Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time\ud windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make\ud that such solutions are not optimal with respect to minimizing the\ud total duty time. Furthermore, VRPTW solutions do not account for\ud complex driving hours regulations, which severely restrict the daily\ud travel time available for a truck driver. To deal with these problems,\ud we consider the vehicle departure time optimization (VDO) problem\ud as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they\ud account for temporal traffic congestions by modeling time-dependent\ud travel times. For the latter, we assume a piecewise constant speed\ud function. Computational experiments show that problem instances\ud of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can\ud be achieved. Finally, the results show that ignoring time-dependent\ud travel times and driving hours regulations during the development of\ud vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions

A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem

This work presents the Vehicle Routing with Time Windows and Loading Problem (VRTWLP) as a multi-objective optimization problem, implemented within a Genetic Algorithm. Specifically, the three dimensions of the problem to be optimized – the number of vehicles, the total travel distance and volume utilization – are considered to be separated dimensions of a multi-objective space. The quality of the solution obtained using this approach is evaluated and compared with results of other heuristic approaches previously developed by the author. The most significant contribution of this work is our interpretation of VRTWLP as a Multi-objective Optimization Problem

Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo

Introduction— Vehicle routing scheduling with service compliance is a necessity for logistics companies in search of their competitive advantage. Objective— The objective of the following work is to determine the routing of vehicles with time windows for a homogeneous fleet applied to the last, mile distribution case with 300 clients, considering the minimization of operating costs, distribution costs and, downtime costs. Methodology— The problem is approached through the approach of a mixed-integer linear programming mathematical model, and the development of an algorithm through the use of the savings method and the use of the swap operator. Results— In the construction phase, the savings algorithm achieves an initial cost focused on the minimum distance. In the upgrade phase, the swap operator improves the initially established solution, very quickly. For a case of 300 clients, 12 iterations were carried out, obtaining an improvement of 71.41% over the initial cost. Conclusions— For calculations of VRPTW cases with 300 nodes, the swap operator achieves computational times of less than 30 seconds.Introducción— La programación de ruteo de vehículos con cumplimiento de servicio es una necesidad de las empresas de logística en busca de su ventaja competitiva. Objetivo— El objetivo del siguiente trabajo es determinar el ruteo de vehículos con ventanas de tiempo para una flota homogénea aplicado a un caso de distribución última milla con 300 clientes, considerando la minimización de los costos operativos, costos de distribución y costos de tiempos de inactividad. Métodología— Se aborda el problema a través del planteamiento de un modelo matemático de programación lineal entera mixta, y el desarrollo de un algoritmo mediante uso del método de ahorros y el uso del operador swap. Resultados— En la fase de construcción, el algoritmo de ahorros logra un costo inicial enfocado en la distancia mínima. En la fase de mejoramiento, el operador swap mejora la solución inicial establecida, de forma muy rápida. Para un caso de 300 clientes, se realizaron 12 iteraciones obteniendo una mejora del 71.41% sobre el costo inicial. Conclusiones— Para cálculos de casos de VRPTW con 300 nodos, el operador swap consigue tiempos computacionales menores a 30 segundos

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. In practice, however, temporary traffic congestion make such solutions non-optimal with respect to minimizing the total duty time. Furthermore, the VRPTW does not account for driving hours regulations, which restrict the available travel time for truck drivers. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing of a VRPTW. We propose an ILP formulation that minimizes the total duty time. The results of a case study indicate that duty time reductions of 15% can be achieved. Furthermore, computational experiments on VRPTW benchmarks indicate that ignoring traffic congestion or driving hours regulations leads to practically infeasible solutions. Therefore, new vehicle routing methods should be developed that account for these common restrictions. We propose an integrated approach based on classical insertion heuristic

An Efficient Extension of Network Simplex Algorithm

In this paper, an efficient extension of network simplex algorithm is presented. In static scheduling problem, where there is no change in situation, the challenge is that the large problems can be solved in a short time. In this paper, the Static Scheduling problem of Automated Guided Vehicles in container terminal is solved by Network Simplex Algorithm (NSA) and NSA+, which extended the standard NSA. The algorithms are based on graph model and their performances are at least 100 times faster than traditional simplex algorithm for Linear Programs. Many random data are generated and fed to the model for 50 vehicles. We compared results of NSA and NSA+ for the static automated vehicle scheduling problem. The results show that NSA+ is significantly more efficient than NSA. It is found that, in practice, NSA and NSA+ take polynomial time to solve problems in this application

Distributing Tourists Among POIs with an Adaptive Trip Recommendation System

Traveling is part of many people leisure activities and an increasing fraction of the economy comes from the tourism. Given a destination, the information about the different attractions, or points of interest (POIs), can be found on many sources. Among these attractions, finding the ones that could be of interest for a specific user represents a challenging task. Travel recommendation systems deal with this type of problems. Most of the solution in the literature does not take into account the impact of the suggestions on the level of crowding of POIs. This paper considers the trip planning problem focusing on user balancing among the different POIs. To this aim, we consider the effects of the previous recommendations, as well as estimates based on historical data, while devising a new recommendation. The problem is formulated as a multi-objective optimization problem, and a recommendation engine has been designed and implemented for exploring the solution space in near real-time, through a distributed version of the Simulated Annealing approach. We test our solution using a real dataset of users visiting the POIs of a touristic city, and we show that we are able to provide high quality recommendations, yet maintaining the attractions not overcrowded

Adaptive Trip Recommendation System

Travel recommendation systems provide suggestions to the users based on di erent information, such as user preferences, needs, or constraints. The recommendation may also take into account some characteristics of the points of interest (POIs) to be visited, such as the opening hours, or the peak hours. Although a number of studies have been proposed on the topic, most of them tailor the recommendation considering the user viewpoint, without evaluating the impact of the suggestions on the system as a whole. This may lead to oscillatory dynamics, where the choices made by the recommendation system generate new peak hours. This paper considers the trip planning problem that takes into account the balancing of users among the di erent POIs. To this aim, we consider the estimate of the level of crowding at POIs, including both the historical data and the e ects of the recommendation. We formulate the problem as a multi- objective optimization problem, and we design a recommendation engine that explores the solution space in near real-time, through a distributed version of the Simulated Annealing approach. Through an experimental evaluation on a real dataset of users visiting the POIs of a touristic city, we show that our solution is able to provide high quality recommendations, yet maintaining the attractions not overcrowded