Ahmed Frikha

Sustainable electricity consumption is considered a pivotal element in the effective governance and growth of any institution. Accurate electricity demand forecasting is essential for strategic planning and decision making. However, due to the numerous existing forecasting approaches, many forecasters find it challenging to select the best model. Currently, there is no robust approach for selecting the best forecasting model when considering conflicting error measures. This paper proposes a novel methodology using a multicriteria decision making (MCDM) approach to determine the most appropriate forecasting model for electricity demand, considering various interdependent error measures. The Analytical Network Process (ANP) was applied to determine the weights of evaluation criteria, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to select the best forecasting model. The proposed methodology was tested and validated with a real case study in Tunisia using the opinions of experts and stakeholders. The results show that multiple regression and exponential smoothing are the best alternatives and outperformed the other models. Additionally, a sensitivity analysis is presented to test the robustness of the final ranking. This serves to assist decision makers to select the best forecasting model.

Objective: The aim of the study is limited to several points, the most important of which is to understand the new concepts related to artificial intelligence and their role in supporting and activating lean management and the extent of their contribution to the success and survival of modern industrial companies. Theoretical Framework: This topic presents the most important concepts and theories on which the research is based. [Artificial Intelligence and Lean] stands out, providing a solid foundation for understanding the context of the investigation. Method: The study population was represented by the factories affiliated with the General Company for Automotive and Equipment Manufacturing. The sample was selected randomly, based on the (simple random sampling) method, where 3 factories were selected (the car and specialized wheel production factory, the body and heavy equipment factory, and the battery factory) out of a total of 6, and the number was Study population (2736) The researcher decided to distribute (300) questionnaires to workers in the factories affiliated with the company, the study sample. (300) questionnaires were distributed to the research sample (10.96%) of them after reviewing previous studies and benefiting from them in the field of research. (289) were recovered at a rate of (96.3%), of which (9) were not suitable for analysis, so the net sample was 278 from the studied population, at a rate of (92.8%) from the research sample. Results and Discussion: The study showed a set of theoretical and field conclusions, the most important of which is that artificial intelligence has gained high acceptance among the staff of the automobile and equipment manufacturing company as a tool for improving administrative work and enhancing the characteristics of lean management. Research Implications: The practical and theoretical implications of this research are discussed, providing insight into how the findings can be applied or impact practices in the field of AI and lean management and these impacts may include companies affiliated with the Ministry of Industry and Minerals and the services they provide. Originality/Value: The importance of this study came from the scarcity of studies that attempted to identify and understand the nature of the relationship between variables (artificial intelligence and lean management), as well as the attempt of the current study to address a realistic problematic problem that directly affects the performance of workers in the general automotive and equipment industry.

This paper presents an innovative approach for supplier selection (SS) within the Tunisian dairy industry, with a specific focus on sustainability and resilience. The proposed method is a hybrid model that integrates multi-criteria decision-making (MCDM) with fuzzy logic. Initially, twelve distinct criteria are identified, categorized into social, environmental, economic, and resilience indexes through a combination of literature review and expert input. To establish the importance of these criteria, a fuzzy analytic hierarchy process (FAHP) is employed to calculate their weights. These weights are then integrated into a new method called fuzzy multi-attribute border approximation area comparison (FMABAC) to rank the suppliers. The methodology aims to provide decision-makers with a more intuitive means of expressing their preferences and evaluating supplier criteria, ultimately identifying the most effective dairy farmers. The research concludes by evaluating the results through two sensitivity analyses: one involving varying criteria weights and the other involving a comparative assessment with alternative fuzzy MCDM techniques.

In recent years, the evaluation of several energy sources is an extremely significant issue that affects socio-environmental development and techno-economic growth in different sectors. To tackle this concern, many researchers have concentrated on preferring desirable energy sources and adopting multi-criteria group decision-making (MCGDM) approaches for only a single type of user (e.g., agricultural, industrial, tourism, or domestic users). However, every energy user plays an important role in shaping energy policy. In fact, for sustainable energy, it is important to include all main energy users at the same time in energy decision-making. The main objective of this work was to propose a fuzzy MCGDM approach to evaluate and prioritize energy sources in Tunisia from various sectors’ point of views. Many criteria are combined, including the following: technical, economic, social, political and environmental. After applying the fuzzy Delphi method, the proposed approach consists of applying a fuzzy TOPSIS method as a multi-criteria approach. Finally, a sensitivity analysis is performed to evaluate the results of the proposed approach and to study how the optimal solution is affected by the objective function coefficients.

Multi-criteria decision aid methods consider decision problems in which many alternatives are evaluated on several criteria. These methods are used to deal with perfect information. However, in practice, it is obvious that this information requirement is too strict. In fact, the imperfect data provided by more or less reliable decision makers usually affect decision results, since any decision is closely linked to the quality and availability of information. In this paper, a PROMETHEE II-BELIEF approach is proposed to help multi-criteria decisions based on incomplete information. This approach solves problems with incomplete decision matrix and unknown weights within PROMETHEE II method. On the basis of belief function theory, our approach first determines the distributions of belief masses based on PROMETHEE II’s net flows, and then calculates weights. Subsequently, it aggregates the distribution masses associated with each criterion using Murphy’s modified combination rule in order to infer a global belief structure. The final alternative ranking is obtained via pignistic probability transformation. A case study of a real-world application concerning the location of a treatment center of waste from healthcare activities with infectious risk in the center of Tunisia is studied to illustrate the detailed process of the PROMETHEE II-BELIEF approach. Keywords: multiple criteria aid, incomplete information, PROMETHEE II method, belief function theory.

We treat a practical application of packing problem in supply chain management. This study was motivated by a real situation encountered in the storage problem of a foam industry in Tunisia. The storage problem consists in allocating, without overlapping, a given set of rectangular items (mattresses) to the minimum number of three dimensional bins with the different bins (rack) dimensions as input variables. Storage problem is a variable - sized bin packing problem that consists in finding an arrangement of set of items that cannot be rotated into the minimum number of variables bins. We develop a new heuristic method and we propose an integer programming model for the storage problem. The algorithm was applied in the case of unlimited number of bins. Numerical results on real-life test instances are reported.