THESIS
1998
xii, 106 leaves : ill. ; 30 cm
Abstract
Material handling hoists are commonly used to transport materials among workstations in electroplating lines, e.g. in printed circuit board (PCB) manufacturing. The control and schedule of such hoists is very crucial to the system performance because there is no buffer between the workstations and the processing time at each station must be within a minimum and a maximum limit. In this thesis, we study the single hoist scheduling problem in both cyclic and dynamic production environments. The objective is to maximize the production throughput and guarantee product quality. The cyclic scheduling problem is formulated as a mixed integer linear programming model. A solution method is then proposed combining heuristic search and linear programming model. The method is also extended to solve...[
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Material handling hoists are commonly used to transport materials among workstations in electroplating lines, e.g. in printed circuit board (PCB) manufacturing. The control and schedule of such hoists is very crucial to the system performance because there is no buffer between the workstations and the processing time at each station must be within a minimum and a maximum limit. In this thesis, we study the single hoist scheduling problem in both cyclic and dynamic production environments. The objective is to maximize the production throughput and guarantee product quality. The cyclic scheduling problem is formulated as a mixed integer linear programming model. A solution method is then proposed combining heuristic search and linear programming model. The method is also extended to solve two hoist cyclic scheduling problem. The dynamic scheduling problem is considered as a series of static problems. A control framework is used to update system status and to formulate the static problem for each new job arrival. The static problems are solved again using search and linear programming model. The methods are implemented and tested on example problems. The results show that the proposed heuristic method is very effective and efficient.
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