Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

Che, ZH; Chiang, Tzu-An; Che, Zhen-Guo

doi:10.1177/0142331211402901

Cited by 8 publications

(7 citation statements)

References 55 publications

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“…They improved the standard PSO and introduced a simple mutation mechanism and a self-adapting inertia weight method by classifying the fitness values. Che et al [264] proposed a novel method based on the analytic network process and turbo PSO to evaluate partners and to determine an optimal supply chain network pattern and production-distribution mode. Hajipour and Pasandideh [265] focused on determination of the number of required facilities along with the relevant allocation process.…”

Section: Operations Researchmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

648

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Operations Researchmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

648

380

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“…The questionnaire was released to experts to determine the relative importance between two criteria and calculate the weight boundary of each criterion according by using the ANP supermatrix approach. The ANP procedure is as following steps (Büyüközkan, Çifçi 2011;Che et al 2012):…”

Section: Determine the Weight Boundary Of Each Criterionmentioning

confidence: 99%

Integrated Methodology for Supplier Selection: The Case of a Sphygmomanometer Manufacturer in Taiwan

Chang

2016

Journal of Business Economics and Management

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Supplier selection is a critical multi-criterion decision-making activity for successful supply chain management. This study involved developing an integrated supplier selection methodology, which is constructed using analytic network process, data envelopment analysis, and multiple objective particle swarm optimization. The proposed integrated methodology can account for multiple supplier selection criteria and set boundaries on weight value for multiple objective data envelopment analysis inputs and outputs. To solve the data envelopment analysis model, a new algorithm based on multiple objective particle swarm optimization is introduced, which embeds with tabu list and group mechanisms, and then, it is found to be superior to the compared algorithms in solving performance on three test functions and the illustrative case. In addition, the proposed integrated methodology was applied to a supplier selection problem of sphygmomanometer manufacturer in Taiwan to verify its applicability of decision-making process. The results show that the methodology can be implemented as an effective decision aid for supplier selection under multiple criteria with weight restrictions.

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“…Che et al (2012) developed an optimization mathematical model solved with PSO in comparison with GA for a reverse supply chain network that contains forward and reverse logistical plans in the multi-echelon system. Che et al (2014) expanded the model considering fuzzy defect ratio, and fuzzy transport loss ratio.…”

Section: Introductionmentioning

confidence: 99%

“…Several papers proposed models for the production-distribution problem in a supply chain. Che et al (2012) and Che et al (2014) developed a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain taking into account such four criteria as cost, quality, delivery and supplier relationship management and considering quantity discount and capacity constraints. The models are solved with PSO and with a methodology based on the analytic network process and turbo particle swarm optimization (TPSO).…”

Section: Introductionmentioning

confidence: 99%

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Grillo

Peidro

Alemany

2015

IJBIC

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InderscienceGrillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3): 157-169. doi:10.1504/IJBIC.2015 Copyright © 200x Inderscience Enterprises Ltd. APPLICATION OF PARTICLE SWARM OPTIMIZATION WITH BACKWARD CALCULATION (PSO-B) TO SOLVE A FUZZY MULTIOBJECTIVE SUPPLY CHAIN MASTER PLANNING MODEL Abstract:Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimization (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.

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Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

Cited by 8 publications

References 55 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Integrated Methodology for Supplier Selection: The Case of a Sphygmomanometer Manufacturer in Taiwan

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

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