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This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems. [ABSTRACT FROM AUTHOR]

Discussions of the resiliency, sustainability, and agility of supply chains are important in the research and management of supply chains in these difficult times, considering the ongoing pandemic of COVID-19. A viable supply chain is often characterized by resiliency, sustainability, and agility in its network design. Resiliency is essential because disruption and demand fluctuations are forced upon SCs, and the effects of these for many managerial supply chains are unknown. In addition, applying novel technology in the supply chain, such as blockchain, Internet-of-Things (IoT), and artificial intelligence (AI) as agility tools can assist and enable the transition to lean production. This special issue of the Foundations of Computing and Decision Sciences is dedicated to advancements in this fields. Besides, the special issue covers instructional information about OR techniques which are useful for addressing real-world applications on such challenges. [ABSTRACT FROM AUTHOR]

The transition to low-carbon agriculture is seen as essential for accelerating the implementation of ecologically friendly and climate-smart agricultural supply chain management strategies. Success in these efforts depends on robust policy frameworks, effective stakeholder participation, and the availability of agricultural products to customers. This study introduces a conceptual and operational structure for the incorporation and management of an agricultural supply chain with a low-carbon scheme through the establishment of a collaborative market environment involving farmers, agricultural enterprises, and consumers. Within this framework, agricultural enterprises and farmers engage in a cooperative Nash game to determine the extent to which they are willing to employ low-carbon practices in response to collaborative market prices and government policies, including subsidies and carbon taxes. The Nash game yields a unique Nash equilibrium, suggesting the possibility of cooperative negotiations among the parties with the goal of improving their financial gains. The proposed model is applied in two Brazilian sugarcane supply chain case studies, integrating with traditional biorefinery mills to produce and market ethanol as a gasoline alternative. Our results suggest that (1) successfully allocating government subsidies and taxes motivates low-carbon adoption; (2) the government taxation policy stabilizes the low-carbon supply chain, improving stakeholder market pricing and consumer affordability; and (3) reduced cost gaps between low-carbon and non-low-carbon alternatives increase low-carbon adoption. As a result, this study provides fresh insights on the Nash collaboration of stakeholders in accordance with government policies to create a cleaner and more affordable agricultural supply chain market. • A novel low-carbon application is introduced under government's regulations. • Existence of the Nash equilibrium encourages low-carbon stakeholder co-operation. • Successfully planning government subsidies and taxes promotes low-carbon adoption. • Two Brazilian sugarcane supply chain case studies show the low-carbon adaptability. [ABSTRACT FROM AUTHOR]

This special issue of the Foundations of Computing and Decision Sciences, titled "Numerical Techniques Meet with OR", is devoted to the numerical techniques and their applications in real-world phenomena. The special issue and its editorial present numerical algorithms as they meet with different research topics such as, e.g., from operational research, supply chain management, geometrical structures and Covid-19 effects on financial applications. Besides, the special issue covers instructional information about numerical techniques which are useful for OR research problems and real-world applications on such issues. [ABSTRACT FROM AUTHOR]

This Special Issue (SI) aimed to investigate the achievement of resiliency and sustainability in supply chains. The concepts of resiliency, sustainability, and agility of supply chains have been at the top of the list more than ever. Resiliency is essential because disruption and demand fluctuations are forced upon supply chains, and the effects of these on many managerial supply chains are unknown. [Extracted from the article]

The world is moving towards a environmental restricted scenario where excellence and growth in business is contingent upon good governance and excellent green sustainability related policies, strategies and long-term values to stay ahead in competition. Green supply chain management (GSCM) practices play an essential role in shaping the way of offering environmental sustainable products and services to customers. Existing research insists that multiple pressure push organizations to navigate towards 'eco design', 'green purchasing' and 'cooperation with customers' leading to green supply chain management. But how these factors exactly impact the organisational performance based on green supply chain management is being explored in this research. Hypothesis developed is tested by using multivariate canonical correlation analysis to model the regression relationship of 54 manufacturing organizations. Green purchasing and eco-design are the main contributors of GSCM's organisational performance. Cooperation with customer as major roadblock for organisational's performance and requires management, to ponder over for improved results. The findings coincide with published research articles that were conducted in developing countries, such as Brazil and Jordan, to ensure environmental sustainability with the integration of GSCM practices. [ABSTRACT FROM AUTHOR]

Determining optimum places for the facilities and optimum transportation from existing sites to the facilities belongs to the main problems in supply chain management. The solid transportation-p-facility location problem (ST-p-FLP) is an integration between the facility location problem and the solid transportation problem (STP). This paper delineates the ST-p-FLP, a generalization of the classical STP in which location of p-potential facility sites are sought so that the total transportation cost by means of conveyances from existing facility sites to potential facility sites will be minimized. This is one of the most important problems in the transportation systems and the location research areas. Two heuristic approaches are developed to solve such type of problem: a locate-allocate heuristic and an approximate heuristic. Thereafter, the performance of the proposed model and the heuristics are evaluated by an application example, and the obtained results are compared. Moreover, a sensitivity analysis is introduced to investigate the resiliency of the proposed model. Finally, conclusions and an outlook to future research works are provided. [ABSTRACT FROM AUTHOR]

Productivity, profitability and sustainability have become the essence of business survival; thus, modern industrial establishments are turning into frameworks consistent with environmental objectives by adopting cleaner technologies, green practices have ultimately resulting in generation of fewer pollutants. Urbanization and burgeoning technological advancement in different sector within India have brought the concept of green supply chain management, to highlight the importance of responsible consumption and production to maintain environmental quality, reduce wastage and bring out economic growth. This paper aims to segregate the sustainable and competitive performers from the average ones in the Indian manufacturing sector and to understand the degree of the impact of green practices of supply chain management based on their contribution towards sustainable environment. Using survey method, data are collected from 54 manufacturing organizations from Pune Nashik area, and a comprehensive framework of sustainability measurement is developed through successive applications of Chi-square Automatic Interaction Detector analysis, CHAID analysis. This paper is unique as it has applied the concept of CHAID analysis for first time to identify green logistics as main driver to achieve a reduction of ecological damage and improve business performance. [ABSTRACT FROM AUTHOR]

Closed-Loop Supply Chain (CLSC) management has attained appreciable attention over the last few years. CLSC management allows companies to manage their recovery and recycling activities of end products. Due to the latest developments in the world, producers are responsible for the collection, refurbishing, repairing and disassembly of end products at the end of their lives. This paper develops a mixed-integer CLSC model that is inspired by the automotive industry. In this model, we consider three Decision Makers (DM): Plant, Dismantler Center and Customer. Each DM has individual objectives and is responsible for only its own objective function under same constraints. In order to tackle the trade-offs among the objectives, we used four different Interactive Fuzzy Programming (IFP) approaches. The applications of the model and solution techniques are investigated in conjectural data. The paper ends with a conclusion and a call for future studies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel mixed integer linear programming model to solve a supply chain network design problem. The proposed model deals with major issues for supply chains; product quality and cost. These issues are usually solved separately, but in this paper, we investigate effects of product quality on supply chain design and transportation flow. A trade-off between raw material quality, its purchasing and reprocessing costs was considered. Assuming decision maker (DM) wishes to work with a supplier which serves a low quality raw material; this raw material should be in need of reprocessing. To avoid the reprocessing costs, a supplier which serves a high quality raw material should be chosen but at this time the DM has to face a high purchasing cost. A supply chain network which consists of multiple suppliers, manufacturers, distribution centers and retailers is tried to be designed to accomplish aforementioned above trade-offs. The paper examines and discusses the relationship between product quality and supply chain design and offers several managerial insights. [ABSTRACT FROM AUTHOR]

Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying to promote green supply chain management (GSCM). In this regard, a multi-criteria decision-making (MCDM) technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers. Then, considering the resource constraint, weight of criteria and a rank of suppliers are taken into account in a multi-objective mixed-integer linear programming (MOMILP) to determine the optimum order quantity of each supplier under uncertain conditions. To deal with the uncertain multi-objectiveness of the proposed model, a robust goal programming (RGP) approach based on Shannon entropy is applied. The offered methodology is applied to a real case study from a green service food manufacturing company in Iran in order to verify its applicability with a sensitivity analysis performed on different uncertainty levels. Furthermore, the threshold of robustness worthiness (TRW) is studied by applying different budgets of uncertainty for the green service food manufacturing company. Finally, a discussion and conclusion on the applicability of the methodology is provided, and an outlook to future research projects is given. [ABSTRACT FROM AUTHOR]

The increasing in industry as well as usage of vehicles for transporting products generate many problems to the environment such as greenhouse gas emission and global warming. As nowadays environmental pollution has become a crucial problem, production companies have now shifted their attention to lower the carbon emission level along with their financial goal. In reality, the values of some parameters such as production cost, holding cost, demand rate, deterioration rate may not fixed due to several factors. Reworking of defective products reduces production cost. This study aims at developing a multi-objective supply chain inventory management by considering deteriorating products together with imperfect quality production under neutrosophic environment. Several carbon reduction policies such as carbon tax, carbon cap-and-trade and carbon cap-and-offset and green technology are implemented here to mitigate the influence of carbon emission by curbing amount of carbon emission. To reduce the deterioration rate, preservation technology is utilized. In real-world situations, all parameters related to an inventory management are not handled as a crisp value, a fuzzy number or an intuitionistic fuzzy number. To confrontation this issue, single valued trapezoidal neutrosophic number is chosen for investigating this type of an inventory problem. A ranking approach is designed to transform all uncertain parameters into deterministic values. A comparison between two types of reworking processes, and a comparison among crisp, intuitionistic fuzzy and neutrosophic models are drawn. A sensitivity analysis regarding important parameters is also taken into consideration to accentual managerial intuitions. • Two SCIM models with synchronous and asynchronous rework processes are developed. • Preservation technology is utilized to reduce deterioration rate. • Carbon tax, offset, cap-and-trade policies are involved to curb carbon emission. • Neutrosophic set is utilized in this study to handle real-life ambiguity. • Neutrosophic compromise programming is used to solve the proposed models. [ABSTRACT FROM AUTHOR]

Recently, large companies have shown a growing tendency to enhance the reliability and sustainability of their supply chains to increase customers' satisfaction in terms of on-time fulfillment of demands and to be compatible with environmental regulations. Therefore, finding the best approaches to achieve companies' goals is a crucial concern in supply chain management, and the majority of organizations prefer to cooperate with reliable and sustainable companies. In designing a supply chain, the supplier selection problem, order size identification, and order allocation are midterm decisions that are needed to be made separately. To this end, three levels of the supply chain, i.e., suppliers, central warehouses, and wholesalers are considered. In the first level, to address the sustainable supplier selection problem, a novel hybrid approach based on the fuzzy logic is implemented. This approach applies the Fuzzy Analytic Network Process (FANP) method to ranking criteria and sub-criteria, the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to identification of the relationships among the main criteria, and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritizing the suppliers. After prioritizing the suppliers, the obtained weights are considered as the input of a tri-objective model designed to optimize the proposed supply chain. The objectives are minimization of the total cost of the chain, maximization of the weighted value of products by taking the account of suppliers' priorities, and maximization of the reliability of the supply chain. Weighted Goal Programming (WGP) method is then used to deal with multi-objectiveness. To assess the applicability of the suggested methodology, a case study of the lamp supply chain was considered and solved using GAMS/CPLEX solver and optimal policies based on suppliers' sustainability. The reliability of the supply chain was tested, and sensitivity analyses were also performed on the main parameters of the model. [ABSTRACT FROM AUTHOR]