Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

Ullah, Saif; Guan, Zailin; Wang, Baoxi; Mirza, Jahanzeb

doi:10.1007/s11465-014-0294-x

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…However, very few researchers considered the stochastic version of such ALB problems. Mixed-model assembly line balancing problems with multiple optimization objectives with consideration of stochastic task time and straight type line configuration was presented in Saif et al (2014), Neda et al (2012 and Yabo et al (2014). Neda et al (2012) presented a mixed-model assembly line balancing in the make-to-order and stochastic environment.…”

Section: Review Of Literaturementioning

confidence: 99%

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Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

Legesse¹,

Tesfaye²,

Berhan³

2020

Int. J. Eng. Sci. Tech

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The objective of this research is to propose a methodology for multi-objective optimization of a mixed-model assembly line balancing problem with the stochastic environment. To do this a mathematical model representing the problems at hand is developed with objectives of minimizing cycle time and minimization of the number of workstations (which is of Type-E ALB problem). And two optimization meta-heuristics are considered to solve it, namely, Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-Objective Genetic Algorithm (MOGA). To test the performance of the algorithms three different size standard problems in Assemble-to-order types of industry are taken and five demand arrival scenarios are considered to incorporate the stochastic nature of the demand arrival for each model in all problems. Both the algorithms are coded and run using MATLAB® 2013a and are compared based on different performance measures. The results indicated that MOGA outperformed NSGA-II in most of the test problems. Nevertheless, both algorithms have resulted in significant improvements in the performance measures in Assemble-to-order types of industry dataset compared to the existing line configuration. Keywords: Assembly Line, Multi-objective optimization, Single model, mixed-model, stochastic environment, Genetic Algorithm

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Section: Review Of Literaturementioning

confidence: 99%

“…A large part of real-world optimization problems are of multi-objective in nature (Naveen and Dalgobind, 2013). Most of the time, these objectives can be conflicting and compromised with each other (Saif et al, 2014, Manavizadeh et al, 2012, Naveen and Dalgobind, 2013, Pavel and Ulrych, 2012, Lapierre et al, 2006.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

Legesse¹,

Tesfaye²,

Berhan³

2020

Int. J. Eng. Sci. Tech

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“…The majority of the approaches existing in the literature for robust ALB are based on considering uncertainty in the input attributes of the tasks, such as operation time, by defining interval values or by setting different plausible scenarios (i.e., set of possible values for the input attributes depending on past actions or historical data). The most used robust criteria rely on the worst case by using traditional min-max or variations of it (Dolgui and Kovalev 2012, Simaria et al 2009, Xu and Xiao 2011, Saif et al 2014. Dolgui and Kovalev (2012) proposed an ALB model and a dynamic programming method to minimize the cycle time by following a worst scenario approach; while Li and Gao (2014) characterized unstable demand in manual mixed-model assembly lines by several representative scenarios.…”

Section: Robust Assembly Line Balancingmentioning

confidence: 99%

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

2018

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Changing conditions and variations in the demand are frequent in real industrial environments. Decision makers have to take into account this uncertainty and manage it properly. One clear example is the automotive industry where manufacturers have to assume an uncertain and heterogeneous demand. For instance, automotive manufacturers must adapt their decisions when balancing the assembly line by considering different flexible solutions. Our proposal is using robust multiobjective optimization and simulation techniques to provide managers with a set of robust and equally-preferred solutions for assembly line balancing. We study a Nissan case where the demand of each product family is uncertain. The problem is addressed by considering a robust multiobjective model for assembly line balancing based on a high number of production plans. After the selection of six different assembly line configurations, we study the implications of robustness metrics based on workstations' overload. We show that the adverse managerial effects of not having flexible line configuration when demand changes are alleviated. For the real Nissan automotive case, our analysis and conclusions show the managerial and industrial benefits of using robust assembly lines. We also encourage decision makers to use robust multiobjective optimization methods for selecting the most flexible decisions.

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“…A previous work of the same authors used a min-max relative regret mechanism instead, which is a more conservative approach (Xu and Xiao 2009). Saif et al (2014) presented a similar work but for a multiobjective problem by defining a Pareto lexicographic α-robust dominance relationship. Chica et al (2013) also defined a set of scenarios and proposed novel robustness functions and a graphical representation to respectively measure and represent how robust the assembly line configuration is on this set of scenarios (product plans).…”

Section: Robust Optimization For Assembly Line Balancingmentioning

confidence: 99%

“…The most used robust criteria rely on the worst case by using traditional min-max or variations of it (Dolgui and Kovalev 2012, Simaria et al 2009, Xu and Xiao 2011, Saif et al 2014. The existing approaches are summarized in Battaïa and Dolgui (2013).…”

Section: Robust Optimization For Assembly Line Balancingmentioning

confidence: 99%

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Chica

Bautista

Cordón

et al. 2016

Omega

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Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

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Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

Cited by 6 publications

References 23 publications

Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

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