The Vehicle Routing Problem has been around for more than 50 years and has been of major interest to the operations research community. The VRP pose a complex problem with major benefits for the industry. In every supply chain transportation occurs between customers and suppliers. In this thesis, we analyze the use of a multiple pheromone trial in using Ant Systems to solve the VRP. The goal is to find a reasonable solution for data environments of derivatives of the basic VRP. An adaptive object model approach is followed to allow for additional constraints and customizable cost functions. A parallel method is used to improve speed and traversing the solution space. The Ant System is applied to the local search operations as well as the data objects. The Tabu Search method is used in the local search part of the solution. The study succeeds in allowing for all of the key performance indicators, i.e. efficiency, effectiveness, alignment, agility and integration for an IT system, where the traditional research on a VRP algorithm only focuses on the first two. / Thesis (PhD)--University of Pretoria, 2010. / Industrial and Systems Engineering / unrestricted

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Objective of this thesis is optimalization of route for inspectory technician. There were described traveling Salesman problem, vehicle Routing problem and it's modifications. Problem was solved by this three heuristics: nearest neighbour algorithm, savings method and insert method.

The goal of the diploma thesis is to introduce and describe a heuristic method for finding a sub-optimal system of circuits in a generalized vehicle routing problem with time windows and time-dependent unit costs. Proposed method was built up on Clarke and Wright's savings method for the standard vehicle routing problem. Additionally, there has been described an algorithm for improving current solution via finding an optimal time harmonogram for a journey on each circuit within the found sub-optimal system of circuits.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2018-01-24T20:35:47Z No. of bitstreams: 1 Luigi Tavolaro Santini.pdf: 2357766 bytes, checksum: b70528f7db6bf88f1285744982eb4234 (MD5) / Made available in DSpace on 2018-01-24T20:35:47Z (GMT). No. of bitstreams: 1 Luigi Tavolaro Santini.pdf: 2357766 bytes, checksum: b70528f7db6bf88f1285744982eb4234 (MD5) Previous issue date: 2017-02-23 / The present work deals with the Capacitated Vehicle Routing Problem with Three-Dimensional Loading Constraints. This problem is difficult to solve exactly, still relatively little studied, but important in the logistics activities of movement, warehousing and transportation. This problem consists in minimizing the total traveled distance by a homogeneous fleet of vehicles that address the issue of deliveries of customer demands, in which these demands are composed of items that have three relevant spatial dimensions. The objective of the present work is to develop heuristic and metaheuristic algorithms to solve the problem in question. The algorithms are based on the Clarke & Wright and George & Robinson heuristics, and on the Iterated Local Search and Adaptive Large Neighborhood Search metaheuristics. In the proposed algorithm, the routing problem is firstly addressed by adapting the Clarke & Wright heuristic, creating routes that are used to verify the loading pattern, thus obtaining an initial solution. In the following, an extensive search in the solution neighborhood is applied with the Iterated Local Search metaheuristic. For the best results of this search, it is checked if the loading pattern is feasible using an adapted George & Robinson algorithm. If it is not feasible, the Adaptive Large Neighborhood Search metaheuristic is executed in an attempt to find a feasible solution to the loading problem. Instances from the literature are used to evaluate the efficiency of the developed methods. The results obtained for the routing problem individually were of paramount importance to ensure the effectiveness of the Iterated Local Search metaheuristic. For the loading problem individually, the tests were also satisfactory, allowing for several feasible loading patterns using the adapted George & Robinson algorithm and the Adaptive Large Neighborhood Search metaheuristic. The results obtained with the proposed algorithm for the integrated problem were also good, being very close to those in the literature and with computational time relatively lower. As perspectives for future research, it is intended to investigate more efficient ways of exploring the solution space of the integrated problem, as well as the use of other metaheuristics. / O presente trabalho trata do Problema de Roteamento de Veículos Capacitado com Restrições de Carregamento Tridimensional. Este é um problema de difícil solução exata, ainda relativamente pouco estudado, porém importante nas atividades logísticas de movimentação, armazenagem e transporte de produtos. Este problema consiste em minimizar a distância total percorrida por uma frota homogênea de veículos que supram a questão das entregas das demandas de clientes, em que tais demandas são compostas por itens que possuem três dimensões espaciais relevantes. O objetivo do presente trabalho consiste em desenvolver algoritmos heurísticos e meta-heurísticos para resolver o problema em questão. Os algoritmos são baseados nas heurísticas de Clarke & Wright e de George & Robinson, e nas meta-heurísticas Iterated Local Search e Adaptive Large Neighborhood Search. No algoritmo proposto, primeiro trata-se o problema de roteamento adaptando-se a heurística de Clarke & Wright, criando roteiros que são utilizados para a verificação do padrão de carregamento, tendo-se assim uma solução inicial. Em seguida, é aplicada uma busca extensiva na vizinhança com a meta-heurística Iterated Local Search. Para os melhores resultados desta busca, verifica-se se o padrão de carregamento é viável utilizando o algoritmo de George & Robinson adaptado. Nos casos em que não é viável, a meta-heurística Adaptive Large Neighborhood Search é executada na tentativa de se encontrar soluções viáveis para o problema de carregamento. Instâncias da literatura são utilizadas para avaliar a eficiência dos métodos desenvolvidos. Os resultados obtidos para o problema de roteamento separadamente foram de suma importância para assegurar a eficiência do meta-heurística Iterated Local Search. Para o problema de carregamento separadamente, os testes utilizando o algoritmo de George & Robinson adaptado e a meta-heurística Adaptive Large Neighborhood Search também foram satisfatórios, permitindo a obtenção de vários padrões de carregamento factíveis. Os resultados obtidos com o algoritmo proposto para o problema integrado também foram bons, sendo bastante próximos aos da literatura e com tempo computacional relativamente menor. Como perspectivas de pesquisas futuras, pretende-se estudar formas mais eficientes de se explorar o espaço de busca do problema integrado, bem como a utilização de outras meta-heurísticas.

This thesis deals with optimization problems with main focus on logistic Vehicle Routing Problem (VRP). In the first part term optimization is established and most important optimization problems are presented. Next section deals with methods, which are capable of solving those problems. Furthermore it is explored how to apply those methods to specific VRP, along with presenting some enhancement of those algorithms. This thesis also introduces learning method capable of using knowledge of previous solutions. At the end of the paper, experiments are performed to tune the parameters of used algorithms and to discuss benefit of suggested improvements.

This master's thesis deals with mathematical model building for routing problems and ways to solve them. There are discussed and implemented deterministic and heuristic approaches that are suitable to be utilized. A big effort is put into building of the mathematical model describing a real world problem from the field of waste management. Appropriate algorithms are developed and modified to solve a particular problem effectively. An original graphical environment is created to illustrate acquired results and perform testing computations.

Bike share system (BSS) has received increasing attention in research for its potential economic and environmental benefits. However, some research has pointed out the negative sustainability impacts of BSS from rebalancing activity, due to its greenhouse gas (GHG) emissions and additional vehicle travels. Additionally, bike and station manufacturing also bring considerable emissions to the system. Therefore, it is important to analyze the current rebalancing efficiency and sustainability of BSSs, and to assist the BSS operators in optimizing the BSS design. Existing studies lack tools to estimate the real-world rebalancing activities and vehicle usage for system sustainability evaluation and improvements. To address this gap, this research first proposed a framework to estimate rebalancing activities and applied a clustering-based method to estimate the rebalancing vehicle use. Applying the framework to the BSSs in Chicago, Boston, and Los Angeles, this study estimated the rebalancing operation and compared the rebalancing efficiencies among the three systems. The analysis results show that 1) only a small proportion of stations and bikes were involved in the daily rebalancing activities; 2) most rebalancing activities were operated during the daytime, while the overnight rebalancing was limited; 3) the system scale, trip demand, and station types are critical for the rebalancing efficiency; and 4) reducing the rebalancing activities at self-rebalance stations could help to improve the rebalancing efficiency and benefits system sustainability. Additionally, the sustainability performance (e.g., carbon emissions) of BSS is not only decided by the rebalance, but also the manufacturing of bikes and stations. It is important to consider all these factors when optimizing a BSS. The existing literature on system improvement for the BSSs lacks an integrated view, and a well-designed integrated model for current BSS improvement is needed. The second part of this thesis built a simulation-based optimization model and generated 2400 scenarios for evaluation. This model aims to minimize the expansion investment, rebalancing mileage, and maximize the system demand and service rate. A Weight Sum Model is applied to solve the multi-criteria decision analysis. The model results show that the best system improvement is to build a new station with a small capacity and initial bikes. The investment and location impacts are discussed to find the tradeoff among expansion strategies. A sensitivity analysis is conducted to evaluate how different weight combinations (refer to different preferences in decision making) impact the preferred station configuration (docks and bikes) and new station locations.

<p> Orbital Debris is a growing challenge for the Space Industry. The increasing density of derelict objects in high-value orbital regimes is resulting in more conjunction warnings and break-up events with cascading repercussions on active satellites and spacecraft. The recent rapid growth of the commercial space industry, in particular proliferated satellite constellations, has placed orbital debris remediation at the forefront of Space Industry efforts. The need to remove existing debris, combined with a growing demand for active satellite life extension services, has created an emerging market for space logistics, in particular spacecraft capable of rendezvous and docking, orbital refueling, debris deorbiting, or object relocation. This market has seen numerous companies emerge with multi-purpose on-orbit servicing platforms. This ecosystem poses technological, economical, and policy questions to decision-makers looking to acquire platforms or invest in technologies and requires a System-of-Systems approach to determine mission and system concepts of merit. An open-source modeling, analysis, and simulation software toolbox has been developed which enables rapid early-stage analysis and design of diverse fleets of on-orbit servicing platforms, with a specific emphasis on active debris removal applications. The toolbox provides fetching and processing of real-time orbital catalog data, clustering and scoring of high-value debris targets, flexible and efficient multi-vehicle multi-objective time-varying routing optimization, and fleet-level lifecycle cost estimation. The toolbox is applied to a diverse sample of promising commercial platforms to enable government decision-makers to make sound investment and acquisition decisions to support the development of ADR technologies, missions, and companies. </p>

In this thesis we consider three challenging vehicle routing problems representing specific aspects of complex real-world problems: (i) the vehicle routing problem with unit demands, (ii) the preemptive stacker crane problem and (iii) a multi-period vehicle and technician routing problem. For the vehicle routing problem with units demands we continue research on the exponential multi-insertion neighborhood, investigate its properties and propose heuristic solution methods utilizing the neighborhood. For the preemptive stacker crane problem we study structural properties and provide bounds on the benefits of preemption and the benefits of so-called explicit drop nodes that are used exclusively to facilitate preemption. We propose construction heuristics that improve on the state-of-the-art in computational time and solution quality. The multi-period vehicle and technician routing problem is the subject of the VeRoLog Solver Challenge 2019. We develop a solution method that adapts to the limited computational budget and the given instance parameters. In summary, this thesis contributes to the structural analysis of the considered problems and proposes efficient heuristic solution methods that are effective even on large-scale instances and under tight restrictions of the computational budget. The methods combine global and local search approaches and take the available computational budget into account to realize an adaptive best-effort allocation of the resources.