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.

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.

This paper provides a review on recent efforts and development in urban distribution centers' location selection's problem in two categories including uncertain environment and certain environment problems and their solution methods. Also, it provides an overview on various objectives and criteria used. While there are a few papers related to this topic, we have not seen any comprehensive review papers that can cover it. We believe this paper can be used as a complementary and updated version.

The location selection of distribution centers is one of important strategies to optimize the logistics system. To solve this problem, this paper presents a new multi-actor multi-attribute decision-making method based on ELECTRE I. The proposed method helps decision-makers to select a preferred location from a given set of locations for implementing. The strength of the proposed method is to incorporate the preferences of a set of decision-makers into account, notably the role of their experience into the decision-making process, consider both quantitative and qualitative criteria, take into account both desirable directions (Min and Max) and validate the selected location by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method.