@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 {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 {Abouaomar2022,
	title = {Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach},
	journal = {IEEE Systems Journal},
	year = {2022},
	note = {cited By 0},
	pages = {1-12},
	abstract = {Multiaccess edge computing (MEC) and network virtualization technologies are important enablers for fifth generation networks to deliver diverse services. Services are often provided as fully connected virtual network functions (VNFs), through service function chaining (SFC). However, the problem of allocating SFC resources at the edge still faces many challenges related to the way VNFs are placed, chained, and scheduled. In this article, to solve these problems, we propose a game theory-based approach with the objective to reduce service latency in the context of SFC at the edge. The problem of allocating SFC resources can be divided into two subproblems: 1) the VNF placement and routing subproblem, and 2) the VNF scheduling subproblem. For the former subproblem, we formulate it as a mean-field game in which VNFs are contending over edge resources aiming at reducing the resource consumption of MEC nodes and reducing latency for users. We also propose a reinforcement learning-based technique, where the Ishikawa{\textendash}Mann learning algorithm is used. For the later subproblem, we formulate it as a matching game between VFNs and edge resources to find the execution order of the VNFs while reducing the latency. To efficiently solve it, we propose a modified version of the many-to-one deferred acceptance algorithm (DAA), called the enhanced multistep DAA. To illustrate the performance of the proposed approaches, we perform extensive simulations. The results show that the approaches achieve up to 40\% less resource consumption, and up to 38\% less latency than the benchmarked state-of-the-art methods. IEEE},
	keywords = {Computation theory, Computer games, Edge computing, Game, Game theory, Learning algorithms, Network function virtualization, Problem solving, Processor scheduling, Reinforcement learning, Resource allocation, Resource allocation/provisioning, Resource Management, Resources allocation, Routings, Scheduling, Service function chaining, Service functions, Transfer functions, Virtual reality, Virtualizations},
	doi = {10.1109/JSYST.2022.3171232},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131721006\&doi=10.1109\%2fJSYST.2022.3171232\&partnerID=40\&md5=e7ecad4d011f9a66bf06f1e9f3f4d176},
	author = {Abouaomar, A. and Cherkaoui, S. and Mlika, Z. and Kobbane, A.}
}
@conference {Fellir2020377,
	title = {A multi-Agent based model for task scheduling in cloud-fog computing platform},
	booktitle = {2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020},
	year = {2020},
	note = {cited By 9},
	pages = {377-382},
	abstract = {The Internet-of-Things (IoT) is a network of physical objects that communicate and exchange data between them over the Internet. These objects, or things, generate a massive amount of data that require powerful analytics approaches and data storage platforms - this is where cloud computing may well come into play. The classic cloud computing paradigm, however, faces several serious issues, due to centralization, such as low performance, latency issues, security issues, bandwidth obstacle, etc.. The Fog (Edge) computing paradigm has been proposed to improve the performance and to address other weaknesses, by providing IoT data processing and storage capabilities locally at the edges (IoT devices), instead of sending all the data to the centralized cloud. Whether in cloud or in fog computing, with billions of devices and a huge number of simultaneous requests, managing limited resources becomes essential. One of the important steps in resource management is task scheduling. In this paper, we consider task scheduling in a cloud-fog computing platform, we propose a multi-agent based model that aims at serving the most important task first, taking into consideration the task priority, its wait time, its status and the resources required to complete it successfully. Furthermore, in the scheduling process, we first propose an update to the priority value of the task, while taking into consideration its dependencies to other tasks and their priorities. Simulation results show that our proposed model can lead to better resource utilization and improve performance. {\textcopyright} 2020 IEEE.},
	keywords = {Autonomous agents, Computational methods, Computing paradigm, Computing platform, Data handling, Digital storage, Fog, Fog computing, Improve performance, Internet of thing (IOT), Internet of things, Multi agent systems, Multi-agent based modeling, Multitasking, Resource Management, Resource utilizations, Scheduling, Scheduling algorithms, Simulation platform, Storage capability},
	doi = {10.1109/ICIoT48696.2020.9089625},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085496418\&doi=10.1109\%2fICIoT48696.2020.9089625\&partnerID=40\&md5=4ee0e2ab76422fc76603ec3b6f6942cb},
	author = {Fellir, F. and El Attar, A. and Nafil, K. and Chung, L.}
}
@conference {Maha2020,
	title = {The Two Phases Method for operating rooms planning and scheduling},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 2},
	abstract = {Over the last three or four decades, there have been numerous optimization problems in Healthcare which have been approached by researchers. Hospital logistics which must be organized and structured in order to secure patient satisfaction in terms of quality, quantity, time, security and least cost, forms part of the quest for global performance.According to the literature review, the problem of operating rooms planning and scheduling involves different conflicting objectives while considering constraints on availability of rooms, patients and doctors. In this paper, we proposed the Two Phases Method (TPM), which is a general technique that is likely to solve multi-objective combinatorial optimization (MOCO) problems.As it is known, TPM has never been applied to solve operating room planning and scheduling problem. In this paper, we developed the TPM to resolve the cited issue, while focusing on optimizing both total completion time and patients{\textquoteright} waiting time. {\textcopyright} 2020 IEEE.},
	keywords = {Combinatorial Optimization, Conflicting objectives, Constraint satisfaction problems, Decision making, Hospital logistics, Industrial management, Literature reviews, Multiobjective combinatorial optimization, Operating rooms, Optimization problems, Patient satisfaction, Planning and scheduling, Scheduling, Total completion time},
	doi = {10.1109/ICTMOD49425.2020.9380584},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103741668\&doi=10.1109\%2fICTMOD49425.2020.9380584\&partnerID=40\&md5=a5086170077f1fe352eb34de4e820aac},
	author = {Maha, T. and Achchab, S. and Omar, S.}
}
@article {9954595520141215,
	title = {Solving a mixed integer linear program with times setup for the steel-continuous casting planning and scheduling problem.},
	journal = {International Journal of Production Research},
	volume = {52},
	number = {24},
	year = {2014},
	pages = {7276 - 7296},
	abstract = {In this paper, we present a novel model that generalise the steelmaking continuous casting planning and scheduling problem. The model includes three stages: converters namely CV, refining stands namely RS and continuous castings namely CC. The obtained problem can be formulated as a mathematical program with typical constraints, namely the times setup constraints. We develop a mixed integer linear program (MILP) to schedule several sequences of charges for more than two continuous castings devices. The aim is: (i) on one hand, to schedule a list of several sequences that are already ordered for each continuous casting with times setup between two successive sequences, (ii) on the other hand, to determine the optimal order of the successive sequences. We use a benchmark and randomly generated instances to test the model and solved by mean of the commercial software solverCplex v12.5. These problems span a variety of instances varying from small to large sized instances with different i},
	keywords = {Continuous casting, Linear programming, Mathematical models, Mathematical optimization, MILP, Mixed integer linear programming, optimisation, planning, Scheduling, setup, steel},
	issn = {00207543},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=99545955\&site=ehost-live},
	author = {Sbihi, Abdelkader and Bellabdaoui, Adil and Teghem, Jacques}
}
