OPTIMIZING THE PROCESS OF PICK-UP AND DELIVERY WITH TIME WINDOWS USING ANT COLONY AND TABU SEARCH ALGORITHMS

The provision of goods shuttle services sometimes faces several constraints, such as the limitation on the number of vehicles, vehicle capacity, and service time, or the vehicle used has single transport access. To avoid losses, a strategy is needed in determining the optimal route and policy for arranging goods in the vehicle especially if there are two types of goods involved. Traveling Salesman Problem and Pick-up and Delivery with Handling Costs and Time Windows (TSPPDHTW) is a model of an optimization problem that aims to minimize the total travel and goods handling costs in the goods pick-up and delivery with the constraints previously mentioned. Solving that model using the exact method requires a very long computation time so it’s not effective to be implemented in real-life. This study aims to develop a (meta)heuristic based on Ant Colony Optimization (ACO) and Tabu Search (TS) to be ACOTS to solve TSPPDHTW with reasonable computation time. The development is carried out by adding functions of clustering, evaluating constraints, cutting tours, arranging of goods, and evaluating moves on the TS, as well as modifying transition rules. The result has a deviation of about 22% and 99.99% less computational time than the exact method

A Multiple Ant Colony Metaheuristic for the Air Refueling Tanker Assignment Problem

The performance of the Nuclear Facility (NFAC) incident module in modeling a nuclear reactor accident is evaluated. Fallout predictions are compared with air concentration measurements of I-131 in Europe over a five-day period. Two categories of source term specifications are used: NFAC-generated source terms based on plant conditions and accident severity, and user-defined source terms based on specifying the release of I-131. The Atmospheric Transport Model Evaluation Study report source term provided the needed detailed release information. The Air Force Combat Climatology Center provided weather data covering Europe during the release\u27s 11-day duration. For the NFAC-generated source terms as few as 20% and as many as 52% of the values are within the intended accuracy, depending on which source term specification was selected. For the user-defined source terms, values ranged 35% to 56% being within the intended accuracy, again depending on which source term specification was used. Performance improved in all cases for monitoring sites closest to Chernobyl, with up to 87% of the values falling within the intended accuracy. This indicates there may be a limit for selecting the spatial domain, making HPAC more useful as a tool for smaller spatial domains, rather than on a continental scale

Waste Collection Vehicle Routing Problem: Literature Review

Waste generation is an issue which has caused wide public concern in modern societies, not only for the quantitative rise of the amount of waste generated, but also for the increasing complexity of some products and components. Waste collection is a highly relevant activity in the reverse logistics system and how to collect waste in an efficient way is an area that needs to be improved. This paper analyzes the major contribution about Waste Collection Vehicle Routing Problem (WCVRP) in literature. Based on a classification of waste collection (residential, commercial and industrial), firstly the key findings for these three types of waste collection are presented. Therefore, according to the model (Node Routing Problems and Arc Routing problems) used to represent WCVRP, different methods and techniques are analyzed in this paper to solve WCVRP. This paper attempts to serve as a roadmap of research literature produced in the field of WCVRP

A Tabu search metaheuristic algorithm for the multiple depot vehicle routing problem with Time Windows

The problems encountered by courier companies in directing their eets along road networks to visit customers, which are geographically distributed, are common problems which are encountered frequently. These problems are by no means isolated to courier companies. Any set of vehicles which is involved in delivery, collection or a combination of both delivery and collection encounter a variation of the vehicle routing problem (VRP). Several variations of the vehicle routing problem exist. The algorithmic solutions to the individual variants of the vehicle routing problem seek to optimise the routes assigned to a eet of vehicles in visiting an array of nodes (which represent points of delivery or collection or from another perspective, a set of customers). The optimal solution of a VRP instance is the shortest, quickest or cheapest set of routes assigned to a eet of vehicles which satis es all customer demand without contravening any of the instance-speci c constraints. The vehicle routing problem has been identi ed as an non-determinant polynomial-time (NP) hard problem. This classi cation gives an indication of the computational complexity of the problem. Problems of this class require an inordinate amount of time to be solved to optimality for large problem instances. To overcome such obstacles, heuristic and metaheuristic search algorithms are often utilised to arrive at near-optimal or satisfactory solutions in less time.Thesis (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2012

Revisiting the Evolution and Application of Assignment Problem: A Brief Overview

The assignment problem (AP) is incredibly challenging that can model many real-life problems. This paper provides a limited review of the recent developments that have appeared in the literature, meaning of assignment problem as well as solving techniques and will provide a review on   a lot of research studies on different types of assignment problem taking place in present day real life situation in order to capture the variations in different types of assignment techniques. Keywords: Assignment problem, Quadratic Assignment, Vehicle Routing, Exact Algorithm, Bound, Heuristic etc

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

An Efficient Improvement Of Ant Colony System Algorithm For Handling Capacity Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is considered as one of the most famous specialized forms of VRP that has attracted considerable attention from researchers. This problem belongs to complex combinatorial optimization problems included in the NP-Hard Problem category, which is a problem that needs difficult computation. This paper presents an improvement of Ant Colony System (ACS) to solve this problem. In this study, the problem deals with a few vehicles which are used for transporting products to specific places. Each vehicle starts from a main location at different times every day. The capacitated vehicle routing problem (CVRP) is defined to serve a group of delivery customers with known demands. The proposed study seeks to find the best solution of CVRP by using improvement ACS with the accompanying targets: (1) To decrease the distance as long distances negatively affect the course of the process since it consumes a great time to visit all customers. (2) To implement the improvement of ACS algorithm on new data from the database of CVRP. Through the implementation of the proposed algorithm better results were obtained from the results of other methods and the results were compared

Optimizing routes using the vehicle routing problem

With the emergence of e-commerce and its significant growth during the COVID-19 pandemic, more people aim to ship their products as fast as possible. More organizations such as DHL and USPS invest vast amounts of money in minimizing transportation costs while finding the shortest route that the delivery man should take to reach different destinations. My Work Project will evolve around studying the Vehicle Routing Problem that uses different algorithms to find the shortest route depending on various constraints. This project will include four parts: a literature review expressing an overview of the vehicle routing issue; then a state of the art of algorithm to understand the meaning behind the main algorithms. Finally, build a code (coding platform) using the algorithms discussed previously to solve a situation for the traveling salesman person and VRP

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

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