Using Local Search with adaptive operator selection to solve the Progressive Party Problem

This paper investigates the use of adaptive operator selection in the context of Local Search to solve a constraint satisfaction problem, namely the Progressive Party Problem. Operators are selected according to a utility value which is computed, for each operator, from the solution quality and from the distance of the candidate solution to recently visited solutions in the search trajectory. We show that using several non-problem-specific operators gives comparable successful resolution rates to an algorithm customized for the problem, albeit with slower run times

Hybrid metaheuristics for solving multi-depot pickup and delivery problems

In today's logistics businesses, increasing petrol prices, fierce competition, dynamic business environments and volume volatility put pressure on logistics service providers (LSPs) or third party logistics providers (3PLs) to be efficient, differentiated, adaptive, and horizontally collaborative in order to survive and remain competitive. In this climate, efficient computerised-decision support tools play an essential role. Especially, for freight transportation, e efficiently solving a Pickup and Delivery Problem (PDP) and its variants by an optimisation engine is the core capability required in making operational planning and decisions. For PDPs, it is required to determine minimum-cost routes to serve a number of requests, each associated with paired pickup and delivery points. A robust solution method for solving PDPs is crucial to the success of implementing decision support tools, which are integrated with Geographic Information System (GIS) and Fleet Telematics so that the flexibility, agility, visibility and transparency are fulfilled. If these tools are effectively implemented, competitive advantage can be gained in the area of cost leadership and service differentiation. In this research, variants of PDPs, which multiple depots or providers are considered, are investigated. These are so called Multi-depot Pickup and Delivery Problems (MDPDPs). To increase geographical coverage, continue growth and encourage horizontal collaboration, efficiently solving the MDPDPs is vital to operational planning and its total costs. This research deals with designing optimisation algorithms for solving a variety of real-world applications. Mixed Integer Linear Programming (MILP) formulations of the MDPDPs are presented. Due to being NP-hard, the computational time for solving by exact methods becomes prohibitive. Several metaheuristics and hybrid metaheuristics are investigated in this thesis. The extensive computational experiments are carried out to demonstrate their speed, preciseness and robustness.Open Acces

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap with an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures, and point out important connections between their properties and those of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e. composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis are valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research.Comment: Accepted Manuscrip

Multi-energy retail market simulation with autonomous intelligent agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

Automated Negotiation Among Web Services

Software as a service is well accepted software deployment and distribution model that is grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of these services in a composite system. It allows users to automatically find and bind these services, as to maximize the productivity of their composed systems, meeting both functional and non-functional requirements. In this paper we present a framework for modeling the dependency relationship of different Quality of Service parameters of a component service. Our proposed approach considers the different invocation patterns of component services in the system and models the dependency relationship for optimum values of these QoS parameters. We present a service composition framework that models the dependency relations ship among component services and uses the global QoS for service selection

Discovering rules for rule-based machine learning with the help of novelty search

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than -ES which allows a good balance between low error and low complexity in the resulting models