Author
Listed:
- Gang Wang
(Kean University, Union, USA)
- Lei Lei
(Rutgers University, New Brunswick, USA)
- Kangbok Lee
(The City University of New York, New York, USA)
Abstract
We study the operations scheduling problem with delivery deadlines in a three-stage supply chain process consisting of (1) heterogeneous suppliers, (2) capacitated processing centres (PCs), and (3) a network of business customers. The suppliers make and ship semi-finished products to the PCs where products are finalized and packaged before they are shipped to customers. Each business customer has an order quantity to fulfil and a specified delivery date, and the customer network has a required service level so that if the total quantity delivered to the network falls below a given targeted fill rate, a non-linear penalty will apply. Since the PCs are capacitated and both shipping and production operations are non-instantaneous, not all the customer orders may be fulfilled on time. The optimization problem is therefore to select a subset of customers whose orders can be fulfilled on time and a subset of suppliers to ensure the supplies to minimize the total cost, which includes processing cost, shipping cost, cost of unfilled orders (if any), and a non-linear penalty if the target service level is not met. The general version of this problem is difficult because of its combinatorial nature. In this paper, we solve a special case of this problem when the number of PCs equals one, and develop a dynamic programming-based algorithm that identifies the optimal subset of customer orders to be fulfilled under each given utilization level of the PC capacity. We then construct a cost function of a recursive form, and prove that the resulting search algorithm always converges to the optimal solution within pseudo-polynomial time. Two numerical examples are presented to test the computational performance of the proposed algorithm.
Suggested Citation
Gang Wang & Lei Lei & Kangbok Lee, 2015.
"Supply chain scheduling with receiving deadlines and non-linear penalty,"
Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 380-391, March.
Handle:
RePEc:pal:jorsoc:v:66:y:2015:i:3:p:380-391
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Cited by:
- Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016.
"Big data analytics in logistics and supply chain management: Certain investigations for research and applications,"
International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
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