Abstract

The stochastic joint replenishment and delivery scheduling (JRD) problem is a key issue in supply chain management and is a major concern for companies. So far, all of the work on stochastic JRDs is under explicit environment. However, the decision makers often have to face vague operational conditions. We develop a practical JRD model with stochastic demand under fuzzy backlogging cost, fuzzy minor ordering cost, and fuzzy inventory holding cost. The problem is to determine procedures for inventory management and vehicle routing simultaneously so that the warehouse may satisfy demand at a minimum long-run average cost. Subsequently, the fuzzy total cost is defuzzified by the graded mean integration representation and centroid approaches to rank fuzzy numbers. To find optimal coordinated decisions, a modified adaptive differential evolution algorithm (MADE) is utilized to find the minimum long-run average total cost. Results of numerical examples indicate that the proposed JRD model can be used to simulate fuzzy environment efficiently, and the MADE outperforms genetic algorithm with a lower total cost and higher convergence rate. The proposed methods can be applied to many industries and can help obtaining optimal decisions under uncertain environment.

Highlights

  • The joint replenishment problem (JRP) has been heavily researched since the early work of Shu [1]

  • According to the recommendation of Neri and Tirronen [37] and Liu and Lampinen [38], we set the relevant parameters of the modified adaptive differential evolution algorithm (MADE) as follows: NP = 100, Fmin = 0.2, Fmax = 1.2, CR = 0.1, and Gen M = 150

  • We will design three different scenarios to compare the results of joint replenishment and delivery scheduling (JRD) and fuzzy JRD

Read more

Summary

Introduction

The joint replenishment problem (JRP) has been heavily researched since the early work of Shu [1]. Cha et al [9] developed a joint replenishment and transportation model in a onewarehouse, n-retailer system based on improved well-known heuristic named RAND and Genetic Algorithm (GA). They extended their model with constraints and showed the flexibility of GA. Unlike the studies above that consider deterministic demand, Qu et al [5] discussed a multiitem JRD with modified periodic-review policy under stochastic demand. They considered nonlinear transportation cost and proposed an efficient heuristic algorithm to solve the problem.

Objectives
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call