Showing papers on "Assignment problem published in 1993"

The concept assignment problem in program understanding

Abstract: Concept assignment is a process of recognizing concepts within a computer program and building up an understanding of the program by relating the recognized concepts to portions of the program, its operational context and to one another. The problem of discovering individual human oriented concepts and assigning them to their implementation oriented counterparts for a given program is the concept assignment problem. The authors argue that the solution to this problem requires methods that have a strong plausible reasoning component. They illustrate these ideas through example scenarios using an existing design recovery system called DESIRE. DESIRE is evaluated based on its usage on real-world problems over the years. >

Channel assignment for cellular radio using simulated annealing

Abstract: The channel assignment problem, i.e. the task of assigning the channels to the radio base stations in a spectrum-efficient way, is an NP-complete optimization problem occurring during design of cellular radio systems. Previously, this problem has been solved by graph coloring algorithms. An alternative approach is presented. The problem is solved using simulated annealing, which is a general approach to combinatorial optimization. The algorithm has been successfully applied to practical radio network planning situations. One major benefit of the approach consists in the enhanced flexibility it gives to the engineer. >

The concept assignment problem in program understanding

Abstract: The problem of discovering individual human oriented concepts and assigning them to their implementation-oriented counterparts for a given program is the concept assignment problem. It is argued that the solution to this problem requires methods that have a strong plausible reasoning component. These ideas are illustrated through recovery system called DESIRE. DESIRE is evaluated based on its use on real-world problems over the years. >

A linear programming approach for the weighted graph matching problem

Abstract: A linear programming (LP) approach is proposed for the weighted graph matching problem. A linear program is obtained by formulating the graph matching problem in L/sub 1/ norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a simplex-based algorithm. Then, approximate 0-1 integer solutions are obtained by applying the Hungarian method on the real solutions of the linear program. The complexity of the proposed algorithm is polynomial time, and it is O(n/sup 6/L) for matching graphs of size n. The developed algorithm is compared to two other algorithms. One is based on an eigendecomposition approach and the other on a symmetric polynomial transform. Experimental results showed that the LP approach is superior in matching graphs than both other methods. >

Genetic Hybrids for the Quadratic Assignment Problem.

Abstract: A new hybrid procedure that combines genetic operators to existing heuristics is proposed to solve the Quadratic Assignment Problem (QAP). Genetic operators are found to improve the performance of both local search and tabu search. Some guidelines are also given to design good hybrid schemes. These hybrid algorithms are then used to improve on the best known solutions of many test problems in the literature.

A new class of instantaneous dynamic user-optimal traffic assignment models

Abstract: The instantaneous dynamic user-optimal (DUO) traffic assignment problem is to determine vehicle flows on each link at each instant of time resulting from drivers using instantaneous minimal-time routes. Instantaneous route time is the travel time incurred if traffic conditions remain unchanged while driving along the route. In this paper, we introduce a different definition of an instantaneous DUO state. Using the optimal control theory approach, we formulate two new DUO traffic assignment models for a congested transportation network. These models include new formulations of the objective function and flow propagation constraints, and are dynamic generalizations of the static user-optimal model. The equivalence of the solutions of the two optimal control programs with DUO traffic flows is demonstrated by proving the equivalence of the first-order necessary conditions of the two programs with the instantaneous DUO conditions. Since these optimal control problems are convex programs with linear constraints...

A Lagrangian Relaxation Algorithm for Multidimensional Assignment Problems Arising from Multitarget Tracking

Abstract: The central problem in multitarget tracking is the data association problem of partitioning the observations into tracks in some optimal way so that an accurate estimate of the true tracks can be recovered. This work considers what is perhaps the simplest multitarget tracking problem in a setting where the issues are easily delineated, i.e., straight lines in two-dimensional space-time with an error component introduced into the observations. A multidimensional assignment problem is formulated using gating techniques to introduce sparsity into the problem and filtering techniques to generate tracks which are then used to score each assignment of a collection of observations to a filtered track. Problem complexity is further reduced by decomposing the problem into disjoint components, which can then be solved independently. A recursive Lagrangian relaxation algorithm is developed to obtain high quality suboptimal solutions in real-time. The algorithms are, however, applicable to a large class of sparse mul...

Network performance under system optimal and user equilibrium dynamic assignments: implications for advanced traveler information systems

Abstract: A comparative assessment was undertaken of network cost and performance under time-dependent system optimal (SO) and user equilibrium (UE) assignment patterns, with particular reference to the effectiveness of advanced traveler information systems (ATIS). Both SO and UE solutions were found using a new simulation-based algorithm for the time-dependent assignment problem. Experiments were conducted using a test network with signal-controlled junctions under progressively increasing network loading intensities. A diagnosis of system performance for various intensities of loading was effected using network-level traffic descriptors for both SO and UE assignments. The results affirm the validity of a meaningful demarcation between SO and UE assignments in urban traffic networks and provide useful insights for macroscopic network-level relations among traffic descriptors. These results suggest that ATIS information supply strategies based on SO route guidance could considerably outperform descriptive noncooperative information strategies, especially at moderate to high congestion levels in the network. The results also illustrate the time-dependent nature of the gains achieved by an SO assignment vis-a-vis a UE assignment in a congested traffic network.

Network Flows and Matching: First DIMACS Implementation Challenge

Abstract: Goldberg's algorithm for maximum flow in perspective: A computational study by R. J. Anderson and J. C. Setubal Implementations of the Goldberg-Tarjan maximum flow algorithm by Q. C. Nguyen and V. Venkateswaran Implementing a maximum flow algorithm: Experiments with dynamic trees by T. Badics and E. Boros Implementing the push-relabel method for the maximum flow problem on a connection machine by F. Alizadeh and A. V. Goldberg A case study in algorithm animation: Maximum flow algorithms by G. E. Shannon, J. MacCuish, and E. Johnson An empirical study of min cost flow algorithms by R. G. Bland, J. Cheriyan, D. L. Jensen, and L. Ladanyi On implementing scaling push-relabel algorithms for the minimum-cost flow problem by A. V. Goldberg and M. Kharitanov Performance evaluation of the MINET minimum cost netflow solver by I. Maros A speculative contraction method for minimum cost flows: Toward a practical algorithm by S. Fujishige, K. Iwano, J. Nakano, and S. Tezuka An experimental implementation of the dual cancel and tighten algorithm for minimum-cost network flow by S. T. McCormick and L. Liu A fast implementation of a path-following algorithm for maximizing a linear function over a network polytope by A. Joshi, A. S. Goldstein, and P. M. Vaidya An efficient implementation of a network interior point method by M. G. C. Resende and G. Veiga On the massively parallel solution of linear network flow problems by S. Neilsen and S. Zenios Approximating concurrent flow with unit demands and capacities: An implementation by J. M. Borger, T. S. Kang, and P. N. Klein Implementation of a combinatorial multicommodity flow algorithm by T. Leong, P. W. Shor, and C. Stein Reverse auction algorithms for assignment problems by D. A. Castanon An approximate dual projective algorithm for solving assignment problems by K. G. Ramakrishnan, N. K. Karmarkar, and A. P. Kamath An implementation of a shortest augmenting path algorithm for the assignment problem by J. Hao and G. Kocur The assignment problem on parallel architectures by M. Brady, K. K. Jung, H. T. Nguyen, R. Raghavan, and R. Subramonian An experimental comparison of two maximum cardinality matching programs by S. T. Crocker Implementing an $O(\sqrt {N}M)$ cardinality matching algorithm by R. B. Mattingly and N. P. Richey Solving large-scale matching problems by D. Applegate and W. Cook Appendix A: Electronically available materials by C. C. McGeoch Appendix B: Panel discussion highlights by D. S. Johnson.

Scheduling medical residents to rotations: solving the large-scale multiperiod staff assignment problem

Abstract: The resident scheduling problem is a specific case of the multiperiod staff assignment problem where individuals are assigned to a variety of tasks over multiple time periods. As in many staffing and training situations, numerous limitations and requirements may be placed on those assignments. This paper presents a procedure for addressing two major problems inherent in the determination of a solution to this type of problem: infeasibilities that naturally occur in the scheduling environment but are obscured by complexity; and the intractable nature of large-scale models with this structure. The procedure developed describes a systematic approach that allows decision makers to resolve system-inherent infeasibilities, and a heuristic based on rounding to develop good feasible solutions to the model. The procedure is illustrated via a case example of resident assignments for teaching and training modules in a university affiliated teaching hospital.

Deadline assignment in a distributed soft real-time system

Abstract: In a distributed environment, tasks often have processing demands on multiple sites. A distributed task is usually divided up into several subtasks, each one to be executed at some site in order. In a real-time system, an overall deadline is usually specified by an application designer indicating when a distributed task is to be finished. To study the subtask deadline assignment problem a simple model of the system and tasks is postulated. The focus is on soft real-time systems. In such systems, it is very difficult to guarantee that all deadlines will be met, and hence one tries to minimize the number of deadlines that are missed. The authors examine (through simulations) four strategies for subtask deadline assignment in a distributed soft real-time environment. >

Output feedback eigenstructure assignment using two Sylvester equations

Abstract: The eigenstructure assignment problem with output feedback is studied for systems satisfying the condition p+m>n. The main tool used is the concept of (C, A, B)-invariance and two coupled Sylvester equations, the solution of which leads to the computation of an output stabilizing feedback. A computationally efficient algorithm for the solution of these two coupled equations, which leads to the computation of a desired output feedback, is presented. >

Performance and fault-tolerance of neural networks for optimization

Abstract: The fault-tolerance characteristics of time-continuous, recurrent artificial neural networks (ANNs) that can be used to solve optimization problems are investigated. The performance of these networks is illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to up to 13 simultaneous stuck-at-1 or stuck-at-0 faults for network sizes of up to 900 neurons. The effect of these faults on the performance is demonstrated, and the cause for the observed fault-tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations and the potential benefits of delegating a critical task to a fault-tolerant network are discussed. >

Practical problem solving with cutting plane algorithms in combinatorialoptimization.

Abstract: Cutting plane algorithms have turned out to be practically successful computational tools in combinatorial optimization, in particular, when they are embedded in a branch and bound framework. Implementations of such \branch and cut" algorithms are rather complicated in comparison to many purely combinatorial algorithms. The purpose of this article is to give an introduction to cutting plane algorithms from an implementor's point of view. Special emphasis is given to control and data structures used in practically successful implementations of branch and cut algorithms. We also address the issue of parallelization. Finally, we point out that in important applications branch and cut algorithms are not only able to produce optimal solutions but also approximations to the optimum with certi ed good quality in moderate computation times. We close with an overview of successful practical applications in the literature.

A multisensor-multitarget data association algorithm for heterogeneous sensors

Abstract: The problem of associating data from three spatially distributed heterogeneous sensors with three simultaneous detections for all three is discussed. The sensors can be active or passive. The source of a detection can be either a real target, in which case the measurement is the true observation variable of the target plus measurement noise, or a spurious one, i.e. a false alarm. The sensors may have nonunity detection probabilities. The problem is to associate the measurements from sensors to identify the real targets, and to obtain their position estimates. Mathematically this leads to a generalized 3D assignment problem, which is known to be NP-hard. An algorithm suited for estimating the positions of a large number of targets in a dense cluster, using a fast, but nearly optimal, 3D assignment algorithm, is presented. Performance results for several representative test cases with 64 targets are presented. >

Path assignment for call routing: an application of tabu search

Abstract: We describe an implementation of tabu search that solves the path assignment problem, which is the problem of routing video data through an undercapacitated telecommunications network. Two versions of the tabu search were studied. Our results compare very favourably with those from other methods.

An Implementation of the Dual Affine Scaling Algorithm for Minimum-Cost Flow on Bipartite Uncapacitated Networks

Abstract: This paper describes an implementation of the dual affine scaling algorithm for linear programming specialized to solve minimum-cost flow problems on bipartite uncapacitated networks. This implementation uses a preconditioned conjugate gradient algorithm to solve the system of linear equations that determines the search direction at each iteration of the interior point algorithm. Two preconditioners are considered: a diagonal preconditioner and a preconditioner based on the incidence matrix of an approximate maximum weighted spanning tree of the network. Under dual nondegeneracy this spanning tree allows for early identification of the optimal solution. By applying an $\epsilon $-perturbation to the cost vector, an optimal extreme-point primal solution is produced in the presence of dual degeneracy. The implementation is tested by solving several large instances of randomly generated assignment problems, comparing solution times with the network simplex code NETFLO and the relaxation algorithm code RELAX....

A Network-Based Primal-Dual Heuristic for the Solution of Multicommodity Network Flow Problems

Abstract: In this paper, we present a primal-dual, heuristic solution approach for large-scale multicommodity network flow problems. The original problem is solved indirectly by repeatedly solving restated feasibility problems. Restrictions on problem size imposed by exact methods are overcome by solving the restated problems with a pure network-based heuristic procedure. To control the heuristic solution process, the network-based procedure is embedded within an iterative primal-dual framework. At each iteration, feasible dual solutions are generated, the dual objective function value strictly ascends, and primal solutions that are measurably closer to feasibility are determined. The algorithm terminates when the heuristic network-based procedure cannot determine an improved primal solution or when optimality is achieved. To demonstrate the effectiveness of the network-based solution strategy, a large-scale freight assignment problem encountered in the trucking industry is formulated as a multicommodity network flow problem. Two linear programming based, exact solution strategies a primal-dual algorithm and a price-directive algorithm are unable to achieve even an initial solution for this problem due to excessive memory requirements. The network-based heuristic, however, determines an optimal solution. We compare the performance of the new heuristic with that of the exact procedures using a set of smaller test problems. The effects of problem formulation and congestion are evaluated for each of the alternative solution strategies.

A parallel simulated annealing algorithm

Abstract: In this paper we present a generalization of the sequential simulated annealing algorithm for combinatorial optimization problems. By performing a parallel study of the current solution neighbourhood we obtain an algorithm that can be very efficiently implemented on a massively parallel computer. We test the convergence and the quality of our algorithm by comparing it to the sequential algorithm for two classical problems: the minimization of an unconstrained 0–1 quadratic function and the quadratic sum assignment problem.

On linear-time deterministic algorithms for optimization problems in fixed dimension

Abstract: We show that with recently developed derandomization techniques, one can convert Clarkson’s randomized algorithm for linear programming in fixed dimension into a linear-time deterministic algorithm. The constant of proportionality is dO Žd ., which is better than those for previously known algorithms. We show that the algorithm works in a fairly general abstract setting, which allows us to solve various other problems, e.g., computing the minimum-volume ellipsoid enclosing a set of n points and finding the maximum volume ellipsoid in the intersection of n halfspaces. Q 1996 Academic Press, Inc.

A knowledge based aircraft-gate assignment advisor

Abstract: An expert system based solution to the aircraft-gate assignment problem is discussed. The prototype system developed works as a planning tool. It assists in the strategic planning of schedules and with projected aircraft-gate assignments.

Parallel Asynchronous Hungarian Methods for the Assignment Problem

Abstract: In this paper, we discuss the parallel asynchronous implementation of the Hungarian method for solving the classical assignment problem. Multiple augmentations and price rises are simultaneously attempted starting from several unassigned sources and using possibly outdated price and assignment information. The results are then merged asynchronously subject to rather weak compatibility conditions. We show the validity of this algorithm and we demonstrate computationally that an asynchronous implementation is often faster than its synchronous counterpart. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Categorized Assignment Scheduling: a Tabu Search Approach

Abstract: The assignment problem (AP) and bottleneck assignment problem (BAP) are well studied in operational research literature. In this paper we consider two related problems which simultaneously generalize both AP and BAP. Unlike AP and BAP, these generalizations are strongly NP-complete. We propose two heuristics to solve these generalized problems: one based on a greedy principle and the other based on tabu search. Computational results are presented which show that these heuristics, when used together, produce good quality solutions. Our adaptation of tabu search also gives some new insight into the application of tabu search on permutation problems.

Data association problems posed as multidimensional assignment problems: problem formulation

Abstract: The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensor, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields high track identification. However, to achieve this accurate state estimation and track identification, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. The primary objective in this work is to formulate a general class of these data association problems as a multidimensional assignment problem to which new, fast, near-optimal, Lagrangian relaxation based algorithms are applicable. The dimension of the formulated assignment problem corresponds to the number of data sets, and the constraints define a feasible partition of the data sets. The linear objective function is developed from Bayesian estimation and is the negative log likelihood function, so that the optimal solution yields the maximum likelihood estimate. After formulating this general class of problems, the equivalence between solving data association problems by these multidimensional assignment problems and by the currently most popular method of multiple hypothesis tracking is established. Track initiation and track maintenance using an N-scan sliding window are then used as illustrations.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.