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Minimizing Completion Time for Order Scheduling: Formulation and Heuristic Algorithm

delete2017-10-01
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PRE
AI
Z
Zhongshun Shi *
L
Longfei Wang
P
Pai Liu
L
Leyuan Shi
DOI:10.1109/TASE.2015.2456131delete
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Abstract

Abstract

En 中文
In this study, the customer order scheduling problem is investigated to minimize total weighted completion time. A quadratic formulation is proposed to address this problem. This formulation is converted into an equivalent mixed-integer linear programming model by applying the linearization technique and the special structure of the problem. The problem size that can be solved to optimality is then investigated and reported based on the final linearized formulation. Furthermore, a hybrid nested partitions algorithm is developed to solve large-scale problems. Numerical results illustrate the advantages of the proposed model and demonstrate that the proposed algorithm can obtain high-quality solutions within a reasonable computational time. Note to Practitioners-At present, many manufacturing enterprises are adopting the multi-variety and small batch manufacturing mode. In this mode, every customer order consists of different product types, and the entire order should be delivered simultaneously. Thus, committing a delivery due date for an order and increasing the utilization rate of manufacturing resources are key issues among manufacturing managers. This study presents an efficient linearized model to address the aforementioned problem. A hybrid nested partitions-based algorithm is designed and implemented to solve large-scale problem instances. An advantage of the proposed algorithm is that it can be potentially extended by incorporating domain knowledge and management experience. This approach indicates that several specific rules or techniques can be used when designing and implementing algorithms to improve efficiency.
Keywords:
Completion time
hybrid nested partitions
linearization
order scheduling
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Journal

IEEE Transactions on Automation Science and Engineering cover
IEEE Transactions on Automation Science and Engineering
IF:
6.4
Papers:
5.1K
Citations:
1.6W

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P
peking university
Scholars:
11.9W
Papers: 8.7W
Citations: 146
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