A multi-criteria decision framework for sustainable supplier selection and order allocation using multi-objective optimization and fuzzy approach

An integrated decision-making process for sustainable supplier selection and order allocation in the automotive industry

An integrated approach of fuzzy risk assessment model and data envelopment analysis for route selection in multimodal transportation networks

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

One of the most significant competitive strategies for organizations is sustainable supply chain management (SSCM). The vital part in the administration of a sustainable supply chain is the sustainable supplier selection, which is a multi-criteria decision-making issue, including many conflicting criteria. The valuation and selection of sustainable suppliers are difficult problems due to vague, inconsistent and imprecise knowledge of decision makers. In the literature on supply chain management for measuring green performance, the requirement for methodological analysis of how sustainable variables affect each other, and how to consider vague, imprecise and inconsistent knowledge, is still unresolved. This research provides an incorporated multi-criteria decision-making procedure for sustainable supplier selection problems (SSSPs). An integrated framework is presented via interval-valued neutrosophic sets to deal with vague, imprecise and inconsistent information that exists usually in real world. The analytic network process (ANP) is employed to calculate weights of selected criteria by considering their interdependencies. For ranking alternatives and avoiding additional comparisons of analytic network processes, the technique for order preference by similarity to ideal solution (TOPSIS) is used. The proposed framework is turned to account for analyzing and selecting the optimal supplier. An actual case study of a dairy company in Egypt is examined within the proposed framework. Comparison with other existing methods is implemented to confirm the effectiveness and efficiency of the proposed approach.

A regional information-based multi-attribute and multi-objective decision-making approach for sustainable supplier selection and order allocation

Supply chain management begins with supplier evaluation and selection. The supplier selection deals with various criteria with different contexts which makes it a complex multi-criteria decision-making (MCDM) method. In this paper, a novel MCDM method, called the alternative ranking process by alternatives’ stability scores (ARPASS), is proposed to solve supplier selection problems. ARPASS considers each alternative as a system that is constructed on integrated components. To perform properly, a system requires high integrity and stability. ARPASS utilizes the stability of alternatives as an effective element for ranking the alternatives. The ARPASS is developed in two forms, ARPASS and ARPASS*. The new method utilizes standard deviations and Shannon’s entropy to compute the alternatives’ stabilities. In this paper, in addition to the new MCDM methods, a new method called the grey equilibrium product (GEP) is introduced to convert grey linguistic variables into crisp values, using decision makers’ subjective perceptions and judgments. To highlight and validate the novel methods’ performance, they are applied to two sustainable supplier selection problems. For evaluation of the reliability of ARPASS and ARPASS*, their results were compared with the results of the popular MCDM methods. We compared the methods in terms of calculation time, simplicity, transparency, and information type.

Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic requirement in supplier selection decisions for a competitive supply chain. The aim of this research is to develop quantitative resilient criteria for supplier selection and order allocation in a fuzzy environment. To serve the purpose, a possibilistic fuzzy multi-objective approach was proposed and an interactive fuzzy optimization solution methodology was developed. Using the proposed approach, organizations can tradeoff between cost and resilience in supply networks. The approach is illustrated using a supply chain case from a garments manufacturing company.

In the supply chain management, configuration of supply chain is the most important decision in the long term and supplier selection and order allocation are the most important decision in the medium-term that are considered separately. Considering these together can overcome the sub-optimality. This paper deals with an integrated model that has two phases. In the first phase, we present a framework for supplier selection criteria in Closed Loop Supply Chain (CLSC). In addition, we define two performance levels for each supplier based on the quantity and capability of purchasing from it to be closer to real world problem. The output of this phase is the score of each supplier in each criterion in each level. In the second phase, we propose a nonlinear multi-objective mixed integer model that determines the number and location of all facilities (strategic decision), flow in each echelon of CLSC (tactical decision) and supplier selection and order allocation (hybrid decision). The objective functions maximize profit and scores of suppliers and minimize total pollution. To solve the model, we have created a transformation based on the piecewise linearization method. The mathematical programming model illustrated by a real numerical example.

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) model (economic, environmental, and social aspects) and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized to identify the weight of all criteria in the second stage. The technique for order preference by similarity to an ideal solution (TOPSIS) is a multicriteria decision analysis method, which is used for ranking potential suppliers in the final stage. As a result, decision-making unit 10 (DMU/10) is found to be the best supplier for the garment industry. The contribution of this research includes modeling the supplier selection decision problem based on the TBL concept. The proposed model also addresses different complex problems in supplier selection, is a flexible design model for considering the evaluation criteria, and is applicable to supplier selection in other industries.

A comprehensive multi-stage decision-making model for supplier selection and order allocation approach in the digital economy

Multimodal transportation network with cargo containerization technology: Advantages and challenges

An integrated methodology for the selection of sustainable suppliers and order allocation problem with quantity discounts, lost sales and varying supplier availabilities

This paper explores the implementation of a kanban system within a multi-echelon pharmaceutical supply chain (MEPSC). The article tackles the multi-echelon supply chain problem and the role of collaboration to enhance its performance specifically while using a pull system. The study provides also insights on the different existing Kanban systems that were created to overcome the difficulties and challenges faced by the original kanban system. This paper presents how a kanban system could manage pharmaceuticals inventory and enhance the information sharing through a multi-echelon supply chain. There are two primary findings: a) that the implementation of Kanban system provides a strategic benefit and improves the quality of services and information sharing through a multi-echelon supply chain; b) It also provides a foundation to leverage innovation in pharmaceutical supply chains; it gives the opportunity to the organisation to move away from the current push delivery and logistics systems towards improved logistics strategy models to empower front-line caregivers.

Supplier selection is crucial decision for buying companies to develop sustainable supply chain strategy. Not only first-tier suppliers but also lower-tier suppliers would influence the buyer’s sustainability performance. Thus, buying companies need to consider extending their sustainable supply chain plan to multitier suppliers, and develop multitier sustainable supply chain management (MT-SSCM). Sustainable supplier selection (SSS), as an important SSCM practice, can effectively enhance the sustainable competitiveness of supply chains. However, there are few theoretical studies on SSS under MT-SSCM, which affects the decision-making efficiency of organisations. In general, there are three multitier supply chain (MTSC) structures: open MTSC, closed MTSC and transitional MTSC structures. Because some traditional decision models cannot deal with a variety of structures at the same time, this study proposes a hybrid model that integrates fuzzy set theory, stepwise weight assessment ratio analysis and a Bayesian network to evaluate the critical SSS criteria in three MTSC structures. Finally, a case of a Chinese buying company is used to verify the flexibility and applicability of the proposed model, and some suggestions are provided for the practitioners to select sustainable suppliers in different MTSC structures.

Sustainable supplier selection has become a critical factor for organizations striving to align their supply chains with environmental, economic, and social sustainability goals. The increasing need for sustainable practices in procurement has prompted the development of frameworks that address these dimensions effectively. This study develops a comprehensive framework for evaluating sustainable suppliers in the textile industry, integrating Triple Bottom Line (TBL) principles with SWARA and TOPSIS methods. Initially, 20 sustainability criteria were identified through a rigorous review of the literature and expert consultations. These criteria were then narrowed down to 15 key criteria based on expert evaluations using a 5-point Likert scale. The study found that the most important economic criteria were chemical quality (0.0929), price (0.0830), and delivery reliability (0.0798), while sustainability concerns were highlighted by environmental management systems (0.0798) and employee health and safety (0.0775). These findings reflect the increasing emphasis on both operational efficiency and environmental and social responsibility in supplier selection. Using TOPSIS, the suppliers were ranked based on their performance across these 15 criteria. The results revealed Supplier A (0.7555) as the most sustainable, followed by Supplier B (0.7337), while Supplier E (0.2573) ranked last due to weaker sustainability performance. These rankings highlight the significance of sustainability practices, especially in environmental and social dimensions, which are increasingly critical in the procurement decision-making process. The study provides valuable insights for procurement managers, particularly in emerging economies like Bangladesh, where SMEs dominate the textile supply chain. By using this SWARA-TOPSIS framework, companies can select suppliers that align with sustainability goals, improving both environmental performance and social responsibility. This research contributes to the field of sustainable supply chain management by offering a structured, transparent, and objective method for supplier evaluation.