Supplier Selection Problem Research Articles

A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society’s health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Addressing Environmental Protection Supplier Selection Issues in a Fuzzy Information Environment Using a Novel Soft Fuzzy AHP–TOPSIS Method

With the current heightened promotion of environmental awareness, issues related to environmental protection have become a critical component of economic development. The emergence of new environment-friendly materials and simple packaging, and other environmental awareness demands in recent years, have prompted manufacturers to pay more attention to planning greener production and supply processes than before. Many scholars have been urged to investigate the issues related to environmental protection and the sustainable economy of green suppliers. However, many factors needed to be considered, such as the price, cost, benefit, reputation, and quality involved in the process of green supplier selection. These factors require quantitative and qualitative analysis information, making the issue of environmental protection a multi-criteria decision making (MDCM) problem. Traditional research methods are unable to effectively and objectively handle the MCDM problem of green supplier selection due to the problem’s complexity and the method’s inclination towards biased conclusions. To resolve the complicated problem of green supplier selection, this study combined the fuzzy analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and the 2-tuple fuzzy linguistic model (2-tuple FLM) and corrected the ranking of the possible green suppliers. The computation results were also compared with the typical TOPSIS and AHP–TOPSIS methods. Through the numerical verification of the actual case for the green supplier, the test results suggested that the proposed method could perform an objective evaluation of expert-provided information while also retaining all their valuable insights.

On utilizing modified TOPSIS with R-norm q-rung picture fuzzy information measure green supplier selection.

The present communication introduces a new discriminant measure coined as R-norm q-rung picture fuzzy discriminant information measure which is more generalized in nature and has the capability to handle more flexibility inherited in the inexact information. The notion of q-rung picture fuzzy set (q-RPFS) has an integrated advantage of picture fuzzy set and q-rung orthopair fuzzy set with flexibility of qth level relations. The proposed parametric measure is then applied in the conventional "technique for order preference by similarity to the ideal solution (TOPSIS) method" for solving a green supplier selection problem. The numerical illustration to exhibit the proposed methodology for the green supplier selection problem has been presented in an empirical form to establish the consistency of the model. Also, the advantageous features of the proposed scheme in the setup of impreciseness have been discussed.

Data‐driven distributionally robust supplier selection and order allocation problems considering carbon emissions

AbstractUnder the constraints of uncertainty and carbon emission policies, reasonable and flexible supplier selection and order allocation can minimize the total cost, which is of great significance to the supply chain. We study sustainable supplier selection and order allocation problems with multiple periods, multiple suppliers, and multiple products under a carbon cap‐and‐trade policy. We exploit distributionally robust versions of supplier selection and order allocation problems with an explicit distributional uncertainty set and derive computationally tractable reformulations of the distributionally robust optimization model with chance constraints by cone duality theory and conditional value‐at‐risk measure. Numerical experiments show that the proposed distributionally robust optimization models with nice computational tractability and probability guarantees are superior to the existing distributionally robust chance‐constrained optimization models in terms of cost and risk aversion. Relevant management insights on different carbon emission regulations and the application of distributionally robust optimization models are provided for policymakers and enterprises' decision‐makers based on the derived results.

A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem.

The supplier selection problem is one of the most important issues in supply chain management. So, many papers have investigated the mentioned problem. However, the related literature shows that researchers had less attention to the sustainability and resilience aspects based on the customer preferences in supplier selection problem. To cover this gap, this research tries to investigate the customer-based sustainable-resilient supplier selection problem. In this way, a Markovian-based fuzzy decision-making method is proposed. At the outset, the customer preferences are evaluated using a combination of the quality function deployment and the Markov transition matrix. Then, by combining the transition matrix and the fuzzy best-worst method, the weights of the indicators are calculated. Finally, the decision matrix is formed and the performance of suppliers is measured based on the multiplication of the decision matrix and vector of sub-criteria weights. Regarding the recent pandemic disruption (COVID-19), the importance of online marketplaces is highlighted more than the past. Hence, this study considers an online marketplace as a case study. Results show that in a pandemic situation, the preferences of customers when they cannot go shopping normally will change after a while. Based on the Markov steady state, these changes are from the priority of price, availability, and performance in initial time to serviceability, reliability, and availability in the future. Finally, based on the FBWM results, from the customer point of view, the top five sub-criteria for sustainable-resilient supplier selection include cost, quality, delivery, responsiveness, and service. So, based on these priorities, the case study potential suppliers are prioritized, respectively.

Green supplier selection problems with data scaling and production frontier estimations in a DEA model

AbstractConsidering ecological issues in supplier evaluation and management alongside business considerations is getting more recognition among firms. Data envelopment analysis (DEA) is one of those methods, which is frequently suggested by the literature to support management decisions. However, the data requirements of the method should be an important consideration. The literature often addresses the issue of desirable outputs and undesirable input as an important data related problem in case of the ecological use of DEA. This paper will present a new solution to manage these data problems along with connecting the evaluation of management criteria, environmental criteria and total cost aspects. The proposed environmental supplier selection problem is an extension of a former paper. The new model examines the effect of inventory related costs, such as EOQ costs of inventory holding or ordering costs on the selected supplier, extended with newly introduced scaled values of input and output indicators. The usage of scaled values is motivated by the problem of invariance to data alteration. In addition to the uncertainty of the data, the paper looks for a functional relationship between the input and output criterion values and the efficiency that can be assigned to them using DEA.

A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is an important problem in GSCM and seeks to satisfy a firm’s environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve this particular problem, we propose a multi-objective robust optimization (RO) model. This specific model is an effective approach to handle uncertainty. A numerical example using randomly generated data, accompanied by subsequent discussion of the proposed approach, is deployed to validate the model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that decreasing the allowance amount (cap) and increasing the allowance prices in the cap-and-trade system escalate the firms’ costs, but lower the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system for a specific range of carbon allowance.

Rough DT2R2ML for renewable energy supplier selection in the presence of big data

SummarySupplier selection is a substantial problem in supply chain management due to concurrent decision on key performance indicators on multi‐dimension data. Recently, more studies investigated the supplier section problem with respect to variety of criteria within the contexts of applied cases. The problem is more significant when industries with high amount of investment explore appropriate suppliers such as renewable energy. This article concerns with developing a new method encompass all indices effective on supplier selection. The proposed algorithm first group by decision tree (DT) indices to criteria and sub‐criteria; then, to include large amount of uncertain data, rough comparisons and weighing are fulfilled using a machine learning (RML). Further, transformation (T) to crisp value and ranking (R) of suppliers are delivered. The new method is implemented for a renewable energy supplier selection problem as a case study. The outputs show the effectiveness of the proposed method in practice since it can handle big data through a machine learning technique. Managerial implications as decision supports are discussed.

A Modified Simulated Annealing (MSA) Algorithm to Solve the Supplier Selection and Order Quantity Allocation Problem with Non-Linear Freight Rates

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.

Neutrosophic programming approach to multilevel decision-making model for supplier selection problem in a fuzzy situation

In supply chain management, the selection of suppliers is vital and plays a valuable role in the performance of organizations. A hierarchical structure, with different levels in the selection of suppliers, is employed, wherein sequential decisions are made from the highest to the lowest level. Decision variables, called controlling factors, are divided into several categories. In the decision-making process, often because of the lack of confidence or uncertainty, It becomes challenging for decision-makers to give explicit/crisp values to any parameter, resulting in uncertainty in the problem. In this paper, we address a multi-level supplier selection problem with fuzzy supply and demand. To avoid decision conflicts, superior or upper-level decision-makers give tolerances that could be used as a possible relaxation. Thus, the problem is employed with fuzzy constraints. Based on a neutrosophic decision set, the neutrosophic compromise programming approach (NCPA) is used as a solution technique with the idea of an indeterminacy degree as well as different objectives for membership and non-membership degrees. Membership functions (Linear-type) are used to develop satisfactory solutions by fuzzily describing objective functions and controlling factors. A numerical illustration is provided to demonstrate the validity and appropriateness of NCPA.

Integrated Supplier Selection and Distribution in Perishable Food Supply Chain

In this study, we investigate the classical supplier selection and distribution problem in an integrated framework. The combined problem is originated from a local producing tomato sauce producer, whom we have been collaborated with since 2022. The suppler selection strategies are made based not only on each supplier’s product price and corresponding product quality, but also on the reliability and lead time of each protentional supplier. We proposed a quantitative framework with a conditional value-at-risk (CVaR) technique in the purpose of assessing the supplier performance. Then the selection and distribution decisions are simultaneously considered in a mixed integer linear programming (MILP) model, with the objective of maximizing the value added to the purchasing portfolio and distribution plans. Our experiments and computational results reveal that the combined approach is more beneficial than separated approaches. Finally, future research topics and managerial insights are provided.

A flexible group decision-making method for green supplier selection integrating MABAC and CRITIC method under the linguistic Z-numbers environment

This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective.

Flexible supplier selection and order allocation in the big data era with various quantity discounts.

This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of the modeling and solution difficulty. All suppliers offering the same discount are far from reality, especially when the number of suppliers is large. The proposed model is a variant of the NP-hard knapsack problem. The greedy algorithm, which solves the fractional knapsack problem optimally, is applied to cope with the challenge. Three greedy algorithms are developed using a problem property and two sorted lists. Simulations show the average optimality gaps are 0.1026%, 0.0547%, and 0.0234% and the model can be solved in centiseconds, densiseconds, and seconds for supplier numbers 1000, 10000, and 100000. This allows the full use of data in the big data era.

İnternet Servis Sağlayıcı Seçim Probleminin Çözümünde Bulanık Sıralama Ağırlık Tabanlı Bulanık MARCOS Yöntemi

İnternet hizmeti, belli bir ücret karşılığında bireylere ve kurumlara İnternet Servis Sağlayıcı (İSS) tarafından sunulmaktadır. Dünyada ve ülkemizde çok sayıda İSS olup, elektronik haberleşme sektöründeki hızlı gelişmeler nedeniyle İSS’ler arasında yoğun bir rekabet yaşanmaktadır. Bu durumda kullanıcılar, İSS seçim problemi ile karşı karşıya kalmaktadır. Kullanıcının amacı doğrultusunda ihtiyacını karşılayacak tüm nitel ve nicel kriterler düşünüldüğünde en iyi hizmeti sunacak İSS seçimi, belirsizlik içeren Çok Kriterli Karar Verme (ÇKKV) problemi olarak tasarlanabilir. Bu çalışmada, belirsizliği modelleyebilmek için bulanık küme teorisi kullanılmıştır. Çalışmada, fiber teknoloji hizmeti almak isteyen ev kullanıcılarının İSS seçim süreci, iki aşamada gerçekleştirilmiştir. İlk aşamada, kullanıcıların İSS seçimini etkileyen kriterler belirlenmiş ve bu kriterlerin ağırlıkları, Bulanık Sıra Toplam (BST), Bulanık Sıra Karşılıklı (BSK) ve Bulanık Sıra Ağırlık Merkezi (BSAM) ile hesaplanmıştır. İkinci aşamada ise aynı kullanıcılar için fiber teknoloji hizmeti veren İSS alternatifleri, ilk aşamada belirlenen kriterler dikkate alınarak bulanık MARCOS yöntemi ile sıralanmıştır. Bu şekilde bulanık MARCOS yöntemi, farklı bulanık sıralama ağırlıklandırma yöntemleri ile birlikte değerlendirilerek karşılaştırmalı bir analiz yapılmış ve sonuçlar yorumlanmıştır.

An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision

With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.

Picture fuzzy Additive Ratio Assessment Method (ARAS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-attribute decision problem and their application

The purpose of this paper is to study the multi-attribute decision-making problem under the fuzzy picture environment. First, a method to compare the pros and cons of picture fuzzy numbers (PFNs) is introduced in this paper. Second, the correlation coefficient and standard deviation (CCSD) method is used to determine the attribute weight information under the picture fuzzy environment regardless of whether the attribute weight information is partially unknown or completely unknown. Third, the ARAS and VIKOR methods are extended to the picture fuzzy environment, and the proposed PFNs comparison rules are also applied in the PFS-ARAS and PFS-VIKOR methods. Fourth, the problem of green supplier selection in a picture-ambiguous environment is solved by the method proposed in this paper. Finally, the method proposed in this paper is compared with some methods and the results are analyzed.

A new integrated approach based on base-criterion and utility additive methods and its application to supplier selection problem

Supplier selection is one of the biggest challenges facing manufacturing companies in an increasingly competitive environment. Selecting the right supplier can minimize the cost of raw materials, which constitute a large portion of the final production cost. During the past decade, attention has been drawn to the supplier selection problem, a complex process in which decision-makers assess multiple quantitative and qualitative characteristics. Approaches such as applied individual or hybrid Multi-Criteria Decision-Making (MCDM) methods have also been developed to elucidate this problem. This research aims to offer a novel integrated MCDM method for the supplier selection problem based on the combination of Base Criterion (BC) and Utility Additive (UA) methods. In this approach, one of the criteria is selected as the base criterion. This criterion is compared with other criteria by considering the DM's preferences. The main contribution of this study is to present an integrated approach that simultaneously calculates the criteria weights and the rank of alternatives, leading to fewer pairwise comparisons and more consistency. Also, the robust mathematical foundation of the suggested model, which incorporates utility functions, assures that the results align with decision-maker preferences as much as possible. The Base Criterion Utility Additive (BCUA) model proposed in this paper, formulated as a nonlinear programming (NLP) model, is solved using the Lingo software package. A numerical example and a real case in the electronics industry have been presented to corroborate the applicability and effectiveness of the BCUA model. Sensitivity analysis and a comparative analysis of the proposed method are discussed in comparison to already existing MCDM methods, including the Fully Consistency Method (FUCOM), Ordinal Priority Approach (OPA), Level Based Weight Assignment (LBWA), and Defining Interrelationships Between Ranked Criteria (DIBR). These analyses indicate that the criteria weights and alternative rankings are robust to changing the parameters δ and λ. In addition to selecting the most suitable supplier, the model presented here can also be used in any decision-making problem with multiple attributes and alternatives, particularly when decision-makers are faced with many alternatives.

Selecting an Optimal Scenario for Addressing Supplier Selection Problem by Considering Sustainable Scheduling: A Hybrid Approach

The iron and steel industry is a pillar industry of the national economy in many countries and is also a source of high energy consumption and pollution gas emissions. In addition to the economic aspect, there have been increasing concerns over how to minimise the negative environmental impact and enhance the awareness of social responsibility for iron and steel enterprises. Therefore, this study proposes an intelligent scheduling system for addressing the supplier selection problem by considering sustainable scheduling (SS) (ISS-AFLCSS) to achieve maximised benefits of logistics costs, carbon emission and fatigued driving for the Chengsteel Company. In the ISS-AFLCSS, first, a multiobjective mathematical optimisation model is formulated. Second, this study proposed a hybrid approach using an improved genetic algorithm (GA) to optimise multiple objectives of scenarios and adopting the technique for order preference by similarity to an ideal solution (TOPSIS) method with the analytic hierarchy process (AHP) to precisely optimise and select a best-ideal scenario. The results confirm that the proposed ISS-AFLCSS can provide accurate guidance in practicing SS for managers of enterprises.

An extension of multi-attribute group decision making method based on quantum-like Bayesian network considering the interference of beliefs

Multi-attribute group decision making (MAGDM) problem has become one of the most remarkable topics recently. MAGDM is made up of multiple decision makers (DMs) who perform opinions on a set of alternatives and then choose the appropriate one. In the classical decision-making process, the multiple DMs are usually regarded as independent and the opinions usually come from the preference of DMs when determining weights of attributes or giving evaluation of alternatives. However, MAGDM is a complicated cognitive process that involves the diverse and uncertainty cognition of DMs from different backgrounds and fields, and the opinions from DMs are likely to interfere with each other. This paper proposes an extension method based on quantum-like Bayesian network (QLBN) and belief entropy considering the interference of beliefs on the basis of our previous research. The purpose is to model subjectivity sourced from the interference of DMs’ beliefs at different decision-making stages, including the aggregation of attributes probabilities and alternatives probabilities. In this paper, a QLBN for MAGDM problem is constructed firstly. The beliefs of DMs are in a superposition state in it, that is, the opinions of DMs are regarded as wave functions occurring in QLBN. Then the probabilities of attributes and the probabilities of alternatives in QLBN are aggregated across all DMs, and the alternatives are sorted. In the process of aggregation, the beliefs from different DMs will interfere with each other. Belief entropy, an index to calculate the uncertainty of probability is introduced to calculate the interference value. When the interference between DMs is considered only in a certain decision-making stage, the proposed method will degenerate into the existing QLBN methods; and when all DMs are regarded as independent, the QLBN will degenerate into a classical BN. Finally, a supplier selection problem is introduced to illustrate and validate our model. The comparison analyses show that the proposed approach can fully consider the interference between DMs and the ranking result are more reasonable and realistic.

Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues

The development of artificial intelligence and the recent COVID-19 pandemic as well as the emergence of other diseases has led to dramatic changes in the overall supply chain development. The choice of a suitable supplier will be the key to ensuring sustainable development of the company and the normal operation of the overall supply chain. However, the evaluation data of the supplier selection contains both qualitative and quantitative data simultaneously. In addition, the evaluation information provided by experts often includes some incomplete and hesitant information. These reasons lead to the complexity of supplier selection. Traditional supplier selection calculation methods ignore the objective weight considerations and thus lead to biased assessment results. The main goal of this study is to overcome the limitations of conventional supplier selection methods, fully consider the subjective and objective weights of the evaluation criteria and deal with incomplete information for providing more correct supplier ranking results. A stepwise weight assessment ratio analysis (SWARA) method, the 2-tuple linguistic representation method, and the combined compromise solution (CoCoSo) were applied in this study to solve the problem of supplier selection. To verify the rationality and correctness of the proposed method, the third-party logistics supplier selection was used as the illustrated example in the numerical validation section. The simulation results confirm that the proposed method can effectively deal with supplier selection with unclear information and can provide more reasonable supplier ranking results.