Optimal decision making in multi-product dual sourcing procurement with demand forecast updating |
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Affiliation: | 1. School of Economics, Renmin University of China, Beijing 100872, China;2. Post Doctor Laboratory, Institute of Finance and Banking, the People''s Bank of China, PBC School of Finance, Tsinghua University, Beijing, China;3. Research Center for Applied Finance, School of Banking and Finance, University of International Business & Economics, Beijing 100029, China;4. School of Economics, Shanghai University of Finance and Economics; Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai 200433, China;1. Department of Supply Chain Management and Management Science, University of Cologne, D-50923 Cologne, Germany;2. Department of Management Science, Lancaster University, Lancaster, United Kingdom |
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Abstract: | In this paper, we will investigate a buyer's decision making problem in procuring multiple products, each treated as a newsvendor, from two markets. The contract market has a long lead time, a fixed wholesale price and resource constraints. While the spot market has an instant lead time and a highly volatile price. The purchasing decision at the spot market can be made near the beginning of the selling season to take the advantage of the most recent demand forecast. The buyer needs to determine the purchasing quantity for each product at the two markets to maximize the expected profit by trading off between the resource availability, demand uncertainty and price variability. The procurement decision making is modeled as a bi-level programming problem under both a single resource constraint and under multiple resource constraints. We show that this bi-level programming problem can be formulated as a single-level concave programming problem. We then develop a sequential algorithm which solves for a linear approximation of the concave programming problem in each iteration. This algorithm can be used to solve a real world problem with up to thousands of kinds of products, and is found to be highly efficient and effective. |
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Keywords: | Two-stage procurement Forecast updating Multiple-products and dual market Resource constraints Successive linear programming algorithm |
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