| Summary: | 碩士 === 國立臺灣大學 === 資訊管理研究所 === 91 === This study focuses on the master planning of “Advanced planning and scheduling.” By considering a final product and its relationship with the global supply chain structure, the objective is to minimize the sum of production cost, processing cost, transportation cost and inventory holding cost under the constraints of limited capacities and due time requirements of orders.
This planning problem, formulated as a basic model, is extended into two kinds of multiple-goal optimization problems for production planning in this study. In one extension, the extra capacities for some production nodes are allowed. With this extension, an additional objective to minimize the total amounts of extra capacities used is added into the original problem. In the other extension, the delays of orders are allowed but minimized in the added objective.
In the previous study, “Linear Programming,” “Mixed Integer Linear Programming” and “Goal Programming” are some popular used to solve these kinds of problems related to supply chain management. However, with the increasing complexities of the supply chain related problems, the numbers of variables and constraints in the LP models grow rapidly. It takes a lot of computer time to solve these problems if there are feasible. Nevertheless, if the LP models result to no feasible solutions, the cause of infeasible can not be identified. Therefore, this study proposes a heuristic algorithm that is more informative and flexible then LP to solve supply chain related problems. The heuristic algorithm can indicate the status of orders and allocations of capacities and searches out feasible solutions more quickly.
The heuristic algorithm first prepares the information for the supply chain by scaling the information of capacities and costs based on the unit of a final product. It then sorts all the orders to determine the sequence for planning. Finally, the orders are to be planned and scheduled one-by-one in sequence. The production plan for each order may be scheduled more than once. In each time, a minimum unit cost production tree is found at first. Following that, the available capacity of the production tree is determined. If there is no more capacity available, the supply chain network structure will be adjusted. If the cost of the schedule is larger than the one for the minimum unit cost production tree, the costs of the supply chain network will be modified and a new production tree will be found. Otherwise, the capacity of production and transportation at each node in the production tree will be allocated for this specific order. The algorithm will repeat these steps until all the orders are fulfilled.
In the extension related to extra capacities, the heuristic algorithm adjusts supply chain structure by treating the nodes with extra capacities as two kinds of nodes: one physical node with regular capacity and one virtual node with extra capacity. In the extension related to order delays, the heuristic algorithm compares the schedules when order is delayed with the schedule when order is not delayed, and chooses the schedule with the lowest cost including the delay cost and the sum of the other costs.
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