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|Title:||Coordinated batch processing machine scheduling with job delivery in semiconductor manufacturing||Authors:||Fu, Qing.||Keywords:||DRNTU::Business::Operations management::Supply chain management||Issue Date:||2013||Abstract:||In the make-to-order (MTO) business model, manufacturing and outbound transportation are intimately linked due to little or no finished product inventory. Collaborative planning of the production and delivery is desired. This research seeks to explore a new collaborative scheduling method for production and distribution in supply chain for discrete manufacturing and distribution environment. A coordinated production and delivery scheduling problem, in which jobs are processed on a single batch processing machine (BPM) and then delivered to geographically dispersed customers, is addressed. The coordinated production and distribution model is applicable in many industries such as the integrated circuit (IC) burn-in process and delivery in the subcontract semiconductor assembly and test environment. Burn-in operation is operated in burn-in processor, which is considered as a type of batch processing machine in the semiconductor manufacturing. IC products after the burn-in operation are shipped to customers in different locations for the final test. In this thesis, two delivery methods, namely ‘Individual Delivery’ and ‘Batch Delivery’, are studied. For the ‘Individual Delivery’ method, a job after completion will be directly delivered to a customer. For the ‘Batch Delivery’ method, jobs completed on BPM will be delivered to multiple customers in batches. Especially, vehicle routing problem is considered in the batch delivery problem. Specifically, this thesis studies three subproblems: 1) coordinated production and individual and immediate delivery scheduling problem, 2) coordinated production and individual delivery scheduling problem with limited buffers, and 3) coordinated batch production and batch delivery problem with discrete split and vehicle routing. The objective of this research is to coordinate job schedule both on the production and distribution stages so as to minimize the time and cost related criteria. The objective functions studied in this research includes the makespan, delivery time and transportation cost. This research starts from the analysis of computational complexity of each problem. For those problems which are solvable, algorithms are developed in this research to obtain an optimal job schedule for the coordinated production and distribution problems. For those problems which are proved to be NP-hard, this research characterizes properties of optimal schedule. These optimal properties are beneficial to develop heuristic algorithms. Then both effective meta-heuristics and simple heuristics are designed to obtain promising job sequences. In particular, different efficient Differential Evolution algorithms combined with a dynamic programming algorithm, local search procedure or simple heuristics are developed in this thesis. Numerical experiments are conducted to evaluate the performance of proposed algorithms.||URI:||http://hdl.handle.net/10356/52659||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Theses|
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Updated on Dec 4, 2020
Updated on Dec 4, 2020
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