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2022Riadh Ghlala, Zahra Kodia, Lamjed Ben Said
Enhancing Decision-Making Consistency in Business Process using a Rule-Based Approach: Case of Business Intelligence Process
Journal of Telecommunications and the Digital Economy 10(2):44-61, 2022
Abstract
Decision-making in Business Process is a real challenge, given its technical complexity and organizational impact. Mostly, decision-making is based on business rules fired by an inference engine using facts reflecting the context of the current process task. Focus on a task alone and in isolation from the rest of the process can easily lead to inconsistency in decision-making. In this paper, we aim to improve the importance of consistency of decision-making throughout the process. To fulfill this aim, our contribution is to propose Consistency Working Memory RETE (CWM-RETE): a Framework based on the Rete Algorithm as a pattern-matching algorithm to simulate inference; and MongoDB as a document-oriented database to serialize business rules. This framework enables the compatibility of decision-making throughout the business process. The experimentation is based on the Business Intelligence process as a case study and it is shown that the decision-making process can generate different results depending on whether consistency functionality is enabled or not.
Besma Ben Amara, Hédia Sellemi, Lamjed Ben SaidThe Principal Characteristics of a Serious Game to Ensure Its Effective Design
Proceedings of DiGRA 2022 Conference: Bringing Worlds Together 2022, Published: 2022-01-01, 2022
Abstract
Serious games (SG) adoption increased in multiple fields. As a first step towards a global SG design approach, it is crucial to characterize the game intended. However, there is still a lack of what the principal and necessary characteristics are to specify SG. This paper explores SG Characteristics (SGCs) to bridge this gap by first analyzing features from SG studies in different domains (education, health, business) and purposes (SG classification, learning impacts, design, and evaluation), then identifying shared features. The findings showed 12 high-level abstraction classes of characteristics, which we named Common SGCs (CSGCs), reducing features overlapping and describing the general structure of the game. The CSGCs set serves as a foundation for SG design and reusability. It also provides the main criteria for SG classification and evaluation. Designers could implement CSGCs by matching each one of them with related concrete game mechanics plethora. We present future research directions in the scope of the SG design approach using the CSGCs proposal.
Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben SaidEfficient bilevel multi-objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment
Applied Intelligence, 1-29, 2022
Abstract
Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.
Malek Abbassi, Abir Chaabani, Lamjed Ben SaidAn efficient chemical reaction algorithm for multi-objective combinatorial bi-level optimization
Engineering Optimization, 54(4), 665-686, 2022
Abstract
The Bi-Level Optimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objective bi-level problems. Regarding Multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated with solving these complex problems. Additionally, a recently proposed metaheuristic, called Non-dominated sorting Chemical Reaction Optimization (NCRO), has been successfully applied to solve single-level Multi-Objective Problems (MOPs). NCRO applies a quick-non-dominated sorting technique that makes it one of the most powerful search algorithms in solving MOPs. Based on these observations, a new Bi-level Multi-objective CRO method, called BMCRO, is proposed in this article for solving MBLOPs. The main idea behind BMCRO is to come up with good solutions in an acceptable execution time within the bi-level framework. Experimental results on well-established benchmarks reveal the outperformance of the proposed algorithm against a bi-level variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) which is developed for this purpose.
Malek Abbassi, Abir Chaabani, Lamjed Ben SaidAn elitist cooperative evolutionary bi-level multi-objective decomposition-based algorithm for sustainable supply chain
International Journal of Production Research, 60(23), 7013-7032, 2022
Abstract
Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP’s high complexity. ECODBEMA is applied to the production–distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms.
, Ameni Azzouz, , , , , Lamjed Ben Said,A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics
Complex & Intelligent Systems, 8(1), 199-212., 2022
Abstract
This paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective
is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another
agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a
lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing
algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to fnd the
near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for
the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms., , Ameni Azzouz, , , ,A bicriterion single-machine scheduling problem with step-improving processing times
Computers & Industrial Engineering, 171, 108469., 2022
Abstract
Time-dependent scheduling problems, where the real processing time of jobs is dependent on the starting time, have received growing attention in recent decades. In particular, scheduling problems on a single machine have been widely studied in many facets that address the learning effect and diverse processing environments or time-dependent processing scheduling. Motivated by this observation, we introduce a variant based on the industrial procedure consideration; that is, owing to due date pressure, the processing time of the remaining jobs should be shortened after a period of manufacturing process. We consider a new single-machine scheduling problem with step-improving processing times where the objective function is to find a schedule to minimize a linear combination of the total weighted completion time and total tardiness of all jobs. The proposed problem without a critical date is an NP-hard problem. Therefore, a mixed integer programming model as well as a branch-and-bound (B&B) along with several dominance properties and a lower bound on the completion of an active partial schedule is utilized for solving the problem under study. Subsequently, four variants of the water wave optimization algorithm and four variants of the simulated annealing algorithms were proposed to solve this problem. The simulation results showed that the branch-and-bound method can solve instance problems for up to twelve jobs. The results also showed that all four variants of the wave optimization algorithm did not perform uniformly better than all three variants of the simulated annealing algorithms.
, , , Wassim AyadiBOBEA: a bi-objective biclustering evolutionary algorithm for genome-wide association analysis.
GECCO Companion 2022: 344-347, 2022
Abstract
The behavior of many diseases is still not well understood by researchers. Genome-Wide Association (GWA) analyzes have recently become a popular approach to discovering the genetic causes of many complex diseases. These analyzes could lead to the discovery of genetic factors potentially involved in certain disease susceptibility. These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP). Indeed, many complex diseases have been revealed to be associated with combinations of SNP interactions. However, the identification of such interactions is considered difficult. Therefore, various unsupervised data mining methods are often developed in the literature to identify such variation involved in disease. In this work, a biclustering method is adopted to detect possible associations between SNP markers and disease susceptibility. It is an unsupervised classification technique, which plays an increasingly important role in the study of modern biology. We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association. An experimental study is achieved on synthetic data to evaluate the performance of the proposed algorithm. Promising results are obtained.
, Lilia Rejeb,Route planning for electric vehicles
IET Intell. Transp. Syst. 16, 875–889 (2022). https://doi.org/10.1049/itr2.12182, 2022
Abstract
This work addresses the multi-objective route planning and charging problem for Battery Electric Vehicles (BEVs). The proposed solution is based on the NSGA-II algorithm to derive the Pareto-optimal set of eco-routes with the minimum travel and charging time, distance, energy consumption and charging costs while complying with the constraints related to the battery State of Charge (SoC) and the status of charging stations. Influencing factors, such as, vehicle and battery parameters, weather conditions, driver behaviour, road grade, Time Of Use (TOU) electricity cost, and Charging Stations (CS) status and type, are taken into account. The final results of the proposed method are compared with the well-established simulator SUMO.
, Lilia Rejeb, , Yasmine Amor, , Lamjed Ben SaidOptimal electric vehicles route planning with traffic flow prediction and real-time traffic incidents
International Journal of Electrical and Computer Engineering Research, 2(1), 1–12. doi:10.53375/ijecer.2022.93, 2022
Abstract
Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.
