Exact Models, Heuristics, and Supervised Learning Approaches for Vehicle Routing Problems

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical workforce scheduling problem, formulated as a specific type of vehicle routing problem. The objective here is to efficiently assign consultants to various clients and plan their trips. This computational challenge is addressed by using a two-stage approach: the first stage employs a mathematical model, while the second stage refines the solution with a heuristic algorithm. In the final chapter, we explore methods that integrate machine learning with traditional approaches to address the Traveling Salesman Problem, a foundational routing challenge. Our goal is to utilize supervised learning to predict information that boosts the efficiency of existing algorithms. Taken together, these three chapters offer a comprehensive overview of methodologies for addressing vehicle routing problems

Cooperative Particle Swarm Optimization for Combinatorial Problems

A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms. In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution. Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space

Meta-heuristic combining prior online and offline information for the quadratic assignment problem

The construction of promising solutions for NP-hard combinatorial optimization problems (COPs) in meta-heuristics is usually based on three types of information, namely a priori information, a posteriori information learned from visited solutions during the search procedure, and online information collected in the solution construction process. Prior information reflects our domain knowledge about the COPs. Extensive domain knowledge can surely make the search effective, yet it is not always available. Posterior information could guide the meta-heuristics to globally explore promising search areas, but it lacks local guidance capability. On the contrary, online information can capture local structures, and its application can help exploit the search space. In this paper, we studied the effects of using this information on metaheuristic's algorithmic performances for the COPs. The study was illustrated by a set of heuristic algorithms developed for the quadratic assignment problem. We first proposed an improved scheme to extract online local information, then developed a unified framework under which all types of information can be combined readily. Finally, we studied the benefits of the three types of information to meta-heuristics. Conclusions were drawn from the comprehensive study, which can be used as principles to guide the design of effective meta-heuristic in the future

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Preferences in Case-Based Reasoning

Case-based reasoning (CBR) is a well-established problem solving paradigm that has been used in a wide range of real-world applications. Despite its great practical success, work on the theoretical foundations of CBR is still under way, and a coherent and universally applicable methodological framework is yet missing. The absence of such a framework inspired the motivation for the work developed in this thesis. Drawing on recent research on preference handling in Artificial Intelligence and related fields, the goal of this work is to develop a well theoretically-founded framework on the basis of formal concepts and methods for knowledge representation and reasoning with preferences

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms