An ant colony algorithm for the time-independent and time-dependent vehicle routing problem with time windows

The Vehicle Routing Problem (VRP) determines a set of vehicle routes originating and terminating at a single depot such that all customers are visited exactly once and the total demand of the customers assigned to each route does not violate the capacity of the vehicle. The objective is to minimize the total distance traveled by all vehicles. An implicit primary objective is to use the least number of vehicles The Vehicle Routing Problem with Time Windows (VRPTW) is a variant of VRP in which lower and upper limits are imposed to the delivery time of each customer. The arrival at a customer outside the specified delivery times is either penalized (soft time windows) or strictly forbidden (hard time windows). In the time-dependent VRP, the travel times between the customers vary due to different traffic conditions in time intervals throughout the scheduling horizon beside different road types. In this thesis, both the time-independent and -dependent VRP with hard time windows are addressed. We tackle these problems using an Ant Colony Optimization approach. The performance of the proposed algorithm is tested on the well-known benchmark instances from the literature

Minimizing the carbon emissions on road networks

The models and algorithms developed for transportation planning, vehicle routing, path finding and the software that utilize them are usually based on distance and constant travel times between the relevant locations and aim at minimizing total distance or travel time . However, constant travel time assumption is not realistic on road networks as the traffic conditions may vary from morning/evening rush hours to off-peak noon/night hours, from the weekends to business days, even from one season to another. Thus, distance/time based optimization does not exactly reflect the real fuel consumptions, hence the actual costs; neither can they be used to accurately account for the greenhouse gas (GHG) emissions. A distance/constant time based optimization model may even yield an infeasible solution when time-windows exist or the route length is time limited. In this study, we first analyze the peculiar characteristics of the Greenest Path Problem (GPP) where the objective is to find the least GHG generating path from an origin to a destination on the road network. We then propose a fast heuristic method for determining the greenest path, by incorporating fuel consumption and GHG emission objectives. Finally, we integrate the proposed algorithm into the Green Vehicle Routing Problem that minimizes the GHG emissions rather than the total distance or travel time. The developed heuristic is benchmarked against the existing algorithms by using synthetic traffic data on a real road network to illustrate potential savings and sustainability benefits

New approaches for determining greenest paths and efficient vehicle routes on transportation networks

Road transportation has hazardous and threatening impacts on the environment. However, the traditional logistics models and approaches used in transportation planning have mainly focused on minimizing the internal costs and lack the environmental aspect. Therefore, new planning techniques and approaches are needed in road transport by explicitly accounting for these negative impacts. In this thesis, we address these issues by first concentrating on solution methods for the Greenest Path Problem (GPP) where fuel consumption and GHG emission objectives are incorporated to find the least GHG generating path, namely the greenest path, and propose a fast and effective heuristic. Taking the strong relation between the speed and the GHG emission into account, we also address the speed embedded minimum cost path problem in the most general case where the speed is also a decision variable as well as the departure time Within this context, we develop a new networkconsistent (which implies spatially and temporally consistent speeds) time-dependent speed and travel time layer generation scheme since real data is difficult to acquire. In the second part, we mainly focus on Vehicle Routing Problems (VRP). First, we propose an Ant Colony Optimization (ACO) approach for solving the Vehicle Routing Problem with Time Windows (VRPTW). Then, we adapt this method to solve the environment friendly VRP, namely the Green VRP, where the greenest paths between all customer pairs are used as input. Finally, we extend the ACO algorithm to a parallel matheuristic approach for solving a class of VRP variants

A parallel matheuristic for solving the vehicle routing problems

In this chapter, we present a matheuristic approach for solving the Vehicle Routing Problems (VRP). Our approach couples the Ant Colony Optimization (ACO) algorithm with solving the Set Partitioning (SP) formulation of the VRP. As the ACO algorithm, we use a rank-based ant system approach where an agent level- based parallelization is implemented. The interim solutions which correspond to single vehicle routes are collected in a solution pool. To prevent duplicate routes, we present an elimination rule based on an identification key that is used to differentiate the routes. After a pre-determined number of iterations, the routes accumulated in the solution pool are used to solve the SP formulation of the problem to find a complete optimal solution. Once the optimal solution is obtained it is fed back to ACO as an elite solution that can be used in the pheromone reinforcement procedure. Our experimental study using the well-known VRP with Time-Windows benchmark instances of Solomon shows that the proposed methodology provides promising results

An enhanced network-consistent travel speed generation scheme on time-dependent shortest path and routing problems

The time-dependent shortest path and vehicle routing literature depends on realistic and reasonable test data for demonstration and performance evaluation. Despite the advancements in GPS and tracking technologies there is still lack and inaccessibility of publicly available real-world road networks with time-dependent arc costs and speeds. Since most of the time-dependent travel time layer generation models proposed for vehicle routing problems (VRPs) are mainly developed for synthetic networks, they cannot capture some realistic features of the real road networks and cannot be used in time-dependent shortest path problems (TDSPPs). In this paper, we first exploit spatial and temporal behavior of travel times using real life road network and speed data, and discuss the cases where the existing methods in the literature are not applicable. Then, we propose an enhanced method that is best fitted for TDSPP and time-dependent VRP (TDVRP). The proposed method can be implemented on both synthetic and real road networks. Finally, we apply our method to generate realistic speed data on Istanbul road network and demonstrate the applicability in TDSPP and TDVRP