A heuristic algorithm based on Ant Colony Optimization for an integrated production and distribution scheduling problem
碩士 === 國立交通大學 === 工業工程與管理系所 === 96 === More and more enterprises have chosen to use make-to-order or direct-order business models in order to be competitive in the demanding market. In such business models, enterprises are forced to reduce their inventory but still have to respond quickly to custome...
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| Format: | Others |
| Language: | zh-TW |
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2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/52749291482360516392 |
| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 96 === More and more enterprises have chosen to use make-to-order or direct-order business models in order to be competitive in the demanding market. In such business models, enterprises are forced to reduce their inventory but still have to respond quickly to customer’s requirements. The reduction of inventory results in closer interaction between production and distribution activities and thus increases the usefulness of an integrated model. This study considers a problem in which orders are first processed by a set of unrelated parallel machines and then distributed to the corresponding customers directly by vehicles with limited capacity without intermediate inventory. This objective is to find a joint schedule of production and distribution such that an objective function which takes into account both customer service level and total distribution cost is optimized. A heuristic algorithm based on ant colony optimization (ACO) is developed to solve such a problem. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions in reasonable computational times. Furthermore, we also investigate the benefits of using the integrated model relative to a sequential model where production and distribution operations are considered separately and sequentially. The test results show the value of integration and the importance of integrated model for an integrated production and distribution scheduling problem.
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