Applying modified NSGA-II for bi-objective supply chain problem

Bandyopadhyay, Susmita; Bhattacharya, Ranjan

doi:10.1007/s10845-011-0617-2

Cited by 38 publications

(21 citation statements)

References 29 publications

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“…The performance of the PSO/SA is compared with a Pareto-based algorithm and a selfadapted weighted algorithm, i.e. NSGA-II (Bandyopadhyay and Bhattacharya 2013;Deb et al 2002) and PSO (Nearchou 2011), respectively. All algorithms are coded in MATLAB and run on a Core i7 3.40 GHz PC.…”

Section: Computational Results and Comparisonsmentioning

confidence: 99%

“…Finally, the parameter setting is determined according to the Taguchi method, and extensive experiments are carried out with several benchmark problems to evaluate the performance of the PSO/SA. Comparisons with NSGA-II (Bandyopadhyay and Bhattacharya 2013;Deb et al 2002) and PSO (Nearchou 2011) show the promising higher performance of the PSO/SA in terms of the diversity, convergence and spread metrics of the obtained solutions.…”

Section: Introductionmentioning

confidence: 97%

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A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

2015

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This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0-1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.

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Section: Computational Results and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

2015

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“…total set-ups Min. the maximum number of set-ups between the two stages supply chain Bandyopadhyay & Bhattacharya (2013) Min. total cost Min.…”

Section: Tablementioning

confidence: 99%

“…Kleeman et al (2007) used modified NSGA II to solve a variation of the multi-commodity capacitated network design problem which satisfies multiple objectives must including costs, delays, robustness, vulnerability, and reliability. Bandyopadhyay and Bhattacharya (2013) conducted modification toward NSGA II called modified NSGA II. Modification of NSGA II was conducted by applying crossover and mutation algorithm for the entire population where in original NSGA II, the mutation performed only relies on selected chromosomes.…”

Section: Non-dominated Sorting Genetic Alhorithm II (Nsga Ii)mentioning

confidence: 99%

“…Modification of NSGA II was conducted by applying crossover and mutation algorithm for the entire population where in original NSGA II, the mutation performed only relies on selected chromosomes. Bandyopadhyay and Bhattacharya (2013) applied modified NSGA II to minimize the value of total cost and bullwhip effect in a two-echelon supply chain. They also compared the results of the modified NSGA-II with NSGA II.…”

Section: Non-dominated Sorting Genetic Alhorithm II (Nsga Ii)mentioning

confidence: 99%

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Multi-objective optimization for supply chain management problem: A literature review

Trisna¹,

Marimin²,

Arkeman³

et al. 2016

10.5267/j.dsl

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Multi-objective optimization is an optimization problem with some conflicting objectives to be attained, simultanously. This paper reviewed literature about multi-objective optimization problems for supply chain management. The review aimed to provide the lastest research views and recomendations for further studies. We discussed the lastest ten years publications about multi-objective optimization for supply chain management. The scope of this review was classified into five categories i.e. problem statements, multi-objective frameworks, mathematical formulation modeling, optimization techniques, and representation of supply chain. Multi-objective optimization approaches, both classical and metaheuristic approaches, were discussed, accordingly. In this review, we conducted conclusion and recomendations about likelihood research directions in future.Science Ltd. All rights reserved. Growing 6 © 201

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On Some Aspects of Nature-Based Algorithms to Solve Multi-Objective Problems

Bandyopadhyay

Bhattacharya

2013

Studies in Computational Intelligence

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Applying modified NSGA-II for bi-objective supply chain problem

Cited by 38 publications

References 29 publications

A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

Multi-objective optimization for supply chain management problem: A literature review

On Some Aspects of Nature-Based Algorithms to Solve Multi-Objective Problems

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