(3), 586–591.
 
Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32, 909–932.
 
Borba, L., Ritt, M., Miralles, C. (2018). Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem. European Journal of Operational Research, 270(1), 146–156.
 
Buyukozkan, K., Kucukkoc, I., Satoglu, S.I., Zhang, D.Z. (2016). Lexicographic bottleneck mixed-model assembly line balancing problem: Artificial bee colony and tabu search approaches with optimised parameters. Expert Systems with Applications, 50, 151–166.
 
Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, United States.
 
Demirli, K., Yimer, A.D. (2008). Fuzzy scheduling of a build-to-order supply chain. International Journal of Production Research, 46, 3931–3958.
 
Fattahi, A., Turkay, M. (2015). On the MILP model for the U-shaped assembly line balancing problems. European Journal of Operational Research, 242(1), 343–346.
 
Hajipour, V., Fattahi, P., Tavana, M., Di Caprio, D. (2016). Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics. Applied Mathematical Modelling, 40(7–8), 4948–4969.
 
Hazır, Ö., Dolgui, A. (2015). A decomposition based solution algorithm for U-type assembly line balancing with interval data. Computers & Operations Research, 59, 126–131.
 
Jablonsky, J. (2007). Measuring the efficiency of production units by AHP models. Mathematical and Computer Modelling, 46(7–8), 1091–1098.
 
Jablonsky, J. (2014). MS Excel based software support tools for decision problems with multiple criteria. Procedia Economics and Finance, 12, 251–258.
 
Jackson, J.R. (1956). A computing procedure for a line balancing problem. Management Science, 2, 261–271.
 
Keshavarz Ghorabaee, M., Amiri, M., Turskis, Z. (2017). A new approach for solving bi-objective redundancy allocation problem using DOE, simulation and ε-constraint method. Informatica, 28(1), 79–104.
 
Kovács, G., Marian, M. (2002). Viability results in control of one-dimensional discrete time dynamical systems defined by a multi-function. Pure Mathematics and Applications, 13(1–2), 185–195.
 
Kucukkoc, I., Zhang, D.Z. (2014). Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines. International Journal of Production Economics, 158, 314–333.
 
Kucukkoc, I., Zhang, D.Z. (2016). Mixed-model parallel two-sided assembly line balancing problem: a flexible agent-based ant colony optimization approach. Computers & Industrial Engineering, 97, 58–72.
 
Lea, D., Gub, X. (2016). Variable neighborhood search for the second type of two-sided assembly line balancing problem. Computers & Operations Research, 72, 183–188.
 
Mardani-Fard, H.A., Hadi-Vencheh, A., Mahmoodirad, A., Niroomand, S. (2018). An effective hybrid goal programming approach for multi-objective straight assembly line balancing problem with stochastic parameters. Operational Research. doi:10.1007/s12351-018-0428-8.
 
Mitchell, J. (1957). A Computational Procedure for Balancing Zoned Assembly Lines. Research Report 6-94801-1-R3, Westinghouse Research Laboratories, Pittsburgh.
 
Ogan, D., Azizoglu, M. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. Journal of Manufacturing Systems, 36, 46–54.
 
Ojha, A., Das, N., Mondal, S., Maiti, M. (2009). An entropy based solid transportation problem for general fuzzy costs and time with fuzzy equality. Mathematical and Computer Modelling, 50, 166–178.
 
Pereira, J., Álvarez-Miranda, E. (2018). An exact approach for the robust assembly line balancing problem. Omega, 78, 85–98.
 
Ramezanian, R., Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87, 74–80.
 
Salehi, M., Maleki, H.R., Niroomand, S. (2018). A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment. Applied Intelligence, 48(8), 2137–2156.
 
Samouei, P., Fattahi, P., Ashayeri, J., Ghazinoory, S. (2016). Bottleneck easing-based assignment of work and product mixture determination: fuzzy assembly line balancing approach. Applied Mathematical Modelling, 40(7–8), 4323–4340.
 
Selim, H., Ozkarahan, I. (2008). A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology, 36, 401–418.
 
Sepahi, A., Jalali Naini, S.J. (2016). Two-sided assembly line balancing problem with parallel performance capacity. Applied Mathematical Modelling, 40(13–14), 6280–6292.
 
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.
 
Sun, H., Chen, R., Qin, Y., Wang, S. (2017). Holo-entropy based categorical data hierarchical clustering. Informatica, 28(2), 303–328.
 
Tavana, M., Abtahi, A.R., Khalili-Damghani, K. (2014a). A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems. Expert Systems with Applications, 41(4), 1830–1846.
 
Tavana, M., Fazlollahtabar, H., Hassanzade, R. (2014b). A bi-objective stochastic programming model for optimising automated material handling systems with reliability considerations. International Journal of Production Research, 52(19), 5597–5610.
 
Tavana, M., Li, Z., Mobin, M., Komaki, G.M., Teymourian, E. (2016). Multi-objective control chart design. optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Systems with Applications, 50, 17–39.
 
Tonge, F.M. (1960). Summary of a heuristic line balancing procedure. Management Science, 7, 21–39.
 
Torabi, S.A., Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159, 193–214.
 
Tuncel, G., Aydin, D. (2014). Two-sided assembly line balancing using teaching–learning based optimization algorithm. Computers & Industrial Engineering, 74, 291–299.
 
Yang, C., Gao, J. (2016). Balancing mixed-model assembly lines using adjacent cross-training in a demand variation environment. Computers & Operations Research, 65, 139–148.
 
Yuguang, Z., Bo, A., Yong, Z. (2016). A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy. Computers & Industrial Engineering, 98, 53–62.
 
Zacharia, Th. Nearchou A. C, P. (2016). A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem. Engineering Applications of Artificial Intelligence, 49, 1–9.
 
Zimmermann, H.J. (1996). Fuzzy Set Theory and Its Applications. Kluwer-Nijhoff, Boston, United States.
 
Zeng, S., Gonzalez, J., Lobato, C. (2015). The effect of organizational learning and Web 2.0 on innovation. Management Decision, 53(9), 2060–2072.
 
Zeng, S., Palacios-Marques, D., Zhu, F. (2016). A new model for interactive group decision making with intuitionistic fuzzy preference relations. Informatica, 27(4), 911–928.

Biographies

Mahmoodirad Ali

A. Mahmoodirad is an assistant professor of applied mathematics (operation research) in Masjed-Soleiman Branch of Islamic Azad University in Iran. His research interests include fuzzy mathematical programming, supply chain management, transportation problems and decomposition methods.

Heydari Ahmad

A. Heydari is a faculty member at Firouzabad Institute of Higher Education in Iran. He received his master’s degree in mathematics from Shahid Beheshti University (in Iran).

Niroomand Sadegh
sadegh.niroomand@yahoo.com

S. Niroomand is an assistant professor of industrial engineering in Firouzabad Institute of Higher Education in Iran. He received his PhD degree in industrial engineering from Eastern Mediterranean University (in Turkey), in 2013. His research interests are operations research, fuzzy theory, and exact and meta-heuristic solution approaches.


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© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
assembly line balancing problem entropy function bounded variable linearization method fuzzy programming approach

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