A Multi-Criterion Genetic Algorithm for Supply Chain Collaboration
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A Multi-Criterion Genetic Algorithm for Supply Chain Collaboration

A Multi-Criterion Genetic Algorithm for Supply Chain Collaboration


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This dissertation, "A Multi-criterion Genetic Algorithm for Supply Chain Collaboration" by Sai-ho, Chung, 鍾世豪, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled A multi-criterion genetic algorithm for supply chain collaboration Submitted by Chung Sai Ho For the Degree of Master of Philosophy at the University of Hong Kong in August 2003 A product is valuable only when it can be delivered to the user/customer at the right time, and to the right place. This is particularly essential in today's supply chain environment especially with quick response, and short product life cycle. Delivery deviates from schedule can seriously induce extra costs, including those of tangible or intangible penalty, product depreciation, high inventory level, interruption of production, poor customer satisfaction, or even loss of market share. Efficient coordination of material suppliers, manufacturing plants, wholesalers, retailers, warehouses, and distribution centres for delivering products as scheduled is the ultimate goal of supply chain management. However, this is an extremely challenging and difficult task. Supply chain management includes many decision variables and ii decision factors that can influence the delivery. In addition, these factors, such as costs, and service level are closely related to, and have influences on each other. A systematic organization methodology is crucial to handle them. Many industries have been seeking computerized supply chains that can automatically take into account the operational constraints, and decision factors of the supply chain networks to establish schedule planning for the input of customer orders. In this connection, the objective of this research is designed to develop a Decision Support System (DSS) for supply chains in the problem area of order distribution. The DSS will analyze various input data of the supply chain, and present to the industries the optimization results generated by the incorporated optimization methodology, named GAAHP. It adopts the optimization methodology of Genetic Algorithms (GA), and the Multi- Criterion Decision-Making (MCDM) technique of Analytic Hierarchy Process (AHP). GAAHP adopts a new optimization approach. It will holistically optimize the problems of demand allocation, transportation, and rough production scheduling. It can help to achieve better optimization results, but not just obtain the sub-optimal. In addition, the deployment of AHP enhances the means of organizing the complex decision factors. In this thesis, the optimization methodology of GAAHP will be explained. A number of hypothetical distribution network models will be solved for verification of its optimization reliability, and demonstration of its significance especially when fulfillment of customer order due date is required. iii S.H. CHUNG (350 words) iv DOI: 10.5353/th_b2935727 Subjects: Decision support systems Genetic algorithms Business logistics


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Product Details
  • ISBN-13: 9781374715509
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 184
  • Weight: 440 gr
  • ISBN-10: 1374715506
  • Publisher Date: 27 Jan 2017
  • Binding: Paperback
  • Language: English
  • Spine Width: 10 mm
  • Width: 216 mm


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