Sensitive Ants in Solving the Generalized Vehicle Routing Problem

The idea of sensitivity in ant colony systems has been exploited in hybrid ant-based models with promising results for many combinatorial optimization problems. Heterogeneity is induced in the ant population by endowing individual ants with a certain level of sensitivity to the pheromone trail. The variable pheromone sensitivity within the same population of ants can potentially intensify the search while in the same time inducing diversity for the exploration of the environment. The performance of sensitive ant models is investigated for solving the generalized vehicle routing problem. Numerical results and comparisons are discussed and analysed with a focus on emphasizing any particular aspects and potential benefits related to hybrid ant-based models

Optimizing the Replication of Multi-Quality Web Applications Using ACO and WoLF

This thesis presents the adaptation of Ant Colony Optimization to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied and replica update loads are minimized. The algorithm proposed, AntDA, for solving this problem is novel in several respects: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Experiments show that AntDA outperforms several other solution methods, but there was room for improvement in the convergence rates of the ants. Therefore, in an attempt to achieve the goals of faster convergence and better solution values for larger problems, AntDA was combined with the variable-step policy hill-climbing algorithm called Win or Learn Fast (WoLF). In experimentation, the addition of this learning algorithm in AntDA provided for faster convergence while outperforming other solution methods

Vehicle routing problem considering reconnaissance and transportation

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 문일경.Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This thesis proposes vehicle routing problem considering reconnaissance and transportation (VRPCRT) for troop movements in wartime. VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements. For this thesis, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed and computational experiments were conducted to compare the performance of the ACO algorithm and that of the mixed-integer programming model. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.Abstract iii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Research Motivation and Contribution 4 1.2 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Review of pickup and delivery problem 6 2.2 Review of ant colony optimization algorithms 9 Chapter 3 Mathematical model 10 3.1 Problem description 10 3.2 The model formulation 14 3.3 Numerical example 17 Chapter 4 Ant colony optimization algorithm 20 4.1 Construction of a solution 21 4.2 Pheromone updating 23 Chapter 5 Computational experiment 26 5.1 Experiment 1 26 5.2 Experiment 2 29 Chapter 6 Conclusion 34 5.1 Findings 34 5.2 Future direction 35 Bibliography 36 국문초록 40 감사의 글 41Maste

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Metaheuristics for the Vehicle Routing Problem with Loading Constraints

We consider a combination of the capacitated vehicle routing problem and a class of additional loading constraints involving a parallel machine scheduling problem. The work is motivated by a real-world transportation problem occurring to a wood-products retailer, which delivers its products to a number of customers in a specific region. We solve the problem by means of two different metaheuristics algorithms: a Tabu Search and an Ant Colony Optimization. Extensive computational results are given for both algorithms, on instances derived from the vehicle routing literature and on real-world instances

An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

The vehicle routing problem (VRP) is a well-known NP-Hard problemin operation research which has drawn enormous interest from many researchers duringthe last decades because of its vital role in planning of distribution systems andlogistics. This article presents a modified version of the elite ant system (EAS) algorithmcalled HEAS for solving the VRP. The new version mixed with insert and swapalgorithms utilizes an effective criterion for escaping from the local optimum points.In contrast to the classical EAS, the proposed algorithm uses only a global updatingwhich will increase pheromone on the edges of the best (i.e. the shortest) route andwill at the same time decrease the amount of pheromone on the edges of the worst(i.e. the longest) route. The proposed algorithm was tested using fourteen instancesavailable from the literature and their results were compared with other well-knownmeta-heuristic algorithms. Results show that the suggested approach is quite effectiveas it provides solutions which are competitive with the best known algorithms in theliterature

Solving Combinatorial Optimization Problems Using Genetic Algorithms and Ant Colony Optimization

This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems. Ant colony optimization for the split delivery vehicle routing problem An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution time. In some instances, the ACO method achieves the best known results to date for the benchmark problems. Hybrid genetic algorithm for the split delivery vehicle routing problem (SDVRP) The Vehicle Routing Problem (VRP) is a combinatory optimization problem in the field of transportation and logistics. There are various variants of VRP which have been developed of the years; one of which is the Split Delivery Vehicle Routing Problem (SDVRP). The SDVRP allows customers to be assigned to multiple routes. A hybrid genetic algorithm comprising a combination of ant colony optimization, genetic algorithm, and heuristics is proposed and tested on benchmark SDVRP test problems. Genetic algorithm approach to solve the hospital physician scheduling problem Emergency departments have repeating 24-hour cycles of non-stationary Poisson arrivals and high levels of service time variation. The problem is to find a shift schedule that considers queuing effects and minimizes average patient waiting time and maximizes physicians’ shift preference subject to constraints on shift start times, shift durations and total physician hours available per day. An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed. The approach is tested on real world datasets for physician schedules