1,424 research outputs found
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
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem
This work presents the Vehicle Routing with Time Windows and Loading Problem (VRTWLP) as a multi-objective optimization problem, implemented within a Genetic Algorithm. Specifically, the three dimensions of the problem to be optimized – the number of vehicles, the total travel distance and volume utilization – are considered to be separated dimensions of a multi-objective space. The quality of the solution obtained using this approach is evaluated and compared with results of other heuristic approaches previously developed by the author. The most significant contribution of this work is our interpretation of VRTWLP as a Multi-objective Optimization Problem
A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives
This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria
The trade-off between taxi time and fuel consumption in airport ground movement
Environmental impact is a very important agenda item in many sectors nowadays, which the air transportation sector is also trying to reduce
as much as possible. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface.
Aircraft have to be routed from a gate to a runway and vice versa and it is
still unknown whether fuel burn and environmental impact reductions will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used
A dynamic programming approach to multi-objective time-dependent capacitated single vehicle routing problems with time windows
A single vehicle performs several tours to serve a set of geographically dis- persed customers. The vehicle has a finite capacity and is only available for a limited amount of time. Moreover, tours' duration is restricted (e.g. due to quality or security issues). Because of road congestion, travel times are time-dependent: depending on the departure time at a customer, a different travel time is incurred. Furthermore, all customers need to get delivered in their specicified time windows. Contrary to most of the literature, we con- sider a multi-objective cost function: simultaneously minimizing the total time traveled including waiting times at customers due to time windows, and maximizing the total demand fulfilled. Efficient dynamic programming algorithms are developed to compute the Pareto set of routes, assuming a specific structure for time windows and travel time profiles
Multi-objective Optimization For The Dynamic Multi-Pickup and Delivery Problem with Time Windows
The PDPTW is an optimization vehicles routing problem which must meet
requests for transport between suppliers and customers satisfying precedence,
capacity and time constraints. We present, in this paper, a genetic algorithm
for multi-objective optimization of a dynamic multi pickup and delivery problem
with time windows (Dynamic m-PDPTW). We propose a brief literature review of
the PDPTW, present our approach based on Pareto dominance method and lower
bounds, to give a satisfying solution to the Dynamic m-PDPTW minimizing the
compromise between total travel cost and total tardiness time. Computational
results indicate that the proposed algorithm gives good results with a total
tardiness equal to zero with a tolerable cost.Comment: arXiv admin note: text overlap with arXiv:1101.339
The Dynamic Multi-objective Multi-vehicle Covering Tour Problem
This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front
- …