Day
Thursday, October 16, 2008 Room Crowchild
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1h35 PM- 2h10 PM |
Simulation Optimization of Supply Chain Replenishment Strategies Using OptQuest |
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Chandandeep S. Grewal,
PhD Student, Department of Mechanical and
Manufacturing Engineering, University of Calgary,
Calgary, Alberta;
csgrewal@ucalgary.ca Paul Rogers, Associate Professor, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta; rogers@ucalgary.ca Silvanus T. Enns, Associate Professor, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta; enns@ucalgary.ca A selection of appropriate control parameters in different supply chain replenishment strategies is a challenging issue. Different approaches are used by researchers and practitioners to find control parameters. Simulation optimization approach results in finding optimal choice of control parameters for each supply chain replenishment policy. This paper demonstrates the use of simulation optimization approach to find the optimal control parameters for reorder point and Kanban replenishment strategies and compares the performance of these policies under optimal parameters. A hypothetical single stage supply chain system is modeled and experiments are conducted using discrete-event simulation. The optimal control parameters for each replenishment policy are determined using OptQuest®. The OptQuest® is a powerful general-purpose heuristic optimization tool which is embedded in simulation software. The two primary performance measures of interest, considered in this study, are average total inventory and customer service level. The control parameters whose values are being determined are: lot size and reorder point (for the reorder point system); and Kanban size and the number of containers (for the Kanban system). In each set of optimum-seeking simulation trials, the objective is to minimize the average total inventory subject to a specified lower limit on the customer service level. |
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2h15 PM- 2h50 PM |
Maximizing Supply Chain Profits by Simultaneously Fulfilling Desirable Amount of Order and Reducing the Residual Capacity |
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Amir Hossein Khataie,
PhD Student, Department of Mechanical and Industrial
Engineering, Concordia University, Montreal, Quebec,
Canada;
a_khatai@encs.concordia.ca Fantahun Melaku Defersha, Post Doctoral Fellow, Department of Mechanical and Industrial Engineering, Concordia, Montreal, Quebec, Canada; md_fanta@encs.concordia.ca Akif Asil Bulgak, Associate Professor, Department of Mechanical and Industrial Engineering, Concordia, Montreal, Quebec, Canada; bulgak@encs.concordia.ca In literature, several techniques have been proposed to develop supply chain management decision making models. Most of these models were mainly based on materials flow and capacity constraints. Recently, methods that explicitly consider the profitability issue in addition to material flow and capacity constraints have emerged. These approaches are mostly based on methodologies such as “Activity-Based Costing” (ABC) and “Throughput Accounting” (TA). In this paper, we develop a Mixed Integer (MI) model based on the Goal Programming approach in order to effectively manage order acceptance decision in supply chain and to maximize profitability subject to capacity constraints. In the proposed models, a desirable amount of orders should be responded fully and in turn to minimize the amount of residual capacity and increase profitability selective number of orders could be accepted partially. Numerical examples have been presented to illustrate the features of the proposed models and demonstrated the advantages. |