Improvement of the Fusing Genetic Algorithm and Ant Colony Algorithm in Virtual Enterprise Partner Selection Problem

Yao, Zhong; Pan, Ranran; Lai, Fujun

doi:10.1109/csie.2009.220

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The basic idea of this research is to enhance the search speed and accuracy by taking advantage of the rapid convergence of GA in the initial search period, and the ACO uses the initial solution provided by GA as the initial pheromone. The authors' research is different from the approach by Yao et al (2009). In this research, the ACO is improved to solve the partnership selection problem by generating more dispersed solutions and modifying the scheme for updating the trail intensity.…”

Section: Literature Reviewmentioning

confidence: 95%

“…In their research, the max-min Ant system is adopted in the local pheromone updating process to limit the upper and lower bounds of the pheromone value. Yao et al (2009) integrated the GA into a max-min ACO to optimise the partner-selection problems. Their experiments demonstrated that the hybrid algorithm is feasible for partner selection, and in some aspects the hybrid algorithm is superior to GA and ACO.…”

Section: Literature Reviewmentioning

confidence: 99%

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An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

Niu

Ong

Nee

2012

International Journal of Production Research

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Increasing global competition drives enterprises, especially small and medium-sized enterprises, to collaborate in order to respond faster to customers' needs, reduce operating costs, increase capacity, and produce customised products to reach the market quicker. A virtual enterprise (VE) is an important manufacturing paradigm to address this trend in the dynamic global economy. Partner selection is a key issue tightly coupled to the success of a VE coalition, and because of its complexity, it is considered a multi-attribute optimisation problem. In this paper, an enhanced ant colony optimiser (ACO) is proposed to address the partner selection problem. Five attributes (namely, cost, time, quality, reputation, and risk) considering both qualitative and quantitative aspects have been investigated to evaluate the candidate partners. Experiments have been conducted to validate the performance of the enhanced ACO algorithm, and the results show that the enhanced ACO algorithm can produce better results in terms of search accuracy and computing time. IntroductionAs competition among enterprises is continuously increasing in the global market, enterprises, especially small and medium-sized enterprises (SMEs), need to cooperate with each other in order to enhance their capability and capacity. A virtual enterprise (VE) is an important manufacturing paradigm for enterprises to collaborate with each other. Generally, VEs are formed for two reasons, namely, a faster response to job opportunities and meeting the requirements of mass customisation (Niu et al. 2009).A VE is a dynamic, temporary, and logical alliance of enterprises that collaborate with each other to exploit fastchanging market opportunities or cope with specific needs. Their operation is achieved by a coordinated sharing of skills, resources, information, and knowledge, as well as risks and benefits (Drissen-Silva and Rabelo 2009). VEs offer new opportunities to companies in a global business environment to better satisfy their customer needs with lower pricing by allowing member enterprises to cooperate and make full use of their core strengths to survive.There are four phases in the life cycle of a VE, namely, the creation, operation, evolution, and dissolution phases (Wu and Su 2005, Drissen-Silva and Rabelo 2009). The success of a VE is strongly dependent on its composition; thus, this research focuses on the VE creation phase. In the creation phase, the selection of partners is the most crucial issue, as selecting proper partners involves multiple selection criteria and constraints, and a poor selection may result in a higher manufacturing time and cost, and less competitive products. Partner selection can be viewed as a multi-criteria decision-making problem where the decision-makers assess the trade-offs between conflicting tangible and intangible criteria, and indicate their preferences based on incomplete or non-available information. In general, this is a very complex problem owing to the large number of alternatives and different types of criteria (...

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Section: Literature Reviewmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

Niu

Ong

Nee

2012

International Journal of Production Research

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“…Each of these has its own advantages and disadvantages; thus, numerous researchers have considered investigations of multiple methods to be notable and hold promise for overcoming the defects of individual algorithms as well as achieving complementary advantages. The hybridization of a GA and ACO has been applied to solve numerous complex combinatorial optimization problems, such as the capacitated vehicle routing problem [26], logistics distribution route optimization [9], the 0-1 knapsack problem and quality of service [10], optimization of cloud database route scheduling [11], the virtual enterprise partner selection problem [12,13], and some NP-complete problems, including the satisfaction problem, the tripartite matching problem, and the TSP [27].…”

Section: Fused Algorithm Of a Ga And Aco A Review Of The Extant Algomentioning

confidence: 99%

“…The Concept of Fusing a GA and ACO. In this paper, the basic concept of the dynamic integration of a GA and ACO comes from Yao et al [12,13] and Xiong et al [29]. We adopted a GA to generate available solutions and update initial pheromone values.…”

Section: Concept Of Fusing a Genetic Algorithm And Ant Colony Optimizmentioning

confidence: 99%

“…Approaches in the first category, such as the work of Acan et al [7,8], apply a GA to select ACO parameter values that optimize the performance of ant populations. Methods in the second category generate initial pheromone distributions through GAs, which are subsequently optimized with ACO [9][10][11][12][13]. Methods in the third category add genetic operations to ACO to diversify solutions [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Zhao

Yao

Luan

et al. 2016

Mathematical Problems in Engineering

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A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

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A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization

Luan

Yao

Zhao

et al. 2019

Mathematics and Computers in Simulation

105

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Improvement of the Fusing Genetic Algorithm and Ant Colony Algorithm in Virtual Enterprise Partner Selection Problem

Cited by 4 publications

References 7 publications

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization

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