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Price discount is an important research topic in the field of inventory management. The existing research on this topic mainly considers fixed price discount, but ignores the situation in which stochastic short-term price discount may be involved. In this paper, we study an inventory problem considering stochastic short-term price discount and partial backordering. To address this problem, we propose an optimal replenishment and stocking model to maximise the retailers' profit. After that, a cost–benefit analysis-based heuristic method for solving the developed model is presented by considering two scenarios depending on whether a replenishment point belongs to a discount period or not. Furthermore, an algorithm is provided to elicit an optimal ordering policy from multiple solutions derived from the given heuristic solution method. Finally, a real case is offered to demonstrate the application of the proposed model, followed by a sensitivity analysis. The results indicate that a retailer can identify the optimal replenishment policy with the aim of achieving maximal profit in situations where stochastic short-term price discount and partial backordering are considered for certain inventory problems at hand. In addition, sensitivity analysis illustrates a fact that different values of the introduced parameters may influence the optimal replenishment policy. [ABSTRACT FROM AUTHOR]

This paper considers an inventory mechanism in which the supplier may provide a short-term price discount to the retailer at a future time with some uncertainty. To maximize the retailer's profit in this setting, we establish an optimal replenishment and stocking strategy model. Based on the retailer's inventory cost-benefit analysis, we present a closed-form solution for the inventory model and provide an optimal ordering policy to the retailer. Numerical experiments and numerical sensitivity are given to provide some high insights to the inventory model. [ABSTRACT FROM AUTHOR]

Multiple‐attribute group decision making (MAGDM) under linguistic environment is an important part of modern decision sciences, and information aggregation operator plays an import role in solving MAGDM problems. In this paper, an approach for solving MAGDM problem with q‐rung orthopair fuzzy 2‐tuple linguistic information is developed. First, the q‐rung orthopair fuzzy 2‐tuple linguistic weighted averaging (q‐ROFTLWA) operator and the q‐rung orthopair fuzzy 2‐tuple linguistic weighted geometric (q‐ROFTLWG) operator are presented. Furthermore, the q‐rung orthopair fuzzy 2‐tuple linguistic Muirhead mean (q‐ROFTLMM) operator and the q‐rung orthopair fuzzy 2‐tuple linguistic dual Muirhead mean (q‐ROFTLDMM) operator are proposed on the basis of Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator. Then, an approach is developed to deal with MAGDM problem under q‐rung orthopair fuzzy 2‐tuple linguistic environment based on the proposed operators. Finally, a numerical example for selecting desirable emergency alternative(s) in the process of designing emergency preplan is given to illustrate the application of the developed method and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers an inventory mechanism with a non-deterministic short-term price discount. In the mechanism, the supplier has a tendency to offer retailers a short-term price discount for a future occasion. Subsequently, when the discount occurs, the discount stock would start to reduce gradually. Therefore the discount might terminate ahead of schedule once the discount stock is depleted, which means that the discount possibility will gradually decrease from 1 ever after the start of the discount. For this system, to maximize the retailer's profit, we establish an optimal replenishment and stocking strategies model. Based on the retailer's cost-benefit analysis, we present a solution method for the model and provide an optimal ordering policy to the retailer. The given numerical experiments illustrate the validity of the model. [ABSTRACT FROM AUTHOR]