Department of Economics and Business Economics

A heuristic for computing a new value-based stationary policy for the stochastic joint replenishment problem

Research output: Working paperResearch

A stationary control policy is constructed for the mulit-item stochastic joint replenishment problem. The cornerstone in the construction is a policy-iteratin improvement step which assumes that there is a single possibility for making a joint order and thus deviate from the rule that the items are governed by independent, re-order and order-up-to, (s, S) polities. However this policy-iteration improvement step is done repeatedly at each demand epoch. For the policy-iteration step it is only required the development of one-dimensional functions of relative values where in general these must be multi-dimensional. Knowledge about good values is important. So in order to secure that these order-up-to values are hit when making a joint order, an allocated order cost is constructed for each item. Furthermore, a relaxation parameter α is introducted such that one can, if convenient, make it easier or more difficult to issue a joint order when following the policy-iteration step. Also, a vector of can-orders is employed, in case a joint order is made, to include additional items. The policy is denoted a (c, S, α ) policy. Numerical results show that is performs almost as well as the Q (s, S) policy and contrary to the Q(s, S) policy, it is stationary. Also some of the peculiarities of the Q (s, S) that can appear for a constructed data set may be better handled by the (c, S, α) policy. Numerical results also indicate that given a can-order policy one can always constuct a better (c,S, α) policy
Original languageEnglish
Number of pages16
Publication statusPublished - 19 Dec 2014

Bibliographical note

Paper haves på AU Library, Fuglesangs alle: AU Research 2014 / Paper is available in print at AU Library, Fuglesangs alle: AU Research 2014

    Research areas

  • Inventory, Stochastic demand, Joint replenishment, Control policies

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ID: 83883617