Supplier Selection Problem Research Articles

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

An extended goal programming approach with piecewise penalty functions for uncertain supplier-material selection problem in cardboard box manufacturing systems

In this study a real case multi-objective material and supplier selection problem in cardboard box production industries is studied. This problem for the first time optimizes the objective functions such as total wastage amounts remained from all raw sheets, total costs of the system including purchasing cost and transportation cost (including fixed and variable costs) of the raw sheets, and total overplus of produced cardboard boxes. To be closer to the real situations, as a novelty, the problem is formulated in belief-degree-based uncertain environment with normal distribution where this type of uncertainty applies the ideas of experts. A solution approach including two steps is proposed to solve the problem. In the first step, the proposed uncertain formulation is converted to a crisp form using a typical chance constrained programming scheme. In the second step, a new goal programming approach containing a piecewise penalty function is developed in order to solve the obtained multi-objective crisp formulation. In this approach, based on the ideas of experts, multiple goals are considered with different penalty values. A case study from cardboard box industries is considered to evaluate the proposed formulations and solution approach. According to the obtained results, the proposed solution approach is compared to similar approaches of the literature and its efficiency is studied.

Shadowed AHP for Multi-Criteria Supplier Selection

Numerous techniques of multi-criteria decision-making (MCDM) have been proposed in a variety of business domains. One of the well-known methods is the Analytical Hierarchical Process (AHP). Various uncertain numbers are commonly used to represent preference values in AHP problems. In the case of multi-granularity linguistic information, several methods have been proposed to address this type of AHP problem. This paper introduces a novel method to solve this problem using shadowed fuzzy numbers (SFNs). These numbers are characterized by approximating different types of fuzzy numbers and preserving their uncertainty properties. The new Shadowed AHP method is proposed to handle preference values which are represented by multi-types of uncertain numbers. The new approach converts multi-granular preference values into unified model of shadowed fuzzy numbers and utilizes their properties. A new ranking approach is introduced to order the results of aggregation preferences. The new approach is applied to solve a supplier selection problem in which multi-granular information are used. The features of the new approach are significant for decision-making applications.

Sustainable Supplier Selection Using Fuzzy AHP (AHP-F) and Fuzzy ARAS (ARAS-F) Techniques for Fertilizer Supply in the Agricultural Supply Chain

Implementing the right strategies in the agricultural supply chain in the supply of seeds, pesticides, fertilizer, energy, fuel and agricultural mechanization tools and equipment has a great role in increasing agricultural productivity. The main purpose of the study is to rank and evaluate alternatives in choosing a sustainable fertilizer supplier in the agricultural supply chain by using AHP-F and ARAS-F techniques. In an environment of uncertainty and complex supply chain structure, multi-criteria decision making (MCDM) methods are widely used to solve supplier selection problems. In this study, the importance levels and weights of the criteria in the selection of sustainable fertilizer suppliers were measured by the AHP-F method. The criteria that are important for fertilizer supplier selection were evaluated by taking expert opinions, the uncertain and uncertain opinions of the decision makers were modeled with the AHP-F approach and the weights of the criteria were determined. Among the criteria, resource consumption (FSC05) has the highest weight. Then, alternative rankings were obtained with the ARAS-F method. Fertilizer supplier alternatives in the agricultural supply chain were ranked with the ARAS-F method, using the criterion weights found with AHP-F. In the ranking of alternatives, alternative fertilizer supplier FS03 ranked first with the highest value. This study provides a resource for businesses and other stakeholders to make decisions regarding sustainable fertilizer supplier selection.

Research on Supplier Selection of Pharmaceutical Enterprises under the Dual Carbon Target

With the proposal of national carbon peak and carbon neutrality targets, green supply chain management has become one of the important means of reducing carbon emissions. Based on elaborating on the connotation of new quality productivity, this article analyzes the supplier selection problem of pharmaceutical enterprises under the dual carbon target. A pharmaceutical supplier evaluation index system has been established from five aspects related to low-carbon and environmental protection, including technological innovation, research and development capabilities, digitization, intelligent production, environmental protection technology, and so on, with five primary indicators and 18 secondary indicators. Secondly, a game theory combination weighting method combining the G1 method and entropy weight method is used to determine the weights of indicators. A pharmaceutical supplier evaluation model is established by combining the matter element extension model. A company is used as an example to evaluate its 8 candidate suppliers and select the best supplier. Finally, corresponding measures are proposed to promote the healthy and sustainable development of pharmaceutical enterprises.

Dynamic grey relational analysis-based supplier selection in a health-care unit

Purpose In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem. Design/methodology/approach In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores. Findings Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A2 and A11 occupy the top two positions having almost the same performance with respect to the considered financial metrics. Originality/value Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

Correction: A hybrid decision‑making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry

Correction: A hybrid decision‑making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry

A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand

This study addresses the multi-item multi-sourcing supplier selection and order allocation problem in a Combinatorial Auction (CA) framework. First, we propose a new flexible procurement CA bidding language, then prove that it can convey the same information as existing bidding languages while allowing more efficient auction outcomes and reduced computational complexity. Suppliers submit combinatorial bids to a single manufacturer. Each bid carries information about the supplier’s cost structure and potential discounts. The manufacturer purchases the items and assembles one or more product types that experience price-sensitive demand rates by end consumers. We use the realistic logit function to represent the price-sensitive demand rates of finished products. A Mixed Integer Non-Linear Programming (MINLP) model is developed to help the manufacturer make optimal supplier selection, order allocation, and pricing decisions under the proposed bidding language. The MINLP model considers purchasing, transportation, ordering, administrative, holding, and manufacturing costs. These considerations make the results of this study realistic with the potential to be used in practical applications. Also, we derive a set of necessary conditions that must exist in at least one optimal solution to the problem. These conditions can be utilized to design efficient solution mechanisms for this difficult problem in the future. Finally, an extensive numerical analysis is performed to illustrate the bidding language and the key results.

A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection

Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability, posing a significant barrier to adopting intelligent approaches in supply chains. To address this issue, we propose a hybrid supplier selection framework that combines interpretable data-driven AI techniques with multi-criteria decision-making (MCDM) approaches: the former aims to reduce the complexity of the supplier selection problem, while the latter ensures familiarity to supply chain stakeholders by retaining MCDM at the heart of the supplier selection process. The framework is validated through two real-world case studies supporting supplier selection decisions in oil, gas, and aerospace manufacturing companies. Preliminary results from our case studies suggest that the framework can achieve comparable performance to approaches utilising only machine learning while offering the added benefits of end-to-end explainability and increased familiarity.

Supplier selection for global service providers: a decision support system

In this paper, we develop a decision support system (DSS) aimed at solving a real-world supplier selection problem (SSP) for a global service provider (GSP) operating in the facility management (FM) industry. The GSP provides its customers with FM services, which are subcontracted to external suppliers selected on the basis of multiple criteria, like economic soundness, quality of service, capacity, and closeness. The SSP is formulated as a multi-objective generalized assignment problem, where the quality and the closeness of the selected suppliers are maximized, whereas a penalty produced by overcapacity assignments is minimized. The quality of each supplier is computed by applying a weighted sum method, resulting from a multi-criteria decision analysis in which the criteria weights are determined through an Analytic Hierarchy Process. The DSS is developed using a modular architecture with a relational database, a supplier evaluator, and a simulator, as well as an additional user-friendly interface. The simulator relies on a rolling horizon algorithm and three alternative configurations to assign contracts to suppliers. The effectiveness of the DSS is assessed by means of extensive computational experiments on historical data. The results show a significant average improvement of 25% compared to the solution adopted by the company.

Evaluation and Selection of Manufacturing Suppliers under the Perspective of Smart Manufacturing Combined with Bilevel Programming

To address the issue of supplier evaluation and selection in the current intelligent manufacturing industry, a supplier intelligent evaluation and selection model combining bilevel programming is proposed. Firstly, based on existing research results and the current situation of the manufacturing market, a manufacturing supplier evaluation index system is constructed, and the principal component analysis method is used to simplify it; Then, the simplified indicator system is input into the back propagation neural network to build a supplier intelligent evaluation model and achieve supplier intelligent evaluation; A mathematical model for supplier selection problem is constructed based on bilevel programming, and an optimized sparrow search algorithm is put forward to solve it. A supplier intelligent selection model is constructed to achieve intelligent supplier selection. The lab findings denote that the F1 value of the supplier intelligent evaluation model is 0.953, the accuracy is 0.985, the area under the curve is 0.962, and the error of the output comprehensive evaluation value is 0.003; The output accuracy of the supplier intelligent selection model is 0.8. The above results indicate that the model raised in the article has good application effects in supplier evaluation and selection, and has a certain promoting effect on the development of the intelligent manufacturing industry.

A TOPSIS Framework for Supplier Selection Problem

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) stands as a widely recognized and extensively utilized method for addressing the complexities of supplier selection across diverse industries. Acknowledged for its systematic framework, TOPSIS serves as a valuable aid for decision-makers in meticulously evaluating and ranking potential suppliers, taking into account a variety of criteria. This paper provides a exploration of the TOPSIS framework in the field of supplier selection, aiming to empower decision-makers with insights to effectively utilize TOPSIS as a strategic tool. The objective is to facilitate informed and optimal supplier selections. The findings can contribute to both theoretical understanding and practical guidance, emphasizing the strategic significance of TOPSIS in decision-making.

Analysis of coffee bean supplier selection using AHP-TOPSIS methods: case study on a ready-to-use coffee powder company

The global market competition that companies must face along with increasingly competitive market developments means that each company has its own challenges in preparing competitive strategies. PT. XYZ is a company that operates in the field of coffee bean processing. The related problems that occur are frequent delays in sending raw materials by suppliers which results in delivery delays, which means they are related to supplier selection problems. Therefore, in this research, the AHP TOPSIS method was used to conduct supplier analysis, AHP is utilized to make decisions and consider multiple factors, subsequently ranking them. Meanwhile, TOPSIS is employed to address multi-criteria problems by comparing the best alternative with the worst alternative among many options. The results of this research are that Supplier 4 is in first place with a preference value of 0.6510, while the second and third places are occupied by Supplier 1 and Supplier 3 respectively.

A decision support system for sustainable supplier selection problem: Evidence from a radiator manufacturing industry

Supplier selection is the basis of a successful supply chain. It is also a key factor in improving the competitiveness of an organization. Being a complex process, the supplier selection plays an important role in upgrading the supply chain. The purpose of this research is to present an integrated approach based on fuzzy best-worst method (BWM) and failure mode and effects analysis (FMEA) for sustainable supplier selection considering seasonal quantity discounts and supplier risk for a radiator manufacturing company. Therefore, as the aim implies, this study addresses how organizations develop a sustainable supplier selection framework by integrating linear programming and decision-making in presence of seasonal discounts. To this end, fuzzy BWM and FMEA were employed to calculate weighting factors for different selection criteria and evaluate the supplier risk, respectively. Then, suppliers were ranked using the so-called technique for order of preference by similarity to ideal solution (TOPSIS). Finally, the discount type was examined by the LP-metric method. Results of the fuzzy BWM showed that the customer satisfaction was the criterion of highest priority, followed by long-term relationship, and then pollution control, among the total of 13 criteria considered in this work. Outputs of the TOPSIS referred to the supplier 3 as the top-ranked supplier, followed by supplier 6 and then supplier 1.

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

Decision-making for supplier selection problems based on QUALIFLEX technique using likelihood method in LIVIFS environment

The notion of linguistic interval-valued intuitionistic fuzzy set (LIVIFS) is one of the best tools in order to deal with the qualitative decision making problems. Therefore, in this paper a linguistic interval-valued intuitionistic fuzzy (LIVIF) QUALIFLEX method with a likelihood-based comparison approach is proposed. First, the notion of likelihood of fuzzy preference relation (FPRs) to compare the linguistic interval valued intuitionistic fuzzy numbers (LIVIFNs). By employing a criterion-wise preference assessment of alternatives through the comparison of likelihoods, we introduce a novel QUALIFLEX-based model. This model aims to quantify the degree of concordance in the complete preference order for effective management of decisions involving multiple criteria. We demonstrate the practicality and applicability of the proposed methods through an illustrative example, specifically focusing on the context of Supplier Selection Problems. To validate the efficacy of the proposed methodology, a comparative analysis is performed against other existing methods.

A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem

The current business climate has generated considerable uncertainty and disrupted supply chain processes. Suppliers have frequently been identified as the primary source of hazards responsible for supply chain disruptions. Using a strategic approach to supplier selection that prioritizes providers with resilience features, mitigating the risk exposure inherent in supply chains is possible. This study proposes a comprehensive gray multiple-criteria decision making (MCDM) method incorporating resilience attributes to supplier selection. To determine criteria weights, the gray PSI and gray BWM methodologies were used, and to evaluate and prioritize resilient providers, the gray MCRAT and gray COBRA methodologies were applied. According to the results obtained by the suggested methodology, the supplier that demonstrated the greatest degree of resilience was determined to be the provider categorized as SPIR 4. The sequential sequence of the SPIR numbers is as follows: SPIR 5, SPIR 1, SPIR 3, SPIR 2, and SPIR 6. The data demonstrate that the developed approach produced accurate results.