@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.}
}
@conference {Walid2021,
	title = {Modeling and optimization of anaerobic digestion: A review},
	booktitle = {E3S Web of Conferences},
	volume = {229},
	year = {2021},
	note = {cited By 2},
	abstract = {Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models. {\textcopyright} The Authors, published by EDP Sciences, 2021.},
	keywords = {Anaerobic digestion, Biogas production, Data-driven model, Machine learning models, Management techniques, Meta-heuristic techniques, Modeling and optimization, Monitoring parameters, Optimization, Renewable energies},
	doi = {10.1051/e3sconf/202122901022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100132508\&doi=10.1051\%2fe3sconf\%2f202122901022\&partnerID=40\&md5=5d2367868bbe9ebe8e867ca9622eda34},
	author = {Walid, F. and El Fkihi, S. and Benbrahim, H. and Tagemouati, H.}
}
@article {Abid20211463,
	title = {Simulation optimisation methods applied in reverse logistics: a systematic review},
	journal = {International Journal of Sustainable Engineering},
	volume = {14},
	number = {6},
	year = {2021},
	note = {cited By 0},
	pages = {1463-1483},
	abstract = {Reverse logistics has grown in importance with the increase in environmental sustainability issues. However, reverse logistics is often associated with complex and difficult uncertainty issues, the vagueness of information, and the numerous decision variables and constraints. To address these issues, researchers deployed a class of effective methods called simulation-based optimisation. The aim of this study is to perform a systematic review of the available reverse logistics literature, discussing reverse logistic and simulation-based optimisation, highlighting the research gap and setting future directions. This work identifies: (1) various research design and methodologies used in reverse logistics literature; (2) the most explored areas of reverse logistics and discusses the sector where the exploration can be redirected; (3) the various simulation-based optimisation (SO) methods; and (4) the most dominant SO methods for solving reverse logistics problems. This study will be useful to researchers and practitioners in finding new research opportunities in the field of reverse logistics. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Decision constraints, Decision variables, Environmental sustainability, Optimization, Optimization method, Reverse logistics, Simulation optimization method, Simulation-based optimizations, Sustainability issues, Sustainable development, Systematic Review, Uncertainty},
	doi = {10.1080/19397038.2021.2003470},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119952129\&doi=10.1080\%2f19397038.2021.2003470\&partnerID=40\&md5=dd9095deb5eaf7d2eb4fffd798e2e34d},
	author = {Abid, S. and Mhada, F.Z.}
}
@article {Zou2021,
	title = {Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer},
	journal = {Applied Energy},
	volume = {304},
	year = {2021},
	note = {cited By 4},
	abstract = {Wind energy is one of the most promising solutions to energy crisis and environmental pollution, so it is being developed rapidly. Wind turbine power curves (WTPCs) play an important role in wind energy assessment, turbine condition monitoring, and power grid dispatching. However, there are two challenges in WTPC modeling: model selection and parameter optimization. Many parametric and non-parametric models have been developed to characterize WTPCs, but none can always perform the best due to the complex wind regimes. In this paper, considering the simple structure and interpretability of model parameters, a set of parametric WTPC models is constructed to adapt to the variability of wind regimes, and the optimal candidate will be selected from the set according to their performances. As to the process of parameter optimization, a novel loss function, which considers the asymmetric error characteristic of WTPC modeling, is proposed, and a hybrid intelligent optimization method named GWO-BSA, which makes full use of the advantages of grey wolf optimizer and backtracking search algorithm, is designed. Finally, a novel WTPC modeling strategy, which combines the candidate model set, error characteristic-based loss function, and GWO-BSA, is proposed to obtain better power curves. Experimental results show that (1) GWO-BSA shows faster convergence speed and higher optimization accuracy than single optimization algorithms; (2) the proposed error characteristic-based loss function has better performance than the commonly used symmetric loss functions; and (3) compared with some popular artificial intelligence-based models, the designed WTPC modeling strategy produces better WTPCs under different wind regimes. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {alternative energy, Asymmetric error characteristic, Asymmetric errors, Condition monitoring, Curve fitting, Curve modeling, Electric load dispatching, Electric power transmission networks, energy efficiency, Energy policy, energy use, Error characteristic-based loss function, Error characteristics, Errors, hybrid, Hybrid optimization algorithm, Loss functions, Optimization, Power curves, power generation, Wind power, wind turbine, Wind turbine power, Wind turbine power curve modeling, Wind turbines},
	doi = {10.1016/j.apenergy.2021.117707},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114406958\&doi=10.1016\%2fj.apenergy.2021.117707\&partnerID=40\&md5=2247666b1228f85e03abc68add3c45d2},
	author = {Zou, R. and Yang, J. and Wang, Y. and Liu, F. and Essaaidi, M. and Srinivasan, D.}
}
@article {Yatribi2020,
	title = {Gradient-descent decoding of one-step majority-logic decodable codes},
	journal = {Physical Communication},
	volume = {39},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, a new low-complexity gradient-descent based iterative majority-logic decoder (GD-MLGD) is proposed for decoding One-Step Majority-Logic Decodable (OSMLD) codes. We give a formulation of the decoding problem of binary OSMLD codes, as a maximization problem of a derivable objective function. The optimization problem is solved using a pseudo gradient-descent algorithm, which performs iteratively an update towards the optimal estimated codeword been transmitted, based on the first-order partial derivatives of each variable calculated in the previous iteration. The proposed decoding scheme achieves a fast convergence to an optimum codeword compared to other decoding techniques reviewed in this paper, at the cost of lower computational complexity. The quantized version (QGD-MLGD) is also proposed in order to further reduce the computational complexity. Simulation results show that the proposed decoding algorithms outperform all the existing majority-logic decoding schemes, and also various gradient-descent based bit-flipping algorithms, and performs nearly close to the belief propagation sum{\textendash}product (BP-SP) decoding algorithm of LDPC codes, especially for high code lengths, providing an efficient trade-off between performance and decoding complexity. Moreover, the proposed quantized algorithm has shown to perform better than all the existing decoding techniques. The proposed decoding algorithms have shown to be suitable for ultra reliable, low latency and energy-constrained communication systems where both high performances and low-complexity are required. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {AWGN channel, Backpropagation, Computational complexity, Computer circuits, Decoding complexity, Difference sets, Economic and social effects, Electronic trading, Gradient descent, Gradient methods, Iterative decoding, LDPC codes, Majority logic, Majority logic decoding, Maximum likelihood, Maximum likelihood decoding, Optimization, OSMLD codes},
	doi = {10.1016/j.phycom.2019.100999},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077749482\&doi=10.1016\%2fj.phycom.2019.100999\&partnerID=40\&md5=830156cfb12a8ec3dbff2e002b3ade63},
	author = {Yatribi, A. and Belkasmi, M. and Ayoub, F.}
}
@article {Oudouar2020,
	title = {A novel approach based on heuristics and a neural network to solve a capacitated location routing problem},
	journal = {Simulation Modelling Practice and Theory},
	volume = {100},
	year = {2020},
	note = {cited By 12},
	abstract = {In this work, we describe a method to solve the capacitated location-routing problem (CLRP) to minimize the delivery distance for a vehicle. The CLRP consists of locating depots, assigning each customer to one depot, and determining routes. The objective is to minimize the cost (distance). In the locating problem, we use a self-organizing map (SOM) to determine the depots and assign customers to depots. The SOM is an unsupervised learning method with two layers and has proven effective in several research areas, such as clustering. In the routing problem, we use the Clarke and Wright technique to determine routes. In the present work, we propose an improvement of the capacitated self-organizing map (CSOM) to optimize the location of depots and the Or-Opt algorithm to ameliorate the routes obtained by Clarke and Write (CSOM\&CW). The numerical results show that the proposed method can meet many benchmarks of small and medium instances. Computational results assess the higher performance of our approach and demonstrate its efficiency in solving large-size instances. {\textcopyright} 2019 Elsevier B.V.},
	keywords = {Capacitated location, Clustering, Computational results, Conformal mapping, Its efficiencies, Location, Location routing problem, Machine learning, Network routing, Neural networks, Numerical methods, Numerical results, Optimization, Routing problems, Self organizing maps, Unsupervised learning, Unsupervised learning method},
	doi = {10.1016/j.simpat.2019.102064},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077516665\&doi=10.1016\%2fj.simpat.2019.102064\&partnerID=40\&md5=a650efb5ef7f1f150f0e4f6f9af68e91},
	author = {Oudouar, F. and Lazaar, M. and Miloud, Z.E.}
}
@article {OUDOUAR2020102064,
	title = {A novel approach based on heuristics and a neural network to solve a capacitated location routing problem},
	journal = {Simulation Modelling Practice and Theory},
	volume = {100},
	year = {2020},
	pages = {102064},
	abstract = {In this work, we describe a method to solve the capacitated location-routing problem (CLRP) to minimize the delivery distance for a vehicle. The CLRP consists of locating depots, assigning each customer to one depot, and determining routes. The objective is to minimize the cost (distance). In the locating problem, we use a self-organizing map (SOM) to determine the depots and assign customers to depots. The SOM is an unsupervised learning method with two layers and has proven effective in several research areas, such as clustering. In the routing problem, we use the Clarke and Wright technique to determine routes. In the present work, we propose an improvement of the capacitated self-organizing map (CSOM) to optimize the location of depots and the Or-Opt algorithm to ameliorate the routes obtained by Clarke and Write (CSOM\&CW). The numerical results show that the proposed method can meet many benchmarks of small and medium instances. Computational results assess the higher performance of our approach and demonstrate its efficiency in solving large-size instances.},
	keywords = {Clustering, Location routing problem, Neural network, Optimization, Self-organizing map, Unsupervised learning},
	issn = {1569-190X},
	doi = {https://doi.org/10.1016/j.simpat.2019.102064},
	url = {https://www.sciencedirect.com/science/article/pii/S1569190X19301959},
	author = {Fadoua Oudouar and Mohamed Lazaar and Zaoui El Miloud}
}
@article {Amine2020,
	title = {Two-sided matching framework for optimal user association in 5G multi-RAT UDNs},
	journal = {International Journal of Communication Systems},
	volume = {33},
	number = {8},
	year = {2020},
	note = {cited By 2},
	abstract = {Recently, academic and industrial research communities are paying more explicit attention to the 5G multiple radio access technology ultra-dense networks (5G multi-RAT UDNs) for boosting network capacity, especially in UD urban zones. To this aim, in this paper, we intend to tackle the user association problem in 5G multi-RAT UDNs. By considering the decoupled uplink/downlink access (DUDA), we divide our user association problem into two distinct subproblems representing, respectively, the uplink and the downlink channels. Next, we formulated each one as a nonlinear optimization problem with binary variables. Then, to solve them, we were restricted by the hard complexity, as well as the hard feasibility of centralized user association schemes. Thus, to resolve our user association problem in a reasonable time and distributed manner, we formulated each subproblem as a many-to-one matching game based on matching theory. Next, we provide two fully distributed association algorithms to compute the uplink and downlink stable matching among user equipments (UEs) and base stations (BSs). Simulation results corroborate our theoretical model and show the effectiveness and improvement of our achieved results in terms of the overall network performance, quality of service (QoS), and energy efficiency (EE) of UEs. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {5G mobile communication systems, 5G multi-RAT UDNs, Association algorithms, decoupled uplink/downlink access (DUDA), energy efficiency, Game theory, Industrial research, Matching game, Matching theory, Network performance, Nonlinear programming, Optimization, Quality of service, Rats, Stable matching},
	doi = {10.1002/dac.4346},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078931379\&doi=10.1002\%2fdac.4346\&partnerID=40\&md5=159a3a40021d6b340c2873e4db73e2ac},
	author = {Amine, M. and Kobbane, A. and Ben-Othman, J. and Walid, A.}
}
