866 research outputs found
Metaheuristics for the Vehicle Routing Problem with Loading Constraints
We consider a combination of the capacitated vehicle routing problem and a class of additional loading constraints involving a parallel machine scheduling problem. The work is motivated by a real-world transportation problem occurring to a wood-products retailer, which delivers its products to a number of customers in a specific region. We solve the problem by means of two different metaheuristics algorithms: a Tabu Search and an Ant Colony Optimization. Extensive computational results are given for both algorithms, on instances derived from the vehicle routing literature and on real-world instances
A matheuristic approach to the integration of three-dimensional Bin Packing Problem and vehicle routing problem with simultaneous delivery and pickup
This work presents a hybrid approach to solve a distribution problem of a Portuguese company in the automotive industry. The objective is to determine the minimum cost for daily distribution operations, such as collecting and delivering goods to multiple suppliers. Additional constraints are explicitly considered, such as time windows and loading constraints due to the limited capacity of the fleet in terms of weight and volume. An exhaustive review of the state of the art was conducted, presenting different typology schemes from the literature for the pickup and delivery problems in the distribution field. Two mathematical models were integrated within a matheuristic approach. One model reflects the combination of the Vehicle Routing Problem with Simultaneous Delivery and Pickup with the Capacitated Vehicle Routing Problem with Time Windows. The second one aims to pack all the items to be delivered onto the pallets, reflecting a three-dimensional single bin size Bin Packing Problem. Both formulations proposedāa commodity-flow model and a formulation of the Three-Dimensional Packing Problem must be solved within the matheuristic. All the approaches were tested using real instances from data provided by the company. Additional computational experiments using benchmark instances were also performed.This research was funded by national funds through FCTāFundaĆ§Ć£o para a CiĆŖncia e
a Tecnologia, under the projects UIDB/00285/2020, UIDB/00319/2020. This work was supported
by the Research Unit on Governance, Competitiveness and Public Policies (UIDB/04058/2020) +
(UIDP/04058/2020), funded by national funds through the Foundation for Science and Technology, IP. This work was also funded by FEDER in the frame of COMPETE 2020 under the project
POCI-01-0247-FEDER-072638
Sea Container Terminals
Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations
A flexible metaheuristic framework for solving rich vehicle routing problems
Route planning is one of the most studied research topics in the operations research area. While the standard vehicle routing problem (VRP) is the classical problem formulation, additional requirements arising from practical scenarios such as time windows or vehicle compartments are covered in a wide range of so-called rich VRPs. Many solution algorithms for various VRP variants have been developed over time as well, especially within the class of so-called metaheuristics. In practice, routing software must be tailored to the business rules and planning problems of a specific company to provide valuable decision support. This also concerns the embedded solution methods of such decision support systems. Yet, publications dealing with flexibility and customization of VRP heuristics are rare. To fill this gap this thesis describes the design of a flexible framework to facilitate and accelerate the development of custom metaheuristics for the solution of a broad range of rich VRPs.
The first part of the thesis provides background information to the reader on the field of vehicle routing problems and on metaheuristic solution methods - the most common and widely-used solution methods to solve VRPs. Specifically, emphasis is put on methods based on local search (for intensification of the search) and large neighborhood search (for diversification of the search), which are combined to hybrid methods and which are the foundation of the proposed framework.
Then, the main part elaborates on the concepts and the design of the metaheuristic VRP framework. The framework fulfills requirements of flexibility, simplicity, accuracy, and speed, enforcing the structuring and standardization of the development process and enabling the reuse of code. Essentially, it provides a library of well-known and accepted heuristics for the standard VRP together with a set of mechanisms to adapt these heuristics to specific VRPs. Heuristics and adaptation mechanisms such as templates for user-definable checking functions are explained on a pseudocode level first, and the most relevant classes of a reference implementation using the Microsoft .NET framework are presented afterwards.
Finally, the third part of the thesis demonstrates the use of the framework for developing problem-specific solution methods by exemplifying specific customizations for five rich VRPs with diverse characteristics, namely the VRP with time windows, the VRP with compartments, the split delivery VRP, the periodic VRP, and the truck and trailer routing problem. These adaptations refer to data structures and neighborhood search methods and can serve as a source of inspiration to the reader when designing algorithms for new, so far unstudied VRPs. Computational results are presented to show the effectiveness and efficiency of the proposed framework and methods, which are competitive with current state-of-the-art solvers of the literature. Special attention is given to the overall robustness of heuristics, which is an important aspect for practical application
Vehicle routing with multi-dimensional loading constraints
Zwei der wichtigsten Problemstellungen in der Transportlogistik behandeln einerseits das
Verladen von Produkten auf LKWs und andererseits die ressourceneffiziente Belieferung
der Kunden auf dem gegebenen StraĆennetz.
Bis dato wurden diese zwei Probleme mit Hilfe von kombinatorischer Optimierung getrennt
von einander behandelt und es existieren zahlreiche Publikationen zu beiden Themen in
den einschlƤgigen Fachzeitschriften. Erst in den letzten drei Jahren wurde einem integrierten
Ansatz, der beide Problemstellungen zu einem Optimierungsproblem vereint betrachtet.
Somit werden die Bestellungen einzelner Kunden nicht bloĆ Ć¼ber ihre Gewichte,
sondern auch Ć¼ber ihre Abmessungen definiert. Der klare Vorteil dieses Ansatzes liegt
darin, dass die einzelnen LKW Routen auch tatsƤchlich so gefahren werden kƶnnen, da
die tatsƤchliche Beladung auch berĆ¼cksichtigt wurde. Andererseits steigt die kombinatorische
KomplexitƤt drastisch, weil das kapazitierte Vehicle Routing Problem (CVRP)
mit Bin Packing Problemen (BPP) kombiniert wird und beide Probleme fĆ¼r sich alleine
NP schwer sind.
Diese Dissertation prƤsentiert drei verschiedene Probleme, die sich neben der Frage welches
Fahrzeug beliefert welchen Kunden auch der Frage widmet, wie die bestellten Produkte
auf den LKW geladen werden kƶnnen.
- Das Multi-Pile Vehicle Routing Problem (MP-VRP) bindet in das klassische CVRP
eine Beladekomponente ein, die zwischen eindimensionalem und zweidimensionalem
Bin Packing Problem angesiedelt ist. Die Problemstellungen wurden durch einen
ƶsterreichischen Holzzulieferer motiviert.
- Beim kapazitierten Vehicle Routing Problem mit zweidimensionalen Beladenebenbedingungen
(2L-CVRP) bestellt jeder Kunden rechteckige Objekte, welche auf der
rechteckigen BeladeflƤche des LKWs untergebracht werden mĆ¼ssen.
- Das allgemeinste Beladeproblem stellt das dreidimensionale Bin Packing Problem
dar. Hier bestellt jeder Kunde dreidimensionale Objekte, welche auf dem dreidimensionalen
Laderaum des LKWs untergebracht werden mĆ¼ssen. Das klassische dreidimensionale Bin Packing
Problem wird durch zusƤtzliche Beladenebenbedingungen
erweitert.
Momentan gibt es zu diesen kombinierten Problemen nur wenige Publikationen. Exakte
AnsƤtze gibt es momentan nur zwei, einen fĆ¼r das MP-VRP (hier kƶnnen Probleme bis
zu 50 Kunden gelƶst werden) und fĆ¼r das 2L-CVRP (hier kƶnnen Probleme bis zu 30
Kunden exakt gelƶst werden). FĆ¼r Realweltanwendungen mĆ¼ssen jedoch Heuristiken gefunden
werden, welche grƶĆere Probleminstanzen lƶsen kƶnnen. In dieser Arbeit wird
fĆ¼r alle drei Problemstellungen ein Ameisenalgorithmus verwendet und mit bestehenden
LƶsungsansƤtzen aus dem Bereich der Tabu-Suche (TS) verglichen. Es wird gezeigt, dass
der prƤsentierte Ameisenansatz fĆ¼r die zur VerfĆ¼gung stehenden Benchmarkinstanzen die
besten Ergebnisse liefert. DarĆ¼ber hinaus wird der Einfluss verschiedener Beladenebenbedingungen
auf die LƶsungsgĆ¼te untersucht, was eine wichtige Entscheidungsgrundlage fĆ¼r
Unternehmen darstellt.Two of the most important problems in distribution logistics concern the loading of the
freight into the vehicles, and the successive routing of the vehicles along the road network,
with the aim of satisfying the demands of the clients.
In the combinatorial optimization field, these two loading and routing problems have been
studied intensively but separately yielding a large number of publications either for routing
or packing problems. Only in recent years some attention has been brought to their
combined optimization. The obvious advantage is that, by considering the information on
the freight to be loaded, one can construct more appropriate routes for the vehicles. The
counterpart is that the combinatorial difficulty of the problem increases consistently. One
must not forget that both the vehicle routing problem and the bin packing problem are
NP hard problems!
This thesis presents three different problems concerning the combination of routing and
loading (packing) problems.
- The Multi-Pile Vehicle Routing Problem (MP-VRP) incorporates an interesting
loading problem situated between one dimensional and two dimensional bin packing.
This problem has been inspired by a real world application of an Austrian wood
distributing company.
- The Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints
(2L-CVRP) augments the classical Capacitated Vehicle Routing Problem
by requiring the satisfaction of general two dimensional loading constraints. This
means that customers order items represented by rectangles that have to be feasibly
placed on the rectangular shaped loading surface of the used vehicles.
- The most general packing problem to be integrated is the Three Dimensional Bin
Packing Problem (3DBPP) resulting in the Capacitated Vehicle Routing Problem
with Three-Dimensional Loading Constraints (3L-CVRP). Here the order of each
customer consists of cuboid shaped items that have to be feasibly placed on the loading space of the vehicle. A feasible placement is influenced by additional constraints
that extend the classical 3DBPP.
Concerning the literature solving these problems with exact methods it becomes clear that
this is only possible to some very limited extent (e.g.: the MP-VRP can be solved up to
50, the 2L-CVRP can be solved exact up to 30 customers, for the 3L-CVRP no exact
approach exists). Nevertheless for real world applications the problem instances are much
larger which justifies the use of (meta-)heuristics.
The rank-based Ant System was modified and extended to solve the combined problem
by integrating different packing routines. The designed methods outperform the existing
techniques for the three different problem classes.
The influence of different loading constraints on the objective value is investigated/is
intensively studied to support the decision makers of companies facing similar problems
Study of capacitated vehicle routing problem based on particle swarm optimization
Vehicle Routing Problem (VRP) is one of the common problems that happen in human life. There are many applications of VRP such as garbage disposal, mail delivery, school bus routing, airline schedule and many more. The main objective of VRP is to minimize the distance of the route starting from a depot, serves all of customers demand, and return back to depot. VRP is one of the optimization problems that belong to NP- hard (Non-deterministic Polynomial-time hard) problem and difficult to solve. VRP has also becomes one of the important topic to discuss and analyze. There are many types of VRP; this research is focusing on capacitated VRP (CVRP). CVRP is defined as the problem of determining optimal routes to be used by vehicles starting from one or more depots to serve all customersā demand, observing some constraints. Particle Swarm Optimization (PSO) method will be used to solve the VRP problems because there are lots of advantages of PSO. PSO is a population based stochastic optimization technique, inspired by social behavior of bird flocking or fish schooling. The experiment has been done to test this algorithm. Three variants of PSO have been used which are PSO with inertia weight, PSO without inertia weight, and PSO with constriction factor. The results show that the PSO with inertia weight strategy which include PSO with inertia weight and PSO with constriction factor have the best total distance. It can be concluded that PSO with inertia weight strategies have better performance because they take less iteration to arrive at the optimum value. The second comparison also showed that small range of inertia weight has the best total distance
ProbleĢmes de tourneĢes de veĢhicules avec contraintes de chargement
Cette theĢse sāinteĢresse aux probleĢmes de tourneĢes de veĢhicules ouĢ lāon retrouve des contraintes de chargement ayant un impact sur les seĢquences de livraisons permises. Plus particulieĢrement, les items placeĢs dans lāespace de chargement dāun veĢhicule doivent eĢtre directement accessibles lors de leur livraison sans quāil soit neĢcessaire de deĢplacer dāautres items. Ces probleĢmes sont rencontreĢs dans plusieurs entreprises de transport qui livrent de gros objets (meubles, eĢlectromeĢnagers).
Le premier article de cette theĢse porte sur une meĢthode exacte pour un probleĢme de confection dāune seule tourneĢe ouĢ un veĢhicule, dont lāaire de chargement est diviseĢe en un certain nombre de piles, doit effectuer des cueillettes et des livraisons respectant une contrainte de type dernier entreĢ, premier sorti. Lors dāune collecte, les items recueillis doivent neĢcessairement eĢtre deĢposeĢs sur le dessus de lāune des piles. Par ailleurs, lors dāune livraison, les items doivent neĢcessairement se trouver sur le dessus de lāune des piles. Une meĢthode de seĢparation et eĢvaluation avec plans seĢcants est proposeĢe pour reĢsoudre ce probleĢme.
Le second article preĢsente une meĢthode de reĢsolution exacte, eĢgalement de type seĢparation et eĢvaluation avec plans seĢcants, pour un probleĢme de tourneĢes de veĢhicules avec chargement dāitems rectangulaires en deux dimensions. Lāaire de chargement des veĢhicules correspond aussi aĢ un espace rectangulaire avec une orientation, puisque les items doivent eĢtre chargeĢs et deĢchargeĢs par lāun des coĢteĢs. Une contrainte impose que les items dāun client soient directement accessibles au moment de leur livraison.
Le dernier article aborde une probleĢme de tourneĢes de veĢhicules avec chargement dāitems rectangulaires, mais ouĢ les dimensions de certains items ne sont pas connus avec certitude lors de la planification des tourneĢes. Il est toutefois possible dāassocier une distribution de probabiliteĢs discreĢte sur les dimensions possibles de ces items. Le probleĢme est reĢsolu de manieĢre exacte avec la meĢthode L-Shape en nombres entiers.In this thesis, we study mixed vehicle routing and loading problems where a constraint is imposed on delivery sequences. More precisely, the items in the loading area of a vehicle must be directly accessible, without moving any other item, at delivery time. These problems are often found in the transportation of large objects (furniture, appliances).
The first paper proposes a branch-and-cut algorithm for a variant of the single vehicle pickup and delivery problem, where the loading area of the vehicle is divided into several stacks. When an item is picked up, it must be placed on the top of one of these stacks. Conversely, an item must be on the top of one of these stacks to be delivered. This requirement is called āLast In First Outā or LIFO constraint.
The second paper presents another branch-and-cut algorithm for a vehicle routing and loading problem with two-dimensional rectangular items. The loading area of the vehicles is also a rectangular area where the items are taken out from one side. A constraint states that the items of a given customer must be directly accessible at delivery time.
The last paper considers a stochastic vehicle routing and loading problem with two- dimensional rectangular items where the dimensions of some items are unknown when the routes are planned. However, it is possible to associate a discrete probability distribution on the dimensions of these items. The problem is solved with the Integer L-Shaped method
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