Journal of Investment and Management 2015; 4(1): 1-8 Published online July 1, 2015 (http://www.sciencepublishinggroup.com/j/jim) doi: 10.11648/j.jim.20150401.11 ISSN: 2328-7713 (Print); ISSN: 2328-7721 (Online) New Procedure for Assigning Drivers to Work Schedules at a Container Terminal Khaled Mili 1 , Ilhem Elghoul 2 1 Department of Economics and Quantitative Methods and Computer, Institute of Companies Administration of Gafsa, Gafsa, Tunisia 2 Department of Computer Science, Mediterranean Institute of Nabeul, Nabeul, Tunisia Email address: Khaled.mili.1@ualaval.ca (K. Mili), ilhem.el.ghoul@gmail.com (I. Elghoul) To cite this article: Khaled Mili, Ilhem Elghoul. New Procedure for Assigning Drivers to Work Schedules at a Container Terminal. Journal of Investment and Management. Vol. 4, No. 1, 2015, pp. 1-8. doi: 10.11648/j.jim.20150401.11 Abstract: One of the success factors of a terminal is related to the time in port for the retrieval and transport of containers. Straddle carriers (SCs) are the pivotal axis around which the terminal transportation system evolves and the success or failure of that process is an indicator of the reliability of the container terminal. Over the last years, the deficiency of efficient control and coordination mechanisms in practice produced a relaxation of transportation principles. The valorization of the academic environment represents nowadays one of the most important research challenges. In this paper, we present a collaborative filtering recommender system able to manage the work schedule’s assignment to straddle carrier’s drivers in a container terminal and provide preliminary results on customer’s satisfaction. Keywords: Recommendation System, Collaborative Filtering, Straddle Carrier’s Assignment 1. Introduction The RADES container terminal in Tunisia, which is the leading Tunisian port for the movement of containers (Africa infrastructure Country Diagnostic 2009) well equipped with necessary modern facilities and academic experience, has opted to provide a Straddle carrier’s assignment recommendation system in order to reduce the malpractices that threaten the integrity of the transportation process and enter a new era in which growing number of e-transparency systems will be employed. The goal of this work is to explore all aspects of the transportation problem and employ an intelligent system that can be more accurate and provide better recommendations to the straddle carrier’s drivers. A work schedule for a straddle carrier driver is provided in the form of a sequence of container groups and a number of containers. The adopted strategy is based on a collaborative filtering mechanism by analyzing user’s behaviors. The remainder of this paper is arranged as follows: Section 2 gives the necessary background on collaborative filtering recommendation systems. Section 3 introduces the transportation management problem and the optimization model to solve it. In section 4, we give the details of the proposed implementation. Concluding remarks and future works are given in Section 5. 2. Background Recommendation systems help users find and select items of interest based on their explicit and implicit preferences, the needs of other users and the item attributes (S. S. Anand , B. Mobasher, 2005).Three parallel categories have emerged in the context of recommender systems: Collaborative filtering, content-based filtering and hybrid methods (J. Herlocker et all, 2004). Collaborative filtering is usually addressed under the assumption that if users rate items similarly or have similar tastes, they will rate other items similarly (M. Khaled, 2013; D. Jannach et all, 2011). Content-based filtering makes recommendations based solely on a user’s profile built up by analyzing the content of items that the user has previously evaluated and/or user’s profile and preferences (G. Adomavicius, A. Tuzhilin, 2005). Hybrid approaches combine aspects of both content-based and collaborative filtering (N. Belkin , W. Croft, 1992). From those categories, collaborative filtering is still considered as the most promising and efficient technologies in practice (Y. Blanco-Fernandez et all, 2011; G. Karypis, 2001)Collaborative filtering solutions usually employ user- item rating matrix to make predictions and recommendations, avoiding by this way the need of providing extensive