A supplier-selection model with classification and joint replenishment of inventory items

Mohammaditabar, Davood; Ghodsypour, Seyed Hassan

doi:10.1080/00207721.2014.951418

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…They can find the optimum solutions because they can avoid being trapped in a local optimum and can search very large spaces of candidate solutions . Besides, from the literature, the selected algorithms are the most used algorithms to solve the provider selection problem especially in MIVS problem …”

Section: Solution Algorithmsmentioning

confidence: 99%

A multicriteria optimization model for cloud service provider selection in multicloud environments

Mohamed

Abdelsalam

2020

Softw Pract Exp

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Multicloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock-in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing[SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)-as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution. K E Y W O R D Scloud service provider, genetic algorithm, multicloud, particle swarm optimization, simulated annealing, Taguchi method 1 Softw: Pract Exper. 2020;50:925-947.wileyonlinelibrary.com/journal/spe

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Section: Solution Algorithmsmentioning

confidence: 99%

A multicriteria optimization model for cloud service provider selection in multicloud environments

Mohamed

Abdelsalam

2020

Softw Pract Exp

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“…Their model is used for evaluating and selecting suppliers under the sustainable supply chain environment. Mohammaditabar and Ghodsypour [18] developed a model in capacity-constrained supplier-selection and order-allocation problem, which considered the joint replenishment of inventory items with a direct grouping approach. Lot-sizing with supplier selection (LS-SS) is a fast-growing offspring of two major problem parents in logistics and supply chain management.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Supplier Selection Model with Multiple Criteria: A Case Study in the Automotive Parts Industry

Phumchusri¹,

Tangsiriwattana²

2019

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This research proposes a mathematical model for supplier selection for a casestudy car seat manufacturer. This research is divided into 2 parts. The first part is the raw material supplier evaluation method using Analytic Hierarchy Process. This part weights the importance of main decision criteria and sub-decision criteria, complying with part makers' satisfaction. The result from the first part is scores for each raw material supplier resulting from multiple evaluation criteria. The second part proposes a mathematical model for supplier selection using integer programming. The scores of each supplier from the first part will be considered along with raw material consumption to select the suitable raw material suppliers that maximize overall part makers' satisfaction. The results from the first part of this research show that the most important criterion for supplier evaluation is cost, which is about 41%. Quality, Delivery, Service, and Risk factors are approximately 24%, 14%, 12% and 9%, respectively. The result from the second part shows that the model can effectively match material suppliers to part makers according to their preferences. Comparing with current situation, the satisfaction is increased by 26% with this proposed framework. It means the proposed model can help matching the right supplier to each part maker that can increase overall satisfactions for this case-study's supply chain.

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“…Details related with the inventory control system and supplier selection can be found in Haq and Kannan, 20 Mendoza and Ventura, 21 Guo and Li, 22 and Mohammaditabar and Ghodsypour. 23 To ensure continued improvement for control policy and supplier selection, we used the SO-based model to make a strategic difference to a company’s ability. Basically, our study serves the following purposes:…”

Section: Literaturementioning

confidence: 99%

Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization

et al. 2017

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A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and continuous (s, S) policy for the supply chain environment; how to apply statistical analysis skills to compare these SO models with a greater level of detail. According to the cost analysis, the total supply chain cost of Model 1 is improved approximately 22% with Model 2. Also, Model 2 is the best one according to quantity-based analysis, order-based analysis, probability-based analysis, and lead-time-based analysis. Model 2 can be successfully applied for the actual situation of the supply chain inventory system and companies can obtain a remarkable amount of saving while increasing their competitive edge.

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A supplier-selection model with classification and joint replenishment of inventory items

Cited by 15 publications

References 35 publications

A multicriteria optimization model for cloud service provider selection in multicloud environments

A multicriteria optimization model for cloud service provider selection in multicloud environments

Optimal Supplier Selection Model with Multiple Criteria: A Case Study in the Automotive Parts Industry

Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization

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