Topic
Assignment problem
About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.
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01 Jan 1998
TL;DR: This is the first study designed recognizing the nonconvexity of the combined problem and examining quality of different algorithm solutions with convergence pattern analysis, and developing a hybrid algorithm of global and local/iterative search utilizing their exclusive merits simultaneously and efficiently.
Abstract: The nonconvex combined traffic signal control and traffic assignment problem is examined using four different algorithms and four example networks. The heuristic iterative approach has been widely used (1) without any justification regarding solution quality and (2) without any treatment of the problem of nonconvexity. This is the first study designed recognizing the nonconvexity of the combined problem and examining quality of different algorithm solutions with convergence pattern analysis. Drivers are assumed to follow Wardrop's first principle and link performance is described by the Webster curve. Origin-destination matrices are assumed fixed, green time per cycle ratios and cycle length are decision variables, and total system travel time minimization is the control objective. The iterative approach sequentially performing assignment and signal optimization finds mutually consistent points where flow is at user equilibrium and signal setting is optimal. Three different local search algorithms with six variations regarding gradient calculation are implemented. To counter the nonconvexity, two stochastic global searches, simulated annealing and a genetic algorithm are applied. Complex signal schemes with overlapping movements and multiple phases are included in the developed codes. The codes are preliminarily tested to address characteristics of the algorithms. Comprehensive experiments are designed using five different demand levels and four different size networks. An aggregate measure to determine the similarity of solutions by different algorithms indicated that the mutually consistent solutions are quite different from the other algorithm solutions and the difference grows as demand increases. Regarding solution quality, each algorithm has a relatively superior combination of demand level and network size. The iterative approach and local searches converge quickly but the two global searches converge slowly. Numerical results confirm that the iterative approach is not always desirable and should be carefully applied at high demand in networks. At high demand, however, no algorithm is always outperforming. To improve code performance, a hybrid algorithm of global and local/iterative search utilizing their exclusive merits simultaneously and efficiently should be developed.
43 citations
TL;DR: Arkin et al. as mentioned in this paper proposed an approximation algorithm with a constant performance guarantee, 4, under the assumption that the weights in B satisfy the triangle inequality (TI) and provided a constant-time algorithm with the same guarantee.
43 citations
01 Jan 2006
TL;DR: Results show that this method can find solutions with a minimum required bandwidth in comparison with the other algorithms investigated in the paper.
Abstract: In the channel assignment problem, frequencies are assigned to the requested calls in a cellular mobile network subject to electromagnetic compatibility constraints such that a required bandwidth is minimized. In this paper, a new method based on genetic algorithm is proposed to solve these problems. The performance of the proposed method is evaluated by solving 3 channel assignment problems. Results show that this method can find solutions with a minimum required bandwidth in comparison with the other algorithms investigated in the paper.
43 citations
TL;DR: An approach based on regret theory with hesitant fuzzy analysis is presented in a context of multiattribute matching decision making where the relative weights are uncertain and an optimal matching model is programmed to generate the matching results based on the MSDs.
Abstract: An approach based on regret theory with hesitant fuzzy analysis is presented in a context of multiattribute matching decision making where the relative weights are uncertain. There are two steps being addressed in this approach. First, we put forward a maximizing differential model to determine the relative weights of hesitant fuzzy attributes, and calculate collective utilities of each attribute according to regret theory. The matching satisfaction degrees (MSDs) are then acquired by aggregating the collective utilities with relative weights. Secondly, an optimal matching model is programmed to generate the matching results based on the MSDs. This model belongs to a sort of multiobjective assignment problem and can be solved using the min–max method. A case study of matching outsourcing contractors and providers in Fuzhou National Hi-tech Zone is conducted to demonstrate the proposed approach and its potential applications.
43 citations
TL;DR: In this article, the pole-assignment problem for discrete-time linear periodic systems with state-variable feedback control is considered and it is shown that if the N-periodic system with m inputs and n states is completely reachable, then the problem can be reduced to the pole assignment problem for a discrete time linear invariant system with Nm inputs and N states.
Abstract: This paper considers the pole-assignment problem for discrete-time linear periodic systems through the use of linear periodic state-variable feedback control. It is shown that if the N-periodic system with m inputs and n states is completely reachable then the problem can be reduced to the pole-assignment problem for a discrete-time linear invariant system with Nm inputs and n states.
43 citations