An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model

Wang, Lin; Dun, Cai-Xia; Bi, Wenjie; Zeng, Yu‐Rong

doi:10.1016/j.knosys.2012.06.007

Cited by 66 publications

(53 citation statements)

References 33 publications

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“…Results in [4] and [10] show that DE is a feasible algorithm to solve the JRDs effectively and efficiently.…”

Section: Different Approaches Selected For Comparisonmentioning

confidence: 99%

“…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%

“…However, many of these guidelines have good initial starting values that can be used directly. Wang, et.al [4] and Wang, Wang, and Liu [25] suggested that the guideline for the number of population is 2 2 0…”

Section: Parameters Setting Analysis For Hde-samentioning

confidence: 99%

“…Moon, Cha, and Lee [6] developed joint replenishment and consolidated freight-delivery models and four efficient algorithms are used to resolve the models. Wang, Dun, et al [4] proposed a two-level stochastic JRD by extending the model of Qu, Bookbinder, and Iyogun [7] and verified that the differential evolution algorithm is effective and efficient. Scholars have also developed several relevant extensions such as JRD under fuzzy environment [8], radio-frequency identification technology investment evaluation model of JRD [9], JRD with delivery constraint [10], locationinventory problem with joint replenishment policy [11], and JRD with multi-warehouse [12].…”

Section: Introductionmentioning

confidence: 98%

“…A corresponding review has been conducted by Khouja and Goyal [3]. Adopting a joint replenishment policy can result in cost savings, so managers are aware that using a joint replenishment and delivery (JRD) policy can obtain a scale effect of replenishment and transportation simultaneously, thereby further reducing total cost [4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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“…Results in [4] and [10] show that DE is a feasible algorithm to solve the JRDs effectively and efficiently.…”

Section: Different Approaches Selected For Comparisonmentioning

confidence: 99%