Optimizing multi-objective sequencing problem in mixed-model assembly line on just-in-time: particle swarm optimization algorithm

Alaghebandha, Mohammad; Hajipour, Vahid; Hemmati, Marziyeh

doi:10.1080/17509653.2016.1258593

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…The efficiency of all algorithms is measured in small, medium and large-scale problems. Results show that DPSO in comparison to the other algorithms is more efficient (Alaghebandha et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Abbasi

Mokhtari

Shahvar

et al. 2019

JAMR

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Purpose The purpose of this paper is to solve large-scale many-to-many hub location-routing problem (MMHLRP) using variable neighborhood search (VNS). The MMHLRP is a combination of a single allocation hub location and traveling salesman problems that are known as one of the new fields in routing problems. MMHLRP is considered NP-hard since the two sub-problems are NP-hard. To date, only the Benders decomposition (BD) algorithm and the variable neighborhood particle swarm optimization (VNPSO) algorithm have been applied to solve the MMHLRP model with ten nodes and more (up to 300 nodes), respectively. In this research, the VNS method is suggested to solve large-scale MMHLRP (up to 1,000 nodes). Design/methodology/approach Generated MMHLRP sample tests in the previous work were considered and were added to them. In total, 35 sample tests of MMHLRP models between 10 and 1,000 nodes were applied. Three methods (BD, VNPSO and VNS algorithms) were run by a computer to solve the generated sample tests of MMHLRP. The maximum available time for solving the sample tests was 6 h. Accuracy (value of objective function solution) and speed (CPU time consumption) were considered as two major criteria for comparing the mentioned methods. Findings Based on the results, the VNS algorithm was more efficient than VNPSO for solving the MMHLRP sample tests with 10–440 nodes. It had many similarities with the exact BD algorithm with ten nodes. In large-scale MMHLRP (sample tests with more than 440 nodes (up to 1,000 nodes)), the previously suggested methods were disabled to solve the problem and the VNS was the only method for solving samples after 6 h. Originality/value The computational results indicated that the VNS algorithm has a notable efficiency in comparison to the rival algorithm (VNPSO) in order to solve large-scale MMHLRP. According to the computational results, in the situation that the problems were solved for 6 h using both VNS and VNPSO, VNS solved the problems with more accuracy and speed. Additionally, VNS can only solve large-scale MMHLRPs with more than 440 nodes (up to 1,000 nodes) during 6 h.

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Section: Literature Reviewmentioning

confidence: 99%

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Abbasi

Mokhtari

Shahvar

et al. 2019

JAMR

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“…If parts usage variation is high or parts consumption is uneven, the production planning will become difficult and the possibility of parts shortage is high. The sixth objective can be formulated as follows [40].…”

Section: Problem Definitionmentioning

confidence: 99%

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Chutima¹,

Kirdphoksap²

2019

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The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in the Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergencerelated metrics.

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“…The optimal management technique has been widely used in many fields, such as engineering management and decision-making 30,31 , and it has been applied for water resources management in coal mines in the past two decades, but, recently it shifted from solely relying on water supply to paying attention to increasing water supply, saving water resources, and managing water demand, as well as from single objective to multiple objective management 32–36 . Many places in China have conducted the related study with various techniques of water resources utilization to ease the contradiction between supply and demand and solve drought emergency problems 37–41 .…”

Section: Introductionmentioning

confidence: 99%

Water Resources Utilization and Protection in the Coal Mining Area of Northern China

Dong

Yin

et al. 2019

Sci Rep

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This study investigates multiple types of water resources in the western mining area in China, their supply-demand balance by using the same dimension gray recurrence dynamic model GM (1, 1), and water content coefficients of mines to ease water supply-demand contradiction. A multi-objective programming model is proposed for optimal water resources allocation management. Optimal technical schemes for water resources allocation among different users are obtained. The optimization model improves upon the previous studies by using water demand and water supply forecast. Coordinated development for mining safety, mine water utilization, and remediation and control of water environment is achieved.

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Optimizing multi-objective sequencing problem in mixed-model assembly line on just-in-time: particle swarm optimization algorithm

Cited by 10 publications

References 30 publications

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Water Resources Utilization and Protection in the Coal Mining Area of Northern China

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