A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs

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

A Revised ant colony system approach to vehicle routing problems /

Vehicle routing problems have various extensions such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. The objectives of all these problems are to design optimal routes minimizing total distance traveled, minimizing number of vehicles, etc that satisfy corresponding constraints. In this study, an ant colony optimization based heuristic that can be used to solve various vehicle routing problems is proposed. The objective function considered to minimize the total distance traveled by all vehicles. The heuristic is applied to vehicle routing problem with time windows and vehicle routing with simultaneous delivery and pick-up. Vehicles are identical and capacities of the vehicles are finite. The time window constraints in the first problem are assumed to be strict. The proposed heuristic consists of four steps. First, a candidate list is formed for each customer in order to reduce computational time. Second, a feasible solution is found, and initial pheromone trails on each arc is calculated using it. Then, routes are constructed based on Dorigo et al. (1997). While visibility is calculated during route construction process, the distance between two customers, customers' distance to the depot and the time window associated with the customer to whom the ant is considered to move are considered. Pheromone trails are modified by both local and global pheromone update. Finally, constructed routes are improved using 2-opt algorithm. The algorithm have been tested on the benchmark problem instances of Solomon (1987) for vehicle routing problem with time windows, and benchmark problem instances of Min (1989) and Dethloff (2001) for vehicle routing with simultaneous delivery and pick-up. The algorithm is proven to give good results when compared to the best known results in the literature

Automated design of local search algorithms for vehicle routing problems with time windows

Designing effective search algorithms for solving combinatorial optimisation problems presents a challenge for researchers due to the time-consuming experiments and experience required in decision-making. Automated algorithm design removes the heavy reliance on human experts and allows the exploration of new algorithm designs. This thesis systematically investigates machine learning for the automated design of new and generic local search algorithms, taking the vehicle routing problem with time windows as the testbed. The research starts by building AutoGCOP, a new general framework for the automated design of local search algorithms to optimise the composition of basic algorithmic components. Within the consistent AutoGCOP framework, the basic algorithmic components show satisfying performance for solving the VRPTW. Based on AutoGCOP, the thesis investigates the use of machine learning for automated algorithm composition by modelling the algorithm design task as different machine learning tasks, thus investigating different perspectives of learning in automated algorithm design. Based on AutoGCOP, the thesis first investigates online learning in automated algorithm design. Two learning models based on reinforcement learning and Markov chain are investigated to learn and enhance the compositions of algorithmic components towards automated algorithm design. The Markov chain model presents a superior performance in learning the compositions of algorithmic components during the search, demonstrating its effectiveness in designing new algorithms automatically. The thesis then investigates offline learning to learn the hidden knowledge of effective algorithmic compositions within AutoGCOP for automated algorithm design. The forecast of algorithmic components in the automated composition is defined as a sequence classification task. This new machine learning task is then solved by a Long Short-term Memory (LSTM) neural network which outperforms various conventional classifiers. Further analysis reveals that a Transformer network surpasses LSTM at learning from longer algorithmic compositions. The systematical analysis of algorithmic compositions reveals some key features for improving the prediction. To discover valuable knowledge in algorithm designs, the thesis applies sequential rule mining to effective algorithmic compositions collected based on AutoGCOP. Sequential rules of composing basic components are extracted and further analysed, presenting a superior performance of automatically composed local search algorithms for solving VRPTW. The extracted sequential rules also suggest the importance of considering the impact of algorithmic components on optimisation performance during automated composition, which provides new insights into algorithm design. The thesis gains valuable insights from various learning perspectives, enhancing the understanding towards automated algorithm design. Some directions for future work are present

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Automated design of local search algorithms for vehicle routing problems with time windows

Designing effective search algorithms for solving combinatorial optimisation problems presents a challenge for researchers due to the time-consuming experiments and experience required in decision-making. Automated algorithm design removes the heavy reliance on human experts and allows the exploration of new algorithm designs. This thesis systematically investigates machine learning for the automated design of new and generic local search algorithms, taking the vehicle routing problem with time windows as the testbed. The research starts by building AutoGCOP, a new general framework for the automated design of local search algorithms to optimise the composition of basic algorithmic components. Within the consistent AutoGCOP framework, the basic algorithmic components show satisfying performance for solving the VRPTW. Based on AutoGCOP, the thesis investigates the use of machine learning for automated algorithm composition by modelling the algorithm design task as different machine learning tasks, thus investigating different perspectives of learning in automated algorithm design. Based on AutoGCOP, the thesis first investigates online learning in automated algorithm design. Two learning models based on reinforcement learning and Markov chain are investigated to learn and enhance the compositions of algorithmic components towards automated algorithm design. The Markov chain model presents a superior performance in learning the compositions of algorithmic components during the search, demonstrating its effectiveness in designing new algorithms automatically. The thesis then investigates offline learning to learn the hidden knowledge of effective algorithmic compositions within AutoGCOP for automated algorithm design. The forecast of algorithmic components in the automated composition is defined as a sequence classification task. This new machine learning task is then solved by a Long Short-term Memory (LSTM) neural network which outperforms various conventional classifiers. Further analysis reveals that a Transformer network surpasses LSTM at learning from longer algorithmic compositions. The systematical analysis of algorithmic compositions reveals some key features for improving the prediction. To discover valuable knowledge in algorithm designs, the thesis applies sequential rule mining to effective algorithmic compositions collected based on AutoGCOP. Sequential rules of composing basic components are extracted and further analysed, presenting a superior performance of automatically composed local search algorithms for solving VRPTW. The extracted sequential rules also suggest the importance of considering the impact of algorithmic components on optimisation performance during automated composition, which provides new insights into algorithm design. The thesis gains valuable insights from various learning perspectives, enhancing the understanding towards automated algorithm design. Some directions for future work are present

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW