An Adaptive Iterated Local Search for the Mixed Capacitated General Routing Problem

We study the mixed capacitated general routing problem (MCGRP) in which a fleet of capacitated vehicles has to serve a set of requests by traversing a mixed weighted graph. The requests may be located on nodes, edges, and arcs. The problem has theoretical interest because it is a generalization of the capacitated vehicle routing problem (CVRP), the capacitated arc routing problem (CARP), and the general routing problem. It is also of great practical interest since it is often a more accurate model for real-world cases than its widely studied specializations, particularly for so-called street routing applications. Examples are urban waste collection, snow removal, and newspaper delivery. We propose a new iterated local search metaheuristic for the problem that also includes vital mechanisms from adaptive large neighborhood search combined with further intensification through local search. The method utilizes selected, tailored, and novel local search and large neighborhood search operators, as well as a new local search strategy. Computational experiments show that the proposed metaheuristic is highly effective on five published benchmarks for the MCGRP. The metaheuristic yields excellent results also on seven standard CARP data sets, and good results on four well-known CVRP benchmarks, including improvement of the best known upper bound for one instance

Advanced Optimization Models in Waste Management

Diplomová práce se zabývá optimalizací svozu odpadu pro středně velká města. Model úlohy zohledňuje požadavky vzešlé z praxe. K jejímu řešení byl navržen původní memetický algoritmus, který byl implementován v jazyce C++.This thesis deals with an optimization of waste collection in a mid-sized town. The model is formulated based on requirements from a real process. To deal with this problem, the original memetic algorithm was developed and implemented in C++.

Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips

This work is motivated by solving a problem faced by big agriculture companies implementing precision agriculture operations for spraying practices using two types of operators, namely a tender tanker and a fleet of sprayers. We model this problem as a vehicle routing problem with multiple synchronization constraints and multiple trips with the objective of minimizing the waiting time of the sprayers and the total routing distance of the sprayers. The resulting mixed integer programming model that we develop is hard to solve using a commercial solver, owing to the dependencies caused by the spatio-temporal synchronization between the two operators. To solve large-scale instances effectively, we present an adaptive large neighborhood search metaheuristic that uses an intensive local search mechanism. We conduct an extensive computational analysis to assess the effectiveness of our solution approach and gain managerial insights into the problem by analyzing various models. The proposed metaheuristic yields high-quality solutions quickly, with an overall average improvement of 5.61% over what is implemented in practice implying significant savings in time and cost

Benchmarking Optimization Algorithms for Capacitated Vehicle Routing Problems

The Vehicle Routing Problem (VRP) originated in the 1950s when algorithms and mathematical approaches were applied to find solutions for routing vehicles. Since then, there has been extensive research in the field of VRPs to solve real-life problems. The process of generating an optimal routing schedule for a VRP is complex due to two reasons. First, VRP is considered to be an NP-Hard problem. Second, there are several constraints involved, such as the number of available vehicles, the vehicle capacities, time-windows for pickup or delivery etc. The main goal for this project was to compare different optimization algorithms for solving Capacitated Vehicle Routing Problems (CVRP). The three specific aims for this project were to (1) survey existing research and identify suitable optimization algorithms for CVRP and (2) implement a work-flow in the Python programming lan- guage, to evaluate their performance, (3) perform different computational experiments on existing CVRP benchmarks. Experiments were conducted by leveraging Google’s OR-Tools library on the well-known benchmarks. Different strategies were evaluated to see if there exists a solution or a better solution than the best-known solutions for these benchmarks. The results show that almost 60% of the problems in the benchmarks have a better solution than the current best-known solution. The second finding of this project is that there is not one strategy which can provide the best solution for all types of CVRPs

A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection  Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company

Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm

An updated annotated bibliography on arc routing problems

The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio