The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system

Cha, Byung-Chul; Moon, Ilkyeong; Park, J.H.

doi:10.1016/j.tre.2007.05.010

Cited by 63 publications

(72 citation statements)

References 27 publications

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“…Moreover, results in [5] show that the performance of the GA is extremely good as compared with other solution of each JRD. Therefore, GA is also a good choice to find good solutions for the practical JRD.…”

Section: Different Approaches Selected For Comparisonmentioning

confidence: 94%

“…JRDs have been proven to be NP-hard problems and designing highquality algorithms is difficult. Current approaches include iterative algorithms, genetic algorithm (GA) [5], and differential evolution algorithm (DE) [4] [9]. Solving JRDs effectively by traditional approaches is obviously difficult for the following reasons: (1) Available heuristics are too problem-specific and difficult to design, and versatile approaches do not exist.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

“…The JRD policy aims to determine the replenishment frequencies and delivery frequencies of items to minimize the total system cost. The JRD in this study has the following assumptions similar to those in the study of Cha et al [5]: (a) Demand rates and costs for these items are known and constant; (b) Shortages are not allowed; (c) : set of items with a replenishment cycle that is longer than or equal to the credit period; and : set of items with a replenishment cycle that is shorter than the credit period.…”

Section: Assumptions and Notationmentioning

confidence: 99%

“…The proposed JRD is in fact a contrastive small-scale numeric example without trade credit based on the study of Cha et al [5]. The data are shown in Table 3.…”

Section: Experiments 1: Proposed Jrd Without Trade Creditmentioning

confidence: 99%

“…Cha, Moon, and Park [5] focused on the JRD of a onewarehouse, n-retailer system. A heuristic and an intelligent algorithm were proposed to solve this JRD.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Hybrid Differential Evolution Algorithm Incorporating Simulated Annealing for Joint Replenishment and Delivery Problem with Trade Credit

Zeng¹,

Peng²,

Zhang³

et al. 2016

IJCIS

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In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade credit. However, because of the complex mathematical properties of JRD, high-quality solutions to the problem have eluded researchers. We design an effective hybrid differential evolution algorithm based on simulated annealing (HDE-SA) that can resolve this non-deterministic polynomial hard problem in a robust and precise way. After determining the suitable parameters by a parameter-tuning test, we verify the performance of the HDE-SA through numerical JRD examples. Compared with the results of other popular evolutionary algorithms, results of randomly generated JRDs indicate that HDE-SA can always obtain slightly lower total costs than differential evolution algorithm (DE) and genetic algorithm (GA) under different situations. Moreover, the convergence rate of the HDE-SA is higher than that of DE and GA. Thus, the proposed HDE-SA is a potential tool for the JRD with trade credit.

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Section: Different Approaches Selected For Comparisonmentioning

confidence: 94%

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Section: Assumptions and Notationmentioning

confidence: 99%

“…The proposed JRD is in fact a contrastive small-scale numeric example without trade credit based on the study of Cha et al [5]. The data are shown in Table 3.…”

Section: Experiments 1: Proposed Jrd Without Trade Creditmentioning

confidence: 99%

“…Cha, Moon, and Park [5] focused on the JRD of a onewarehouse, n-retailer system. A heuristic and an intelligent algorithm were proposed to solve this JRD.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations