A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

Sadeghi, Javad; Sadeghi, Saeid; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.cor.2013.07.024

Cited by 111 publications

(34 citation statements)

References 35 publications

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“…In general, the scientific community has proposed numerous studies to adaptation to the incertitude of the market and the fast demand variations aiming to optimize and integrate different manufacturing problems, inventory and SCM so that in an agile and flexible way, it minimizes costs and attend efficiently the client's orders. (consult Bandyopadhyay and Bhattacharya 2014;Baud-Lavigne et al 2014;Diabat et al 2015;Ghiami et al 2013;Hiremath et al 2013;Haq and Boddu 2014;Kumar et al 2014;Mousavi et al 2013Mousavi et al , 2014Park and Kyung 2014;Sadeghi et al 2014;Triki et al 2014;Xiang et al 2014;Yao and Huang 2014;İnkaya and Akansel 15).…”

Section: Contextualization and Rationalementioning

confidence: 96%

A Genetic Algorithm applied to pick sequencing for billing

2015

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This article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes.

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Section: Contextualization and Rationalementioning

confidence: 96%

A Genetic Algorithm applied to pick sequencing for billing

2015

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“…From orthogonal arrays, each problem is run three times and since a solution with the highest MOCV is desired, the aim is to find minimize S/N calculated by Eq. 14 (Sadeghi et al 2014).…”

Section: Parameter Tuningmentioning

confidence: 99%

JIT single machine scheduling problem with periodic preventive maintenance

et al. 2016

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This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.

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“…Thus, for achieving near optimal solutions in large size instances, applying MOEAs are helpful. Sadeghi et al (2014) presented a multi-objective combinatorial optimization model of a supply chain problem including one-vendor multi-retailers considering a vendormanaged inventory approach. Their proposed model included two objectives, minimization of inventory cost as the first objective and maximization of the system reliability of the machines that produce the goods as the second.…”

Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

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A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

Cited by 111 publications

References 35 publications

A Genetic Algorithm applied to pick sequencing for billing

A Genetic Algorithm applied to pick sequencing for billing

JIT single machine scheduling problem with periodic preventive maintenance

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

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