This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the delivery of relief supplies to the most affected areas. Additionally, the demands and transportation times are represented using fuzzy numbers to more accurately reflect real-world conditions. The problem was formulated using a fuzzy multi-objective integer programming model. To solve it, a hybrid algorithm combining a multi-objective ant colony system and simulated annealing algorithm was proposed. This algorithm adopts two ant colonies to obtain a set of nondominated solutions (the Pareto set). Numerical analyses have been conducted to determine the optimal parameter values for the proposed algorithm and to evaluate the performance of both the model and the algorithm. Furthermore, the algorithm’s performance was compared with that of the multi-objective cat swarm optimization algorithm and multi-objective fitness-dependent optimizer algorithm. The numerical results demonstrate the computational efficiency of the proposed method.

This paper deals with knapsack problem in fuzzy nature, where both the objective function and constraints are considered to be fuzzy. Three different models for fuzzy knapsack problem are proposed including, expected value model, chance-constrained model, and dependent-chance model. Credibility ranking method is applied to convert the fuzzy models into a crisp equivalent linear one considering triangular and trapezoidal fuzzy numbers. The solution of the fuzzy problem is obtained with respect to different satisfaction degrees in the objective function and constraints. Several numerical examples are given to demonstrate different models and concepts. The proposed approaches are applied to model and to solve a fuzzy pre-disaster investment decision problem.

The Coronavirus Disease 2019 (COVID-19) pandemic highlighted the urgent need for effective vaccination strategies to control the virus’s spread and reduce mortality. Machine learning (ML) algorithms offer promising tools for predicting vaccine effectiveness and aiding public health decisions. This study explores the application of various ML techniques, including artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) to model and forecast the impact of vaccination on COVID-19 mortality. The algorithms were evaluated using accuracy, precision, recall, specificity, F-measure, and area under the curve (AUC) metrics. The findings revealed that DT outperformed other ML algorithms, achieving the highest metrics across multiple evaluation criteria. It recorded an accuracy of 92.27%, precision of 92.54%, recall of 91.95%, specificity of 87.92%, F-measure of 92.24%, and an AUC of 94.50%, highlighting its exceptional predictive performance. Moreover, DT demonstrated this high level of accuracy while maintaining minimal computational time. These findings suggest that ML models, particularly DTs, can be valuable in assessing vaccine effectiveness and informing health strategies against COVID-19.

One of the most important problems in network design applications is the hub location problem, which is an extension of the facility location problem. The purpose of the problem is to select the least hub nodes from the available nodes so by establishing faster connections between hub nodes, the cost of transferring the entire network traffic is minimised. To deal with uncertainty and hesitation, the traffic amount between origin and destination nodes, the transfer cost, and the cost of establishing hub nodes are considered to be trapezoidal intuitionistic fuzzy numbers. The problem is formulated, and a new approach and a linearisation technique are shown to transform the Intuitionistic Fuzzy Hub Location Problem into a classical one. The transformed problem is solved using integer linear programming algorithms. The feasibility and efficiency of the obtained solutions applied to some airline passenger distribution problem applications are illustrated.

This paper deals with multi-depot bus scheduling (MDBS) problem. Depot workload balancing constraints are introduced. In this case, the problem can be stated as a two-objective multi-commodity flow problem with soft constraints. Two state-of-the-art heuristics are developed including schedule-based and cluster-based heuristics, both of them extend auction algorithm. Also three different approaches are proposed to satisfy depot workload balancing constraints. The convergence of each algorithm is investigated and its complexity obtained. To illustrate the main concepts and results, a small example is solved. Also, to demonstrate the performance of the proposed algorithms, some benchmark examples are considered and CPU-time and optimality gap are compared. The results show a great improvement in the CPU time and quality of the solution of the proposed algorithms. Also the extension of the proposed algorithms under epsilon-scaling approach is analysed.

In this paper bus scheduling problem under the constraints that the total number of buses needed to perform all trips is known in advance and the energy level of buses is limited, is considered. Each depot has a different time processing cost. The goal of this problem is to find a minimum ...