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Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants. [ABSTRACT FROM AUTHOR]

• Developing a framework for the wellness tourism supply chain. • Designing a mathematical model for the wellness tourism supply chain. • Observing the concept of sustainability in wellness tourism. • Using the 3 V big data concept in data collection. • Considering a real case study from Sarein spa in Iran. Nowadays, the most successful and prominent wellness tourism centers are those that, consider the aspects of sustainable development, while providing memorable services with the best quality and reasonable prices. A wellness tourism center is seeking to meet the purposeful journey of wellness tourists by improving their mental, physical, and spiritual happiness. This paper presents a three-objective mathematical model to formulate the supply chain of services provided by a wellness tourism center. The purpose of this model is to establish a balance between sustainability aspects in this supply chain. The first objective seeks to maximize profits from the supply chain and the second and third objectives are to maximize customer satisfaction concerning environmental and social impacts. The proposed model includes quantitative parameters of operating costs, capacity increase cost, service capacity, and service demand. Due to the multi-objective nature of the model, the improved multi-choice goal programming method is used. Real data are collected from the Ardabil-Sarein wellness tourism center in Iran and used as a case study to validate and analyze the model. In collecting the dataset for the parameters, the three aspects of big data (3 V's) are considered. Findings show that the model offers different types of discounts for various periods following the tourist demand. Additionally, the wellness tourism center can use the original cost to expand its range of services by offering different tours. Finally, the limitations and managerial suggestions are discussed. [ABSTRACT FROM AUTHOR]

Proposed closed-loop supply chain network for the olive industry. [Display omitted] • Designing a sustainable closed-loop supply chain network for the olive industry. • Developing a multi-objective optimization framework for analyzing economic, environmental and social factors. • Proposing two hybrid optimization algorithms consisting of four meta-heuristics for the first time. • Studying a real-case study to implement the sustainability goals. A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO 2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran. [ABSTRACT FROM AUTHOR]