A multi-objective optimization model of the partner selection problem in a virtual enterprise and its solution with genetic algorithms

2005 ◽  
Vol 28 (11-12) ◽  
pp. 1246-1253 ◽  
Author(s):  
Zhao Fuqing ◽  
Hong Yi ◽  
Yu Dongmei
Keyword(s):  
Genetic Algorithms ◽  
Optimization Model ◽  
Virtual Enterprise ◽  
Partner Selection ◽  
Selection Problem ◽  
Multi Objective Optimization ◽  
Multi Objective

Using NSGA-II to solve multi-objective competitive location problem with cooperative coverage for distance-based attractiveness

2021 ◽  
pp. 1-12
Author(s):  
Sheng-Chuan Wang ◽  
Ta-Cheng Chen
Keyword(s):  
Genetic Algorithms ◽  
Optimization Model ◽  
Location Problem ◽  
Solution Procedure ◽  
Competitive Location ◽  
Multi Objective Optimization ◽  
Demand Point ◽  
Nsga Ii ◽  
Solution Sets ◽  
Multi Objective

Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II.

A partner selection model for collaborative product innovation from the viewpoint of knowledge collaboration

Kybernetes ◽  
2019 ◽  
Vol 49 (6) ◽  
pp. 1623-1644 ◽  
Author(s):  
Jie Jian ◽  
Milin Wang ◽  
Lvcheng Li ◽  
Jiafu Su ◽  
Tianxiang Huang
Keyword(s):  
Optimization Model ◽  
Product Innovation ◽  
Partner Selection ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Np Hard Problem ◽  
Selection For ◽  
Knowledge Collaboration

Purpose Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration. Design/methodology/approach First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection. Findings A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance. Originality/value Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

scholarly journals Non-dominated Sorting Genetic Algorithms-IIBased on Multi-objective Optimization Model in the Water Distribution System

2012 ◽  
Vol 37 ◽  
pp. 309-313 ◽  
Author(s):  
Mingming Li ◽  
Shuming Liu ◽  
Ling Zhang ◽  
Huanhuan Wang ◽  
Fanlin Meng ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Optimization Model ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Multi Objective Optimization ◽  
Multi Objective
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