Reinforcement learning of fuzzy joint replenishment problem in supply chain

2015 ◽  
pp. 779-782
Author(s):  
C Li ◽  
S Zhao ◽  
T Zhang ◽  
X Wang
Keyword(s):  
Supply Chain ◽  
Reinforcement Learning ◽  
Joint Replenishment ◽  
Joint Replenishment Problem

Bi-objective optimisation of the joint replenishment problem in a two-echelon supply chain

2021 ◽  
Vol 38 (3) ◽  
pp. 336
Author(s):  
Pejman Ahmadi ◽  
Seyed Hamid Reza Pasandideh ◽  
Leopoldo Eduardo Cárdenas Barrón ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Supply Chain ◽  
Joint Replenishment ◽  
Joint Replenishment Problem

scholarly journals Bi-objective optimization of the joint replenishment problem in a two-echelon supply chain

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Leopoldo Eduardo Cardenas Barron ◽  
Seyed Taghi Akhavan Niaki ◽  
Seyed Hamid Reza Pasandideh ◽  
Pejman Ahmadi
Keyword(s):  
Supply Chain ◽  
Joint Replenishment ◽  
Joint Replenishment Problem

Vendor-managed inventory in the joint replenishment problem of a multi-product single-supplier multiple-retailer supply chain

2018 ◽  
Vol 13 (1) ◽  
pp. 156-178 ◽  
Author(s):  
Seyed Hamid Reza Pasandideh ◽  
Seyed Taghi Akhavan Niaki ◽  
Pejman Ahmadi
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Random Search ◽  
Optimal Number ◽  
Vendor Managed Inventory ◽  
Joint Replenishment ◽  
Content Type ◽  
Joint Replenishment Problem ◽  
Nonlinear Programming Model ◽  
Practical Implications

Purpose In this paper, the joint replenishment problem is modeled for a two-level supply chain consisting of a single supplier and multiple retailers that use the vendor-managed inventory (VMI) policy for several products. This paper aims to find the optimal number of products to order in both policies, the optimal times at which each retailer orders the products in the traditional policy and the optimal times at which the supplier orders the product in the VMI policy. Design/methodology/approach The problem is first formulated into the framework of a constrained integer nonlinear programming model; then, the problem is solved using a teacher-learner based optimization algorithm. As there are no benchmarks available in the literature, a genetic algorithm is used as well to validate the results obtained. Findings The solutions obtained using both the algorithms for several numerical examples are compared to the ones of a random search procedure for further validation. A real case is solved at the end to demonstrate the applicability of the proposed methodology and to compare both the policies. Research limitations/implications The paper does not have any special limitations. Practical implications The study has significant practical implications for the sellers and for the suppliers who have to get the most profit. Also, satisfying the constraints make decision more complicated. Originality/value This paper has two main originalities. The authors have developed the model of the joint replenishment problem and have contributed in the problem-solving process. They have used a new meta-heuristic and then compared it to a classic one.

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