We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a ...review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.
In the real world, there are complex decision‐making problems in which a variety of intertwined factors and hierarchical structures exist, and it is difficult or impossible to solve these problems ...using classical methods. The prioritization of circular economy (CE) adoption barriers is one such complex problem that cannot be solved using current classical methods. Hence, in this paper, for the first time, a new method, namely, FBWDS, is developed. FBWDS derives from the combination of fuzzy best–worst method (BWM), fuzzy decision‐making trial and evaluation laboratory (DEMATEL), and Supermatrix structure. This proposed method establishes weights of the intertwined factors in hierarchical networks under uncertainty; these weighted factors and their interdependencies are calculated using the fuzzy BWM and fuzzy DEMATEL techniques, respectively. In addition, the Supermatrix structure is applied to integrate the results of these two techniques. The proposed approach efficiency is evaluated in the field of the CE adoption barriers using data from a cable and wire industry in Iran. The results showed that “high setup costs,” “financial limitations,” and “absence of public awareness about CE” rank as the most challenging barriers, and “lack of standards for designing recycled products” and “absence of standard system to evaluate performance” are the least important barriers to CE adoption in the cable and wire industry.
•Proposes a multiobjective model to design an optimized reverse logistic network.•Optimizes economic, social and environmental objectives simultaneously.•Fuzzy approach to dealing with uncertainties ...in many parameters of the model.•Multi-objective genetic algorithm for solving the model efficiently.
Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order to deal with uncertain parameters, fuzzy mathematical programming is used, and to obtain solutions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is applied. The validity of the proposed solution procedure has been analyzed in small and large size test problems based on four comparison metrics and computational time using analysis of variance. Finally, in order to indicate the applicability of the suggested model and the practicality of the proposed solution procedure, the model has been implemented in a medical syringe recycling system. The results reveal that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality of the solutions as well as computational time. Proper use of the proposed process could help managers efficiently manage the flow of recycled products with regard to environmental and social considerations, and the process offers a sustainable competitive advantage to corporations.
PurposeThe purpose of this study is to systematically review the state-of-art literature on the net-zero economy in the field of supply chain management.Design/methodology/approachA systematic ...literature review of 79 articles published from 2009 to 2021 has been conducted to minimise the researchers' bias and maximise the reliability and replicability of the study.FindingsThe thematic analysis reveals that studies in the field of net-zero economy have mostly been done on decarbonisation in the supply chain, emission control and life cycle analysis and environmental and energy management. The findings highlight the strong positive association between digitalisation, circular economy and resources optimization practices with net-zero economy goals. The study also addresses the challenges linked with the net-zero economy at the firm and country levels.Research limitations/implicationsPractitioners in companies and academics might find this review valuable as this study reviews, classifies and analyses the studies, outlines the evolution of literature and offers directions for future studies using the theory, methodology and context (TMC) framework.Originality/valueThis is the first study that uses a structured approach to analyse studies done in the net-zero field by assessing publications from 2009 to 2021.
Real-life challenges require proactive measures. Good transportation services and greener alternatives demand steadfast research in the area. Here, an attempt has been made to address such a problem. ...A particular case of VRP, with deliveries split into bags and triangular fuzzy travel times, has been modelled to minimise fuel emissions. The concepts of fuzzy rule-based implication for ranking and for comparing fuzzy numbers with numeric values, an expected value model, have been drawn upon. A discrete fuzzy-hybridised GA has been developed. Multiple experiments on data in existing works, parameter tuning, and comparative analysis have been performed, thereby corroborating the model's efficacy.
► Developed a carbon footprint based reverse logistics network design model. ► Proposed model is tested with various test problems. ► Proposed model is validated with a case study from plastic ...sector.
Due to the environmental legislation and regulations, manufacturing firms have realized the importance of adopting environmental friendly supply chain management (SCM) practices. In this paper, a mixed integer linear model is developed for a carbon footprint based reverse logistics network design. The proposed model aims at minimizing climate change (specifically, the CO2 footprint), and it employs reverse logistics activities to recover used products, hence combining the location/transportation decision problem. The proposed model is validated by examining a case study from the plastic sector.
Supply chain management aims to designing, managing and coordinating material/product, information and financial flows to fulfill the customer requirements at low costs and thereby increasing supply ...chain profitability. In the last decades, data envelopment analysis has become the main topic of interest as a mathematical tool to evaluate supply chain management. While, various data envelopment analysis models have been suggested to measure and evaluate the supply chain management, there is a lack of research regarding to systematic literature review and classification of study in this field. Regarding this, some major databases including Web of Science and Scopus have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. Accordingly, a review of 75 published articles appearing in 35 scholarly international journals and conferences between 1996 and 2016 have been attained to reach a comprehensive review of data envelopment analysis models in evaluation supply chain management. Consequently, the selected published articles have been categorized by author name, the year of publication, technique, application area, country, scope, data envelopment analysis purpose, study purpose, research gap and contribution, results and outcome, and journals and conferences in which they appeared. The results of this study indicated that areas of supplier selection, supply chain efficiency and sustainable supply chain have had the highest frequently than other areas. In addition, results of this review paper indicated that data envelopment analysis showed great promise to be a good evaluative tool for future evaluation on supply chain management, where the production function between the inputs and outputs was virtually absent or extremely difficult to acquire. The facility of multiple inputs and multiple outputs of the data envelopment analysis model was definitely an attractive one to most researchers and, therefore, the data envelopment analysis procedure had found many applications beyond supply chain management into organization and industry.
Formal decision-making methods can be used to help improve the overall sustainability of industries and organisations. Recently, there has been a great proliferation of works aggregating ...sustainability criteria by using diverse multiple criteria decision-making (MCDM) techniques. A number of review papers summarising these techniques have been published. During the past few years, new approaches for hybrid MCDM (HMCDM) methods have been developed, but they have not yet been completely reviewed. This article aims to fill this gap and to summarise publications related to the application of HMCDM. The current study is limited solely to papers available in the Thomson Reuters Web of Science Core Collection database. The main findings report that HMCDM methods have been increasingly applied for supporting decisions in different domains of sustainability. The most frequently used methods emphasise the advantages of hybrid approaches over individual methods, and we conclude that they can assist decision-makers in handling information such as stakeholders' preferences, interconnected or contradictory criteria, and uncertain environments. The main contribution of this work is identifying hybrid approaches as improvements for decision-making related to sustainability issues, while also promoting future application of the approaches.
This paper reviews the literature on additive manufacturing (AM) technologies and equipment, and spare parts classification criteria to propose a systematic process for selecting spare parts which ...are suitable for AM. This systematic process identifies criteria that can be used to select spare parts that are suitable for AM. The review found that there is limited research that addresses identifying processes for spare parts selection for AM, even though companies have identified this to be a key challenge in adopting AM. Seven areas for future research are identified relating to the methodology of spare parts selection for AM, processes for cross-functional integration in selecting spare parts for AM, broadening the spare parts portfolio that is suitable for AM (by considering usage of AM in conjunction with conventional technologies), and potential impact of AM on product modularity and integrality.
This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The ...problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran’s wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%.
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