669 research outputs found
On the use of biased-randomized algorithms for solving non-smooth optimization problems
Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines
The two-echelon capacitated vehicle routing problem: models and math-based heuristics
Multiechelon distribution systems are quite common in supply-chain and logistics. They are used by public administrations in their transportation and traffic planning strategies, as well as by companies, to model own distribution systems. In the literature, most of the studies address issues relating to the movement of flows throughout the system from their origins to their final destinations. Another recent trend is to focus on the management of the vehicle fleets required to provide transportation among different echelons. The aim of this paper is twofold. First, it introduces the family of two-echelon vehicle routing problems (VRPs), a term that broadly covers such settings, where the delivery from one or more depots to customers is managed by routing and consolidating freight through intermediate depots. Second, it considers in detail the basic version of two-echelon VRPs, the two-echelon capacitated VRP, which is an extension of the classical VRP in which the delivery is compulsorily delivered through intermediate depots, named satellites. A mathematical model for two-echelon capacitated VRP, some valid inequalities, and two math-heuristics based on the model are presented. Computational results of up to 50 customers and four satellites show the effectiveness of the methods developed
A Survey of Network Design Problems
Network design problems arise in many different application areas such as air freight, highway traffic, and communication systems. The intention of this survey is to present a coherent unified view of a number of papers in the network design literature. We discuss suggested solution procedures, computational experience, relations between various network models, and potential application areas. Promising topics of research for improving, solving, and extending the models reviewed in this survey are also indicated.Supported in part by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
Lagrangian-based methods for single and multi-layer multicommodity capacitated network design
Le problÚme de conception de réseau avec coûts fixes et capacités (MCFND) et le problÚme
de conception de réseau multicouches (MLND) sont parmi les problÚmes de
conception de réseau les plus importants. Dans le problÚme MCFND monocouche, plusieurs
produits doivent ĂȘtre acheminĂ©s entre des paires origine-destination diffĂ©rentes
dâun rĂ©seau potentiel donnĂ©. Des liaisons doivent ĂȘtre ouvertes pour acheminer les produits,
chaque liaison ayant une capacité donnée. Le problÚme est de trouver la conception
du réseau à coût minimum de sorte que les demandes soient satisfaites et que les capacités
soient respectées. Dans le problÚme MLND, il existe plusieurs réseaux potentiels,
chacun correspondant à une couche donnée. Dans chaque couche, les demandes pour un
ensemble de produits doivent ĂȘtre satisfaites. Pour ouvrir un lien dans une couche particuliĂšre,
une chaĂźne de liens de support dans une autre couche doit ĂȘtre ouverte. Nous
abordons le problÚme de conception de réseau multiproduits multicouches à flot unique
avec coĂ»ts fixes et capacitĂ©s (MSMCFND), oĂč les produits doivent ĂȘtre acheminĂ©s uniquement
dans lâune des couches.
Les algorithmes basĂ©s sur la relaxation lagrangienne sont lâune des mĂ©thodes de rĂ©solution
les plus efficaces pour résoudre les problÚmes de conception de réseau. Nous
prĂ©sentons de nouvelles relaxations Ă base de noeuds, oĂč le sous-problĂšme rĂ©sultant se
décompose par noeud. Nous montrons que la décomposition lagrangienne améliore significativement
les limites des relaxations traditionnelles.
Les problÚmes de conception du réseau ont été étudiés dans la littérature. Cependant,
ces derniÚres années, des applications intéressantes des problÚmes MLND sont apparues,
qui ne sont pas couvertes dans ces études. Nous présentons un examen des problÚmes de
MLND et proposons une formulation générale pour le MLND. Nous proposons également
une formulation générale et une méthodologie de relaxation lagrangienne efficace
pour le problÚme MMCFND. La méthode est compétitive avec un logiciel commercial
de programmation en nombres entiers, et donne généralement de meilleurs résultats.The multicommodity capacitated fixed-charge network design problem (MCFND) and
the multilayer network design problem (MLND) are among the most important network
design problems. In the single-layer MCFND problem, several commodities have to
be routed between different origin-destination pairs of a given potential network. Appropriate
capacitated links have to be opened to route the commodities. The problem
is to find the minimum cost design and routing such that the demands are satisfied and
the capacities are respected. In the MLND, there are several potential networks, each
at a given layer. In each network, the flow requirements for a set of commodities must
be satisfied. However, the selection of the links is interdependent. To open a link in a
particular layer, a chain of supporting links in another layer has to be opened. We address
the multilayer single flow-type multicommodity capacitated fixed-charge network
design problem (MSMCFND), where commodities are routed only in one of the layers.
Lagrangian-based algorithms are one of the most effective solution methods to solve
network design problems. The traditional Lagrangian relaxations for the MCFND problem
are the flow and knapsack relaxations, where the resulting Lagrangian subproblems
decompose by commodity and by arc, respectively. We present new node-based
relaxations, where the resulting subproblem decomposes by node. We show that the
Lagrangian dual bound improves significantly upon the bounds of the traditional relaxations.
We also propose a Lagrangian-based algorithm to obtain upper bounds.
Network design problems have been the object of extensive literature reviews. However,
in recent years, interesting applications of multilayer problems have appeared that
are not covered in these surveys. We present a review of multilayer problems and propose
a general formulation for the MLND. We also propose a general formulation and
an efficient Lagrangian-based solution methodology for the MMCFND problem. The
method is competitive with (and often significantly better than) a state-of-the-art mixedinteger
programming solver on a large set of randomly generated instances
Arc Routing with Time-Dependent Travel Times and Paths
Vehicle routing algorithms usually reformulate the road network into a
complete graph in which each arc represents the shortest path between two
locations. Studies on time-dependent routing followed this model and therefore
defined the speed functions on the complete graph. We argue that this model is
often inadequate, in particular for arc routing problems involving services on
edges of a road network. To fill this gap, we formally define the
time-dependent capacitated arc routing problem (TDCARP), with travel and
service speed functions given directly at the network level. Under these
assumptions, the quickest path between locations can change over time, leading
to a complex problem that challenges the capabilities of current solution
methods. We introduce effective algorithms for preprocessing quickest paths in
a closed form, efficient data structures for travel time queries during routing
optimization, as well as heuristic and exact solution approaches for the
TDCARP. Our heuristic uses the hybrid genetic search principle with tailored
solution-decoding algorithms and lower bounds for filtering moves. Our
branch-and-price algorithm exploits dedicated pricing routines, heuristic
dominance rules and completion bounds to find optimal solutions for problem
counting up to 75 services. Based on these algorithms, we measure the benefits
of time-dependent routing optimization for different levels of travel-speed
data accuracy
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