The application of the principles of sustainability to the implementation of urban freight policies requires the estimation of all the costs and externalities involved. We focus here on the case of access time windows, which ban the access of freight vehicles to central urban areas in many European cities. Even though this measure seeks to reduce congestion and emissions in the most crowded periods of the day, it also imposes additional costs for carriers and results in higher emissions and energy consumption. We present here a mathematical model for the Vehicle Routing Problem with Access Time Windows, a variant of the VRP suitable for planning delivery routes in a city subject to this type of accessibility restriction. We use the model to find exact solutions to small problem instances based on a case study and then compare the performance over larger instances of a modified savings algorithm, a genetic algorithm, and a tabu search procedure, with the results showing no clear prevalence of any of them, but confirming the significance of those additional costs and externalities.
Abstract:Choice design building based on D-error minimization can be accomplished either by using predefined β values or by assuming probabilistic distributions for them. Several mathematical techniques have been used for both approaches in the past, resulting in algorithms that obtain efficient designs, which guarantee the high quality of the information that will be provided by the respondents. This paper proposes the use of a genetic algorithm for dealing with the problem of building designs with minimum D-error, describing the technique and applying it successfully to several benchmark cases. Design matrices, D-error values, percentages of level overlap and computation times are provided for each case.
Because of its mathematical and computational components, operations research (OR) is not simple to teach or to learn, despite its innumerable industry applications. However, advanced OR is included in many graduate degrees related to industrial engineering, where students need these techniques to solve complex optimization problems. Faced with the problem of teaching heuristic methods to master's students at the University of Seville, we developed a problem-based approach whereby instead of listening to lectures and taking exams on these techniques, one algorithmic technique is randomly assigned to each student, who must apply it to solving a certain optimization problem. Here we discuss our approach to putting our heuristics course into practice, the problems we faced, how we addressed those problems, the positive results obtained, and the lessons learned.
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