The joint replenishment problem with resource restriction

Moon, Ilkyeong; C., B.

doi:10.1016/j.ejor.2004.11.020

Cited by 63 publications

(42 citation statements)

References 11 publications

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“…The GA is better than the M-RAND and RG. 2 The RG is better than the M-RAND and GA. 3 The GA is equal to RG and both are better than M-RAND. 2 The GA is better than the M-RAND and RG.…”

Section: Second Comparison Partmentioning

confidence: 99%

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Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Yousefi

Sadjadi²

2014

Turk J Elec Eng & Comp Sci

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Abstract:There are many cases in real inventory systems where more than one objective must be optimized. The main purpose of this research is to develop a multiobjective joint replenishment problem (JRP), where one objective is the minimization of the total inventory investment and another is the minimization of the total inventory ordering and holding costs. To solve the suggested model, 3 algorithms are proposed. In the first algorithm, the existing RAND method, called the best heuristic for solving the JRP, is modified and a new heuristic algorithm is developed to be applicable to the JRP with 2 objectives. The second algorithm is a multiobjective genetic algorithm that has shown good performance for solving the JRP. Finally, a third algorithm is developed, using a combination of the 2 previous ones. The performances of these algorithms are then compared. Running the programs shows good performance in solving the 9200 randomly produced problems.

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“…The GA is better than the M-RAND and RG. 2 The RG is better than the M-RAND and GA. 3 The GA is equal to RG and both are better than M-RAND. 2 The GA is better than the M-RAND and RG.…”

Section: Second Comparison Partmentioning

confidence: 99%

“…Because of the major ordering cost, using a group replenishment or group ordering may lead to substantial cost savings [2,3]. Arkin [4] showed that the JRP is a nondeterministic polynomial-time hard (NP-hard) problem.…”

Section: Introductionmentioning

confidence: 99%

Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Yousefi

Sadjadi²

2014

Turk J Elec Eng & Comp Sci

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“…Goyal introduced resource constraint to the JRP, proposing a heuristic algorithm based on the Lagrangian multiplier [1]. Moon and Cha developed two efficient algorithms to solve the JRP with resource restrictions [2]. Moon et al considered the joint replenishment and transportation problem for a third party warehouse to handle multi-variety items, proposing two policies and developing four efficient algorithms to solve optimization model by the two policies [3].…”

Section: Review Of Previous Literaturementioning

confidence: 99%

“…The numerical examples referred by Moon and Cha [2] were used to verify program operation process and performance of AIGA. Samples of data are given in Table I.…”

Section: Examples Of Data Instancesmentioning

confidence: 99%

Constrained Stochastic Joint Replenishment Problem with Option Contracts in Spare Parts Remanufacturing Supply Chain

Liang¹,

Qiao²,

Luo³

2016

Int. j. simul. model.

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In a real business environment, the stochastic joint replenishment with resource restrictions commonly adopts the wholesale sales mechanism. The mechanism causes conflicts of interest between the supplier and the service provider. In order to effectively compete in an uncertain business environment, the option contract is a viable mechanism for coordination between the supplier and the service provider. This paper proposes a fixed-cycle joint replenishment policy based on the option contracts. Stochastic joint replenishment problem of spare parts considers the forward supply process and the reverse recycling process in the remanufacturing supply chain. The optimization model of stochastic joint replenishment problem is developed with a fixed-cycle joint replenishment policy based on the option contracts. According to characteristics of the optimization model, the adaptive immune genetic algorithm is established for solving the optimization problem. Finally, the validity of the optimization model and algorithm are illustrated by data.

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“…Since optimizing the size of our problem took too long time, we proposed GA for deciding can-order level. And, because GA is well used for similar problem (Khouja et al (2000) used GA for classical JRP, Moon and Cha (2006) confirm some extent of effectiveness of GA for constrained JRP, or Yang et al (2012) used GA for SWMR), and GA is well used for multi objective optimization problem. Thus, this research also proposed and applied GA for deciding can-order level.…”

Section: Introductionmentioning

confidence: 99%

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

Nagasawa¹,

Irohara²,

Matoba³

et al. 2015

Industrial Engineering and Management Systems

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In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

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The joint replenishment problem with resource restriction

Cited by 63 publications

References 11 publications

Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Constrained Stochastic Joint Replenishment Problem with Option Contracts in Spare Parts Remanufacturing Supply Chain

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

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