This paper deals with the job shop problem of simultaneous scheduling of production operations and preventive maintenance tasks. To solve this problem, we develop an elitist multi-objective genetic algorithm that provides a set of Pareto optimal solutions minimising the makespan and the total maintenance cost. A deep study was made to choose the best encoding, operators, and the different probabilities. Some lower bounds of the adopted criteria are developed. The computational experiments carried out on a set of published instances validate the efficiency of the proposed algorithm.

Scheduling problems under unavailability constraints has become a popular research topic in the last few years. Despite it’s important application in the real world, the uniform parallel machine scheduling problem was the least studied due to its complexity. In this paper, we investigated the uniform parallel machine scheduling problem under deterministic availability constraints. Each machine is subject to one unavailability period. Different versions of the problem regarding the type of jobs (identical and non-identical) and the performance measures (the total completion times and the makespan) were studied. For the case of identical jobs and for both performance measures, we developed linear programming models and optimal algorithms to provide a solution to the problem. For the case of non-identical jobs, we proved that the problem is NP-hard and propose a quadratic program. Because, this later cannot solve problems with very large number of jobs and machines, a heuristic was developed to find near optimal solutions to the problem especially with very large number of jobs and machines. The computational results showed that the heuristic’s performance is very high regardless the dimensions of problem instances.

The multiple Travelling Salesmen Problem (mTSP) is one of the most popular and important operational research problems. It is a problem where n salesmen have to visit m cities such that each salesman has to visit at least one city and all the cities should be visited exactly once, starting and ending at one specific city. In this paper a new hybrid approach called AC2OptGA is proposed to solve the mTSP. AC2OptGA is a combination of three algorithms: Modified Ant Colony, 2-Opt, and Genetic Algorithm. Ant Colony-based algorithm is used to generate solutions on which the 2-Opt edge exchange algorithm is applied to enhance the obtained solutions. A Genetic Algorithm is then used to again improve the quality of the solutions. The reason behind combining the above-mentioned algorithms is to exploit their strengths in both global and local searches. The proposed approach is evaluated using various data instances from standard benchmarks. Using the TSPLIB benchmarks for large-sized instances, AC2OptGA shows better results than M-GELS, the current best known approach. For medium and small-sized data instances, AC2OptGA shows better results than other approaches and comparable results to M-GELS. Using the MTSP benchmarks (MTSP-51, MTSP-100 and MTSP-150), AC2OptGA outperforms other methods for number of salesmen less than 10 and is competitive with NMACO (BKS) for 10 salesmen.

This research investigates the Three Dimensional-Single Bin-Size Bin-Packing Problem (3D-SBSPP). The aim is to pack $n$ boxes of different dimensions and weights into a minimum number of equally balanced bins. The boxes can be rotated according to six possible orientations. Balancing the bins, volume and weight wise, is of high importance. To achieve this goal, a three-stage heuristic was developed. In the first stage, the different possible layers are generated. Each layer is formed by boxes of same type packed according to a common given orientation. The second stage generates solution candidates using the layers formed in stage 1. In stage 3, the boxes to be shipped are placed in the bins following the solution candidates of stage 2 that give the best volumetric and/or weight utilization. The proposed heuristic was successfully validated through an exhaustive experimental study using a variety of problem instances and involving comparisons with other existing heuristics.

In this paper, a new approach including permutation rules and a genetic algorithm is proposed to solve the symmetric travelling salesman problem. This problem is known to be NP-Hard. In order to increase the efficiency of the genetic algorithm, the initial population of feasible solutions is carefully generated. In addition to that, dynamic crossover and mutation rates were developed. The proposed method was successfully tested using large numbers of different-sized benchmarks. The computational results proved that the proposed solution approach outperforms many existing methods. In addition, for many problem instances the proposed algorithm is able to generate solutions with same value as the best known solutions.

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems.