938 research outputs found
Combining meta-heuristics to effectively solve the vehicle routing problems with time windows
The vehicle routing problems with time windows are challenging delivery problems in which instances involving 100 customers or more can be difficult to solve. There were many interesting heuristics proposed to handle these problems effectively. In this paper, we examined two well-known meta-heuristics and carefully combined the short-term and long-term memory-like mechanisms of both methods to achieve better results. Our prototype was shown to compare favorably against the original search methods and other related search hybrids on the Solomon's test cases. More importantly, our proposal of integration opens up many exciting directions for further investigation.postprin
A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows
The aim of this paper is to propose an algorithm based on the philosophy of the Variable Neighborhood Search (VNS) to solve Multi Depot Vehicle Routing Problems with Time Windows. The paper has two main contributions. First, from a technical point of view, it presents the first application of a VNS for this problem and several design issues of VNS algorithms are discussed. Second, from a problem oriented point of view the computational results show that the approach is competitive with an existing Tabu Search algorithm with respect to both solution quality and computation time
Container Hinterland Drayage - On the Simultaneous Transportation of Containers Having Different Sizes
In an intermodal transportation chain drayage is the term used for the movement by truck of cargo that is filled in a loading unit. The most important intermodal transportation chain is the intermodal container transportation, in which containers represent the loading unit for cargo. Cost effectiveness constitutes a general problem of drayage operations. A major cost driver within container transportation chains is the movement and repositioning of empty containers. The present thesis investigates the potential to reduce drayage costs. Two solution methodologies are developed for operating a fleet of trucks that transports containers of different sizes, which addresses a recent gap in research in seaport hinterland regions
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
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 Hybrid Heuristic for a Broad Class of Vehicle Routing Problems with Heterogeneous Fleet
We consider a family of Rich Vehicle Routing Problems (RVRP) which have the
particularity to combine a heterogeneous fleet with other attributes, such as
backhauls, multiple depots, split deliveries, site dependency, open routes,
duration limits, and time windows. To efficiently solve these problems, we
propose a hybrid metaheuristic which combines an iterated local search with
variable neighborhood descent, for solution improvement, and a set partitioning
formulation, to exploit the memory of the past search. Moreover, we investigate
a class of combined neighborhoods which jointly modify the sequences of visits
and perform either heuristic or optimal reassignments of vehicles to routes. To
the best of our knowledge, this is the first unified approach for a large class
of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants.
The efficiency of the algorithm is evaluated on 643 well-known benchmark
instances, and 71.70\% of the best known solutions are either retrieved or
improved. Moreover, the proposed metaheuristic, which can be considered as a
matheuristic, produces high quality solutions with low standard deviation in
comparison with previous methods. Finally, we observe that the use of combined
neighborhoods does not lead to significant quality gains. Contrary to
intuition, the computational effort seems better spent on more intensive route
optimization rather than on more intelligent and frequent fleet re-assignments
Vehicle routing with arrival time diversification
Unpredictable routes may be generated by varying the arrival time at each customer over successive visits. Inspired by a real-life case in cash distribution, this study presents an efficient solution approach for the vehicle routing problem with arrival time diversification by formulating it as a vehicle routing problem with multiple time windows in a rolling horizon framework. Because waiting times are not allowed, a novel algorithm is developed to efficiently determine whether routes or local search operations are time window feasible. To allow infeasible solutions during the heuristic search, four different penalty methods are proposed. The proposed algorithm and penalty methods are evaluated in a simple iterated granular tabu search that obtains new best-known solutions for all benchmark instances from the literature, decreasing average distance by 29% and reducing computation time by 93%. A case study is conducted to illustrate the practical relevance of the proposed model and to examine the trade-off between arrival time diversification and transportation cost
A multi-objective centralised agent-based optimisation approach for vehicle routing problem with unique vehicles
Motivated by heterogeneous service suppliers in crowd shipping routing problems, vehicles’ similarity assumption is questioned in the well-known logistical Vehicle Routing Problems (VRP) by considering different start/end locations, capacities, as well as shifts in the Time Window variant (VRPTW). In order to tackle this problem, a new agent-based metaheuristic architecture is proposed to capture the uniqueness of vehicles by modelling them as agents while governing the search with centralised agent cooperation. This cooperation aims to generate near optimum routes by minimising the number of vehicles used, total travelled distance, and total waiting times. The innovative architecture encapsulates three individual core modules in a flexible metaheuristic implementation. First, the problem is modelled by an agent-based module that includes its components in representing, evaluating, and altering solutions. A second metaheuristic module is then designed and integrated, followed by a multi-objective module introduced to sort solutions generated by the metaheuristic module based on Pareto dominance. Tests on benchmark instances were run, resulting in better waiting times, with an average reduction of 2.21-time units, at the expense of the other objectives. Benchmark instances are modified to tackle the unique vehicle's problem by randomising locations, capacities, and operating shifts and tested to justify the proposed model's applicability
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