ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

The location selection of distribution centers is one of the important strategies to optimize the logistics system. To solve this problem, under certain environment, this paper presents a new multicriteria decision-making method based on ELECTRE I. The proposed method helps decision-makers to select the best location from a given set of locations for implementing. After having identified decision-makers, the criteria, and the set of locations, the factors influencing the selection are analyzed in order to identify the best location. A sensitivity analysis is then performed to determine the influence of criteria weights on the selection decision. The strength of the proposed method is to incorporate decision-makers' preferences into the decision-making process. In addition, the proposed method considers both quantitative and qualitative criteria. Finally, the selected solution is validated by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method

Multi-attribute and multi-actor decision making methods for solving the selection problem under certain/uncertain environment : case of distribution centers location

Le travail de recherche présenté dans cette thèse s’inscrit dans la continuité des travaux de l’aide à la décision multi-critère de groupe (décideurs), particulièrement dans le champ de sélection de la localisation des centres de distribution. Dans un environnement certain, si la décision de sélection de la localisation des centres de distribution a donné lieu à plusieurs travaux de recherche, elle n’a jamais été l’objet, à notre connaissance, d’une décision prise par plusieurs décideurs. À cet égard, le premier objectif de cette thèse est de proposer une méthode d’aide à la décision multi-attribut et multi-acteur (MAADM) pour résoudre le problème posé. Pour se faire, nous avons adapté et étendu la méthode ELECTRE I. Dans un environnement incertain, au vu de l’incertitude inhérente et l’imprécision du processus décisionnel humain ainsi que les comportements futurs du marché et des entreprises, le deuxième objectif de cette thèse est de développer une méthode floue d’aide à la décision multi-attribut et multi-acteur (FMAADM) pour traiter le problème en question. Pour cela, nous avons couplé la méthode MAADM avec la théorie des ensembles flous. Pour la validation des deux contributions, nous avons conçu un système d’aide à la décision (S-DSS) pour implémenter les algorithmes de la méthode MAADM et la méthode FMAADM. Sur la base du S-DSS, deux études expérimentales ont été menées. Nous avons, aussi, appliqué une analyse de sensibilité pour vérifier la sensibilité de la solution retenue vis-à-vis aux variations de poids des critères d’évaluation. Les résultats obtenus prouvent que les deux méthodes proposées répondent à l’objectif recherché et ainsi retenues pour la sélection de la meilleure localisation dans un contexte certain/incertain de multi-attribut et multi-acteur.The research work presented in this thesis is part of the works’ continuity on multi-criteria group (decision-makers) decision-making, particularly in the field of the distribution centers’ location selection. Under certain environment, although the decision to select the location of the distribution centers has given rise in several research works, it has never been the object, to our knowledge, of a decision taken by several decision makers. In this regard, the first objective of this thesis is to develop a multi-attribute and multi-actor decision-making method (MAADM) to resolve the posed problem. For this purpose, we have adapted and extended the ELECTRE I method. Under uncertain environment, In view of the inherent uncertainty and inaccuracy of human decision-making, the future behavior of the market and companies, the second objective of this thesis is to propose a fuzzy multi-attribute and multi-actor decision-making method (FMAADM) to treat the problem in question. To this end, we have coupled the MAADM method with the fuzzy set theory. To validate the two contributions, we designed a decision support system (S-DSS) to implement the MAADM method and the FMAADM method. Based on the S-DSS, two experimental studies were conducted. We also applied a sensitivity analysis to verify the sensitivity of the solution retained vis-a-vis to weights’ variations of evaluation criteria. The obtained results prove that the MAADM method and the FMAADM method meet the desired objective and thus retained for the selection of the best location under certain/uncertain context of multi-attribute and multi-actor

Méthodes d'aide à la décision multi-attribut et multi-acteur pour résoudre le problème de sélection dans un environnement certain/incertain : cas de la localisation des centres de distribution

The research work presented in this thesis is part of the works’ continuity on multi-criteria group (decision-makers) decision-making, particularly in the field of the distribution centers’ location selection. Under certain environment, although the decision to select the location of the distribution centers has given rise in several research works, it has never been the object, to our knowledge, of a decision taken by several decision makers. In this regard, the first objective of this thesis is to develop a multi-attribute and multi-actor decision-making method (MAADM) to resolve the posed problem. For this purpose, we have adapted and extended the ELECTRE I method. Under uncertain environment, In view of the inherent uncertainty and inaccuracy of human decision-making, the future behavior of the market and companies, the second objective of this thesis is to propose a fuzzy multi-attribute and multi-actor decision-making method (FMAADM) to treat the problem in question. To this end, we have coupled the MAADM method with the fuzzy set theory. To validate the two contributions, we designed a decision support system (S-DSS) to implement the MAADM method and the FMAADM method. Based on the S-DSS, two experimental studies were conducted. We also applied a sensitivity analysis to verify the sensitivity of the solution retained vis-a-vis to weights’ variations of evaluation criteria. The obtained results prove that the MAADM method and the FMAADM method meet the desired objective and thus retained for the selection of the best location under certain/uncertain context of multi-attribute and multi-actor.Le travail de recherche présenté dans cette thèse s’inscrit dans la continuité des travaux de l’aide à la décision multi-critère de groupe (décideurs), particulièrement dans le champ de sélection de la localisation des centres de distribution. Dans un environnement certain, si la décision de sélection de la localisation des centres de distribution a donné lieu à plusieurs travaux de recherche, elle n’a jamais été l’objet, à notre connaissance, d’une décision prise par plusieurs décideurs. À cet égard, le premier objectif de cette thèse est de proposer une méthode d’aide à la décision multi-attribut et multi-acteur (MAADM) pour résoudre le problème posé. Pour se faire, nous avons adapté et étendu la méthode ELECTRE I. Dans un environnement incertain, au vu de l’incertitude inhérente et l’imprécision du processus décisionnel humain ainsi que les comportements futurs du marché et des entreprises, le deuxième objectif de cette thèse est de développer une méthode floue d’aide à la décision multi-attribut et multi-acteur (FMAADM) pour traiter le problème en question. Pour cela, nous avons couplé la méthode MAADM avec la théorie des ensembles flous. Pour la validation des deux contributions, nous avons conçu un système d’aide à la décision (S-DSS) pour implémenter les algorithmes de la méthode MAADM et la méthode FMAADM. Sur la base du S-DSS, deux études expérimentales ont été menées. Nous avons, aussi, appliqué une analyse de sensibilité pour vérifier la sensibilité de la solution retenue vis-à-vis aux variations de poids des critères d’évaluation. Les résultats obtenus prouvent que les deux méthodes proposées répondent à l’objectif recherché et ainsi retenues pour la sélection de la meilleure localisation dans un contexte certain/incertain de multi-attribut et multi-acteur

ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

The location selection of distribution centers is one of the important strategies to optimize the logistics system. To solve this problem, under certain environment, this paper presents a new multicriteria decision-making method based on ELECTRE I. The proposed method helps decision-makers to select the best location from a given set of locations for implementing. After having identified decision-makers, the criteria, and the set of locations, the factors influencing the selection are analyzed in order to identify the best location. A sensitivity analysis is then performed to determine the influence of criteria weights on the selection decision. The strength of the proposed method is to incorporate decision-makers’ preferences into the decision-making process. In addition, the proposed method considers both quantitative and qualitative criteria. Finally, the selected solution is validated by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method

Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm

Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client’s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients’ preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms