Mixed assembly line rebalancing: A binary integer approach applied to real world problems in the automotive industry

Oliveira, Felipe Steiner de; Vittori, Karla; Russel, R. M. O.; Travassos, Xisto Lucas

doi:10.1007/s12239-012-0094-4

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…Oliveira et al [26] presented a case study regarding the rebalancing of the tasks and operations of a mixed-model AL in the automotive industry. The authors proposed a binary integer programming model that aims to minimize the number of stations, solved it using a B&B algorithm, and evaluated the results in terms of the number of stations created, average workload, and imbalance rate.…”

Section: Related Workmentioning

confidence: 99%

A Demand-Driven Model for Reallocating Workers in Assembly Lines

Perez-Wheelock

Yenradee

et al. 2022

IEEE Access

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This paper introduces the demand-driven assembly line rebalancing problem (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to some demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and we address this problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), obtaining useful insights about the dynamic of worker reallocation under different scenarios: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios.

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Section: Related Workmentioning

confidence: 99%

A Demand-Driven Model for Reallocating Workers in Assembly Lines

Perez-Wheelock

Yenradee

et al. 2022

IEEE Access

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“…Different product models are manufactured in an arbitrarily mixed sequence (not in batches) in mixed-model ALs (Bukchin et al, 2002). Twenty-five percent of the works were mixedmodel in the reviewed ALRBP publications (Altemeier et al, 2010;Antoine et al, 2014;Fattahi and Samouei, 2016;Grangeon et al, 2011;Ishigaki, 2018;Lai et al, 2015;Oliveira et al, 2012;Yang et al, 2013;Yang and Gao, 2016;Zhang et al, 2016).…”

Section: Type Of Product MIX In the Assembly Linementioning

confidence: 99%

A detailed review and analysis of assembly line rebalancing problems

Çimen

Baykasoğlu²,

Akyol³

2022

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Purpose Various approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs. Design/methodology/approach A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended. Findings Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested. Originality/value Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.

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“…Altemeier et al (2009) proposed a mathematical model for minimizing the cost of the problem, which constitutes overload situations, reconfiguration measures and regular workers. A binary integer programming and heuristic approach is developed by Oliveria et al (2012) for solving the RAL problem. The number of workstations created, the average load of work and level of unbalance are used in their study as performance measures.…”

Section: Previous Researchmentioning

confidence: 99%

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

Seri̇n

Mete

Çelik

2019

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Purpose Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

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Mixed assembly line rebalancing: A binary integer approach applied to real world problems in the automotive industry

Cited by 17 publications

References 22 publications

A Demand-Driven Model for Reallocating Workers in Assembly Lines

A Demand-Driven Model for Reallocating Workers in Assembly Lines

A detailed review and analysis of assembly line rebalancing problems

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

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