2,150 research outputs found
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
A two-level local search heuristic for pickup and delivery problems in express freight trucking
We consider a multiattribute vehicle routing problem inspired by a freight transportation company operating a fleet of heterogeneous trucks. The company offers an express service for requests including multiple pickup and multiple delivery positions spread in a regional area, with associated soft or hard time windows often falling in the same working day. Routes are planned on a daily basis and reoptimized on-the-fly to fit new requests, taking into account constraints and preferences on capacities, hours of service, route termination points. The objective is to maximize the difference between the revenue from satisfied orders and the operational costs. The problem mixes attributes from both intercity less-than-truckload and express couriers operations, and we propose a two-level local search heuristic. The first level assigns orders to vehicles through a variable neighborhood stochastic tabu search; the second level optimizes the route service sequences. The algorithm, enhanced by neighborhood filtering and parallel exploration, is embedded in a decision support tool currently in use in a small trucking company. Results have been compared to bounds obtained from a mathematical programming model solved by column generation. Experience on the field and test on literature instances attest to the quality of results and the efficiency of the proposed approach
Models and advanced optimization algorithms for the integrated management of logistics operations
Tese de Doutoramento em Engenharia Industrial e de Sistemas.In this thesis, we propose a set of algorithms regarding real combinatorial optimization
problems in the context of transportation of goods. These problems consist in
the combination of the vehicle routing problem with the two-dimensional bin-packing
problem, which is also known as the vehicle routing problem with two-dimensional
loading constraints. We also analyzed two related problems, namely the elementary
shortest path and the vehicle routing problem with mixed linehauls and backhauls.
In both problems, two-dimensional loading constraints are explicitly considered.
Two column generation based approaches are proposed for the vehicle routing
problem with two-dimensional constraints. The rst one relies on a branch-and-price
algorithm with di erent branching schemes. A family of dual valid inequalities is also
de ned, aiming to accelerate the convergence of the algorithm. The second approach
is based on a set of di erent heuristics strategies, which are applied to the reformulated
model.
The elementary shortest path problem with two-dimensional constraints is addressed
due to its importance in solving the subproblem of the column generation
algorithms. To the best of our knowledge, we contribute with the rst approach for
this problem, through di erent constructive strategies to achieve feasible solutions,
and a variable neighborhood search algorithm in order to search for improved solutions.
In what concerns the vehicle routing problem with mixed linehaul and backhauls
and two-dimensional loading constraints, di erent variable neighborhood search algorithms
are proposed. These algorithms explored various neighborhood structures,
being some of those developed based on the features of the problem.
All the proposed methods were implemented and experimentally tested. An exhaustive
set of computational tests was conducted, using, for this purpose, a large
group of benchmark instances. In some cases, a large set of benchmark instances was
adapted in order asses the quality of the proposed models. All the obtained results
are presented and discussed.Nesta tese, propomos um conjunto de algoritmos sobre problemas reais de otimiza c~ao
combinat oria no contexto do transporte de bens. Estes problemas consistem na combina
c~ao do problema de planeamento de rotas de ve culos com o problema de empacotamento
bidimensional, que tamb em e conhecido como o problema de planeamento de
rotas de ve culos com restri c~oes de carregamento bidimensional. Analisamos tamb em
dois problemas relacionados, nomeadamente o problema de caminho mais curto e o
problema de planeamento de rotas ve culos com entregas e recolhas indiferenciadas.
Em ambos os problemas, s~ao explicitamente consideradas restri c~oes de carregamento
bidimensional.
Duas abordagens baseadas em gera c~ao de colunas s~ao propostas para o problema
de planeamento de rotas de ve culos com restri c~oes de carregamento bidimensional.
O primeiro baseia-se num algoritmo de parti c~ao e gera c~ao de colunas com diferentes
estrat egias de parti c~ao. Uma fam lia de desigualdades duais v alidas e tamb em apresentada,
com o objetivo de acelerar a converg^encia do algoritmo. A segunda abordagem
baseia-se num conjunto de diferentes estrat egias heur sticas, que s~ao aplicadas
ao modelo reformulado.
O problema do caminho mais curto com restri c~oes de carregamento bidimensional
e abordado devido a sua import^ancia na resolu c~ao do subproblema dos aos algoritmos
de gera c~ao de colunas. De acordo com o nosso conhecimento, contribu mos com a
primeira abordagem para este problema, atrav es de diferentes estrat egias construtivas
para obter solu c~oes v alidas, e um algoritmo de pesquisa em vizinhan ca vari avel, com
o objetivo de encontrar solu c~oes de melhor qualidade.
No que concerne ao problema de planeamento de rotas de ve culos com entregas e
recolhas indiferenciadas, diferentes algoritmos de pesquisa em vizinhan ca vari avel s~ao
propostos. Estes algoritmos exploram v arias estruturas de vizinhan ca, sendo algumas
destas desenvolvidas com base nas caracter sticas do problema.
Todos os m etodos propostos foram implementados e testados experimentalmente.
Um extenso conjunto de testes computacionais foi efetuado, utilizando um grande
grupo de inst^ancias descritas na literatura. Em alguns casos, um grande conjunto de
inst^ancias descritas na literatura foi adaptado com o objetivo de avaliar a qualidade
dos m etodos propostos
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Optimization models and methods for transportation services
Managing transportation services efficiently is essential to both public and private sectors. This dissertation addresses three scheduling problems in modern transportation systems: the network design problem, the train dispatching problem, and the service route design problem. The transportation network design problem with service requirements designs arcs on a directed network and route commodities on the designed arcs so that i) commodities satisfy service requirements and ii) the total cost is minimized. We develop three mathematical programming models: a compact but weak arc-flow formulation, a large but strong path-flow formulation, and a hybrid formulation that uses both the arc-flow and the path-flow representations. We show that the hybrid formulation can significantly strengthen the LP formulation without introducing many variables. To find a good hybrid formulation, we develop columnization and decolumnization algorithms that uses the LP relaxation information to identify commodities that should use the path-flow representation. We also develop valid inequalities for commodities using the path-flow representation. The train dispatching problem schedules the movements of trains on scarce railroad tracks so as to improve the average velocity of trains. We develop a mathematical programming model and strengthen the model using valid inequalities. Besides, we present a heuristic to find a feasible solution quickly, which can serve as the warm-start solution to the MIP solver. For the third problem, we seek to design vehicle routes to deliver and pickup orders for a major grocery chain. We design a GRASP that can incorporate various operational requirements, including warehouse loading capacity, loading sequence, time window requirements, truck volume and weight capacities, and driver time limits. Our GRASP procedure consists of two phases: the solution construction (Phase I) and the Tabu search (Phase II). We show that the neighborhood structure of solutions is highly degenerate, which limits the solution space explored by the Tabu search. We apply the Tabu search with random variable neighborhood to increase the solution space explored.Operations Research and Industrial Engineerin
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