Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System

The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an ongoing manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristics, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.Comment: 10 pages, 2 figure

THE CHARACTERISTICS STUDY OF SOLVING VARIANTS OF VEHICLE ROUTING PROBLEM AND ITS APPLICATION ON DISTRIBUTION PROBLEM

Vehicle Routing Problem (VRP) is one of the most challenging problems in combinatorial optimization. Objective of VRP is to find minimum length route starts and ends in a depot. There are some additional constraints such as more than one depot, service time, time window, capacity of vehicle, and many more. These are cause of VRP variants. Vehicle Routing Problem with Time Windows (VRPTW) is a variant of VRP with some additional constrains, that are number of requests may not exceed the vehicle capacity, as well as travel time and service time may not exceed the time window. Multi Depot Vehicle Routing Problem (MDVRP) has number of depots serving all customers, a number of vehicles distributing goods to customers with a minimum distance of distribution route without exceeding the capacity of the vehicle. Many researches have presented algorithms to solve VRPTW and MDVRP. This article discusses solution characteristics of VRPTW and MDVRP algorithms, and their performance. VRPTW algorithms reviewed are Tabu Search, Clarke and Wright, Nearest Insertion Heuristics, Harmony Search, Simulated Annealing, and Improved Ant Colony System algorithm. Performance of MDVRP algorithms studied are Self-developed Algorithm, Upper Bound, Clarke and Wright, Ant Colony Optimization, and Genetic Algorithm. Each algorithm is studied on its performance, process, advantages, and disadvantages. This article presents example of distribution problem in VRPTW and MDVRP, based on characteristic of the real problem. A computer program created using Delphi is implemented for VRPTW and MDVRP, to solve distribution problem for any number of vehicles and customer locations. Keywords: VRPTW, MDVRP, Distribution proble

Vehicle Routing Problem with Time Window Constrain using KMeans Clustering to Obtain the Closest Customer

In this paper, the problem statement is solving the Vehicle Routing Problem (VRP) with Time Window constraint using the Ant Colony Algorithm with K-Means Clustering. In this problem, the vehicles must start at a common depot, pickup from various ware houses, deliver to the respective nodes within the time window provided by the customer and returns to depot. The objectives defined are to reduction in usage of number of vehicles, the total logistics cost and to reduce carbon emissions. The mathematical model described in this paper has considered multiple pickup and multiple delivery points. The proposed solution of this paper aims to provide better and more efficient solution while minimizing areas of conflict so as to provide the best output on a large scale in Vehicle Routing Problem, K-Means Clustering, Time Window constraint, Ant Colony Algorithm

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Dynamic vehicle routing with time windows in theory and practice

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon’s benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment

Scheduling Deliveries with Backhauls in Thailand's Cement Industry

In this study, the Truckload Delivery with Backhaul Scheduling Problem (TDBSP) is formulated and an Ant Colony Optimization methodology developed for a related problem, the Vehicle Routing Problem with Backhaul and Time Windows (VRPBTW), is adapted for its solution. The TDBSP differs from the VRPBTW in that shipments are in units of truckloads, multiple time windows in multiple days are available for delivery to customers, limited space for servicing customers is available and multiple visits to each customer may be required. The problem is motivated by a real-world application arising at a leading cement producer in Thailand. Experts at the cement production plant assign vehicles to cement customers and lignite mines based on manual computations and experience. This study provides mathematical and computational frameworks for modeling and solving this real-world application