In this paper, we consider the periodic review joint replenishment problem under the class of cyclic policies. For each item, the demand in the protection interval is assumed stochastic. Moreover, a fraction of shortage is lost, while the other quota is backordered. We suppose that lead times and minor ordering costs are controllable. The problem concerns determining the cyclic replenishment policy, the lead times, and the minor ordering costs in order to minimize the long-run expected total cost per time unit. We established several properties of the cost function, which permit us to derive a heuristic algorithm. A lower bound on the minimum cost is obtained, which helps us to evaluate the effectiveness of the proposed heuristic. The heuristic is also compared with a hybrid genetic algorithm that is specifically developed for benchmarking purposes. Numerical experiments have been carried out to investigate the performance of the heuristic.
Controlling lead times and minor ordering costs in the joint replenishment problem with stochastic demands under the class of cyclic policies / Braglia, M.; Castellano, D.; Santillo, L.; Song, D.. - In: INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH. - ISSN 0969-6016. - 28:1(2021), pp. 376-400. [10.1111/itor.12571]
Controlling lead times and minor ordering costs in the joint replenishment problem with stochastic demands under the class of cyclic policies
Castellano D.;
2021
Abstract
In this paper, we consider the periodic review joint replenishment problem under the class of cyclic policies. For each item, the demand in the protection interval is assumed stochastic. Moreover, a fraction of shortage is lost, while the other quota is backordered. We suppose that lead times and minor ordering costs are controllable. The problem concerns determining the cyclic replenishment policy, the lead times, and the minor ordering costs in order to minimize the long-run expected total cost per time unit. We established several properties of the cost function, which permit us to derive a heuristic algorithm. A lower bound on the minimum cost is obtained, which helps us to evaluate the effectiveness of the proposed heuristic. The heuristic is also compared with a hybrid genetic algorithm that is specifically developed for benchmarking purposes. Numerical experiments have been carried out to investigate the performance of the heuristic.File | Dimensione | Formato | |
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