6,551 research outputs found
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Dynamic approach to solve the daily drayage problem with travel time uncertainty
The intermodal transport chain can become more e cient by means of a good organization of
drayage movements. Drayage in intermodal container terminals involves the pick up and delivery
of containers at customer locations, and the main objective is normally the assignment
of transportation tasks to the di erent vehicles, often with the presence of time windows. This
scheduling has traditionally been done once a day and, under these conditions, any unexpected
event could cause timetable delays. We propose to use the real-time knowledge about vehicle
position to solve this problem, which permanently allows the planner to reassign tasks in case
the problem conditions change. This exact knowledge of the position of the vehicles is possible
using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show
that this additional data can be used to dynamically improve the solution
Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances
Recent studies in maritime logistics have introduced a general ship routing
problem and a benchmark suite based on real shipping segments, considering
pickups and deliveries, cargo selection, ship-dependent starting locations,
travel times and costs, time windows, and incompatibility constraints, among
other features. Together, these characteristics pose considerable challenges
for exact and heuristic methods, and some cases with as few as 18 cargoes
remain unsolved. To face this challenge, we propose an exact branch-and-price
(B&P) algorithm and a hybrid metaheuristic. Our exact method generates
elementary routes, but exploits decremental state-space relaxation to speed up
column generation, heuristic strong branching, as well as advanced
preprocessing and route enumeration techniques. Our metaheuristic is a
sophisticated extension of the unified hybrid genetic search. It exploits a
set-partitioning phase and uses problem-tailored variation operators to
efficiently handle all the problem characteristics. As shown in our
experimental analyses, the B&P optimally solves 239/240 existing instances
within one hour. Scalability experiments on even larger problems demonstrate
that it can optimally solve problems with around 60 ships and 200 cargoes
(i.e., 400 pickup and delivery services) and find optimality gaps below 1.04%
on the largest cases with up to 260 cargoes. The hybrid metaheuristic
outperforms all previous heuristics and produces near-optimal solutions within
minutes. These results are noteworthy, since these instances are comparable in
size with the largest problems routinely solved by shipping companies
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
Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an
extension of the well-known Vehicle Routing Problem (VRP), which takes into
account the dynamic nature of the problem. This aspect requires the vehicle
routes to be updated in an ongoing manner as new customer requests arrive in
the system and must be incorporated into an evolving schedule during the
working day. Besides the vehicle capacity constraint involved in the classical
VRP, DVRPTW considers in addition time windows, which are able to better
capture real-world situations. Despite this, so far, few studies have focused
on tackling this problem of greater practical importance. To this end, this
study devises for the resolution of DVRPTW, an ant colony optimization based
algorithm, which resorts to a joint solution construction mechanism, able to
construct in parallel the vehicle routes. This method is coupled with a local
search procedure, aimed to further improve the solutions built by ants, and
with an insertion heuristics, which tries to reduce the number of vehicles used
to service the available customers. The experiments indicate that the proposed
algorithm is competitive and effective, and on DVRPTW instances with a higher
dynamicity level, it is able to yield better results compared to existing
ant-based approaches.Comment: 10 pages, 2 figure
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