@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.}
}
@article {Filali20222174,
	title = {Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services},
	journal = {IEEE Transactions on Network Science and Engineering},
	volume = {9},
	number = {4},
	year = {2022},
	note = {cited By 3},
	pages = {2174-2187},
	abstract = {Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions. {\textcopyright} 2013 IEEE.},
	keywords = {5G mobile communication systems, Deep learning, EMBB, Heuristic algorithms, Heuristics algorithm, Learning algorithms, Low-latency communication, Markov processes, Multi agent systems, Network slicing, Optimisations, Optimization, Quality of service, Quality-of-service, Radio access networks, Reinforcement learning, Resource allocation, Resource Management, Software agents, Software defined networking, Software-defined networkings, Time measurement, Ultra reliable low latency communication, URLLC},
	doi = {10.1109/TNSE.2022.3157274},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126273319\&doi=10.1109\%2fTNSE.2022.3157274\&partnerID=40\&md5=44d6a41c3d7b8b6d8c4d6918856f36c5},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Toub2022,
	title = {Operating rooms scheduling using Variable Neighborhood Search meta-heuristic},
	booktitle = {2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
	year = {2022},
	note = {cited By 1},
	abstract = {Healthcare systems around the world are faced with rising costs, increasingly complex illnesses and declining reimbursements. In this context, problems related to health optimization are complex, because they concern the fabrication of schedules that absorb the disturbances occurring in the future. The operating room is a complex environment, at risk for the safety of the patient, and which involves many stakeholders. Its organization must be based on a reflection around the management of flows to contribute to the improvement of the patient{\textquoteright}s journey. In this paper, we propose a smart operating rooms scheduling using Variable Neighborhood Search (VNS) meta-heuristic. According to the literature review, VNS meta-heuristic approach still underused to resolve the cited issue. In this work, we have developed two VNS basic schemes of (Variable Neighborhood Descent (VND) and General VNS (GVNS)) in order to show their efficiency to meet operating rooms planning and scheduling problem challenges. {\textcopyright} 2022 IEEE.},
	keywords = {Complex environments, Healthcare systems, Heuristic algorithms, Heuristic methods, Literature reviews, Metaheuristic, Operating rooms, Operating rooms scheduling, Operating theatre, Operational research, Optimisations, Optimization, Scheduling, Surgerie, Variable neighborhood search},
	doi = {10.1109/IRASET52964.2022.9738371},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127982211\&doi=10.1109\%2fIRASET52964.2022.9738371\&partnerID=40\&md5=a5119109d2fa9362b7b5f3b9ba37aa99},
	author = {Toub, M. and Achchab, S. and Souissi, O.}
}
@article {9434372520130201,
	title = {Ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles.},
	journal = {INFOR},
	volume = {51},
	number = {1},
	year = {2013},
	pages = {31 - 40},
	abstract = {This article describes a heuristic method based on an ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles. Test results on different problem instances are presented and compared with those obtained by CPLEX and by a previous constructive heuristic. [ABSTRACT FROM AUTHOR]},
	keywords = {Ant algorithms, ant colony algorithm, Comparative studies, Computer networks, Heuristic algorithms, Information technology, Keywords Distribution, products in large quantities, Vehicle routing problem},
	issn = {03155986},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=94343725\&site=ehost-live},
	author = {Benslimane, Mohammed Taha and Benadada, Youssef}
}
@article {9434372220130201,
	title = {Buffer dimensioning and control stations positioning with a space partitioning-tabu search approach.},
	journal = {INFOR},
	volume = {51},
	number = {1},
	year = {2013},
	pages = {15 - 22},
	abstract = {We propose an efficient heuristic method based on Space Partitioning (SP) and Tabu Search (TS) to solve the buffer sizing problem in unreliable production lines with several inspection stations. In such problem, we have an unreliable production line consisting of a certain number of machines and fixed-size buffers. These machines produce a single part with two different quality levels: conforming and non-conforming parts. The production line may contain inspection stations whose job is to reject the non-conforming parts from the line. The production line must meet a constant rate of demand for the conforming finished parts. The objective is to minimize the average long term combined storage and shortage costs, while also specifying the optimal location of inspection stations. This design problem is a difficult mixed integer nonlinear program. Solving even a small instance of 10 machines and one inspection station using a direct dynamic programming method takes hours. If we especially},
	keywords = {Buffer storage (Computer science), Combinatorial Optimization, Dynamic programming, Heuristic algorithms, Inspection, Machine theory, Meta-heuristics, Production lines, Quality, Tabu search algorithm, Topology},
	issn = {03155986},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=94343722\&site=ehost-live},
	author = {Ouzineb, Mohamed and Mhada, Fatima and El Hallaoui, Issmail}
}
@article {9467097620130101,
	title = {Efficient Heuristic Based on Clustering Approach for OLSR.},
	journal = {Journal of Computer Networks \& Communications},
	year = {2013},
	pages = {1 - 7},
	abstract = {Due to the dynamic nature of mobile ad hoc network (MANET), the quality of service (QoS) requires several improvements. The present paper comes within the framework of research to optimize QoS in MANET. In this paper, we propose a novel version of OLSR based on the clustering approach which is inspired from Lin and Chu heuristic and adapted to be implemented in OLSR. We studied its stability and we compared its performances to those of standard OLSR. The metrics we used in evaluating network performances were average end-to-end delay, control routing overhead, and packet delivery ratio. Experimental results show that our alternative significantly reduces the traffic reserved to monitoring the network, which positively influences other performances such as throughput, delay, and loss. [ABSTRACT FROM AUTHOR]},
	keywords = {Ad hoc networks (Computer networks), Document clustering, Heuristic algorithms, Internet traffic, Performance evaluation, Quality of service, Routing (Computer network management)},
	issn = {20907141},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=94670976\&site=ehost-live},
	author = {Choukri, Ali and Habbani, Ahmed and Koutbi, Mohamed El.}
}
