Sim Heuristic Algorithms For Robust Vehicle Routing Problem With Stochastic Demand

With application to subscription bus routing.

This work extends the Vehicle Routing Problem (VRP) for addressing uncertainties via robust optimization, giving the Robust VRP (RVRP). First, uncertainties are handled on travel times/costs. Then, a bi-objective version (bi-RVRP) is introduced to handle uncertainty in both, travel times and demands. For solving the RVRP and the bi-RVRP different models and methods are proposed to determine robust solutions minimizing the worst case. A Mixed Integer Linear Program (MILP), several greedy heuristics, a Genetic Algorithm (GA), a local search procedure and four local search based algorithms are proposed: a Greedy Randomized Adaptive Search Procedure (GRASP), an Iterated Local Search (ILS), a Multi-Start ILS (MS-ILS), and a MS-ILS based on Giant Tours (MS-ILS-GT) converted into feasible routes via a lexicographic splitting procedure. Concerning the bi-RVRP, the total cost of traversed arcs and the total unmet demand are minimized over all scenarios. To solve the problem, different variations of multiobjective evolutionary metaheuristics are proposed and coupled with a local search procedure: the Multiobjective Evolutionary Algorithm (MOEA) and the Non-dominated Sorting Genetic Algorithm version 2 (NSGAII). Different metrics are used to measure the efficiency, the convergence as well as the diversity of solutions for all these algorithms.

Vehicle routing problems, among the most studied in combinatorial optimization, arise in many practical contexts (freight distribution and collection, transportation, garbage collection, newspaper delivery, etc.). Operations researchers have made significant developments in the algorithms for their solution, and Vehicle Routing: Problems, Methods, and Applications, Second Edition reflects these advances. The text of the new edition is either completely new or significantly revised and provides extensive and complete state-of-the-art coverage of vehicle routing by those who have done most of the innovative research in the area; it emphasizes methodology related to specific classes of vehicle routing problems and, since vehicle routing is used as a benchmark for all new solution techniques, contains a complete overview of current solutions to combinatorial optimization problems. It also includes several chapters on important and emerging applications, such as disaster relief and green vehicle routing.

The vehicle routing problem (VRP) is one of the most famous combinatorial optimization problems. In simple terms, the goal is to determine a set of routes with overall minimum cost that can satisfy several geographical scattered demands. Biological inspired computation is a field devoted to the development of computational tools modeled after principles that exist in natural systems. The adoption of such design principles enables the production of problem solving techniques with enhanced robustness and flexibility, able to tackle complex optimization situations. The goal of the volume is to present a collection of state-of-the-art contributions describing recent developments concerning the application of bio-inspired algorithms to the VRP. Over the 9 chapters, different algorithmic approaches are considered and a diverse set of problem variants are addressed. Some contributions focus on standard benchmarks widely adopted by the research community, while others address real-world situations.

This dissertation, "Optimization of Stochastic Vehicle Routing With Soft Time Windows" by Zigang, Guo, 郭自剛, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Optimization of Stochastic Vehicle Routing with Soft Time Windows submitted by GUO ZIGANG for the degree of Doctor of Philosophy at the University of Hong Kong in July 2006 Routing vehicles to service customers is an important and economically- significant component of many distribution systems. This study seeks to develop efficient methodologies to solve stochastic vehicle routing problems, so that distribution systems can be designed with more cost-effective delivery plans. The study considers a stochastic vehicle routing problem with soft time windows (SVRPSTW). This problem involves utilizing a fleet of vehicles with finite capacity to establish a set of minimum-cost vehicle routes for servicing a set of geographically scattered customers. All routes start and end at a central depot, while each customer is visited once by exactly one vehicle. Both the demand and the presence of a customer are stochastic. Each customer also has a corresponding "time window" defining the period in which it can be serviced. A vehicle arriving early has to wait until the window opens, while a vehicle arriving late is permitted to service the customer subject to an appropriate penalty. A new mathematical model is developed to describe the characteristics of the distribution system. The objective is to minimize the expected total cost associated with the number of vehicles employed, the total distance travelled to service the customers, the additional distance travelled due to ivroute failures, the penalty for violating time window constraints, and the cost-savings due to the omission of absent customers. The SVRPSTW is a complex combinatorial optimization problem which is NP- hard, and a genetic algorithm (GA) is proposed to address it. A novel heuristic edge- based crossover operator is proposed and incorporated in the GA, so that the crossover operation depends not only on the apparent contents of the parent chromosomes, but also some instance-specified information embedded in them. The algorithm performs well, and is used to establish a coarse-grained parallel GA to perform even better. A Tabu Search (TS) algorithm is also designed to solve the SVRPSTW. The algorithm is then combined with the aforementioned GA to develop a hybrid algorithm which is subsequently used to establish a parallel hybrid TS-GA. A distributed computing platform is developed to implement the parallel algorithms. Since no benchmark problems are available in the literature, the study also introduces an approach that can generate test problems for evaluating the performance of the algorithms. Indeed, results obtained from solving some randomly generated problems clearly show that the proposed parallel algorithms are efficient and robust optimization tools for solving the SVRPSTW. Sensitivity analyses are also performed to study the effects of the genetic parameters on the performance of the GA, the communication topology, the migration interval and size on the performance of the parallel GA, and the communication topology and the migration interval on the performance of the parallel hybrid TS-GA. The convergence characteristics of the parallel GA and the parallel hybrid TS- GA are also studied theoretically using the Markov chain model and the axiomatic model respectively. A common conclusion from both studies is that when the overall best candidate solution is maintained over time, the search processes eventually conv

This book presents state-of-the-art research and practice in optimization routing, specifically the vehicle routing problem (VRP). Since its introduction in the late 1950s, the VRP has been a very significant area of research and practice in operations research. Vehicles are used to make deliveries and for pick-ups every day and everywhere. Companies such as Amazon, UPS, FedEx, and DHL use route optimization to reduce mileage, fuel use, number of trucks on the road, and carbon dioxide emissions. The authors compile and analyze 135 survey and review articles on vehicle routing topics published between 2005 and 2022 in an effort to make key observations about publication and trend history, summarize the overall contributions in the field, and identify trends in VRP research and practice. The authors have compiled published research on models, algorithms, and applications for specific areas, including: alternative and multiple objectives; arc routing and general routing; drones, last-mile delivery, and urban distribution; dynamic and stochastic routing; green routing; inventory routing; loading constraints; location-routing; multiple depots; pickup and delivery and dial-a-ride problems; rich and multi-attribute routing; routing over time; shipping; two-echelon, collaborative, and inter-terminal problems; specific variants, benchmark datasets, and software; and exact algorithms and heuristics. In addition, the book discusses how vehicle routing problems are among the most widely studied problems in combinatorial optimization due to the mathematical complexity and practical significance.

This book is dedicated to metaheuristics as applied to vehicle routing problems. Several implementations are given as illustrative examples, along with applications to several typical vehicle routing problems. As a first step, a general presentation intends to make the reader more familiar with the related field of logistics and combinatorial optimization. This preamble is completed with a description of significant heuristic methods classically used to provide feasible solutions quickly, and local improvement moves widely used to search for enhanced solutions. The overview of these fundamentals allows appreciating the core of the work devoted to an analysis of metaheuristic methods for vehicle routing problems. Those methods are exposed according to their feature of working either on a sequence of single solutions, or on a set of solutions, or even by hybridizing metaheuristic approaches with others kind of methods.