Roya Soltani is an Assistant Professor at the Department of industrial engineering, Khatam University, Tehran, Iran. She received her Ph.D. degree in Industrial Engineering from Iran University of Science and Technology, Tehran, Iran. Her current research interests include Operations Research and Management Science, Optimization under Uncertainty, Robust and Stochastic Optimization, Reliability Optimization, Logistics Network Design, Heuristic and Meta-heuristic applications.
Shahla Paslar is an Assistant Professor at the Department of industrial engineering, Islamic Azad University Bandar Abbas branch (IAUBA), Bandar Abbas, Iran, since 2016. She received the Ph.D. degree in industrial engineering from the University Putra Malaysia, Selangor, Malaysia, in 2014. She currently teaches the course on integer programming and facility location problem in the M.S. program in IAUBA, and combinatorial optimization problem in the Ph.D. program in Islamic Azad University Qeshm Branch, Qeshm, Iran. Her current research interests include combinatorial optimization problem, and the application of metaheuristic algorithms to real-world problems.
Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find high-quality solutions to combinatorial optimization problems. Through this workshop, first metaheuristics and their building blocks are introduced so that
the scholars and researchers will be able to learn the main concepts relevant for the design and implementation of metaheuristics for practical problems such as logistic and supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and production, timetabling, sports scheduling, facility location and layout, network design, power generation, finance, marketing, among others.
Finally, the essential steps for implementing a metaheusristic algorithm is described to solve a real world problem.
Souhail Dhouib is a Full Professor at the University of Sfax, Tunisia. His teaching and research interests are related to the areas of Decision Science, Computer Science and Management Science. He is the inventor of Dhouib-Matrix concept which gathers several optimization methods: heuristics (Dhouib-Matrix-TSP1, Dhouib-Matrix-AP1, Dhouib-Matrix-TP1, ... etc.) and metaheuristics (Far-to-Near, Dhouib-Matrix-3, Dhouib-Matrix-4, ... etc.). He is former vice president of Tunisian Operational Research Society.
Taicir Moalla Loukil has received her State doctorate from the Faculty of Economics and Management of Sfax, Tunisia in 2001. She is the President of The Tunisian Operational Research Society. She worked as a chief of the department development and studies at the ‘’Office des ports Aériens de Tunisie’’ prior to joining the University of Sfax. Her research activities include decision aid, combinatorial optimization, multicriteria optimization, and scheduling and logistics problems. She acted as a guest editor of a special issue on ‘’Developments in Multiple Objective Programming and Goal Programming’’ at International Transactions in Operations Research (ITOR). She has authored or co-authored more than 50 scientific papers published in specialized reviews and book chapters. She has supervised 20 PhD thesis and more than 30 master thesis.
Generally, the approximation methods (Heuristics and Metaheuristics) provide a solution for complex problems in a polynomial computational time. These methods are basically developed for certain environment with crisp data. However, in real live data of industrial problems are generally presented under fuzzy, intuitionistic or neutrosophic environments.
In this workshop we will discuss about the enhancement of existent optimization methods for combinatorial problems (Scheduling Problem, Knapsack Problem, Shortest Path Problem, Transportation Problem, etc.) or continuous problems (Engineering Design Problem, etc.) under fuzzy, intuitionistic or neutrosophic domains. Real world application with step by step explication will be highly appreciated.
Svetlana Rastvortseva is a professor at the World Economy Department, Higher School of Economics. She received her Doctoral Dissertation on topic Management of Social and Economic Effectiveness of Regional Development at St. Petersburg State University of Economics and Finance. Her major research interests are Regional Economics, Economic Geography, Agglomeration economy, Innovation, Economic Growth, Path-dependence, International Business. She has published in top scientific journals and served as keynote speaker in a variety of international conferences. Her research portfolio includes also several research grants that she led with success in Russia. Svetlana is a chief editor of Journal of regional and international competitiveness and a member of the editorial board in such journals as City governance: theory and practice, Russian Journal of Industrial Economics, Strategizing: Theory and Practice and University proceedings. Volga region. Social sciences. Her teaching activities consists in a wide range of undergraduate and graduate courses on Regional Economics, Strategic Aspects in the Regional Economy, International Business, International Strategic Management, Competition and Competitiveness in International Business.
The positive impact of information and communication technologies (ICT) on economic development and growth in a country or region is beyond doubt. However, when conducting empirical tests based on different countries and regions of the world, this relationship looks ambiguous. The study uses two approaches, static and dynamic, represented by the Cobb-Douglas production function and the neoclassical growth model. The static approach assesses how the main components of ICT (fixed telephone subscriptions per 100 inhabitants, mobile cellular subscription per 100 inhabitants, fixed broadband subscription per 100 inhabitants, internet users as percentage of population) affect economic development (GDP or GRP per capita in the current period of time). The dynamic approach shows this impact on the rate of economic growth, i.e. in the long run. The object of the research is (1) 27 countries of the European Union during the period from 1960 to 2020, (2) 83 regions of Russia during the period from 2000 to 2019, but not before a particular technology implementation. The analysis has shown that ICTs have a permanent positive impact on economic development (both in EU countries and in Russia). At the same time, the impact of different types of technologies on economic growth is observed predominantly in the early stages of development and has a lag shift.
Safa Bhar Layeb is a professor of industrial engineering and a member of the OASIS Lab at the National Engineering School of Tunis, Tunisia. She is Polytechnic Engineer and has obtained her Master’s in Mathematical Engineering, PhD in Applied Mathematics, and HDR in Industrial Engineering. She is the founding chair of the African Working Group in Health Systems, affiliated with the African Federation of Operational Research Societies (AFROS). She is particularly interested in data science and industrial engineering approaches and their applications in network design, logistics and healthcare.
Marwa Hasni is Assistant professor in Industrial Engineering at ISSIG Gabes – University of Gabes. She holds a PhD in Industrial Engineering from Ecole Nationale d'Ingénieurs deTunis - University of Tunis El Manar and member of the Laboratory of Economics and Applied Finance (LEFA) since 2021. She also holds a diploma of Engineer in Industrial Engineering and Logistics of the National Engineering School of Carthage-University of Carthage. Her research focuses on the development and the analysis of forecasting techniques applied to production systems and to financial and banking systems. The interest being non-parametric sampling techniques and those associated with the Machine learning and deep learning approaches. Marwa Hasni has published and reviewed a number of research studies on the side of several international journals namely: International Journal of Production Economics, International Journal of Production Research, International Journal of Decision Sciences, Risk and Management, Managerial and Decision Economics, International Journal of Economics, and Strategic Management of Business Processes.
Machine learning is the science of programming machines to perform human tasks without being explicitly programmed to Email spam recognition, spelling checkers, and platform video recommenders are commonly encountered machine learning applications that we are exploring in our everyday life. In this workshop, two learning objectives are targeted. First, acquire practice implementation of machine learning algorithms using Python. Second, give key criteria to help to select adequate machine learning algorithm given a particular case study.
For the purpose of the first objective, a comprehensive review of algorithms covering major machine learning models is provided. Afterwards, specified labs are animated using python. We propose the Simple Linear regression, the Multiple Linear regression and the Logistic regression to deal with the regression models. The Decision Tree, the Random Forest and the Naïve Bayes for classification models; and the K-means, the Nearest Neighbors (NN) and the Support Vector Machine (SVM) for the clustering ones.
To accomplish the second objective, we will introduce some popular use cases of Machine Learning and go through Machine Learning interview questions to assess practical market expectations.
Sadia Samar Ali (Ph. D. in Operations Research ) is an Associate Professor with Department of Industrial Engineering, Faculty of Engineering ,King Abdulaziz University, Jeddah, Saudi Arabia . She has extensive experience in engineering and management teaching, training, research, mentor and specializes in Sustainable practices in Supply Chain Management, Technology and Innovation related to developing countries by using Optimization and Quantitative Analysis. She is associated with EURO and IFORS groups as part of the working team in promoting ' Sustainable and Optimization practices' . She offers courses related to supply chain management & logistics, industrial quality control, probability distribution, optimization, design of experiments, and decision making. She is associated with EURO and IFORS groups as part of the working team in promoting ' Sustainable and Optimization practices' and ' Smart Technologies practices of developing countries’. She has authored many research articles, papers, reports, and books in ISI, SCI/SSCI, SCOPUS and SCIRUS indexed journals such as Journal of Cleaner Production, International Journal of Production Research , International Journal of Production Economics, Annals of Operations Research, IEEE Transactions on Engineering Management, Central European Journal of Operations Research, International Journal of Quality & Reliability Management , Mathematics , Benchmarking: An International Journal , Optimization : A Journal of Mathematical Programming and Operations Research . Her recent titles for the book contributed are ‘Best Practices of Green Supply Chain Management: A Developing Countries Perspectives; Emerald Global Publications’ and ‘Logistics 4.0: Digital Transformation of Supply Chain Management’ CRC Press| Taylor & Francis Group. She has been a keynote speaker at international conferences , editorial board member and guest editor for the topics based on data analytics, optimization and industrial engineering.
The present study focusses on evaluating the impact green practices on Low carbon performance which affects the Sustainable manufacturing and societies. So, the author proposed a theoretical model to evaluate the proposed hypotheses for the given study. To test, the theoretical model, a survey was conducted using the modified Dillman’s approach. The data was collected from the manufacturer and 380 useable responses were obtained. Using the collected data, the measurement model was tested in the PLS-SEM package. The validity and reliability ensured the data is appropriate and can be used for further analysis. From, the structural model analysis, the authors found that proposed hypotheses are found to significant and support the theoretical model. However, in order to check the robustness of the proposed model, Machine Learning (ML) classifiers were used to test the hypotheses proposed in the study. Different ML classifiers were used and found ANN classifiers is apt for the study. Further it was found that hypotheses were valid with overall accuracy of 92.68% with an error value of 0.25. Moreover, the distinctness of the Regulatory Framework (RF) and LCP (Low Carbon Performance) were not widely observed. Therefore, a post hoc analysis was conducted to check the usability of the RF and LCP using the Item Response Theory (IRT) and found that RF and LCP are appropriate for the study. The present study is unique in terms of testing the theoretical model with different ML classifiers. Further, scale validation was carried out with the IRT to validate the efficacy of the proposed model.