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In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to the wheat supply chain during the global crises. The use of resilience strategies is one of the solutions to face the supply chain disruptions. In addition, there is a possibility of multiple crises occurring in global societies simultaneously.

In this research, the resilience strategies of backup suppliers (BS) and inventory pre-prepositioning (IP) were discussed in order to cope with the wheat supply chain disruptions. Furthermore, the p-Robust Scenario-based Stochastic Programming (PRSSP) approach was used to optimize the wheat supply chain under conditions of disruptions from two perspectives, feasibility and optimality.

After implementing the problem of a real case in Iran, the results showed that the use of resilience strategy reduced costs by 9.33%. It was also found that if resilience strategies were used, system's flexibility and decision-making power increased. Besides, the results indicated that if resilience strategies were used and another crisis like the COVID-19 pandemic occurred, supply chain costs would increase less than when resilience strategies were not used.

In this study, the design of the wheat supply chain was discussed according to the wheat supply disruptions due to the Russia–Ukraine war and its implementation on a real case. In the following, various resilience strategies were used to cope with the wheat supply chain disruptions. Finally, the effect of the COVID-19 pandemic on the wheat supply chain in the conditions of disruptions caused by the Russia–Ukraine war was investigated.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.