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Organization's sustainability performance is influenced by its suppliers' sustainability performance. This relationship makes sustainable supplier development a strategic competitive option for a buyer or focal organization. When considering sustainable supplier development practices (SSDPs) adoption, organizations have to balance and consider their limited financial resources and operational constraints. It becomes necessary to both select the best SSDPs set and investment allocation among the selected SSDP set such that the organization can maximize overall sustainability performance level. In this paper, an integrated formal modeling methodology using DEMATEL, the NK model, and multi-objective linear programming model is used support this objective. The proposed methodology is evaluated in a practical sustainable supply chain field study of an equipment manufacturing company in China. Through case study, we found that the interdependency among SSDPs must be considered in SSDPs selection and investment allocation problem. Theoretical, managerial and methodology implications, conclusions, and directions for future research are also presented. [ABSTRACT FROM AUTHOR]

With the emergence of digital technologies, we have witnessed a rapid growth of the sharing economy in recent years. In fashion industry, sharing economy concept is also widely applied. Instances can be found all over the world, like Rent the Runway, Golden Tote, Stitch Fix, and Le Tote in the US, Air Closet in Japan, Y Closet, Ms Paris, and Dora's Dream in China, etc. In this paper, motivated by the industrial practices of fashion sharing platforms, we first conduct a literature review on business operations of sharing platforms, including the sharing platform operations, digital and information technologies employed in sharing economy, business models, and sustainability and circularity in business operations. Afterwards, we analyze the challenges faced by fashion sharing platforms by exploring the two leading fashion sharing platforms in the US and in China, respectively. At last, we propose future research directions on fashion sharing platform operations and conclude the paper. [ABSTRACT FROM AUTHOR]

The literature review shows research gaps into the food supply chain design. In that context, this paper deals with the design of a sustainable supply chain. A multi-objective mixed-integer linear programming model includes four decisions and three sustainable criteria (economic—total network costs—, environmental—carbon emissions—, and social—work conditions and societal development—). The model aims to determine the optimal location and capacity of processing and distribution facilities, to choose the suppliers from a set of potential candidates, to determine transportation modes between all the actors, and to define the quantity of product, in order to satisfy the demand of dairy products in a set of regions. The applicability of the model is tested in a realistic case in the dairy sector in the central region of Colombia. The results show the existent trade-offs between the three dimensions of sustainability. The unweighted balance results, giving more priority to the social dimension, which obtains the least deviation, affecting the environmental performance of the chain. The analysis carried out in this paper does help decision-makers that will have at hand a set of possible configurations to be chosen in order to comply with environmental and social regulations without neglecting economic performance. [ABSTRACT FROM AUTHOR]

For decades, research on the relationship between environmental sustainability and firm performance focused on determining whether the effect of the former on the latter was positive or negative. That potentially controversial approach had driven mainstream research to ask two focal questions: Does it pay to be green? and Does it cost to be green? Recent papers have explored a curvilinear effect (i.e., U-shaped and inverted U-shaped) by tackling another question: When does it pay to be green? These efforts, however, have either oversimplified the complex linkage between these two things into a linear relationship or confined the complexity within a formatted frame. This paper overcomes these shortfalls by exploring the holistic effect of environmental sustainability on firm performance, contingent upon corporate reputation. In conjunction with direct effect analysis, this paper uniquely explores the synergistic effect of environmental sustainability and corporate reputation on performance. In addition to detecting the nonlinear synergy pattern, we found that the synergy effect is asymmetric. Specifically, the effect is complementary for low-reputation firms but substitutive for high-reputation firms. In addition to its theoretical contributions and pragmatic implications, this paper uniquely presents PROCESS-Neural network as a promising analytic paradigm. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk. [ABSTRACT FROM AUTHOR]

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields. [ABSTRACT FROM AUTHOR]

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308. [ABSTRACT FROM AUTHOR]

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption. [ABSTRACT FROM AUTHOR]

Global warming, climate change, and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution, greenhouse effect, poor working conditions, child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system, actor, objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the inter-relationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance, reciprocity, fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance, social performance and channel efficiency of the supply chain, thereby ensuring sustainability and socio welfare of all the supply chain. [ABSTRACT FROM AUTHOR]

An appropriate and sustainable waste management plan is required in different scenarios for global development. The main goal of this paper is to evaluate a waste management problem by investigating an integrated multi-objective environment through solid transportation problem. To develop sustainability, three objective functions are optimized by choosing as cost for economical opportunity, time for social satisfaction and carbon emission for environmental view. Cap and trade policy is regarded here to minimize carbon emission and to provide some economical opportunities to the system. To control hesitancy of this scenario, twofold uncertainty (type-2 intuitionistic fuzzy) is incorporated here, and this uncertainty is defuzzified by a ranking operator. A strategy is proposed here to optimize three factors of sustainability by an intellectual model formulation of solid waste management. To check the appropriateness of the proposed model, two numerical problems are evaluated using two advanced methods, namely, neutrosophic linear programming and ϵ -constraint method. The Pareto-optimal solutions are derived by the variation of cap value and fulfilling the criteria of sustainability. The obtained results indicate that cap and trade policy or waste management, or both are highly sophisticated for applying in real-world application. The overall conclusions recommend that a government or NGO should encourage transportation system, or the industry to minimize carbon emission by utilizing several carbon policies. It can assist to establish different new project of waste management in a discrete environment, based on sustainability. [ABSTRACT FROM AUTHOR]

Sustainable freight transportation is aimed at reducing environmental emissions and social inequity along with economic inefficiency to drive the business. Lack of a continuous sustainability assessment and monitoring tool increases the reluctance of freight transporters to adopt world-class sustainability practices. This paper attempts to develop the performance index for a sustainable freight transportation system by innovatively integrating the Consensus Model (CM) with the Fuzzy Evidential Reasoning Algorithm (FERA). A CM has been used to determine the degree of importance of each Key Performance Indicators identified across three dimensions of sustainability. FERA has been used to aggregate subjective judgments with crisp quantitative values. This approach has a unique capability to handle various uncertainties related to impreciseness in decision-making. This study has demonstrated the use of an integrated approach for developing a performance index of the freight transportation system. Sensitivity Analysis of the model provides logical inferences and an understanding of the robustness of the model outputs. It can be interpreted from the results of the study that the firms with higher profitability in the market are focusing more on making their operations sustainable. A proportionate approach of firms towards the economic, environment, and societal well-being, may assist in achieving higher sustainability. [ABSTRACT FROM AUTHOR]

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between − 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health. [ABSTRACT FROM AUTHOR]

The objective of this study is to present a new stochastic two-stage data envelopment analysis (DEA) model for assessing the sustainability of supply chains. Unlike the conventional DEA models, which consider each decision making unit as a black box, two-stage DEA models consider the intermediate products. The main contribution of the current paper is to develop a two-stage DEA model in centralized context in the presence of stochastic data. The efficacy of the developed approach is shown by a case study. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine whether agricultural supply chains (ASC) can be simultaneously sustainable and resilient to ecological disruptions, using the Planetary Boundaries theory. The nine different Planetary Boundaries i.e. climatic change, biodiversity loss, biogeochemical, ocean acidification, land use, freshwater availability, stratosphere ozone depletion, atmospheric aerosols and chemical pollution are examined in relation to ASC sustainability and resilience. Kenya's tea upstream supply chain sustainability and resilience from the ecological point of view is questioned. This study adopts a multi-case study analysis approach of nine producer organisations from Kenya's tea supply chain. The data from the in-depth semi-structured interviews and a focus group discussion are analysed using thematic analysis. The Kenyan tea supply chain producers are not aware of all the nine planetary boundaries, although these impact on their resilience practices. They are engaged in pursuing both sustainability and resilience practices. They implement mainly environmental practices in relation to sustainability, while only a few of them are implementing resilience practices. The sustainability and resilience concepts were found to be interrelated, but resilience does not improve at the same pace as sustainability. It is suggested that the relationship between sustainability and resilience is non-linear. Limitations and future research avenues are also provided. [ABSTRACT FROM AUTHOR]

Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed. [ABSTRACT FROM AUTHOR]

Research on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results. [ABSTRACT FROM AUTHOR]

Despite its proven cost-effectiveness and its capacity to cover the increasing demand for dialysis, especially hemodialysis, home hemodialysis (HHD) is not yet implemented in Tunisia. The aim of this paper is to conduct a socio-economic study of its introduction in Tunisia and provide guidance for nephrologists to select a dialysis modality while considering sustainability dimensions. First, a multi-expert fuzzy analytic hierarchy process (FAHP) based approach was conducted to assess HHD position among the existing dialysis modalities in Tunisia. Several criteria were considered: environmental; social; economic; organizational and medical. Furthermore, pairwise comparisons were provided by judgments of experts from two different contexts: one where HHD was already implemented (France) and another where HHD was not yet implemented (Tunisia). Based on the collected judgments, FAHP generated the ranking of dialysis modalities. Moreover, the impact of criteria weights variation on the ranking was studied. Then a decision model was proposed to guide nephrologists' prescription of dialysis modality based on the generated ranking as well as constraints related to the patient's environment and preferences. Our results highlighted that although in-center hemodialysis is the commonest modality in Tunisia, it is the least sustainable. However, HHD modalities proved to be more sustainable and are well ranked regarding all the considered criteria. Hence, it is worthy to introduce HHD in Tunisia from both medico-organizational and sustainability perspectives. By leveraging the FAHP results, this study demonstrated the practical application of the decision model, specifically designed for use by nephrologists, to foster sustainability in dialysis decision-making processes. [ABSTRACT FROM AUTHOR]

This paper provides results in the area of the analytical derivation of the efficient set of a mean-variance portfolio selection problem that has more than three criteria. By "analytical" we mean derived by formula as opposed to being computed by algorithm. By "more than three criteria", we mean that beyond the mean and variance of regular portfolio selection, the problems addressed have two or more additional linear objectives. The additional objectives might include sustainability, dividend yield, liquidity, and R&D as extra objectives like these are being seen with greater frequency. While not all multiple criteria portfolio selection problems lend themselves to an analytical derivation, a certain class does and the problems in this class are covered by the mathematics of this paper. [ABSTRACT FROM AUTHOR]

In today's world, industries are facing massive pressure to integrate sustainability issues for efficient and successful supply chain management (SCM). Hence, worldwide it has become critically important to make economic operational balance satisfying environment protection norms and social welfare perspectives. Consequently, the industries are investigating their SCM structures in association with a third party logistics (3PL) service provider adopting the triple bottom line framework for improving the overall supply chain performance. Therefore, selection of the right 3PL provider for the sustainable alliance is supremely important for broader perspective of greater business value. Thus, the main objective of this research work is the selection of most appropriate 3PL provider for a food manufacturing company (FMC) after systematic evaluation of six different feasible logistic providers serving over a decade in India. Selection of optimal alternative 3PL provider is very complex and challenging because of the qualitative description of service provider performances and the inherent uncertainty due to subjectivity. The concept of interval-valued fuzzy-rough number (IVFRN) offers perfect treatment of such uncertainty. In this paper, we develop a multi criteria decision making (MCDM) model combining the factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) models based on IVFRN. The proposed model is tested and validated on a case study where the optimal selection of 3PL providers is performed for an Indian FMC. Based on the results obtained in sensitivity analysis, it was shown that the proposed IVFRN based FARE-MABAC model produces stable/consistent solutions. Through the research presented in this paper, it is shown that the new hybrid MCDM method is a useful and reliable tool for rational decision-making. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s11192-021-04042-6 [ABSTRACT FROM AUTHOR]

Over the past fifty years the population of the world has doubled, while resources as water have become increasingly scarce. In particular, water consumption has far exceeded the available of this resource in some regions of the world. In order to address the above problem, the possibility of reusing grey water by installing wastewater treatment plants could be a suitable alternative for several developing countries. This paper seeks to find the best configuration for these facilities in Chile by considering the economic and environmental aspects conjoint to the social dimension. The problem is modeled as a multi-objective optimization including: minimizing costs, minimizing environmental impact, maximizing phosphorus extraction from wastewater and maximizing the number of workers to be required with the goal of analyzing the sustainability of the system. To find the Pareto frontiers of multi-objective problem, a resolution framework based on an adaptation of elitist non-dominated sorting genetic algorithm (NSGA-II) is provided for the problem. From the obtained results, the non-dominated solutions and a compromise solution are computed, reporting configuration alternatives that integrate the three sustainability dimensions, the economic, the environmental and the social as objectives for the design for a sustainable system of wastewater treatment plants. [ABSTRACT FROM AUTHOR]

To reduce carbon emissions and enable sustainable development goals, electric vehicles need to be promoted. As an evolving area of study, significant research gaps exist in the electric vehicle supply chain literature, especially in terms of competition, game structures, decision variables, green investment, etc. This paper considers two competing electric vehicle supply chains with one manufacturer and one retailer under each supply chain. The supply chain members are profit-maximizing in nature. Here, the manufacturers decide on the wholesale price and green level of the product, whereas the retailers or distributors decide on the retail selling price. The market demand is price-sensitive and also positively related to consumers’ green awareness and brand reputation of green products. Nevertheless, the green level comes with a price in the form of green investment for the manufacturer. Therefore, the manufacturer makes a tradeoff between the increased revenue due to greener electric vehicle technologies and the associated green investments. This paper derived the optimal decisions for the supply chain following three game-theoretical approaches. Three game scenarios represent the dynamic power structures of the market. The overall results are presented via four propositions and twelve corollaries, along with proofs. Furthermore, numerical simulations have been conducted to gain further insights into the sensitivities of the optimal actions with respect to self-price, competitor price, green level, and green investment. The overall study may assist electric vehicle businesses in having a better understanding of optimal pricing and green investment strategies under a variety of market scenarios. [ABSTRACT FROM AUTHOR]

Supplier selection has long been a strategic cornerstone for the advancement of companies from the mining sector due to its impact on many sectors as inventory management, production planning, maintenance scheduling, financial resources and environment. In this regard, expert decision-makers should deem a number of performance conflicting criteria and supplier alternatives. Moreover, choosing a suitable Multi-Criteria Decision Making (MCDM) approach has become a key strategic consideration for supplier selection. Hence, this paper presents a combination of powerful MCDM methods to select the most suitable supplier of forklift filters. The Fuzzy Analytic Hierarchy Process (FAHP) is first used to calculate the initial weights of criteria and sub-criteria under uncertainty, followed by the implementation of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL) to assess the interrelations and feedback between them. The fuzzy theory is used in both AHP and DEMATEL methods to represent the human vagueness when making judgments. Then, FAHP and FDEMATEL are combined to obtain the final contributions of both criteria and sub-criteria on the basis of interrelations and uncertainty. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank the supplier alternatives. To validate the effectiveness of the proposed framework, a case study is presented. The results evidence that quality criterion is the most crucial aspect when selecting suppliers of forklift filters. [ABSTRACT FROM AUTHOR]

Sustainable Supply Chain Management (SSCM) is a topic of growing interest among researchers. Many works have investigated the negative impact of CO2 emission, but have put less emphasis on other emission types. Many works have focused on the economic and environmental dimensions of sustainability but lacked content on the social dimension. Since SSCM is concerned with all aspects of sustainability, this work undertakes the three dimensions of sustainability and models particulate pollution (PM2.5) which has been cited in the literature to have a negative impact on human health. The paper constructs a mathematical programming model that minimizes the transportation costs and the undesirable social medical costs for remedying the release of PM2.5 into the atmosphere. The paper solves a model that evaluates different transportation modes, transportation costs, carbon emissions, PM2.5, medical costs, and capturing options. Moreover, the work utilizes a Monte Carlo simulation to best present the inherent variations in the model. Largely, the work provides stakeholders with transportation mode choices and greening opportunity investments to minimize the overall transportation costs while respecting physical and operational constraints. [ABSTRACT FROM AUTHOR]

Since the mid 1990s some European countries (including Italy) implemented a Notional Defined Contribution (NDC) pension system. Such a system is based on pay-as-you-go funding, while the pension amount is a function of the individual lifelong contribution. Despite many appealing features, the NDC system presents some drawbacks: first, it is vulnerable to demographic and economic shocks compromising the financial sustainability; second, it could fail to guarantee adequate pension benefits to pensioners. In order to reduce the first limit, automatic balance mechanisms (ABMs) have been proposed in literature and also implemented in Sweden, while solutions that combine financial sustainability and social adequacy have been applied only in a pay-as-you-go point system. The aim of this paper is to insert into the Italian NDC architecture ABMs that preserve social adequacy under financial sustainability constraints. Godinez-Olivares et al. (Insur Math Econ 69:117–126, 2016) built ABMs for a Defined Benefit pension system using nonlinear optimization techniques to calculate the optimal paths of the control variables representing the main drivers of the system: contribution rate, retirement age and indexation of pensions. Following this line of research, we have developed a nonlinear optimization model for the Italian NDC system based on three control variables: pensions indexation, notional rate and contribution rate. The objective function considers both social adequacy and contribution rate sustainability, under liquidity and sustainability constraints. In the numerical application we apply the model to the Italian pension system and test the sensitivity of the results to different economic scenarios and objective function parameters. [ABSTRACT FROM AUTHOR]

Home healthcare is a product of an aging population, increased awareness of health management and growing demand for medical services. Home healthcare providers need to rationally and efficiently route caregivers to visit their customers. This paper investigates the green home healthcare routing and scheduling problem with simultaneous consideration of physician–patient satisfaction and sustainability based on prospect theory, which simultaneously optimizes four objectives including minimizing total cost, carbon emission, maximizing customer satisfaction, and caregiver satisfaction. Practicalities such as maximum working hours, load balancing, physician–patient skill level matching, customer prioritization, and time windows are also considered. In addition, an improved adaptive reference point-based third generation non-dominated sorting genetic algorithm is proposed to solve the problem. Finally, numerical experiments on various scales were conducted to verify the effectiveness of the algorithm, and the results show that the algorithm can provide decision makers with a larger number of feasible no dominated solutions, and can effectively solve the home healthcare path planning problem. It is also compared with three algorithms, second generation of non-dominated sorting genetic algorithm, third generation non-dominated sorting genetic algorithm, and adaptive reference point-based third generation non-dominated sorting genetic algorithm, to further verify that the proposed improved adaptive reference point-based third generation non-dominated sorting genetic algorithm performs better in terms of solution speed, distribution of Pareto-optimal solutions, convergence, and diversity in various instances. The problem proposed in this study synthesizes the goals of multiple stakeholders in home healthcare and is more conducive to the sustainable forward development of the enterprise. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s10479-021-03994-5 [ABSTRACT FROM AUTHOR]

Sustainable forest management should be pursued in all public forests of the EU countries, as this constitutes a legal requirement within the frame of the FOREST EUROPE policy. However, the forest management sustainability assessment process is a complex task, mainly because it involves integration of multiple environmental, socio-economic and institutional impacts at different spatial scales of different forest management policies, which are considered for implementation at any forest location. Moreover, the conflicts and interests of the various stakeholders related to the forest resources should be included in the assessment process. Much of the on-going research, therefore, focuses on the development of tools that can facilitate the integration of the different type of forest resource impacts and conflicts towards achievement of forest management sustainability. This paper presents a multi-criteria approach, which combined spatial analysis, Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), spatially referenced impact indicator models for pairwise comparisons, the fuzzy extent analysis, fuzzy preference programming and the ideal solution concept in order to assess the performance of forest management sustainability at regional level. The combined multi-criteria approach was successfully implemented to the forests of Eastern Macedonia and Thrace Region in northern Greece through the operation of the National Forest Governance Council. Seven forest management policies were subjected to sustainability assessment and an Overall Forest Sustainability Performance Index (OFSPI) was calculated for each one of them. The final ranking of the forest management policies was based on their OFSPI values. Furthermore, sensitivity analysis was conducted to explore robustness of the final solution. [ABSTRACT FROM AUTHOR]

This study investigated corporate governance factors that could shape the decision for sustainability reporting. Specifically, this paper sets out to investigate the environmentally sensitive properties of distinct corporate governance characteristics vis-à-vis sustainable concerns strengthening the decision to develop environmental disclosures. Regarding explanatory variables, the study focused on corporate governance because it sets the rules and processes by which a firm is managed. Five plausible individual characteristics were employed in our proposed model, namely, "Independent Directors", "presence of Lead Independent Director", "frequency of Audit Committee Meetings", "presence of Sustainable Committee", and the "Age of the Youngest Director". We utilized the Environmental facet of Environmental, Social and Governance score calculated by Bloomberg as a proxy driving the decision to disseminate environmental information. Hypotheses were tested using a Logit model for a sample of a total of 278 firms from the United States listed S&P 500. Results revealed that the "Age of the Youngest Director" has a negative effect, while "Independent Directors" and the "presence of Lead Independent Director" variables seem to strengthen the decision to develop environmental disclosures. Implications are valuable to different stakeholders and policy makers interested in improving corporate governance initiatives to reducing agency costs and enhancing corporate sustainable transparency. [ABSTRACT FROM AUTHOR]

In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models. [ABSTRACT FROM AUTHOR]

In spite of the increasing awareness apparent in the previous studies regarding the evaluation suppliers considering sustainability aspects, there are limitations on the incorporation of sustainable performance in terms of traditional, green and social aspects in supplier selection and order allocation. This paper presents the development of an integrated fuzzy TOPSIS-possibilistic multi objectives model to (1) solve a two-stage sustainable supplier selection problem; and (2) allocate the optimal flow of products quantity that should be ordered from suppliers towards the minimization of expected costs, environmental impact and travel time and maximization of social impact. Suppliers' sustainable performance was based on traditional, green and social criteria, and quantified by using fuzzy TOPSIS and then integrated into the possibilistic multi objective model. The latter helps decision makers in having an order allocation plan that considers sustainability aspect. Furthermore, the multi-objective optimization model was re-developed as a possibilistic multi-objective optimization model (PMOOM) to handle the dynamic nature in some of the input data. Next, the LP-metrics method was employed to derive Pareto solutions out of the PMOOM. The quality of the obtained Pareto solutions was evaluated using the global criterion approach aiming to help decision makers in selecting the final Pareto solution. The applicability of the developed integrated fuzzy TOPSIS-possibilistic multi-objective approach was proven with sensitivity analysis on a case study of a meat supply chain. [ABSTRACT FROM AUTHOR]

Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs' sustainability mainly classified into three categories—the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering Multi-Objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies' structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME. [ABSTRACT FROM AUTHOR]

Emerging issues and new challenges of globalization have forced companies to design their supply chains for not only minimizing cost but also considering other factors. Supply chains are exposed to new environmental regulations to reduce their carbon emissions and compelled to consider other overlooked factors, such as risk. In this paper, we consider a multi-echelon multimodal supply chain network design problem with multiple products and components that take economic, environmental and risk factors into account. The problem is modeled as a Mixed Integer Linear Programming model and constrained by a carbon cap-and-trade scheme and a risk threshold. This novel problem realistically portrays the supply chain network design considering sustainability and reliability factors simultaneously. The proposed model has been tested on randomly generated hypothetical but realistic test instances. The impacts of different risk thresholds and unit carbon prices on the supply chain cost, risk and emissions are analyzed. The effects of multimodal transportation modes on cost, risk and emissions are also tested. Results prove that using multimodal transportation decreases supply chain cost and carbon emission. In addition, the total supply chain cost and carbon emission increase if the decision maker is risk-averse. The choice of transportation modes is sensitive only to emission levels. [ABSTRACT FROM AUTHOR]

In this paper, we examine the impact of manufacturers' dishonest reports of product sustainability. We first investigate the Volkswagen emissions scandal and Wal-Mart fake organic food scandal in terms of the overstatement of product sustainability. Based on these two cases, we construct a two-echelon supply chain model consisting of one manufacturer and one retailer. The supply chain members can use inspections or deferred payments in trading. We find that the inspection and deferred payment mechanisms do not always help reduce the overstatements of sustainability. With the inspection mechanism, when the basic demand is higher (lower) than the threshold, inspections can (cannot) effectively reduce the levels of overstatement. With the deferred payment mechanism, when the manufacturer's expected proportion of payments is sufficiently small (large), the deferred payments can (cannot) help reduce the levels of overstatement. Moreover, we find that when overstatements are difficult (easy) to identify by the retailer, the deferred payment (inspection) mechanism is the more effective means for the retailer to reduce overstatements. Furthermore, our numerical results indicate that when a third party organization perceives a low probability of product overstatement, the manufacturer and the retailer use neither inspections nor the deferred payment mechanism. In contrast, when a third party organization perceives a high probability of product overstatement, the inspection mechanism is more beneficial to the manufacturer, while the deferred payment is more beneficial to the retailer. The managerial implications of our findings are discussed. [ABSTRACT FROM AUTHOR]

Throughout the world, carbon emissions have decreased in an unprecedented way as a result of the Covid-19 pandemic. The purpose of this paper is to investigate whether a rebound effect in carbon emissions is anticipated following the extraction of information related to the beliefs of investors. A suitable Markov switching model is used in this paper to adapt the safe haven financial methodology to an environmental sustainability perspective. Analytically, the aforementioned situation is modeled by estimating a two-state dynamic Markov-Switching Regression (MSR), with a state-dependent intercept term to capture the dynamics of the series, across unobserved regimes. In light of the results of the research and the robustness checks, investors are anticipating a rebound effect on the total quantity of carbon emissions. [ABSTRACT FROM AUTHOR]

As policies and regulations related to environmental protection and resource constraints are becoming increasingly tougher, corporations may face the difficulty of determining the optimal trade-offs between economic performance and environmental concerns when selecting product technology and designing supply chain networks. This paper considers weight reduction technology selection and network design problem in a real-world corporation in China which produces, sells and recycles polyethylene terephthalate (PET) bottles used for soft drinks. The problem is addressed while taking consideration of future regulations of carbon emissions restrictions. First, a deterministic mixed-integer linear programming model is developed to analyze the influence of economic cost and carbon emissions for different selections in terms of the weight of PET bottle, raw material purchasing, vehicle routing, facility location, manufacturing and recycling plans, etc. Then, the robust counterpart of the proposed mixed-integer linear programming model is used to deal with the uncertainty in supply chain network resulting from the weight reduction. Finally, results show that though weight reduction is both cost-effective and environmentally beneficial, the increased cost due to the switching of the filling procedure from hot-filling to aseptic cold-filling and the incumbent uncertainties have impacts on the location of the Pareto frontier. Besides, we observe that the feasible range between economic cost and carbon emission shrinks with weightreduction; and the threshold of restricted volume of carbon emission decreases with the increase of uncertainty in the supply chain network. [ABSTRACT FROM AUTHOR]

Humanitarian supply chains (HSCs) contribute significantly to achieving effective and rapid responses to natural and man-made disasters. Though humanitarian organizations have during the last decades made considerable efforts to improve the response to crises in terms of effectiveness and efficiency, HSCs are still faced with so many challenges, one of which is the incorporation of sustainability dimensions (economic, social and environmental) in the management of their supply chains. In the literature, some authors have highlighted that the planning and achievement of sustainability performance objectives in humanitarian operations is hindered by the lack of decision support systems (DSS). Therefore, this paper proposes a multi-objective Master Planning DSS for managing sustainable HSCs. This Master Planning DSS includes: (1) the definition of a set of metrics for measuring the performance of a sustainable HSC; (2) an algorithm to solve the multi-objective problem; and (3) a Master Planning mathematical model to support the tactical planning of the sustainable HSC. Using the information gathered from field research and the literature, an illustrative numerical example is presented to demonstrate the implementation and utility of the proposed DSS. The results show that the order in which the three sustainability dimensions (economic, social and environmental) are prioritized has some impact on the performance measures. Therefore, it is important to fix a tolerance that would enable to obtain an acceptable balance (trade-off) between the three sustainability objectives, in line with the prioritization choice of the decision maker. [ABSTRACT FROM AUTHOR]

As an outcome of economies of scale, demand growth can benefit companies in various ways. However, demand fulfillment and management are just not possible in a supply chain whose managers focus only on cost minimization. For this reason, the present paper proposes a sustainable, efficient, customer-centered and resilient closed-loop supply chain network. Limited access to labor during the Covid-19 pandemic, traffic congestion due to transportation of products, as well as transparency through the Blockchain technology are some of the key considerations in our supply chain network design. To the best of our knowledge, this is the first attempt where such a supply chain network is designed, solved and analyzed based on iterative and integrated models. The fuzzy goal programming is adopted as a solution approach for both of the proposed models. The applications of the proposed models are demonstrated in the context of a case study. Furthermore, the results showed that the iterative model can focus on the expenditure minimization strategy and the integrated model can focus on the transparency and efficiency strategies. [ABSTRACT FROM AUTHOR]

Due to the growing environmental concerns and associated health issues of using fertilisers and pesticides in agriculture, Zero Budget Natural Farming (ZNBF) is gaining traction to guide farmers toward sustainable farming practices. Therefore, to promote sustainability and also to safeguard the interests of small farmers who adopt ZNBF, the government in developing economies are adopting two prevalent intervention mechanisms: (i) cost subsidy and (ii) indirect support. However, little has been done to provide clear-cut directives on when to employ these intervention mechanisms, especially in the presence of heterogeneous farmers and yield uncertainty. Therefore, in this paper, we consider a supply chain comprising a government, small farmers, and consumers, and we perform a comparative analysis to understand the prevailing conditions under which a particular policy outperforms another in maximizing social welfare by promoting ZBNF practices. We develop a government-farmer game-theoretic model wherein the government first selects the intervening mechanism to maximise social welfare, followed by farmers’ sowing decisions. Our analysis reveals that both policies positively impact the adoption of ZBNF practices; however, in the scenario of higher environmental damage, cost subsidy outperforms the indirect support mechanism. On the contrary, for medium and lower environmental damage, the choice of intervention depends on the proportion of environmentally conscious farmers. Specifically, the government should extend indirect support only if there exists a critical mass of environmentally conscious farmers. Our findings from analytical and numerical analysis assist the government in identifying and designing the appropriate intervention mechanisms whereas to farmers in their showing decisions. [ABSTRACT FROM AUTHOR]

A sustainability program is about implementing essential sustainability practices at various intensities that will not harm people because of firm actions. This paper formulates an optimization model to identify an optimal intensity of implementation of selected sustainability practices referred as sustainability program for maximising manufacturing industry sustainability performance for a known set of budgetary and minimum thresholds constraints with respect to economic, social and socio-economic criteria. Besides modelling the paper proposes a random search procedure embedding the NN to determine the optimal sustainability program and explains the usefulness with a sample problem. Sensitivity analysis is carried out to understand the behaviour of the model. It is observed that the sustainability performances depend on the constraints such as budget limitation and threshold values of performance criterion. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic behavior of the interaction between sustainable supply chains and environment. Environmental impact exists at each stage throughout supply chains such as resource extraction, production, distribution, repairs, and waste disposal, among others. As market and competition constantly change over time, however, supply chains interact significantly with environment, so managing supply chains sustainably is dynamic. In this paper, a mathematical model based on nonlinear dynamic system is presented to describe the dynamics of the impact of supply chains on environment while achieving sustainable supply chains. The existence and uniqueness of its solutions are proved, followed by the analysis on its equilibrium and stability. To better understand the dynamic mechanism of this proposed system, performance analysis is conducted with respect to three parameters: (a) design production capacity; (b) environmental cost; and (c) demand rate. Analytical results validate the dynamic interaction of supply chains with environment, and justify the environmental and economic significance of supply chain sustainability. [ABSTRACT FROM AUTHOR]

Due to rising concerns for environmental sustainability, the Indian electronic industry faces immense pressure to incorporate effective sustainable practices into the supply chain (SC) planning. Consequently, manufacturing enterprises (ME) are exploring the option of re-examining their SC strategies and taking a formalized approach towards a sustainable partnership with logistics providers. To begin with, it is imperative to associate with sustainable forward and reverse logistics providers to manage effectively the upward and downstream flows simultaneously. In this context, this paper proposes an integrated SC network for the evaluation and selection of forward distribution partners (FDP) and third party reverse logistic providers (3PRLP) from a sustainable perspective of an Indian electronic ME. The sustainable evaluation of the logistic partners is performed using fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution. The integrated logistics network is modeled as a bi-objective mixed-integer programming problem with the objective of maximizing the profit of the manufacturer and maximizing the sustainable score of the selected forward and reverse logistics providers. The novelty of the study is its ranking of the FDPs and 3PRLPs on the economic, environmental, and social dimensions of sustainability and the simultaneous integration of logistics outsourcing decisions for the forward and reverse flow of products. Goal programming approach is utilized to capture the trade-off between the conflicting objectives and to attain a satisfying solution to the bi-objective problem. The results indicate that integrating the strategic decisions of selection of logistics partners with the operational flow planning decisions can immensely improve the sustainable performance value of the SC network and secure reasonable profits. The managerial implications drawn from the result analysis provide a sustainable framework to the ME for enhancing its corporate image. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of sustainability into supply chain management (SCM) is a key issue for ensuring corporate competitiveness in face of dynamic ecological and social environments. This paper reviews 185 journal publications of the last 20 years that formalize issues related to sustainable supply chain management (SSCM) in quantitative models. In a content analysis, modeling and SCM characteristics as well as sustainability and SSCM constructs are elaborated. The models are assessed numerically by counting frequencies of occurrence and by clustering the paper sample according to selected characteristics. The findings indicate that SSCM models predominantly focus on deterministic approaches and the integration of environmental aspects of sustainability while neglecting stochastic modeling techniques and the consideration of social factors. By now, comprehensive modeling approaches are most often employed on intra-organizational levels whereas broader application areas are assessed by less complex models. The integration of pressures and incentives of external stakeholders or the formalization of sustainable supplier management and sustainability risks are identified as future research perspectives. Furthermore, the interrelationships between the triple bottom line dimensions are to be scrutinized in greater detail in order to avoid focused optimization of selected sustainability criteria. Seven modeling guidelines are derived from the reviewed literature to facilitate future model-based SSCM research. [ABSTRACT FROM AUTHOR]

The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain considering the economical and environmental factors with uncertainty in parameters. The proposed network is modeled as fuzzy multi-objective mixed integer linear programming problem considering multi-customer zones, multi-collection centers, multi-disassembly centers, multi-refurbishing centers, multi-external suppliers, and different product recovery processes; to take care for purchasing cost, transportation cost, processing cost, set-up cost, and capacity constraints simultaneously. The model is solved using an interactive $$\upvarepsilon $$ -constraint method. A case example is solved using LINGO 14.0 to demonstrate the significance and applicability of the developed fuzzy optimization model for closed-loop supply chain. [ABSTRACT FROM AUTHOR]

The advent of mobile channels have changed retail business models, the choice of retail mix, and shopper behavior. As consumers do not differentiate among the channels where they try, purchase and/or take delivery of their product, they also expect maximum flexibility in the product returns process. On average, retailers forecasted returns to reach about 16.6% of the total merchandise that customers purchased in 2021, according to the National Retail Federation, which is an increase from an average return rate of 10.6% in 2020. The resulting cost of returns amounted to $761 billion worth of merchandise in 2021 (Repko in A more than $761 billion dilemma: retailers’ returns jump as online sales grow. https://www.cnbc.com/2022/01/25/retailers-average-return-rate-jumps-to-16point6percent-as-online-sales-grow-.html. Accessed 17 June 2022, 2022). For retailers and manufacturers, integration of different reverse channels is extremely important to deliver the seamless experience demanded by today’s discerning consumer while ensuring the profitable handling of the returned products as well as ensuring the environmental sustainability of the retailing operations. Regardless of which channel receives a return, the reverse logistics network should have the flexibility and the capability to remarket or to recover the value in the returned product in a cost efficient and timely manner that maximizes firm profitability. To the best of our knowledge, this paper is one of the first studies that develops a linear programming model with profit maximization objective to help determine how to optimally decide the returned product touch point(s) in the reverse logistics network. Unlike the extant literature, our model explicitly incorporates the marginal value of time for returns, product characteristics as well as the underling reverse logistics network configuration in return channel selection strategy. We present a comprehensive analysis on how and to what extent the return channel selection is dependent on the product characteristics such as time-based value decay rate, defective rates, and disposal rates as well as the network structure. Using data from HP and Bosch Power tools operations as well as real geographical US data, we show that our decision model can effectively help determine the reverse logistics network and the type of facility where a product is returned as a function of product characteristics and economic parameters. Our work emphasizes that product returns and waste reduction, improved firm sustainability and profitability can co-exist through effective reverse logistics planning. [ABSTRACT FROM AUTHOR]

A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples. [ABSTRACT FROM AUTHOR]

Facility layout design, a NP hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new sustainable stochastic dynamic facility layout problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated are then analyzed by data envelopment analysis to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi attribute decision-making techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, Borda-Kendall and integer linear programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size $$N=12$$ for time period $$T=5$$ having Gaussian demand are considered. [ABSTRACT FROM AUTHOR]

Due to tremendous losses of resources in modern food supply chains, higher priority should be given to reducing food waste and environmental impacts of food production. In practice, multiple production options are available, but must be quantitatively assessed with respect to economic and environmental performances before they are adopted in food supply chains. The objective of this paper is to develop a mathematical model that can be used for such a quantitative assessment of alternative production options that are associated with different ways to deal with waste in food supply chains, i.e. prevention, recycling, and disposal of food waste. We develop a multi-objective mixed integer linear programming model to derive the set of eco-efficient solutions corresponding to production planning decisions. Environmental performance of the chain is expressed with an indicator based on exergy analysis, which has the potential to capture other commonly used indicators, such as energy consumption, fuel consumption, and waste generation, and express them in a single value. This simplifies the calculation of the eco-efficient frontier, and enables its intuitive graphical representation, which is much easier to communicate to the involved decision makers. The applicability of the developed model is demonstrated on a real-life industrial bread supply chain in the Netherlands. Results confirm the findings from literature that prevention is the best waste management strategy from environmental perspective. The advantages of using exergy as an indicator to capture the environmental performance is demonstrated by comparing the outcomes to other commonly used indicators of environmental performance. We illustrate the potential of studying food production planning decision problems in a multi-objective context, and demonstrate the applicability of the model in the assessment of alternative production options. [ABSTRACT FROM AUTHOR]

There does not exist a one-size-fits-all green product strategy in green market, and formulating a strategy that aligns a company's economic and environmental goals is no small feat. In this paper we provide insights into a firm's green product strategy choice with a focus on two alternative strategies: Greening-Up and Greening-Out. In doing so, we incorporate two important characteristics of the customer market where there are substantial numbers of customers who are potentially receptive to a green appeal: (1) Customer market is divided into three distinct and mutually exclusive segments based on large-scale surveys and interviews conducted to measure both customers' willingness-to-pay for products with environmental attribute(s) and their propensity to buy these products; and (2) We factor in the findings of the most recent market behavior studies that even the customers that demonstrate the least environmental responsibility of all the segments can buy green products for non-environmental reasons. The contributions and findings of our work are as follows. First, we characterize the market- and product-related factors a firm should act on for a successful execution of green product strategies. Second, on the basis of customer choice data available, we assess the strategic fit of distinct pricing options under each strategy choice with the objective(s) of a firm. Third, we explore the extent to which a green product strategy creates higher environmental benefits while providing economic payoffs to a firm. In contrast to prevailing view, we show that greening up an existing brown product is not necessarily better at reducing the environmental impact of a firm than designing a new green product. We observe that responsiveness of the least environmentally conscious customers to environmental attributes added into a brown product sets one of the two major constraints on the environmental performance of Greening-Up strategy. We also observe that there does not exist a strict dichotomy between having a better economic performance and achieving a higher environmental performance, and a firm can achieve both goals simultaneously, even if it means leaving out serving some of the customers targeted by the firm's existing brown product. [ABSTRACT FROM AUTHOR]