Combinatorial Benders cuts for assembly line balancing problems with setups

Akpınar, Şener; Elmi, Atabak; Bektaş, Tolga

doi:10.1016/j.ejor.2016.11.001

Cited by 58 publications

(23 citation statements)

References 37 publications

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“…Similarly, Kuo, Huang, Wei, and Tang (1999) used colored time Petri net (CTPN) to balance the MAL using the concept of several levels and multiple modules in each level. The objectives of MALB problems include minimizing the number of stations, cycle time, station idle time, and weighted smoothness index (Akpinar, Elmi, & Bekta, 2017;Erel & Gokcen, 1999;Gokcen & Erel, 1997Karabat & Sayn, 2003;Ozcan & Toklu, 2009;Sparling & Miltenburg, 1998). The maximizing objectives include weighted line efficiency and capacity utilization (Ozcan & Toklu, 2009;Yagmahan, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some of the common heuristic models include COMSOAL (McMullen & Frazier, 1997), ranked positional weight heuristic (Gokcen & Erel, 1998), hybrid genetic algorithm (Noorul Haq, Rengarajan, & Jayaprakash, 2006), simulated annealing (Ozcan & Toklu, 2009), and ant colony optimization (Yagmahan, 2011). Some authors developed an exact algorithm using benders decomposition (Akpinar et al, 2017) and integer programming methods (Gokcen & Erel, 1998). Similarly, some researchers used simulation techniques such as Monte Carlo simulation (McMullen & Frazier, 1997;Thomopoulos, 1967) and tools like Probalance (Boivie & HÖ Glund, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

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Assembly line design using a hybrid approach of lean manufacturing and balancing models

Qattawi

Madathil

2019

Production & Manufacturing Research

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This manuscript presents a method to design automotive production assembly lines that integrate lean manufacturing approaches and line balancing algorithms. In this work, we develop a clustering algorithm and task mutuality index for the assembly tasks to redesign and rebalance an assembly production line with a fixed layout and machinery. Moreover, we consider the product demand variability and the introduction of new product models. This paper describes the current and the future states of an automotive engines assembly production line using quantitative performance parameters and assembly tasks content flow charting, along with the analysis and results obtained from the proposed approach. The results show a reduction in required modeling and optimization efforts and a reduction in the required time to generate a feasible redesign of an assembly line while reaching the required takt time based on the demand forecast.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Assembly line design using a hybrid approach of lean manufacturing and balancing models

Qattawi

Madathil

2019

Production & Manufacturing Research

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“…The last index of the g matrix saves the side selection type value for the assignment of E-type tasks. In the first iteration, g matrix is generated with random integer numbers between [17,34]. The last index is assigned using binary values (0 for the side which has more available time, 1 for the random selection).…”

Section: Initialization Of the Matricesmentioning

confidence: 99%

“…In their study, both forward setups and backward set-ups are considered, but crossover setups are not taken into account. Akpinar et al [34] describes an exact algorithm based on Benders decomposition to solve both simple and mixed-model assembly line balancing problems with sequence-dependent set-up times.…”

Section: Introductionmentioning

confidence: 99%

An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times

et al. 2018

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Some practical arrangements in assembly lines necessitate setup times between consecutive tasks. To create more realistic models of operations, setup times must be considered. In this study, a sequencedependent setup times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included setup times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent setup times between consecutive tasks and consecutive cycles, called the ''forward setup time'' and the ''backward setup time'', respectively. Additionally, we examine the ''crossover setup time'', which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent setup times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.

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“…On the other hand, the assembly line balancing (ALB) refers to the assignment of assembly task into the workstations, so that the workload between the workstations will be balanced or almost balanced [7,8]. ALB problems are categorised as Simple Assembly Line Balancing Problem (SALBP) and Generalise Assembly Line Balancing Problem (GALBP).…”

Section: Introductionmentioning

confidence: 99%

A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

Rashid¹,

Mohamed²,

Rose³

2019

JMES

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A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing ABSTRACTAssembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

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Combinatorial Benders cuts for assembly line balancing problems with setups

Cited by 58 publications

References 37 publications

Assembly line design using a hybrid approach of lean manufacturing and balancing models

Assembly line design using a hybrid approach of lean manufacturing and balancing models

An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times

A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

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