@conference {Toub2022313,
	title = {Accelerated operating room scheduling using Lagrangian relaxation method and VNS meta-heuristic},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2022},
	note = {cited By 0},
	pages = {313-317},
	abstract = {Like any business that produces services, the hospital is part of a process of improving the quality of services provided to patients. As part of this, hospitals are faced with the daunting task of planning operating room patients with budget, time and personnel. Most of the scheduling problems are NP-hard, so researchers have favored the development of heuristics and meta-heuristics to the detriment of exact methods. In a context where high performance computers are in continuous improvement, it is once again interesting to explore exact methods. Here we focus on developing exact methods for solving the operating room planning and scheduling problem. Our contribution is to develop first an accelerated Integer Linear Program (ILP) using the Variable Neighborhood Search (VNS) meta-heuristic to optimize patient waiting time according to the priority of their surgeries. Afterwards, we expose a new lower bound obtained by optimizing the patient waiting time relaxed. The experimental results validated the performance of the accelerated ILP in comparison with the original ILP. Furthermore, we have shown that the Lagrangian relaxation of the original ILP produces a lower bound of good quality. {\textcopyright} 2022 ACM.},
	keywords = {Budget control, Healthcare, Heuristic algorithms, Heuristic methods, Integer linear programs, Integer programming, Lagrange multipliers, Lagrangian relaxations, Low bound, Meta-heuristic., Metaheuristic, Operating rooms, Operation research, Optimisations, Scheduling, Surgery, Variable neighborhood search},
	doi = {10.1145/3529836.3529928},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133432791\&doi=10.1145\%2f3529836.3529928\&partnerID=40\&md5=b98b5eb1fa5eebe63a438dcf238538d7},
	author = {Toub, M. and Achchab, S. and Souissi, O.}
}
@conference {Abbal2022,
	title = {Multi-level multi-capacitated facility location problem: formulation and resolution.},
	booktitle = {2022 IEEE 6th International Conference on Logistics Operations Management, GOL 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Facility location problem is one of the most problems studied since the 1950 years; many variants have been introduced in order to deal with real-world problems. In 2016, a new variant was developed to generalize the p-median problem, called {\textquoteright}budget constraint multi-capacitated location problem{\textquoteright} (BMCLP). This paper aims to generalize the BMCLP onto the multi-level, a problem that arises in many scopes as telecommunication, freight transportation, etc. Therefore, we proposed a mathematical formulation as mixed integer, called {\textquoteright}Multi-level multi-capacitated facility location problem{\textquoteright} (MLMCLP) and generated an artificial set of instances in order to validate the new formulation. Finally, we apply the Branch and Cut solving method using the CPLEX solver. The results obtained from the numerical study show the robustness of the proposed formulation. {\textcopyright} 2022 IEEE.},
	keywords = {Branch Cuts, Budget constraint, Budget control, Capacitated facility location problems, Capacitated location, Facilities locations, Freight transportation, Generalisation, Integer programming, Location, Location problems, Multi-capacity, Multilevels, Problem formulation},
	doi = {10.1109/GOL53975.2022.9820242},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135206286\&doi=10.1109\%2fGOL53975.2022.9820242\&partnerID=40\&md5=d5a9b0d02e9e1d7bfa52d2971ac50263},
	author = {Abbal, K. and El Amrani, M. and Benadada, Y.}
}
@article {Annouch2021607,
	title = {An Adaptive Genetic Algorithm for a New Variant of the Gas Cylinders Open Split Delivery and Pickup with Two-dimensional Loading Constraints},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {1},
	year = {2021},
	note = {cited By 1},
	pages = {607-619},
	abstract = {This paper studies a combination of two well-known problems in distribution logistics, which are the truck loading problem and the vehicle routing problem. In our context, a customer daily demand exceeds the truck capacity. As a result, the demand has to be split into several routes. In addition, it is required to assign customers to depots, which means that each customer is visited just once by any truck in the fleet. Moreover, we take into consideration a customer time windows. The studied problem can be defined as a Multi-depots open split delivery and pickup vehicle routing problem with two-dimensional loading constraints and time windows (2L-MD-OSPDTW). A mathematical formulation of the problem is proposed as a mixed-integer linear programming model. Then, a set of four class instances is used in a way that reflects the real-life case study. Furthermore, a genetic algorithm is proposed to solve a large scale dataset. Finally, preliminary results are reported and show that the MILP performs very well for small test instances while the genetic algorithm can be efficiently used to solve the problem for a widereaching test instances. {\textcopyright} 2021. All rights reserved.},
	keywords = {Adaptive genetic algorithms, Constraint programming, Fleet operations, Genetic algorithms, Integer programming, Large dataset, Loading constraints, Multi depots, Pickups, Routing algorithms, Sales, Split delivery, Split pickups, Test instances, Time windows, Trucks, Two-dimensional, Two-dimensional loading, Vehicle routing, Vehicle Routing Problems},
	doi = {10.14569/IJACSA.2021.0120170},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100379461\&doi=10.14569\%2fIJACSA.2021.0120170\&partnerID=40\&md5=7e2a6546b01de8c70b8cca5545b09a4e},
	author = {Annouch, A. and Bellabdaoui, A.}
}
@conference {Abouaomar2021273,
	title = {A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks},
	booktitle = {Proceedings - Conference on Local Computer Networks, LCN},
	volume = {2021-October},
	year = {2021},
	note = {cited By 4},
	pages = {273-280},
	abstract = {Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance. {\textcopyright} 2021 IEEE.},
	keywords = {Deep learning, Edge computing, Infotainment, Integer programming, Learning schemes, Markov processes, Multi agent systems, Multi-access edge computing, Multiaccess, Q-learning, Quality of service, Reinforcement learning, Reinforcement learning approach, Service migration, Vehicle mobility, Vehicular networks},
	doi = {10.1109/LCN52139.2021.9524882},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118466383\&doi=10.1109\%2fLCN52139.2021.9524882\&partnerID=40\&md5=8e35dfee04f347da3f757f57f5d575c8},
	author = {Abouaomar, A. and Mlika, Z. and Filali, A. and Cherkaoui, S. and Kobbane, A.}
}
@article {11387931120160401,
	title = {Efficient deployment quality analysis for intrusion detection in wireless sensor networks.},
	journal = {Wireless Networks (10220038)},
	volume = {22},
	number = {3},
	year = {2016},
	pages = {991 - 1006},
	abstract = {The intrusion detection in a Wireless Sensor Network is defined as a mechanism to monitor and detect any intruder in a sensing area. The sensor deployment quality is a critical issue since it reflects the cost and detection capability of a wireless sensor network. The quality of deterministic deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. In the intrusion detection application, it is necessary to define more precise measures of sensing range, transmission range, and node density that impact overall system performance. The major question is centred around the quality of intrusion detection in WSN, how we can guarantee that each point of the sensing area is covered by at least one sensor node, and what a sufficient condition to guarantee the network connectivity? In this paper, we propose an appropriate probabilistic model which provides the cover},
	keywords = {Graph theory, Integer programming, Intrusion detection probability, Intrusion detection systems (Computer security), Multiple access protocols (Computer network protocols), Network connectivity, Network coverage, Quality of service, Sensing range, Transmission range, Wireless sensor networks},
	issn = {10220038},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=113879311\&site=ehost-live},
	author = {Assad, Noureddine and Elbhiri, Brahim and Faqihi, Moulay and Ouadou, Mohamed and Aboutajdine, Driss}
}
