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.
Highlights
In real life, the decay or deterioration of products is a natural phenomenon
Our research focuses on a new joint replenishment problem (JRP) model, in which multiple noninstantaneous deteriorating items are joint replenished from multiple suppliers with different quantity discounts
In order to verify the competitive performance of the Improved Moth-flame Optimization (IMFO) algorithm, it is compared with other five meta-heuristic algorithms, named Genetic Algorithm (GA) (Deb et al 2002) [34], Grey Wolf Optimizer Algorithm (GWO) (Mirjalili et al 2014) [35], Fruit-fly Optimization Algorithm (FOA) (Pan, 2012) [36], Particle Swarm Optimization (PSO) Algorithm (Shi, 2001) [37] and original Math-Flame Optimization Algorithm (MFO) (Mirjalili. 2015) [7]
Summary
The decay or deterioration of products is a natural phenomenon. The majority of sales revenue of supermarkets and grocery stores derives from deteriorating merchandises. In the multi-item joint replenishment problem with multiple suppliers, how many perishable items to be replenished altogether and which specific item should be replenished from which supplier become key but challenging decisions for retailers to minimize the total cost Motivated by such a realistic problem, this study firstly considers a multi-supplier joint replenishment problem for non-instantaneous deteriorating items (MS-JRNID) with several different quantity discounts offered by suppliers. As the joint replenishment problem (JRP) and the supplier selection problem are both NP-hard (Arkin et al 1989 [4]; Moon et al 2008 [5]), an additional consideration of non-instantaneous deterioration makes the problem more complicated, and hard to be solved by traditional algorithms of JRP.
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