Showing papers on "Assignment problem published in 2003"

Virtual landmarks for the internet

Abstract: Internet coordinate schemes have been proposed as a method for estimating minimum round trip time between hosts without direct measurement. In such a scheme, each host is assigned a set of coordinates, and Euclidean distance is used to form the desired estimate. Two key questions are: How accurate are coordinate schemes across the Internet as a whole? And: are coordinate assignment schemes fast enough, and scalable enough, for large scale use? In this paper we make contributions toward answering both those questions. Whereas the coordinate assignment problem has in the past been approached by nonlinear optimization, we develop a faster method based on dimensionality reduction of the Lipschitz embedding. We show that this method is reasonably accurate, even when applied to measurements spanning the Internet, and that it naturally leads to a scalable measurement strategy based on the notion of virtual landmarks.

Routing and wavelength assignment of scheduled lightpath demands

Abstract: We present algorithms that compute the routing and wavelength assignment (RWA) for scheduled lightpath demands in a wavelength-switching mesh network without wavelength conversion functionality. Scheduled lightpath demands are connection demands for which the setup and teardown times are known in advance. We formulate separately the routing problem and the wavelength assignment problem as spatio-temporal combinatorial optimization problems. For the former, we propose a branch and bound algorithm for exact resolution and an alternative tabu search algorithm for approximate resolution. A generalized graph coloring approach is used to solve the wavelength assignment problem. We compared the proposed algorithms to an RWA algorithm that sequentially computes the route and wavelength assignment for the scheduled lightpath demands.

Multi-Task Allocation and Path Planning for Cooperating UAVs

Abstract: This paper presents results on the guidance and control of fleets of cooperating Unmanned Aerial Vehicles (UAVs). A key challenge for these systems is to develop an overall control system architecture that can perform optimal coordination of the fleet, evaluate the overall fleet performance in real-time, and quickly reconfigure to account for changes in the environment or the fleet. The optimal fleet coordination problem includes team composition and goal assignment, resource allocation, and trajectory optimization. These are complicated optimization problems for scenarios with many vehicles, obstacles, and targets. Furthermore, these problems are strongly coupled, and optimal coordination plans cannot be achieved if this coupling is ignored. This paper presents an approach to the combined resource allocation and trajectory optimization aspects of the fleet coordination problem which calculates and communicates the key information that couples the two. Also, this approach permits some steps to be distributed between parallel processing platforms for faster solution. This algorithm estimates the cost of various trajectory options using the distributed platforms and then solves a centralized assignment problem to minimize the mission completion time. The detailed trajectory planning for this assignment can then be distributed back to the platforms. During execution, the coordination and control system reacts to changes in the fleet or the environment. The overall approach is demonstrated on several example scenarios to show multi-task allocation and cooperative path planning.

Optimization‐based procurement for transportation services

Abstract: This paper presents the concept of optimization-based procurement for transportation services. The approach allows both the shipper and carriers to benefit through the use of a collaborative approach to securing and managing a strategic relationship. Because the shipper's assignment problem involves a combinatorial number of choices and cannot be easily executed manually, the procurement process benefits strongly from the use of optimization. The authors introduce the optimization-based procurement process, briefly analyze the theoretical issues, and discuss lessons learned from its application in practice over the last several years.

A study of a target tracking algorithm using global nearest neighbor approach

Abstract: This paper compares two algorithms for Multiple Target Tracking (MTT), using Global Nearest Neighbor (GNN) and Suboptimal Nearest Neighbor (SNN) approach respectively. For both algorithms the observations are divided in clusters to reduce computational efforts. For each cluster the assignment problem is solved by using Munkres algorithm or according SNN rules. Results reveal that in some cases the GNN approach gives batter solution than.SNN approach. The computational time, needed for assignment problem solution using Munkres algorithm is studied and results prove that it is suitable for real time implementations.

Robust monitoring of link delays and faults in IP networks

Abstract: In this paper, we develop failure-resilient techniques for monitoring link delays and faults in a service provider or enterprise IP network. Our two-phased approach attempts to minimize both the monitoring infrastructure costs as well as the additional traffic due to probe messages. In the first phase of our approach, we compute the locations of a minimal set of monitoring stations such that all network links are covered, even in the presence of several link failures. Subsequently, in the second phase, we compute a minimal set of probe messages that are transmitted by the stations to measure link delays and isolate network faults. We show that both the station selection problem as well as the probe assignment problem are NP-hard. We then propose greedy approximation algorithms that achieve a logarithmic approximation factor for the station selection problem and a constant factor for the probe assignment problem. These approximation ratios are provably very close to the best possible bounds for any algorithm.

A New Genetic Algorithm for the Quadratic Assignment Problem

Abstract: In this paper we propose several variants of a new genetic algorithm for the solution of the quadratic assignment problem. We designed a special merging rule for creating an offspring that exploits the special structure of the problem. We also designed a new type of a tabu search, which we term aconcentric tabu search. This tabu search is applied on the offspring before consideration for inclusion in the population. The algorithm provided excellent results for a set of 29 test problems having between 30 and 100 facilities.

Evolutionary Algorithms and the Maximum Matching Problem

Abstract: Randomized search heuristics like evolutionary algorithms are mostly applied to problems whose structure is not completely known but also to combinatorial optimization problems. Practitioners report surprising successes but almost no results with theoretically well-founded analyses exist. Such an analysis is started in this paper for a fundamental evolutionary algorithm and the well-known maximum matching problem. It is proven that the evolutionary algorithm is a polynomial-time randomized approximation scheme (PRAS) for this optimization problem, although the algorithm does not employ the idea of augmenting paths. Moreover, for very simple graphs it is proved that the expected optimization time of the algorithm is polynomially bounded and bipartite graphs are constructed where this time grows exponentially.

Adaptive and decentralized operator placement for in-network query processing

Abstract: In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed These algorithms are however centralized and cannot be used in a sensor network We describe an adaptive and decentralized algorithm that progressively refines the placement of operators by walking through neighbor nodes Simulation results illustrate the potential benefits of our approach They also show that our placement strategy can achieve near optimal placement onto various graph topologies despite the risks of local minima

Evolutionary algorithms and the maximum matching problem

Abstract: Randomized search heuristics like evolutionary algorithms are mostly applied to problems whose structure is not completely known but also to combinatorial optimization problems. Practitioners report surprising successes but almost no results with theoretically well-founded analyses exist. Such an analysis is started in this paper for a fundamental evolutionary algorithm and the well-known maximum matching problem. It is proven that the evolutionary algorithm is a polynomial-time randomized approximation scheme (PRAS) for this optimization problem, although the algorithm does not employ the idea of augmenting paths. Moreover, for very simple graphs it is proved that the expected optimization time of the algorithm is polynomially bounded and bipartite graphs are constructed where this time grows exponentially.

Reconstruction of the early Universe as a convex optimization problem

Abstract: We show that the deterministic past history of the Universe can be uniquely reconstructed from knowledge of the present mass density field, the latter being inferred from the three-dimensional distribution of luminous matter, assumed to be tracing the distribution of dark matter up to a known bias. Reconstruction ceases to be unique below those scales – a few Mpc – where multistreaming becomes significant. Above 6 h−1 Mpc we propose and implement an effective Monge–Ampere–Kantorovich method of unique reconstruction. At such scales the Zel'dovich approximation is well satisfied and reconstruction becomes an instance of optimal mass transportation, a problem which goes back to Monge. After discretization into N point masses one obtains an assignment problem that can be handled by effective algorithms with not more than O(N3) time complexity and reasonable CPU time requirements. Testing against N-body cosmological simulations gives over 60 per cent of exactly reconstructed points. We apply several interrelated tools from optimization theory that were not used in cosmological reconstruction before, such as the Monge–Ampere equation, its relation to the mass transportation problem, the Kantorovich duality and the auction algorithm for optimal assignment. A self-contained discussion of relevant notions and techniques is provided.

Instance generators and test suites for the multiobjective quadratic assignment problem

Abstract: We describe, and make publicly available, two problem instance generators for a multiobjective version of the well-known quadratic assignment problem (QAP). The generators allow a number of instance parameters to be set, including those controlling epistasis and inter-objective correlations. Based on these generators, several initial test suites are provided and described. For each test instance we measure some global properties and, for the smallest ones, make some initial observations of the Pareto optimal sets/fronts. Our purpose in providing these tools is to facilitate the ongoing study of problem structure in multiobjective (combinatorial) optimization, and its effects on search landscape and algorithm performance.

Recent advances in the solution of quadratic assignment problems

Abstract: The quadratic assignment problem (QAP) is notoriously difficult for exact solution methods. In the past few years a number of long-open QAPs, including those posed by Steinberg (1961), Nugent et al. (1968) and Krarup (1972) were solved to optimality for the first time. The solution of these problems has utilized both new algorithms and novel computing structures. We describe these developments, as well as recent work which is likely to result in the solution of even more difficult instances.

Coalitional strategy-proof and resource-monotonic solutions for multiple assignment problems

Abstract: We consider the problem of allocating indivisible objects when agents may desire to consume more than one object and monetary transfers are not possible. Each agent receives a set of objects and free disposal is allowed. We are interested in allocation rules that satisfy “appealing” properties from an economic and social point of view. Our main result shows that sequential dictatorships are the only efficient and coalitional strategy-proof solutions to the multiple assignment problem. Adding resource-monotonicity narrows this class down to serial dictatorships.

Quasi-variational inequality formulation of the multiclass dynamic traffic assignment problem ☆

Abstract: We consider the extension of a single user-class macroscopic dynamic traffic assignment model to include multiple user-classes. The distinction between user-classes is typically based on vehicle characteristics, e.g. cars and trucks. Interactions between the user-classes sharing the same road infrastructure are taken into account. To deal with various different asymmetries that may occur, such as interuser-class interaction, interspatial and intertemporal asymmetries, the model is specified as a (quasi) variational inequality problem. A nested modified projection method is proposed to solve the assignment problem. The solution of the problem depends heavily on the choice of some very important input: the multiclass link travel time functions. Under mild restrictions there exists a solution, which needs however not be unique. A case study illustrates the model.

Automata, languages and programming : 30th International Colloquium, ICALP 2003, Eindhoven, the Netherlands, June 30-July 4, 2003 : proceedings

Abstract: Invited Lectures.- Polarized Process Algebra and Program Equivalence.- Problems on RNA Secondary Structure Prediction and Design.- Some Issues Regarding Search, Censorship, and Anonymity in Peer to Peer Networks.- The SPQR-Tree Data Structure in Graph Drawing.- Model Checking and Testing Combined.- Logic and Automata: A Match Made in Heaven.- Algorithms.- Pushdown Automata and Multicounter Machines, a Comparison of Computation Modes.- Generalized Framework for Selectors with Applications in Optimal Group Testing.- Decoding of Interleaved Reed Solomon Codes over Noisy Data.- Process Algebra.- On the Axiomatizability of Ready Traces, Ready Simulation, and Failure Traces.- Resource Access and Mobility Control with Dynamic Privileges Acquisition.- Replication vs. Recursive Definitions in Channel Based Calculi.- Approximation Algorithms.- Improved Combinatorial Approximation Algorithms for the k-Level Facility Location Problem.- An Improved Approximation Algorithm for the Asymmetric TSP with Strengthened Triangle Inequality.- An Improved Approximation Algorithm for Vertex Cover with Hard Capacities.- Approximation Schemes for Degree-Restricted MST and Red-Blue Separation Problem.- Approximating Steiner k-Cuts.- MAX k-CUT and Approximating the Chromatic Number of Random Graphs.- Approximation Algorithm for Directed Telephone Multicast Problem.- Languages and Programming.- Mixin Modules and Computational Effects.- Decision Problems for Language Equations with Boolean Operations.- Generalized Rewrite Theories.- Complexity.- Sophistication Revisited.- Scaled Dimension and Nonuniform Complexity.- Quantum Search on Bounded-Error Inputs.- A Direct Sum Theorem in Communication Complexity via Message Compression.- Data Structures.- Optimal Cache-Oblivious Implicit Dictionaries.- The Cell Probe Complexity of Succinct Data Structures.- Succinct Representations of Permutations.- Succinct Dynamic Dictionaries and Trees.- Graph Algorithms.- Labeling Schemes for Weighted Dynamic Trees.- A Simple Linear Time Algorithm for Computing a (2k - 1)-Spanner of O(n 1+1/k ) Size in Weighted Graphs.- Multicommodity Flows over Time: Efficient Algorithms and Complexity.- Multicommodity Demand Flow in a Tree.- Automata.- Skew and Infinitary Formal Power Series.- Nondeterminism versus Determinism for Two-Way Finite Automata: Generalizations of Sipser's Separation.- Residual Languages and Probabilistic Automata.- A Testing Scenario for Probabilistic Automata.- The Equivalence Problem for t-Turn DPDA Is Co-NP.- Flip-Pushdown Automata: k + 1 Pushdown Reversals Are Better than k.- Optimization and Games.- Convergence Time to Nash Equilibria.- Nashification and the Coordination Ratio for a Selfish Routing Game.- Stable Marriages with Multiple Partners: Efficient Search for an Optimal Solution.- An Intersection Inequality for Discrete Distributions and Related Generation Problems.- Graphs and Bisimulation.- Higher Order Pushdown Automata, the Caucal Hierarchy of Graphs and Parity Games.- Undecidability of Weak Bisimulation Equivalence for 1-Counter Processes.- Bisimulation Proof Methods for Mobile Ambients.- On Equivalent Representations of Infinite Structures.- Online Problems.- Adaptive Raising Strategies Optimizing Relative Efficiency.- A Competitive Algorithm for the General 2-Server Problem.- On the Competitive Ratio for Online Facility Location.- A Study of Integrated Document and Connection Caching.- Verification.- A Solvable Class of Quadratic Diophantine Equations with Applications to Verification of Infinite-State Systems.- Monadic Second-Order Logics with Cardinalities.- ? 2 ? ? 2 ? AFMC.- Upper Bounds for a Theory of Queues.- Around the Internet.- Degree Distribution of the FKP Network Model.- Similarity Matrices for Pairs of Graphs.- Algorithmic Aspects of Bandwidth Trading.- Temporal Logic and Model Checking.- CTL+ Is Complete for Double Exponential Time.- Hierarchical and Recursive State Machines with Context-Dependent Properties.- Oracle Circuits for Branching-Time Model Checking.- Graph Problems.- There Are Spanning Spiders in Dense Graphs (and We Know How to Find Them).- The Computational Complexity of the Role Assignment Problem.- Fixed-Parameter Algorithms for the (k, r)-Center in Planar Graphs and Map Graphs.- Genus Characterizes the Complexity of Graph Problems: Some Tight Results.- Logic and Lambda-Calculus.- The Definition of a Temporal Clock Operator.- Minimal Classical Logic and Control Operators.- Counterexample-Guided Control.- Axiomatic Criteria for Quotients and Subobjects for Higher-Order Data Types.- Data Structures and Algorithms.- Efficient Pebbling for List Traversal Synopses.- Function Matching: Algorithms, Applications, and a Lower Bound.- Simple Linear Work Suffix Array Construction.- Types and Categories.- Expansion Postponement via Cut Elimination in Sequent Calculi for Pure Type Systems.- Secrecy in Untrusted Networks.- Locally Commutative Categories.- Probabilistic Systems.- Semi-pullbacks and Bisimulations in Categories of Stochastic Relations.- Quantitative Analysis of Probabilistic Lossy Channel Systems.- Discounting the Future in Systems Theory.- Information Flow in Concurrent Games.- Sampling and Randomness.- Impact of Local Topological Information on Random Walks on Finite Graphs.- Analysis of a Simple Evolutionary Algorithm for Minimization in Euclidean Spaces.- Optimal Coding and Sampling of Triangulations.- Generating Labeled Planar Graphs Uniformly at Random.- Scheduling.- Online Load Balancing Made Simple: Greedy Strikes Back.- Real-Time Scheduling with a Budget.- Improved Approximation Algorithms for Minimum-Space Advertisement Scheduling.- Anycasting in Adversarial Systems: Routing and Admission Control.- Geometric Problems.- Dynamic Algorithms for Approximating Interdistances.- Solving the Robots Gathering Problem.

Reconstruction of the early Universe as a convex optimization problem

Abstract: We show that the deterministic past history of the Universe can be uniquely reconstructed from the knowledge of the present mass density field, the latter being inferred from the 3D distribution of luminous matter, assumed to be tracing the distribution of dark matter up to a known bias. Reconstruction ceases to be unique below those scales -- a few Mpc -- where multi-streaming becomes significant. Above 6 Mpc/h we propose and implement an effective Monge-Ampere-Kantorovich method of unique reconstruction. At such scales the Zel'dovich approximation is well satisfied and reconstruction becomes an instance of optimal mass transportation, a problem which goes back to Monge (1781). After discretization into N point masses one obtains an assignment problem that can be handled by effective algorithms with not more than cubic time complexity in N and reasonable CPU time requirements. Testing against N-body cosmological simulations gives over 60% of exactly reconstructed points. We apply several interrelated tools from optimization theory that were not used in cosmological reconstruction before, such as the Monge-Ampere equation, its relation to the mass transportation problem, the Kantorovich duality and the auction algorithm for optimal assignment. Self-contained discussion of relevant notions and techniques is provided.

Dynamic traffic modelling and dynamic stochastic user equilibrium assignment for general road networks

Abstract: This paper investigates the requirements of dynamic traffic modelling, and proposes the deterministic queuing model as a plausible link performance function to describe the relationship between inflows, outflows, and link travel costs in time-varying condition. Then, it explains how we can perform logit-based stochastic network loading for general road networks in the dynamic case. In particular, this paper shows how to perform dynamic stochastic network loadings for many-to-many origin–destination pairs, and what should be considered to maintain correct flow propagation in the network loading process. Next, this paper shows how the stochastic dynamic user equilibrium (SDUE) assignment problem can be solved without direct evaluation of the objective function. For this purpose, a quadratic interpolation, the method of successive average, and the pure network loading method are adopted at the line-search step in the solution algorithm. Numerical examples show that the present SDUE assignment model with a quadratic interpolation gives rise to a convergent solution with good quality whilst needing less computation time. Furthermore, it is found that the predictive cost-flow association (Proceedings of the European Transport Conferences, Seminar F, P434, 1999, p. 79) is preferable to the reactive one because the former can produce consistent assignment patterns regardless of the size of dispersion parameter θ in the logit model for route choice.

The minimum range assignment problem on linear radio networks

Abstract: Given a set S of radio stations located on a line and an integer h ≥ 1 , the MIN ASSIGNMENT problem consists in finding a range assignment of minimum power consumption provided that any pair of stations can communicate in at most h hops. Previous positive results for this problem are only known when h=|S|-1 or in the uniform chain case (i.e., when the stations are equally spaced). As for the first case, Kirousis et al. [7] provided a polynomial-time algorithm while, for the second case, they derive a polynomial-time approximation algorithm. This paper presents the first polynomial-time, approximation algorithm for the MIN ASSIGNMENT problem. The algorithm guarantees a 2-approximation ratio and runs in O(hn 3 ) time. We also prove that, for fixed h and for ``well spaced'' instances (a broad generalization of the uniform chain case), the problem admits a polynomial-time approximation scheme . This result significantly improves over the approximability result given by Kirousis {et al}. Both our approximation results are obtained via new algorithms that exactly solve two natural variants of the MIN ASSIGNMENT problem: the problem in which every station must reach a fixed one in at most h hops and the problem in which the goal is to select a subset of bases such that all the other stations must reach one base in at most h-1 hops. Finally, we show that for h=2 the MIN ASSIGNMENT problem can be exactly solved in O(n 3 ) time.

Dynamic Capacity Acquisition and Assignment under Uncertainty

Abstract: Given a set of m resources and n tasks, the dynamic capacity acquisition and assignment problem seeks a minimum cost schedule of capacity acquisitions for the resources and the assignment of resources to tasks, over a given planning horizon of T periods. This problem arises, for example, in the integrated planning of locations and capacities of distribution centers (DCs), and the assignment of customers to the DCs, in supply chain applications. We consider the dynamic capacity acquisition and assignment problem in an environment where the assignment costs and the processing requirements for the tasks are uncertain. Using a scenario based approach, we develop a stochastic integer programming model for this problem. The highly non-convex nature of this model prevents the application of standard stochastic programming decomposition algorithms. We use a recently developed decomposition based branch-and-bound strategy for the problem. Encouraging preliminary computational results are provided.

A case-based expert support system for due-date assignment in a wafer fabrication factory

Abstract: Owing to the complexity of wafer fabrication, the traditional human approach to assigning due-date is imprecise and very prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due date to each order becomes a challenge to the production planning and scheduling staff. Since most production orders are similar to those previously manufactured, the case based reasoning (CBR) approach provides a suitable means for solving the due-date assignment problem. This research proposes a CBR approach that employs the k-nearest neighbors concept with dynamic feature weights and non-linear similarity functions. The test results show that the proposed approach can more accurately predict order due dates than other approaches.

A decomposition scheme for system optimal dynamic traffic assignment models

Abstract: In this paper, a Dantzig-Wolfe decomposition based solution algorithm is developed for the linear programming formulation introduced by Ziliaskopoulos (2000) for System Optimal Dynamic Traffic Assignment problem. The algorithm takes advantage of the network structure in the constraint set of the formulation: the sub-problem is formulated as a minimum-cost-flow problem and the master as a simpler linear programming problem, which allows DTA to be solved more efficiently on meaningful networks. The algorithm is tested on an example network and its performance is analyzed.

Resequencing and feature assignment on an automated assembly line

Abstract: We consider the problem of resequencing a prearranged set of jobs on a moving assembly line with the objective of minimizing changeover costs. A changeover cost is incurred whenever two consecutive jobs do not share the same feature. Features are assigned from a set of job-specific feasible features. Resequencing is limited by the availability of offline buffers. The problem is motivated by a vehicle resequencing and painting problem at a major U.S. automotive manufacturer. We develop a model for solving the joint resequencing and feature assignment problem and an efficient solution procedure for simultaneously determining optimal feature assignments and vehicle sequences. We show that our solution approach is amenable to implementation in environments where a solution must be obtained within tight time constraints. We also show that the effect of offline buffers is of the diminishing kind with most of the benefits achieved with very few buffers. This means that limited resequencing flexibility is generally sufficient. Furthermore, we show that the value of resequencing is sensitive to the feature density matrix, with resequencing having a significant impact on cost only when density is in the middle range.

Power allocation and bit loading for spatial multiplexing in MIMO systems

Abstract: The power assignment problem is important for multiple-input-multiple-output (MIMO) systems to achieve high capacity. Although this problem is solved by the well-known water filling (WF) algorithms, this does not provide an optimal solution if the system is constrained to a fixed raw bit error rate threshold and to discrete modulation orders. An approximate approach, called QoS based WF, is proposed to solve the power assignment problem with such constraints. It is shown to outperform quantization of the conventional water filling solution and a well known bit loading algorithm (Chow's algorithm) used in digital subscriber lines (DSL).

An Extended Branch-and-Bound Method for Locomotive Assignment

Abstract: This paper considers the locomotive assignment problem encountered during the planning of the operations of a freight railroad, which consists of providing sufficient motive power to pull a set of scheduled trains at minimum cost while satisfying locomotive availability and maintenance requirements. In 1997, Ziarati et al. proposed for this problem a heuristic branch-and-price approach that relies on a simple depth-first search strategy without backtracking. In this paper, we present an efficient backtracking mechanism that can be added to this heuristic branch-and-price approach. To do so, we propose and evaluate different branching methods that impose multiple decisions on locomotive routes at each branching node, including one decision that forbids one such route. Finally, we introduce different ways of computing an estimate of the best integer solution value that can be obtained from a branch-and-bound node. These estimates can be used to guide the backtracking process of a two-phase search strategy.

A self-organizing channel assignment algorithm: A cellular learning automata approach

Abstract: Introduction of micro-cellular networks offer a potential increase in capacity of cellular networks, but they create problems in management of the cellular networks. A solution to these problems is self-organizing channel assignment algorithm with distributed control. In this paper, we first introduce the model of cellular learning automata in which learning automata are used to adjust the state transition probabilities of cellular automata. Then a cellular learning automata based self-organizing channel assignment algorithm is introduced. The simulation results show that the micro-cellular network can self-organize by using simple channel assignment algorithm as the network operates.