A preemptive fuzzy goal programming model for generalized supplier selection and order allocation with incremental discount

Mirzaee, Hossein; Naderi, Bahman; Pasandideh, Seyed Hamid Reza

doi:10.1016/j.cie.2018.05.042

Cited by 68 publications

(32 citation statements)

References 43 publications

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“…In the scientific literature, GP has been widely used in different fields such as agriculture (Poux & Aubert, 2018), industry (Jamalnia & Soukhakian, 2009;Huang et al, 2017;Mokhtari & Hasani, 2017; Zhuang & Hocine, 2018), medicine (Oddoye et al, 2009) and logistic (Hildenbrandt et al, 2018;Mirzaee et al, 2018). Regarding methodological development, we note many extensions to the GP model such as: interactive GP, stochastic GP, weighted GP, lexicographical GP, and GP with intervals.…”

Section: Goal Programming (Gp)mentioning

confidence: 99%

Weighted Goal Programming model for Storage Space Allocation problem in a container terminal

Dhahri

Mezghani

Rekik

2020

J. sustain. dev. transp. logist.

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Section: Goal Programming (Gp)mentioning

confidence: 99%

Weighted Goal Programming model for Storage Space Allocation problem in a container terminal

Dhahri

Mezghani

Rekik

2020

J. sustain. dev. transp. logist.

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“…The challenging competitions and globalization of today's markets have converted supplier selection to one of the most significant decisions to be made by decision-makers [35]. Supplier selection problem has been an enticing theme for research since 1960 [5]. There has been a wealth of review papers in this domain such as De Boer et al [36], Aissaoui et al [37], Ho et al…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first step toward this goal is to purchase an environment friendly raw material [4]. In manufacturing companies, approximately 70 percent of the cost of product is related to its raw material [5]. Therefore, purchasing strategies in green supply chain management is vital and significant.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy solution approach for supplier selection and order allocation in green supply chain considering location-routing problem

2019

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In the field of supply chain, selecting a suitable green supplier could significantly help us to decrease the cost and the risk involved in the operations as well as increase in the quality and green. In this paper, we develop an integrated two-stage approach based on fuzzy analytic hierarchy process (FAHP) and multi-objective mixed-integer linear programming to select suppliers and order allocation in green supply chain. In the first stage, suppliers are evaluated using FAHP method, and in the second stage, a multi-product multi-period supply chain considering green location-routing problem, discounting, and time window under uncertainty is developed. Then, a fuzzy solution approach is applied to solve proposed model using the data of a pharmaceutical chain in Iran. Results will verify the efficiency of the proposed model. [38], Ware et al. [39], Chai et al. [40], Igarashi et al. [41], Govindan et al. [42], Zimmer et al. [43] and Keshavarz Ghorabaee et al. [44]. Studies in the field of supplier selection can be mainly divided into three categories: supplier evaluation and selection, order allocation and combination of both [3]. In this research, the papers on the Green Supplier Selection and Order allocation are reviewed. Mafakheri et al. [45] used the AHP to determine the criteria weights and obtain the suppliers' scores. They also used a bi-objective mathematical model and a solution approach based on dynamic programming for order allocation. The objective functions simultaneously minimize costs and maximizes the purchase value. Four main criteria of price, delivery, quality and environmental performance were defined for supplier evaluation. Shaw et al. [46] presented a combined approach based on FAHP and fuzzy MOLP for supplier selection and order allocation problem towards minimizing greenhouse gas emissions. They applied the model on garment manufacturing company in India as for model validation. Kannan et al. [13] developed an integrated approach based on FAHP, FTOPSIS and MOLP for supplier selection and order allocation in a green supply chain management. They measured the performance of each supplier based on the cost, quality, delivery, technology capability and environmental competency criteria, using the combination of FAHP and FTOPSIS methods. Then, the resulted scores were used as a factor in the objective function of maximizing purchases from the fitting suppliers. The other objective function was to minimize total cost of purchasing. Using three environmental criteria including environmental management system, pollution and greenhouse emissions, along with other criteria, Azadnia et al. [47] utilized the FAHP method to evaluate suppliers. They developed a multi-objective programming model for allocating orders to suppliers. Govindan and Sivakumar [14] proposed a MOLP model for simultaneously minimizing costs, minimizing defective product purchases, minimizing delays in products delivery, minimizing recycling losses, and minimizing carbon emissions. They used fuzzy TOPSIS method to calculate th...

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“…Quantity discount is the most frequently-used strategy which consists of all-unit quantity discount and incremental quantity discount (Corbett, 2000;Tsai & Wang, 2010;Zhang & Chen, 2013;Ghaniabadi & Mazinani, 2017;Bohner & Minner, 2017;Manerba & Perboli, 2019). All-unit quantity discount involves multiple quantity breaks, and buyers get a discount for every unit when the purchase quantity equals or exceeds each threshold (Wang et al, 2019), while incremental quantity discount allows buyers to get a discount only for extra units above predetermined thresholds (Mirzaee et al, 2018). Discount policy is often an important characteristic in the realistic problems of procurement (Cheraghalipour & Farsad, 2018;Sabouhi et at., 2018).…”

Section: Introductionmentioning

confidence: 99%

Automobile Components Procurement Using a Dea-Topsis-Fmip Approach With All-Unit Quantity Discount and Fuzzy Factors

Chen

Zhang

Gou

et al. 2020

Technological and Economic Development of Economy

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Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions.

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A preemptive fuzzy goal programming model for generalized supplier selection and order allocation with incremental discount

Cited by 68 publications

References 43 publications

Weighted Goal Programming model for Storage Space Allocation problem in a container terminal

Weighted Goal Programming model for Storage Space Allocation problem in a container terminal

A fuzzy solution approach for supplier selection and order allocation in green supply chain considering location-routing problem

Automobile Components Procurement Using a Dea-Topsis-Fmip Approach With All-Unit Quantity Discount and Fuzzy Factors

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