Partner selection in a virtual enterprise under uncertain information about candidates

Huang, Bin; Gao, Chen; Chen, Liang

doi:10.1016/j.eswa.2011.02.180

Cited by 31 publications

(29 citation statements)

References 17 publications

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“…Yue (2013) developed an approach for partner selection, using linguistic values and intuitionistic fuzzy information, within a group decision-making environment. Huang, Gao, and Chen (2011) propose vague sets to deal with the partner selection problem in the formation of a VE, when information about the candidates and their performances is incomplete and uncertain.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic partner selection for virtual enterprises: a chance-constrained approach

Crispim

Rego

Sousa

2014

International Journal of Production Research

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A virtual enterprise (VE) is a temporary organisation that pools the core competencies of its member enterprises in order to exploit fast-changing market opportunities. Making successful collaborative partnerships is, in this context, a major challenge in today's competitive business environments. The success of such a 'virtual' organisation is strongly dependent on its composition, and the selection of partners becomes therefore a crucial issue. This problem is particularly difficult because of the uncertainties related to information, market dynamics, customer expectations and technology speedup, with a strongly stochastic decision-making context. In this paper, a chance-constrained approach to rank alternative VE configurations in business environments with uncertainty, and vague and random information, is proposed. This approach is based on a two-stage model: a chance-constraint multi-objective directional Tabu Search metaheuristic, complemented by a 2-tuple fuzzy linguistic representation model. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic partner selection for virtual enterprises: a chance-constrained approach

Crispim

Rego

Sousa

2014

International Journal of Production Research

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“…On the one hand, PSO has been proved to be a simple, sound, and effective metaheuristic algorithm (Che 2012). Zhao et al (2008) and Huang et al (2011) pointed out that PSO is an effective and efficient method to solve a complex optimization problem. PSO offers easy programming and can be used on optimization problems that are partially irregular, noisy and changes over time.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In addition, PSO can search very large spaces of possible solutions and so can be used for complex optimization problems (Zhao et al 2008, Huang et al 2011, Che 2012). …”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The global best position of the whole swarm found so far is defined as gbest. The updating rule is: Equation (7) is applied to calculate the particle's new velocity in accordance with its previous velocity and the distances of its current position from the group's best position and its own best position (Huang et al 2011). Rand numbers in the Equation (7) are independent variables which can serve as the weights of speed moving towards gbest and pbest for updating positions.…”

Section: Gsc Partner Selection Optimization Hierarchy Criteria Constrmentioning

confidence: 99%

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Partner selection in green supply chains using PSO – a practical approach

Barnes

2016

Production Planning & Control

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Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected prerequisite in the partner selection process. This paper proposes a three-stage model that combines Dempster-Shafer belief acceptability theory and particle swarm optimization technique for the first time in this application. This enables optimization of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer.

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“…Also, to validate the solution and to compare the performance of the proposed algorithm, a GA was utilized as well. Huang et al [21] used PSO algorithm to solve partner selection problem under uncertainty. Kuo et al [22] developed an intelligent vendor decision support system by the collection of quantitative data such as profit and productivity, a PSO-based fuzzy neural network to derive the rules for qualitative data.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

Ekhtiari

Poursafary

2013

ISRN Artificial Intelligence

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It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem.

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Partner selection in a virtual enterprise under uncertain information about candidates

Cited by 31 publications

References 17 publications

Stochastic partner selection for virtual enterprises: a chance-constrained approach

Stochastic partner selection for virtual enterprises: a chance-constrained approach

Partner selection in green supply chains using PSO – a practical approach

Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

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