Abstract

This article addresses a two-stage, three-machine assembly scheduling problem that considers the learning effect. All jobs are processed on two machines in the first stage and move on to be processed on an assembly machine in the second stage. The objective of the study is to minimize the total completion time of the given jobs. Because the problem is NP hard, the authors first established a lower bound and several adjacent propositions using a branch-and-bound algorithm to search for the optimal solution. Four metaheuristics are proposed to approximate the solutions: genetic algorithms, cloud theory-based simulated annealing, artificial bee colonies and iterated greedy algorithms. Four different heuristics are used as seeds in each metaheuristic to obtain high-quality approximate solutions. The performances of all 16 metaheuristics and the branch-and-bound algorithm are then examined and are reported herein.

Abstract

The two-stage assembly scheduling problem has a lot of applications in industrial and service sectors. Furthermore, truncation-based learning effects have received growing attention in connection with scheduling problems. However, it is relatively unexplored in the two-stage assembly scheduling problem. Therefore, we addressed the two-stage assembly with truncation learning effects with two machines in the first stage and an assembly machine in the second stage. The objective function was to complete all jobs as soon as possible (or to minimize the makespan). Due to the NP-hardness of the considered problem, we proposed several dominance relations and a lower bound for the branch-and-bound method for finding the optimal solution. Moreover, we proposed six versions of hybrids greedy iterative algorithm, where three versions of the local searches algorithm with and without a probability scheme are embedded. They include extraction and backward-shifted reinsertion, pairwise interchange and extraction and forward-shifted reinsertion for searching good-quality solutions. The experimental results of all proposed algorithms are presented on small-size and big-size jobs.

Abstract

Code smells, also known as anti-patterns, are indicators of bad design solutions. However, two different experts may have different opinions not only about the smelliness of a particular software class but also about the smell type. This causes an uncertainty problem that should be taken into account. Unfortunately, existing works reject uncertain data that correspond to software classes with doubtful labels. Uncertain data rejection could cause a significant loss of information that could considerably degrade the performance of the detection process. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose in this paper a new evolutionary detection method, named ADIPOK (Anti-pattern Detection and Identification using Possibilistic Optimized K-NN), that is able to cope with the uncertainty factor using the possibility theory. The comparative experimental results reveal the merits of our proposal with respect to four relevant state-of-the-art approaches.

Abstract

Cloud computing has reached huge popularity for delivering on-demand services on a pay-per-use basis over the internet. However, since the number of cloud users evolves, multi-cloud environment has been introduced where clouds are interconnected in order to satisfy customers’ requirements. Task scheduling in such environments is very challenging mainly due to the heterogeneity of resources. In this paper, a budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment is proposed. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm in terms of makespan.

Abstract

The rise of terrorism over the past decade did not only hinder the development of some countries, but also it continues to destroy humanity. To face this concept of an emerging crisis, every country and every citizen is responsible for the fight against terrorism. As conventional plans became useless against terrorism, governments are required to establish innovative concepts and technologies to support units in this asymmetric war. In this paper, we propose a new multi-agent model for
counter-terrorism characterized by a methodical process and a flexibility to handle different contingency scenarios. The division of labour in our multi-agent model improves decision making and the structuring of organisational plans.

Abstract

Nowadays the world is suffering from the emergence of a new concept of war, it is the asymmetric warfare created by the terrorists’ new combat doctrine. As the plans to face classic enemies have become unusual against terrorism, this calls for innovative concepts and technologies to support the units and to improve the capability of leaders and structure their choices. In this paper, we propose a multi agent architecture for action planning against terrorist attacks. It is characterized by rapid decisive responses and methodical steps to handle the situation, and by the flexibility to adapt a contingency scenario. We aim to create a multi-agent model that describes the relation between actors during the terrorist attack in order to find the best possible units distribution to neutralize the enemy.

Abstract

In this article, the multi-depot heterogeneous fleet vehicle routing problem with time window (MDHFVRPTW) is considered. The objective of this work is to minimize the total traveled distance while delivering goods to geographically dispersed customers. In our research we solved the MDHFVRPTW with a multi-agent approach based on the hybridization of three meta-heuristics which are a particle swarm optimization algorithm (PSO), a genetic algorithm (GA) and a memetic algorithm (MA). A mathematical programming model for the problem is presented. In order to show the performance of the proposed approach we tested it on different benchmarks and we compared it with other results obtained from the literature.

Abstract

Abstract

During last years, several competency management systems (CMSs) have been proposed to support the acquisition, allocation, and improvement of competencies. However, competency information and associated proofs are still not tracked and shared in a trustworthy and immutable way. In this context, blockchain technology provides a prominent manner to keep track of competencies and achievements and to ensure their sharing in a secure and transparent manner. Particularly, given the decentralized nature of immutable and distributed ledgers enabled by blockchain, the potential for using this revolutionary new technology for lifelong competency tracking and assessment is tremendous. In this paper, a functional architecture using smart contracts and blockchain is proposed to support competency tracking and assessment in learning networks. Thus, by implementing the proposed architecture, all the different stakeholders involved can be connected, namely, learners, authors, and assessors. Besides, a full traceability of acquired proofs of competencies and competency profiles is warranted, while ensuring their authenticity and integrity.

Abstract

Automatic sleep scoring has, recently, captured the attention of authors due to its importance in sleep abnormalities detection and treatments. The majority of the proposed works are based on supervised learning and considered mostly a single physiological signal as input. To avoid the exhausting pre-labeling task and to enhance the precision of the sleep staging process, we propose an unsupervised classification model for sleep stages identification based on a flexible architecture to handle different physiological signals. The efficiency of our approach was investigated using real data. Promising results were reached according to a comparative study carried out with the often used classification models.