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

Application of an Open Source Spreadsheet Solver in Single Depot Routing Problem

The VRP has been broadly developed with additional feature such as deliveries, selective pickups time windows. This paper presents the application of an open source spreadsheet solver in single depot routing problem. This study focuses on Fast Moving Consumer Goods (FMCG) Company as a case study. The objective of this research is to minimize the distance travel. This research begins by collecting data from a respective FMCG Company. An FMCG company based in Jakarta, Indonesia provides drinking water packaged in the gallon. This FMCG Company has two distributions characteristic. Head office distribution was used in this case study due to highest internally rejected by the company such as un-routed order, no visit, not enough time to visit and transportation issue. Based on computational results, overall solutions to delivered 214 gallons to 26 customers having total distance traveled 56.76 km, total driving time 2 hour and 49 minutes, the total driver working time 7 hours and 57 minutes. Total savings of distances traveled between current route and the proposed solutions using open source spreadsheet solver is 7.25 km. As a result, by using open source spreadsheet solver in single depot routing problem can be implemented in FMCG Company

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

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

A simheuristic algorithm for time-dependent waste collection management with stochastic travel times

A major operational task in city logistics is related to waste collection. Due to large problem sizes and numerous constraints, the optimization of real-life waste collection problems on a daily basis requires the use of metaheuristic solving frameworks to generate near-optimal collection routes in low computation times. This paper presents a simheuristic algorithm for the time-dependent waste collection problem with stochastic travel times. By combining Monte Carlo simulation with a biased randomized iterated local search metaheuristic, time-varying and stochastic travel speeds between different network nodes are accounted for. The algorithm is tested using real instances in a medium-sized city in Spain

Metaheuristics for The Solution of Dynamic Vehicle Routing Problem With Time Windows (DVRPTW) With Travel Time Variable

This research is focusing on the development of metaheuristic algorithm to solve Dynamic Vehicle Routing Problem With Time Windows (DVRPTW) for the service provider of Inter-city Courier. The algorithm is divided into two stages which is static stage and dynamic stage. In the static stage, modified Ant Colony System is developed and in the dynamic stage, Insertion Heuristic is developed. In DVRPTW, vehicle’s routes are raised dynamically based on real time information, for example the reception of new order. To test the performances of the developed metaheuristic algorithm, the author compares the developed algorithm with the nearest neighbor algorithm and with the combination between the nearest neighbor and insertion heuristics algorithm. Experiment is done using Chen’s standard data test. The developed metaheuristic algorithm was applied on the network data of the roads in Surabaya, where the routes generated not only determine the order that the consumer must visit but also determine the routes that must be passed. After the experiment, the author conclude that the developed algorithm generates a better travel time total than the nearest neighbor and the combination between the nearest neighbor and insertion heuristics and can also generate route dynamically to respond to the new order well

The stochastic vehicle routing problem : a literature review, part II : solution methods

Building on the work of Gendreau et al. (Oper Res 44(3):469–477, 1996), and complementing the first part of this survey, we review the solution methods used for the past 20 years in the scientific literature on stochastic vehicle routing problems (SVRP). We describe the methods and indicate how they are used when dealing with stochastic vehicle routing problems. Keywords: vehicle routing (VRP), stochastic programmingm, SVRPpublishedVersio

Thirty years of heterogeneous vehicle routing

It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems

Meta-RaPS Hybridization with Machine Learning Algorithms

This dissertation focuses on advancing the Metaheuristic for Randomized Priority Search algorithm, known as Meta-RaPS, by integrating it with machine learning algorithms. Introducing a new metaheuristic algorithm starts with demonstrating its performance. This is accomplished by using the new algorithm to solve various combinatorial optimization problems in their basic form. The next stage focuses on advancing the new algorithm by strengthening its relatively weaker characteristics. In the third traditional stage, the algorithms are exercised in solving more complex optimization problems. In the case of effective algorithms, the second and third stages can occur in parallel as researchers are eager to employ good algorithms to solve complex problems. The third stage can inadvertently strengthen the original algorithm. The simplicity and effectiveness Meta-RaPS enjoys places it in both second and third research stages concurrently. This dissertation explores strengthening Meta-RaPS by incorporating memory and learning features. The major conceptual frameworks that guided this work are the Adaptive Memory Programming framework (or AMP) and the metaheuristic hybridization taxonomy. The concepts from both frameworks are followed when identifying useful information that Meta-RaPS can collect during execution. Hybridizing Meta-RaPS with machine learning algorithms helped in transforming the collected information into knowledge. The learning concepts selected are supervised and unsupervised learning. The algorithms selected to achieve both types of learning are the Inductive Decision Tree (supervised learning) and Association Rules (unsupervised learning). The objective behind hybridizing Meta-RaPS with an Inductive Decision Tree algorithm is to perform online control for Meta-RaPS\u27 parameters. This Inductive Decision Tree algorithm is used to find favorable parameter values using knowledge gained from previous Meta-RaPS iterations. The values selected are used in future Meta-RaPS iterations. The objective behind hybridizing Meta-RaPS with an Association Rules algorithm is to identify patterns associated with good solutions. These patterns are considered knowledge and are inherited as starting points for in future Meta-RaPS iteration. The performance of the hybrid Meta-RaPS algorithms is demonstrated by solving the capacitated Vehicle Routing Problem with and without time windows