@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 {Slimani20224243, title = {Automated machine learning: the new data science challenge}, journal = {International Journal of Electrical and Computer Engineering}, volume = {12}, number = {4}, year = {2022}, note = {cited By 0}, pages = {4243-4252}, abstract = {The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a {\textquotedblleft}data science approach{\textquotedblright} is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with {\textquotedblleft}machine learning{\textquotedblright} learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models is compared based on different metrics. {\textcopyright} 2022 Institute of Advanced Engineering and Science. All rights reserved.}, doi = {10.11591/ijece.v12i4.pp4243-4252}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129666214\&doi=10.11591\%2fijece.v12i4.pp4243-4252\&partnerID=40\&md5=1d5f93103617370f5a92a90901bfe815}, author = {Slimani, I. and Slimani, N. and Achchab, S. and Saber, M. and Farissi, I.E. and Sbiti, N. and Amghar, M.} } @article {Toub20221, title = {Operating room scheduling 2019 survey}, journal = {International Journal of Medical Engineering and Informatics}, volume = {14}, number = {1}, year = {2022}, note = {cited By 2}, pages = {1-30}, abstract = {Numerous optimisation problems in healthcare have been approached by researchers over the last three to four decades. Hospital logistics - organised and structured to secure patient satisfaction in terms of quality, quantity, time, security and least cost - forms part of the quest for global performance. We provide herein a review of recent study and applications of operations research in healthcare. In particular, we survey work on optimisation problems, focusing on the planning and scheduling of operating rooms. The latter is a highly strategic place within the hospital as it requires key medical competence and according to Macario (2008) surgical sector expenditure represents nearly a third of a hospital{\textquoteright}s budget. We analyse recent research on operating room planning and scheduling from 2008 to 2019; our evaluation is based on patient characteristics, performance measurement, the solution techniques used in the research and the applicability of the research to real life cases. The searches were based on PubMed, Web of Science, Science Direct and Google Scholar databases. Copyright {\textcopyright} 2022 Inderscience Enterprises Ltd.}, keywords = {adult, agricultural worker, Article, budget, clinical evaluation, computer assisted tomography, Computer simulation, cost effectiveness analysis, eutrophication, febrile neutropenia, female, genetic algorithm, health care cost, health care facility, health care system, hip replacement, hospital cost, hospitalization, human, intensive care unit, length of stay, Machine learning, male, mathematical model, operating room personnel, operation duration, Patient satisfaction, population size, stochastic model, system analysis, Time series analysis, total quality management, vaccination, work environment, workload}, doi = {10.1504/IJMEI.2022.119307}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120773204\&doi=10.1504\%2fIJMEI.2022.119307\&partnerID=40\&md5=a2ac2109f88463231d4e37b0a988843f}, author = {Toub, M. and Souissi, O. and Achchab, S.} } @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 {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 {Bousqaoui2019301, title = {Machine learning applications in supply chains: Long short-term memory for demand forecasting}, journal = {Lecture Notes in Networks and Systems}, volume = {49}, year = {2019}, note = {cited By 1}, pages = {301-317}, doi = {10.1007/978-3-319-97719-5_19}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063274139\&doi=10.1007\%2f978-3-319-97719-5_19\&partnerID=40\&md5=ac9a051fef5a767873929baf29b00612}, author = {Bousqaoui, H. and Achchab, S. and Tikito, K.} } @article {Bousqaoui2018626, title = {Information sharing as a coordination tool in supply chain using multi-agent system and neural networks}, journal = {Advances in Intelligent Systems and Computing}, volume = {745}, year = {2018}, pages = {626-632}, doi = {10.1007/978-3-319-77703-0_62}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045145521\&doi=10.1007\%2f978-3-319-77703-0_62\&partnerID=40\&md5=3a6c9dcdd6ba98d43e615abe474572b2}, author = {Bousqaoui, H. and Slimani, I. and Achchab, S.} } @conference {Bousqaoui20181, title = {Machine learning applications in supply chains: An emphasis on neural network applications}, booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017}, volume = {2018-January}, year = {2018}, pages = {1-7}, doi = {10.1109/CloudTech.2017.8284722}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046694532\&doi=10.1109\%2fCloudTech.2017.8284722\&partnerID=40\&md5=606fee01fc33846e4a9726b198e91983}, author = {Bousqaoui, H. and Achchab, S. and Tikito, K.} } @article {Achchab2017226, title = {A combination of regression techniques and cuckoo search algorithm for FOREX speculation}, journal = {Advances in Intelligent Systems and Computing}, volume = {569}, year = {2017}, note = {cited By 0}, pages = {226-235}, abstract = {This paper describes a hybrid model formed by a mixture of regression techniques and Cuckoo Search algorithm to speculate USD/EUR variations. Inspired by ARMA model we propose a dataset composed of historical data of USD/EUR and (JYN, EUR and BRP) variations. The dataset is used to train four regression algorithms: Multiple linear regression, Support vector regression, Partial Least Squares regression and CRT regression tree; the generated regression weights of these algorithms will be used as inputs to Cuckoo Search algorithm. The effectiveness of the proposed system against classical regression algorithms is confirmed by experiments on exchange rate prediction within the period from January 2014 to January 2016. {\textcopyright} Springer International Publishing AG 2017.}, doi = {10.1007/978-3-319-56535-4_23}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018486816\&doi=10.1007\%2f978-3-319-56535-4_23\&partnerID=40\&md5=8ec7dbda07a48707f78a6b186721c9bf}, author = {Achchab, S. and Bencharef, O. and Ouaarab, A.} } @article {Slimani2017144, title = {Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data}, journal = {International Journal of Logistics Systems and Management}, volume = {28}, number = {2}, year = {2017}, note = {cited By 0}, pages = {144-163}, abstract = {The purpose of any effective supply chain is to find balance between supply and demand by coordinating all internal and external processes in order to ensure delivery of the right product, to the right customer, at the best time and with the optimal cost. Therefore, the estimation of future demand is one of the crucial tasks for any organisation of the supply chain system who has to make the correct decision in the appropriate time to enhance its commercial competitiveness. In an earlier study, where various artificial neural networks{\textquoteright} structures are compared including perceptron, adaline, no-propagation, multi layer perceptron (MLP) and radial basis function for demand forecasting, the results indicate that the MLP structure present the best forecasts with the optimal error. Consequently, this paper focuses on realising a daily demand predicting system in a supermarket using MLP by adding inputs including previous demand, days{\textquoteright} classification and average demand quantities. {\textcopyright} Copyright 2017 Inderscience Enterprises Ltd.}, doi = {10.1504/IJLSM.2017.086345}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029227042\&doi=10.1504\%2fIJLSM.2017.086345\&partnerID=40\&md5=ccd93c50554b8b201bac46a1363f9953}, author = {Slimani, I. and El Farissi, I. and Achchab, S.} } @conference {ElFarissi2017122, title = {Coordination by sharing demand forecasts in a supply chain using game theoretic approach}, booktitle = {Colloquium in Information Science and Technology, CIST}, year = {2017}, note = {cited By 0}, pages = {122-127}, abstract = {Through the literature, authors give a considerable attention to game theory because of its wide range of applications in various fields including economics, political science, psychology or biology. The aim of this case study is to employ game theoretic approach to model information sharing as a coordination mechanism in a basic two-echelon supply chain composed of a single retailer and a single supplier. This paper is the sequel of previous works; where demand is forecasted based on historical data of a supermarket in Morocco using the Multi Layer Perceptron structure of the artificial neural networks. Nevertheless, this work focuses on the implementation of the obtained forecasting results in the studied system modeled as a game of two players with asymmetric and imperfect information, in order to find the equilibrium of the game that guaranties maximum payoff for both players. In fact, this is what game theory is all about. {\textcopyright} 2016 IEEE.}, doi = {10.1109/CIST.2016.7805028}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010210515\&doi=10.1109\%2fCIST.2016.7805028\&partnerID=40\&md5=79f1785352cd1586a09b76b45cdd93d1}, author = {El Farissi, I. and Slimani, I. and Achchab, S.} } @conference {Belhiah2015, title = {The impact of data accuracy on user-perceived business service{\textquoteright}s quality}, booktitle = {2015 10th Iberian Conference on Information Systems and Technologies, CISTI 2015}, year = {2015}, note = {cited By 0}, abstract = {As business processes have become increasingly automated, data quality becomes the limiting and penalizing factor in the business service{\textquoteright}s overall quality, and thus impacts customer satisfaction, whether it is an end-user, an institutional partner or a regulatory authority. The available research that is related to business services{\textquoteright} quality paid very little attention to the impact of poor data quality on good services delivery and customer satisfaction, and to the calculation of the optimal level of data quality. The aim of this paper is to present a customer-oriented approach that will help to understand and analyze how an organization business service{\textquoteright}s overall quality is linked to the quality of upstream business processes and of data objects in use. This paper also introduces a calculation framework that allows the identification of an optimal level of data quality - data accuracy dimension in the case of this paper - taking into account the business processes{\textquoteright} execution accuracy and data accuracy. {\textcopyright} 2015 AISTI.}, doi = {10.1109/CISTI.2015.7170445}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943339073\&doi=10.1109\%2fCISTI.2015.7170445\&partnerID=40\&md5=2f46571af96bd9f471d08ea67fc449f0}, author = {Belhiah, M. and Bounabat, B. and Achchab, S.} } @conference {Belhiah2015189, title = {Towards a context-aware framework for assessing and optimizing Data Quality projects}, booktitle = {DATA 2015 - 4th International Conference on Data Management Technologies and Applications, Proceedings}, year = {2015}, note = {cited By 0}, pages = {189-194}, abstract = {This paper presents an approach to clearly identify the opportunities for increased monetary and non-monetary benefits from improved Data Quality, within an Enterprise Architecture context. The aim is to measure, in a quantitative manner, how key business processes help to execute an organization{\textquoteright}s strategy, and then to qualify the benefits as well as the complexity of improving data, that are consumed and produced by these processes. These findings will allow to clearly identify data quality improvement projects, based on the latter{\textquoteright}s benefits to the organization and their costs of implementation. To facilitate the understanding of this approach, a Java EE Web application is developed and presented here.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964994813\&partnerID=40\&md5=64fb574b1d5d15cd346582c7a58cdd20}, author = {Belhiah, M. and Benqatla, M.S. and Bounabat, B. and Achchab, S.} } @conference {Slimani2014168, title = {Game theory to control logistic costs in a two-echelon supply chain}, booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014}, year = {2014}, note = {cited By 4}, pages = {168-170}, abstract = {As a mathematical tool of the decision maker, game theory is an essential methodology to analyze and solve situations where the decision of each rational agent, called player, affects the other agent{\textquoteright}s payoff. Indeed, in this work game theory is used to analyze an inventory and transportation optimization problem within a supply chain composed of two agents: a retailer who faces a random demand of a final product and his supplier of raw materials that is also responsible of the transportation function. Since the retailer is closer to the market and has a better view of demand forecasts careful attention given to Information sharing and its impacts. {\textcopyright} 2014 IEEE.}, doi = {10.1109/GOL.2014.6887435}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908632329\&doi=10.1109\%2fGOL.2014.6887435\&partnerID=40\&md5=059b1ee51ffe9bf41468fce7e9cd9ed5}, author = {Slimani, I. and Achchab, S.} } @conference {Tikito2013, title = {Combined optimization of shipping and storage costs in a multi-product and multi-level supply chain, under a stochastic demand}, booktitle = {2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013}, year = {2013}, note = {cited By 0}, abstract = {The increasing need for optimality in the presence of uncertainty motivates the development and application of Model Predictive Control. In this paper we apply the Stochastic Model Predictive Control to optimize the cost of storage and transport for a multi-product and a multi-level supply chain under a stochastic demand. We use the dynamic programming to find the control policies resolving the problem. {\textcopyright} 2013 IEEE.}, doi = {10.1109/ICMSAO.2013.6552666}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881436213\&doi=10.1109\%2fICMSAO.2013.6552666\&partnerID=40\&md5=488c37ee005c9d8928f4d1d35738cbe3}, author = {Tikito, K. and Achchab, S. and Benadada, Y.} }