Integration of strategic and tactical decisions for vendor selection under capacity constraints

Keskin, Burcu B.; íster, Halit; Çetinkaya, Sıla

doi:10.1016/j.cor.2010.03.008

Cited by 35 publications

(19 citation statements)

References 40 publications

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Order By: Relevance

“…Since changing the order quantities implies a modification of the ordering frequencies, the transportation costs are directly influenced by the inventory policy. Some researchers consider this impact explicitly (Keskin et al 2010bÜster et al 2008) and some implicitly. On the other hand, some others consider the relationship between inventory and location problems from a lead time perspective and evaluate the impact of lead times on backorders (Drezner et al 2003).…”

Section: Location Modelsmentioning

confidence: 99%

The Utility of EOQ in Supply Chain Design and Operation

Keskin

Çapar

2013

International Series in Operations Research &Amp; Management Science

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Supply chain design and operation problems are complicated in many fronts due to intervened decision making. One of the main complications is related to inventory decisions and costs. In many cases, considering inventory in the overall supply chain domain introduces stochastic and nonlinear formulations. Ignoring inventories, as in the traditional approach, results in inferior supply chain designs and operations in terms of cost performance. Economic order quantity (EOQ) models, with their simplicity, intuitive explanation, and clear implementation, aid in the resolution of these issues in a number of integrated supply chain problem contexts. In this chapter, we summarize the role and utility of EOQ models in these integrated supply chain design and operation problems. Specifically, we consider the three pillars of supply chain management: location, transportation, and inventory. We discuss how EOQ models ease the analysis of integrated supply chain models under each pillar in detail including inventorylocation models, inventory-routing models, and multi-echelon inventory models.

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Section: Location Modelsmentioning

confidence: 99%

The Utility of EOQ in Supply Chain Design and Operation

Keskin

Çapar

2013

International Series in Operations Research &Amp; Management Science

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“…Table 4 (17) is of multi-objective problems, the best solution is the nearest solution to utopia point (where considering the space solution, each and every one of the objectives has its own best value). This issue can be obtained through the least value of V in Program (18). In this study, the most efficient solution is the one which presents the least possible value for V. Hence, from the viewpoint of the quality and efficiency of the solutions obtained here, ABC with the least possible value for V presents the best solution compared to PSO and ICA and Lingo.…”

Section: Solving the Problem Of Main Vendors By Abc Pso Andmentioning

confidence: 99%

“…Therefore, Table 5 presents the results obtained from solving Program (18) by ABC in the presence of 10 main vendors.…”

Section: Solving the Problem Of Main Vendors By Abc Pso Andmentioning

confidence: 99%

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Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

Ekhtiari

Poursafary

2013

ISRN Artificial Intelligence

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It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem.

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“…Furthermore, they assumed that the warehouse cost dominates all the other inventory holding costs. Keskin et al [21] study generalized vendor selection models aimed at optimizing the total logistical costs including not only the vendor-specific fixed management and purchasing costs considered in traditional models, but also the transportation, inventory replenishment, and holding costs. …”

Section: Introductionmentioning

confidence: 99%