로봇 산업의 다중 공급망 환경을 고려한 생산 및 분배 관리를 위한 유전 알고리듬 개발Development of Genetic Algorithm for Production and Distribution Management in Multiple SupplierNetwork Environment of Robot Engineering Industry
- Other Titles
- Development of Genetic Algorithm for Production and Distribution Management in Multiple SupplierNetwork Environment of Robot Engineering Industry
- Authors
- 조성민; 김태영; 황승준
- Issue Date
- Jun-2013
- Publisher
- 한국데이터전략학회
- Keywords
- Robot Engineering Industry; Integration Production and Distribution Planning
- Citation
- Journal of Information Technology Applications & Management, v.20, no.2, pp 147 - 160
- Pages
- 14
- Indexed
- KCI
- Journal Title
- Journal of Information Technology Applications & Management
- Volume
- 20
- Number
- 2
- Start Page
- 147
- End Page
- 160
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/29742
- ISSN
- 1598-6284
- Abstract
- Today, the management environments of intelligence firm are changing the way of production planning and logistics management, and are changing the process of supply chain management system. This paper shows the development of information system software for intelligence enterprises is used in supply chain management for robot engineering industry. Specifically, supply chain management system in this paper has been developed to analyze the impact of multi plant and multi distribution environment, showing the process analysis and system development of hierarchical assembly manufacturing industry.
In this paper we consider a production planning and distribution management system of intelligence firm in the supply chain. We focus on a capacitated production resource and distribution volume allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using a genetic algorithm to solve it efficiently. This method makes it possible for the population to reach the feasible approximate solution easily. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solution converge to the feasible approximate solution quickly.
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