Sel, Cagri2024-09-292024-09-2920220360-83521879-0550https://doi.org/10.1016/j.cie.2022.108141https://hdl.handle.net/20.500.14619/4490In this study, we address the stochastic inventory control problem of green vegetables deteriorating in a fruit and vegetable wholesaler. The problem is to re-order green vegetables periodically to meet demand of the green markets. The aim is to decide the order quantity and the order-up-to level for each period minimising the total inventory cost. In the fruit and vegetable wholesaler, a staff separates the fresh and non-fresh products to sustain freshness in the green vegetable crates. The behaviour of the wholesaler staff on selecting the non-fresh green vegetables is personal reasoning that fuzzy logic can help to deal with the uncertainty. Accordingly, we introduce a stochastic integer linear programming model accounting for the uncertain demand. We represent the deterioration rate indicated in the model by fuzzy numbers and transform the resulting fuzzy model into an alpha-para-metric programming model. As a solution method, we propose a simulated annealing based simulation optimisation algorithm. The models and the heuristic algorithm are applied to a case study that reflects the real settings of a food market in Turkey. The numerical analysis is conducted on the case study to understand the effect of the fuzziness on the solution quality and time under varying cost parameters and deterioration rates. As a result, the numerical analyses demonstrate that the proposed models allow us better to estimate the total costs and the waste quantity. The heuristic algorithm yields near-optimal solutions. The solutions approach approximately 3% difference on average for the medium and long term decisions. The heuristic algorithm results in significantly shorter computation times compared to the proposed model.eninfo:eu-repo/semantics/closedAccessStochastic inventory controlFuzzy deterioration rateParametric programmingSimulation-optimisationSimulated annealing algorithmArticle10.1016/j.cie.2022.1081412-s2.0-85127510763Q1168WOS:000793260700007Q1