The Joint Stock and Capacity Rationings of a Make-To-Stock System with Flexible Demand

Bing Lin, Shaoxiang Chen (), Yi Feng () and Jianjun Xu ()
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Bing Lin: Department of Logistics, School of Business, Jiangsu Normal University, Xuzhou 221116, P. R. China
Shaoxiang Chen: Division of Information Technology and Operations Management, Nanyang Business School, Nanyang Technological University, Nanyang Avenue 639798, Singapore
Yi Feng: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Jianjun Xu: International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance & Economics, Dalian 116025, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2018, vol. 35, issue 01, 1-27

Abstract: This paper studies the joint stock and production capacity rationing polices for a make-to-stock system with two partially substitutable products manufactured at two factories, respectively. Customers are classified into three types with each type further segmented into multiple classes. Type I and Type II customers are selective and accept their favorite products only. Type III accepts either of the two products and hence its demand is regarded as the flexible demand. The management has to decide whether to run or stop production and whether the various classes of demand for each type of customers have to be satisfied from available inventory or not — in which case demand is lost — in order to minimize the firm’s inventory and production costs. We formulate the problem as a make-to-stock queue system and characterize the optimal joint stock and production rationing policies as monotone switching curves. Numerical examples are given to illustrate the optimal policies and demonstrate the value of flexible demand. Moreover, we develop a simple heuristic policy and compare the costs associated with the optimal and heuristic policies for different parameters.

Keywords: Production; inventory; rationing; dynamic programming; Markov decision processes (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0217595918500045

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