International Journal of Industrial Engineering & Production Research

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  Supplier selection is one of the most important activities of purchasing departments. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer’s company. Supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in this problem make the decision making complicated. Simultaneously, in this model, vagueness of input data and varying importance of criteria are considered. In real cases, where Decision- Makers (DMs) face up to uncertain data and situations, the proposed model can help DMs to find out the appropriate ordering from each supplier, and allows purchasing manager(s) to manage supply chain performance on cost, quality, on time delivery, etc. An additive weighted model is presented for fuzzy multi objective supplier selection problem with fuzzy weights. The model is explained by an illustrative example.

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Assessment and selection of suppliers are two most important tasks in the purchasing part in supply chain management. Supplier selection can be considered to be a single or multi-objective problem. From another point of view, it can be a single or multi-sourcing problem. In this paper, an integrated AHP and Fuzzy TOPSIS model is proposed to solve the supplier selection problem. This model makes the decision-maker to be able to solve this problem with different criteria and different weight for each criterion with respect to the purchasing strategy. Finally, the proposed model is illustrated by an example.

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In nowadays world competitive market, on account of the development of electronic media and its influence on shortening distances, companies require some core competencies in order to be able to compete with numerous competitors in industry and sustain their situation in such a market. In addition companies achieve this target are those which their processes perform great and exploit from competitive price, quality, guarantee, etc. Since some parameters such as price and quality are so dependent on the performance of company supply chain management, so the results can highly impress the final price and quality of products. One of the main processes of supply chain management is supplier selection process which its accurate implementation can dramatically increase company competitiveness. In presented article two layers of suppliers have been considered as a chain of suppliers. First layer suppliers are evaluated by two groups of criteria which the first one encompasses criteria belongs to first layer suppliers features and the second group contains criteria belong to the characteristics of second layer suppliers. One of the criteria is the performance of second layer suppliers against environmental issues. Then the proposed approach is solved by a method combined of concepts of fuzzy set theory (FST) and linear programming (LP) which has been nourished by real data extracted from an engineering design and supplying parts company. At the end results reveal the high importance of considering second layer suppliers features as a criteria for selecting the best supplier.

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Supplier selection,   Multi-objective decision making,   Fuzzy Compromise programming,   Supply chain management,   Quantity discount .

Supplier selection is naturally a complex multi-objective problem including both quantitative and qualitative factors. This paper deals with this issue from a new view point. A quantity discount situation, which plays a role of motivator for buyer, is considered. Moreover, in order to find a reasonable compromise solution for this problem, at first a multi-objective modeling is presented. Then a proposed fuzzy compromise programming is utilized to determine marginal utility function for each criterion. Also, group decision makers’ preferences have taken into account and the weight of each criterion has been measured by forming pair-wise comparison matrixes. Finally the proposed approach is conducted for a numerical example and its efficacy and efficiency are verified via this section. The results indicate that the proposed method expedites the generation of compromise solution .

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Capacity Reservation,   Option Contract,   Supplier Selection

A key issue for manufacturing firms is planning for outsourced components. In this research, we have considered a manufacturer in a Make-to-Order production environment who has to outsource a special component from a set of suppliers. One selling season is considered and the manufacturer faces uncertain demand during the selling season. A good strategy for the manufacturer to balance both holding and lost sale costs is to initiate capacity reservation contracts with his suppliers. Thus, unlike the previous researches we have presented a mathematical model based on option mechanism that will help the manufacturer to select appropriate suppliers and order allocation, simultaneously. The considered option mechanism has a two part contract fee structure (option price and exercise price) and it is at the foundation of practical contracts used by different industries. A numerical example is used to illustrate the model and to investigate how option mechanism improves manufacturer's expected profit in comparison with the situation without applying the option mechanism .

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In recent years, Supplier evaluation and selection, an important element in supply chain management, has been gaining attention in both academic literature and industrial practice. The Mixed integer multi-Objective non-Linear programming model (MIMONLP) presented in this paper aimed to evaluate and select the appropriate set of suppliers considering quantitative and qualitative criteria and in addition to selecting the first layer's suppliers which relate directly to the organization, analyses the characteristics of second-layers suppliers, and design a network to determine the flow rate of products and materials between buyers and best suppliers in both layers. Another important feature of this model is considering holding costs of different products over the planning horizon and quantity discounts for the first layer's suppliers at the same time. Finally, the model is solved by using goal programming approach and numerical examples are presented to test the performance of proposed model.

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Considering the major change occurred in business cells from plant to “chain” and the critical need to choose the best partners to form the supply chain for competing in today’s business setting, one of the vital decisions made at the early steps of constructing a business is supplier selection. Given the fact that the early decisions are inherently strategic and therefore hard and costly to change, it’s been a point of consideration for industries to select the right supplier. It’s clear that different criteria must be investigated and interfered in deciding on the best partner(s) among the alternatives. Thereupon the problem might be regarded as a multiple criteria decision making (MCDM) problem. There are a variety of techniques to solve a MCDM problem. In this paper we propose a novel technique by combination of decision making trial and evaluation laboratory and graph theory and matrix approach techniques. Eventually, the results are compared to SAW technique and discussed to come to a conclusion.

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Efficiency and effectiveness of the organization is result ofmanagement performance and supply chain structure.Today, several factors in selection the supplier or the best combination of suppliers have been identified that this issue would increase the complexity of suplier selecting.This study investigates the application of Fuzzy Delphi in order to identify the important factors in selecting a supplier in the steel industry and then provide a comprehensive and holistic model of supplier selection to overcome the complexity.In this context, Interpretive Structural Modeling (ISM) unlike other methods, the holistic, dealing with supplier selection to prioritize components-surfacing and identifying key components, so industry leaders will provide comperhensive map to select the best combination based on their.The results of this study indicate that "technically possible", "financial health" and "geography situation" are the basic components to the selection of suppliers.

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Currently, due to increased competition in the services and manufacturing, many companies are trying to lower price and good quality products offer to the market. In this paper, the multi-criteria decision-making techniques to evaluate and select the best supplier from among the existing suppliers. The first, hierarchical structure for selecting suppliers of raw materials used and the analytic hierarchy process to obtain the relative importance of quantitative and qualitative criteria related to green supply chain is applied.  Then, a fuzzy TOPSIS technique any raw material suppliers is ranked according to the relevant criteria. Finally, with regard to the weight of suppliers and demand of raw material and resource constraints by a multi-objective mathematical model, optimum order is determined. The objectives are to minimize the total cost, maximize amount of purchases of desirable suppliers and minimize of raw materials required are not provide. The proposed method in a case study used Food Company and the relevant results are expressed.

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Recently, sustainable supply chain management (SSCM) has become one of the important subjects in the industry and academia. Supplier selection, as a strategic decision, plays a significant role in SSCM. Researchers use different multi-criteria decision making (MCDM) methods to evaluate and select sustainable suppliers. In the previous studies, evaluation is solely based on the desirable features of suppliers and their risks are neglected. Therefore, current research uses failure mode and effects analysis (FMEA) as a risk analysis technique to consider supplier's risk in combination with the MCDM method. Practically, this study operated in two main stages. In the first stage, the score of the suppliers obtains by integration Fuzzy MOORA and FMEA. In the second stage, the output of the previous stage used as input parameters in developed mix-integer linear programming to select suppliers and order optimum quantity. Finally, to demonstrate the effectiveness of the proposed approach, a case study in a chemical industry and sensitivity analysis is presented.  

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The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations.  The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric. 

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In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms. 

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This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.

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Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. Regarding the fact that decision makers (DMs) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. This paper develops a nadir compromise programming (NCP) model for decision-making under uncertainty on the selection of suppliers within the framework of binary programming. Depending on the condition of uncertainty, three statuses are taken into consideration and a solution approach is proposed for each status. A pure deterministic NCP model is presented for solving the problem in white condition (certainty of data) and a solution approach resulted from combination of NCP and stochastic programming is introduced to solve the model in black (uncertainty of data) situation. The paper also proposes a NCP model under certainty and uncertainty for solving problem under grey (a combination of certainty and uncertainty of data) conditions. The proposed approaches are illustrated for a real problem in steel industry with multiple objectives. Also, a simulation approach has been designed in order to examine the results obtained and also verifies capabilities of the proposed model.

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In the new strategies of purchasing and production, suppliers play a key role in achieving of competitive capability for big companies. The selection of suitable suppliers is a critical component of this strategy. The problem of allocation order to supplier is a multi-objective problem that includes fuzzy parameters and also suppliers usually consider discount due to different levels of purchase amount. Since there is not a multi-objective fuzzy model for allocating orders in the literature that consider discount and shortfall simultaneously in the planning horizon of multi-products, this research proposes a new model includes minimization of costs, delays and the percentage of defective parts as objective functions. Jimenez method is used to defuzzy price, demand, delay and percentage of defective parts. Since the model is NP-hard, the two metaheuristic algorithms, NSGAII and MOPSO that their parameters were set by the Taguchi method has been developed. According to the results of numerical problems, the proposed algorithms can provide a good approximation of the optimal solutions for intended objectives.
 

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