Showing papers in "A Quarterly Journal of Operations Research in 2006"
TL;DR: A new metaheuristic to solve the LRP with capacitated routes and depots is presented, based on an extended and randomized version of Clarke and Wright algorithm and is competitive with a meta heuristics published for the case of uncapacitated depots.
Abstract: As shown in recent researches, the costs in distribution systems may be excessive if routes are ignored when locating depots. The location routing problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. This paper presents a new metaheuristic to solve the LRP with capacitated routes and depots. A first phase executes a GRASP, based on an extended and randomized version of Clarke and Wright algorithm. This phase is implemented with a learning process on the choice of depots. In a second phase, new solutions are generated by a post-optimization using a path relinking. The method is evaluated on sets of randomly generated instances, and compared to other heuristics and a lower bound. Solutions are obtained in a reasonable amount of time for such a strategic problem. Furthermore, the algorithm is competitive with a metaheuristic published for the case of uncapacitated depots.
246 citations
TL;DR: A direct extension of the label setting algorithm proposed by Martins in 1984 for the shortest path problem with multiple objectives, which computes all the efficient paths from a given source vertex, to all the other vertices of the network.
Abstract: This paper presents a direct extension of the label setting algorithm proposed by Martins in 1984 for the shortest path problem with multiple objectives. This extended version computes all the efficient paths from a given source vertex, to all the other vertices of the network. The algorithm copes with problems in which the "cost values" associated with the network arcs are positive. The proposed extension can handle objective functions that are either of the "sum" type or of the "bottleneck" type. The main modifications to Martins' algorithm for multi-objective shortest path problems are linked to the dominance test and the procedure for identifying efficient paths. The algorithmic features are described and a didactic example is provided to illustrate the working principle. The results of numerical experiments concerning the number of efficient solutions produced and the CPU time consumed for several configurations of objectives, on a set of randomly generated networks, are also provided.
91 citations
TL;DR: In this article, the authors extend Mousseau et al. (2003) to incorporate information about the confidence attached to each assignment example, hence providing inconsistency resolutions that the DMs are most likely to accept.
Abstract: Sorting models consist in assigning alternatives evaluated on several criteria to ordered categories. To implement such models it is necessary to set the values of the preference parameters used in the model. Rather than fixing the values of these parameters directly, a usual approach is to infer these values from assign- ment examples provided by the decision maker (DM), i.e., alternatives for which (s)he specifies a required category. However, assignment examples provided by DMs can be inconsistent, i.e., may not match the sorting model. In such situations, it is necessary to support the DMs in the resolution of this inconsistency. In this paper, we extend algorithms from Mousseau et al. (2003) that calculate different ways to remove assignment examples so that the information can be represented in the sorting model. The extension concerns the possibility to relax (rather than to delete) assignment examples. These algorithms incorporate information about the confidence attached to each assignment example, hence providing inconsistency resolutions that the DMs are most likely to accept.
78 citations
TL;DR: It appears that traffic flow on a highway during non-congested hours is best described using a M/G/1 queueing model, while during the congested hours however, the state dependent queueing GI/g/z models are more realistic.
Abstract: In this paper, the use of queueing theory for modeling uninterrupted traffic flows is evaluated. Empirical data on speeds and flows are used to evaluate speeds generated by the different queueing models. Using the Theil inequality coefficient as evaluation criterion, the speeds generated by the queueing models are compared to the empirical speeds. Queueing models that best fit the observed speeds are obtained. It appears that traffic flow on a highway during non-congested hours is best described using a M/G/1 queueing model. During the congested hours however, the state dependent queueing GI/G/z models are more realistic. Because the queueing models describe the empirical data well, they can also be used to evaluate potential improvements in existing traffic conditions.
74 citations
TL;DR: This talk provides a heuristic approach for the integrated solution of two decision problems, which occur consecutively while planning the charge and discharge operations of container ships in container terminals.
Abstract: This talk deals with the combination of two decision problems, which occur consecutively while planning the charge and discharge operations of container ships in container terminals. The Berth Allocation Problem (BAP) considers the allocation of ships to berths in the course of time. The Crane Assignment Problem (CAP) addresses the assignment of quay cranes to ships. We provide a heuristic approach for the integrated solution of these problems and present computational results based on real world data.
59 citations
TL;DR: The objective of this paper is to lay out the general framework of the ejection chain and filter-and-fan methods and present applications to a number of important combinatorial optimization problems and to provide insights into solving challenging problems in other settings.
Abstract: The design of effective neighborhood structures is fundamental to the performance of local search and metaheuristic algorithms for combinatorial optimization. Significant efforts have been made in the creation of larger and more powerful neighborhoods that are able to explore the solution space more extensively and effectively while keeping computation complexity within acceptable levels. The most important advances in this domain derive from dynamic and adaptive neighborhood constructions originating in ejection chain methods and a special form of a candidate list design that constitutes the core of the filter-and-fan method. The objective of this paper is to lay out the general framework of the ejection chain and filter-and-fan methods and present applications to a number of important combinatorial optimization problems. The features of the methods that make them effective in these applications is expected to provide insights into solving challenging problems in other settings.
35 citations
TL;DR: A column generation approach for solving airline crew scheduling problems that is based on a set partitioning model and discusses algorithmic aspects such as the use of bundle techniques for the fast, approximate solution of linear programs, a pairing generator that combines Lagrangean shortest path and callback techniques, and a novel “rapid branching” IP heuristic.
Abstract: The airline crew scheduling problem deals with the construction of crew rotations in order to cover the flights of a given schedule at minimum cost. The problem involves complex rules for the legality and costs of individual pairings and base constraints for the availability of crews at home bases. A typical instance considers a planning horizon of one month and several thousand flights. We propose a column generation approach for solving airline crew scheduling problems that is based on a set partitioning model. We discuss algorithmic aspects such as the use of bundle techniques for the fast, approximate solution of linear programs, a pairing generator that combines Lagrangean shortest path and callback techniques, and a novel “rapid branching” IP heuristic. Computational results for a number of industrial instances are reported. Our approach has been implemented within the commercial crew scheduling system NetLine/Crew of Lufthansa Systems Berlin GmbH.
34 citations
TL;DR: Stochastic semidefinite programs are introduced as a paradigm for dealing with uncertainty in data defining semideFinite programs.
Abstract: Semidefinite programs are a class of optimization problems that have been studied extensively during the past 15 years. Semidefinite programs are naturally related to linear programs, and both are defined using deterministic data. Stochastic programs were introduced in the 1950s as a paradigm for dealing with uncertainty in data defining linear programs. In this paper, we introduce stochastic semidefinite programs as a paradigm for dealing with uncertainty in data defining semidefinite programs.
33 citations
TL;DR: The effectiveness of state-dependent queueing models for analyzing traffic flows is tested by comparing the speeds generated by the queueing Models with the ones obtained by simulation, and an M/G/1 queueing model with Gaussian state-dependency outperforms all other state- dependence models.
Abstract: In this paper, the effectiveness of state-dependent queueing models for analyzing traffic flows is tested by comparing the speeds generated by the queueing models with the ones obtained by simulation. Simulation is thus used to evaluate speeds generated by the different queueing models. Different state-dependency functions are described and their performance is assessed. An M/G/1 queueing model with Gaussian state-dependency outperforms all other state-dependent queueing models. Different test results and insights are provided.
32 citations
TL;DR: A novel approach for scheduling elective surgeries over a short-term horizon is proposed which takes explicit consideration of these aspects and is formulated as a set packing problem and solved optimally through column generation and constraint branching.
Abstract: The efficient scheduling of surgical procedures to operating rooms in a hospital is a complex problem due to limited resources (e.g. medical staff, equipment) and conflicting objectives (e.g. reduce running costs and increase staff and patient satisfaction). A novel approach for scheduling elective surgeries over a short-term horizon is proposed which takes explicit consideration of these aspects. The problem is formulated as a set packing problem and solved optimally through column generation and constraint branching. Good results were obtained for instances from the literature.
31 citations
TL;DR: In this paper, a new heuristic called adaptive genetic algorithm (AGA) was proposed for an efficient exploration of the search space, which was tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation.
Abstract: This is a summary of the main results presented in the author’s PhD thesis. This thesis was supervised by El-Ghazali Talbi, and defended on 21 June 2005 at the University of Lille (France). It is written in French and is available at http://www.lifl.fr/~basseur/These.pdf. This work deals with the conception of cooperative methods in order to solve multi-objective combinatorial optimization problems. Many cooperation schemes between exact and/or heuristic methods have been proposed in the literature. We propose a classification of such schemes. We propose a new heuristic called adaptive genetic algorithm (AGA), that is designed for an efficient exploration of the search space. We consider several cooperation schemes between AGA and other methods (exact or heuristic). The performance of these schemes are tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation.
TL;DR: In this paper, lower bounds and upper bounds for the F2/max delay/Cmax problem were shown for a branch-and-bound procedure, which is shown to be efficient.
Abstract: In this paper, we show lower bounds and upper bounds for the F2/max delay/Cmax problem. These bounds allow us to build a branch-and-bound procedure. Computational experiments reveal that these bounds and the associated branch-and-bound procedure are efficient.
TL;DR: In this paper, the authors consider a dynamic real-life vehicle routing problem which is a combined load acceptance and generalised vehicle routing, incorporating a diversity of practical complexities such as time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, route restrictions associated to orders and vehicles, and drivers working hours.
Abstract: Real-life vehicle routing problems encounter a number of complexities that are not considered by the classical models found in the vehicle routing literature. In this paper we consider a dynamic real-life vehicle routing problem which is a combined load acceptance and generalised vehicle routing problem incorporating a diversity of practical complexities. Among those are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, route restrictions associated to orders and vehicles, and drivers’ working hours. We propose iterative improvement approaches based on Large Neighborhood Search. Our algorithms are characterised by very fast response times and thus, can be used within dynamic routing systems where input data can change at any time.
TL;DR: This work investigates three variants of a multi-commodity flow model for line planning that differ with respect to passenger routings, and compares these models theoretically and computationally on data for the city of Potsdam.
Abstract: The line planning problem is one of the fundamental problems in strategic planning of public and rail transport. It consists in finding lines and corresponding frequencies in a network such that a given demand can be satisfied. There are two objectives. Passengers want to minimize travel times, the transport company wishes to minimize operating costs. We investigate three variants of a multi-commodity flow model for line planning that differ with respect to passenger routings. The first model allows arbitrary routings, the second only unsplittable routings, and the third only shortest path routings with respect to the network. We compare these models theoretically and computationally on data for the city of Potsdam.
TL;DR: The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm, and how it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.
Abstract: This paper presents constraint programming (CP) as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.
TL;DR: A polynomial time algorithm is proposed to construct a solution to the days off scheduling problem when the demand for staffing fluctuates from day to another and when the number of total workdays is fixed in advance for each employee.
Abstract: This paper studies the days off scheduling problem when the demand for staffing fluctuates from day to another and when the number of total workdays is fixed in advance for each employee. The scheduling problem is then to allocate rests to employees with different days off policies: (1) two or three consecutive days off for each employee per week and (2) at least three consecutive days off for each employee per month. For each one, we propose a polynomial time algorithm to construct a solution if it exists.
TL;DR: This is a summary of the main results presented in the author’s PhD thesis, which deals with the structure and the connectivity of the Internet.
Abstract: This is a summary of the main results presented in the author’s PhD thesis. This thesis, written in English, was supervised by Frits Spieksma and defended on September 23, 2005 at the Katholieke Universiteit Leuven. A copy of the thesis is available from the authors website (http://www.econ. kuleuven.be/linda.moonen/public/). The thesis can be roughly split into two parts. The first part is dedicated to the problem of partitioning partially ordered sets into chains of limited size. The second part deals with the structure and the connectivity of the Internet.
TL;DR: This paper will discuss the application of location analysis techniques to the problem of optimally locating new health facilities in Nouna, including an analysis of the current situation, the creation of an appropriate model, solution techniques, and final results.
Abstract: The Nouna health district in Burkina Faso, Africa, has a population of approximately 275,000 people living in 290 villages, who are served by 23 health facilities. At present, the time and effort required in travelling to a health facility (especially during the rainy season) is, for many people, a deterrent to seeking proper medical care. As one step toward improving health care for the people in the district, this study focuses on the problem of optimally locating new health facilities. In light of a government goal that every village should have a health facility within 10 kilometers, the basic model we use for this problem is a covering model, which we then further adapt to the specific situation in Nouna. In this paper we will discuss our application of location analysis techniques to this problem, including an analysis of the current situation, the creation of an appropriate model, solution techniques, and final results.
Journal Article•
TL;DR: New, innovative strategies are sought, which promise on the one hand a long-term customer retention and assure, on the other hand, a more cost-efficient provision of electric energy.
TL;DR: This paper exploits results from moment problem theory and applies upper bound of loss probability of univariate random variable with special properties, given expected value and variance of VaR and CVaR under different type of information on distribution of random parameter.
Abstract: The main goal of this paper is to derive and compare values of worst-case VaR and CVaR under different type of information on distribution of random parameter. To this purpose we exploit results from moment problem theory and apply upper bound of loss probability of univariate random variable with special properties, given expected value and variance. Subsequently, we suppose that except the first two moments of the distributions, we know further characteristics of the class of distributions. We assume symmetry and/or unimodality. The bounds are also illustrated on the case of interbank exchange rate.
TL;DR: Using the popular puzzle game of Sudoku, this article highlights some of the ideas and topics covered in ZR-04-58.
Abstract: Using the popular puzzle game of Sudoku, this article highlights some of the ideas and topics covered in ZR-04-58.
TL;DR: This paper gives a short overview of the operations research techniques currently used to support structural and functional analysis of proteins.
Abstract: Operations Research is probably one of the most successful fields of applied mathematics used in Economics, Physics, Chemistry, almost everywhere one has to analyze huge amounts of data. Lately, these techniques were introduced in biology, especially in the protein analysis area to support biologists. The fast growth of protein data makes operations research an important issue in bioinformatics, a science which lays on the border between computer science and biology. This paper gives a short overview of the operations research techniques currently used to support structural and functional analysis of proteins.
TL;DR: A local search heuristic is proposed that iteratively improves the solution obtained for a related linear problem by applying drop and swap operations and shows the effectiveness and efficiency of the proposed heuristic.
Abstract: The bank-branch restructuring problem seeks to locate bank-branches by maintaining, closing, or opening branches, to provide the service required by clients, at minimum total cost. This nonlinear problem, due to the existence of economies of scale, is formulated as a mixed binary, integer linear model. The model obtained can be solved by a ready-available software. However, due to the problem combinatorial nature, only small size instances can be solved. Thus, we also propose a local search heuristic that iteratively improves the solution obtained for a related linear problem by applying drop and swap operations. The computational experiments performed show the effectiveness and efficiency of the proposed heuristic.
TL;DR: The main result is that the paper can efficiently compute a length-bounded s-t-flow which sends one fourth of the maximum flow value while exceeding the length bound by a factor of at most 2.
Abstract: Classical network flow problems do not impose restrictions on the choice of paths on which flow is sent. Only the arc capacities of the network have to be obeyed. This scenario is not always realistic. In fact, there are many problems for which, e.g., the number of paths being used to route a commodity or the length of such paths has to be small. These restrictions are considered in the length-bounded k-splittable s-t-flowproblem: The problem is a variant of the well known classical s-t-flow problem with the additional requirement that the number of paths that may be used to route the flow and the maximum length of those paths are bounded. Our main result is that we can efficiently compute a length-bounded s-t-flow which sends one fourth of the maximum flow value while exceeding the length bound by a factor of at most 2. We also show that this result leads to approximation algorithms for dynamic k-splittable s-t-flows.
TL;DR: It is discovered that Dănuţ Marcu's paper is a very slightly modified copy of the paper by J. B. Shearer on the independence number of dense graphs with large odd girth.
Abstract: Yesterday I started reading the paper by Danuţ Marcu that you sent me. I liked it: the results and proofs were not great but very neat. At the same time I was surprised that he gives too few references on such an important subject, and had a feeling that I had seen something like this somewhere. So today I started checking and 15 minutes ago I discovered that the paper is a very slightly modified copy of the paper by J. B. Shearer: “The independence number of dense graphs with large odd girth” (The Electronic Journal of Combinatorics 2 (1995), http://www.combinatorics.org/). This is the first time I discovered a 100% plagiarism in math publications. Then it struck me that recently I heard about a plagiarist in a letter from a well known mathematician. I found it it was about Marcu!!! Please let the fact be well known in OR community. Marcu has to get rejected no matter where he sends his or “his” papers.
TL;DR: A modified Clarke-Wright parallel savings algorithm, a nearest insertion algorithm and a tabu search heuristic for the open vehicle routing problem with time deadlines are presented.
Abstract: In the open route version of the well-known vehicle routing problem, vehicles are not required to return to the depot; or if they are required, then they return by traveling the same route back. In this study, we present a modified Clarke-Wright parallel savings algorithm, a nearest insertion algorithm and a tabu search heuristic for the open vehicle routing problem with time deadlines. Some random test problems and a real-life school bus routing problem are solved by these heuristics, and results are compared.
TL;DR: It does not matter too much which performance measure one chooses to evaluate hedge funds, because the newer performance measurement approaches result in rankings that are the same and thus result in the same assessments of hedge funds.
Abstract: It does not matter too much which performance measure one chooses to evaluate hedge funds. Because the newer performance measurement approaches result in rankings that are the same and thus result in the same assessments of hedge funds, use of the classic Sharpe ratio (even if it displays some undesirable features) is justified, at least from a practical perspective.
TL;DR: N P-hard problem as a total distance minimization problem as natural extension of the notorious traveling salesperson problem (TSP) with…
Abstract: N P-hard problem as a total distance minimization problem. Natural extension of the notorious traveling salesperson problem (TSP) with… Delivery vehicles (homogeneous or heterogeneous fleet) have a limited capacity. One or more nodes in the graph (single depot vs. multi-depots) are designated as origins of routes. Each route should emanate from such an origin node. The rest of the nodes are identified as customer nodes. Each route terminates at its own origin (closed route), or either at a different origin (depot) or at a customer node (open route).