Optimizing multi-supplier multi-item joint replenishment problem for non-instantaneous deteriorating items with quantity discounts

Abstract

This paper deals with a new joint replenishment problem, in which a number of non-instantaneous deteriorating items are replenished from several suppliers under different quantity discounts schemes. Involving both joint replenishment decisions and supplier selection decisions makes the problem to be NP-hard. In particular, the consideration of non-instantaneous deterioration makes it more challenging to handle. We first construct a mathematical model integrated with a supplier selection system and a joint replenishment program for non-instantaneous deteriorating items to formulate the problem. Then we develop a novel swarm intelligence optimization algorithm, the Improved Moth-flame Optimization (IMFO) algorithm, to solve the proposed model. The results of several numerical experiments analyses reveal that the IMFO algorithm is an effective algorithm for solving the proposed model in terms of solution quality and searching stableness. Finally, we conduct extensive experiments to further investigate the performance of the proposed model.

Vegetable Product Pricing Replenishment Strategy Based on WOA

“…When is nonstationary and there is an ARMA component, then can be described as (4) where is the white noise process, p and q denote the maximum number of stages of the nonseasonal autoregressive and moving average operators, respectively, and d denotes the number of first-order (non-seasonal) differences . Substituting Equation (4) into Equation (3) into the moving average seasonal time series model can be obtained as (5) Where the subscripts P, Q, p, q denotes the maximum lag order of seasonal and non-seasonal autoregressive and moving average operators, respectively, and d, D denote the number of nonseasonal and seasonal differences, respectively. The above equation is called the order seasonal time series model or the product seasonal model [8].…”

“…For the pricing strategy, the advantage of nonlinear programming is that it can solve nonlinear problems, but the disadvantage is that the solution process may be more complicated and may not be able to find the global optimal solution. The advantage of the whale intelligent optimization algorithm is that it has a high convergence speed and the ability to jump out of the local optimal solution [5]. Therefore, we use the whale intelligent optimization algorithm to solve the pricing of vegetable individual products [6].…”

An Efficient Algorithm for the Joint Replenishment Problem with Quantity Discounts, Minimum Order Quantity and Transport Capacity Constraints

“…Cui [32] simultaneously considered two quantity discounts, an all-unit quantity discount and an incremental quantity discount, in the JRP and solved it by the locust swarms algorithm. Ai [33] constructed a mathematical model with a supplier selection system and a JRP where suppliers have different quantity discount schemes.…”

Complex Production-Inventory Replenishment Problem With Uncertainty in Customer Behaviour

“…The studies of [43] and [44] utilised an inventory replenishment strategy for deteriorating items in finding a solution to the problem of production disruption. The same idea has been used in the supply chain industry by [45] and [46], forming part of the integrated units for the disruption problem.…”

“…On the other hand, the instantaneous method replenishes order inventory instantly. See Table 3 for variable parameters applied in the range: High Order Volume (100-120); High Demand Volume (80-100); Average Order Volume (40-60); Average Demand Volume (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50); Low Order Volume ; Low Demand Volume (20-25); Full Inventory=100; Safe Inventory Volume =50.…”