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.

Design and Evaluation of a Backhaul-Aware Base Station Assignment Algorithm for OFDMA-Based Cellular Networks

Abstract: Existing base station (BS) assignment methods in cellular networks are mainly driven by radio criteria since it is assumed that the only limiting resource factor is on the air interface. However, as enhanced air interfaces have been deployed, and mobile data and multimedia traffic increases, a growing concern is that the backhaul of the cellular network can become the bottleneck in certain deployment scenarios. In this paper, we extend the BS assignment problem to cope with possible backhaul congestion situations. A backhaul-aware BS assignment problem is modeled as an optimization problem using a utility-based framework, imposing constraints on both radio and backhaul resources, and mapped into a Multiple-Choice Multidimensional Knapsack Problem (MMKP). A novel heuristic BS assignment algorithm with polynomial time is formulated, evaluated and compared to classical schemes based exclusively on radio conditions. Simulation results demonstrate that the proposed algorithm can provide the same system capacity with less backhaul resources so that, under backhaul bottleneck situations, a better overall network performance is effectively achieved.

Gate Assignment to Minimize Passenger Transit Time and Aircraft Taxi Time

Abstract: This research focuses on airport gates, which are limited resources in the air transportation system. In spite of its importance, ramp congestion has not been considered in traditional research on gate assignment. This research, therefore, proposes a new perspective on the gate assignment problem that accounts for ramp congestion, as well as passenger flow. The proposed gate assignment problem minimizes weighted nominal taxi time and weighted taxi delay, as well as passenger transit time in the terminal. Nominal taxi time and taxi delay are weighted by the number of passengers on board. As a result, the flow in the passenger terminal and on ramps becomes efficient. To get near-optimal solutions within a reasonable amount of time, two metaheuristic algorithms, tabu search and genetic algorithm, are implemented for two ramp configurations. Also, the tradeoff between passenger flow and ramp congestion is analyzed.

A Cell-based Merchant-Nemhauser Model for the System Optimum Dynamic Traffic Assignment Problem

Abstract: Using a simple piecewise linear exit-flow function, a cell-based variant of the Merchant-Nemhauser (M-N) model is proposed for the system optimum (SO) dynamic traffic assignment (DTA) problem. Once augmented with additional constraints to capture cross-cell interactions, the model becomes a linear program that embeds a relaxed cell transmission model (CTM) for traffic propagation. The proposed cell-based M-N model is different from the existing CTM-based SO-DTA models in that (1) it does not require intersections to be standard merge and diverge; and 2) it does not directly solve for cell-to-cell flows. Generally, the proposed model has a simpler constraint structure and is easier to construct. Path marginal costs are defined using a recursive formula that involves a subset of multipliers from the linear program. This definition is then employed to interpret the necessary condition, which is a dynamic extension of the Wardrop's second principle. An algorithm is presented to solve the flow holding back problem that is known to exist in many existing SO-DTA models. A numerical experiment is conducted to verify the proposed model and algorithm.

Cost-effective barrier coverage formation in heterogeneous wireless sensor networks

Abstract: Barrier coverage is a critical issue in wireless sensor networks (WSNs) for security applications, which however cannot be guaranteed to be formed after initial random deployment of sensors. Existing work on barrier coverage mainly focus on homogeneous WSNs, while little effort has been made on exploiting barrier coverage formation in heterogeneous WSNs where different types of sensors are deployed with different sensing models and costs. In this paper, we study how to efficiently form barrier coverage by leveraging multiple types of mobile sensors to fill in gaps between pre-deployed stationary sensors in heterogeneous WSNs. The stationary sensors are grouped into clusters and a cluster-based directional barrier graph is proposed to model the barrier coverage formation problem. We prove that the minimum cost of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path on the graph. Moreover, we propose a greedy movement algorithm for heterogeneous WSNs to efficiently schedule different types of mobile sensors to different gaps while minimizing the total moving cost. In particular, we formulate the movement problem for homogeneous WSNs as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Extensively experimental results on homogeneous and heterogeneous WSNs demonstrate the effectiveness of the proposed algorithms.

Computing Improved Fuzzy Optimal Hungarian Assignment Problems with Fuzzy Costs Under Robust Ranking Techniques.

Abstract: th job to the i th person. The cost is usually deterministic in nature. In this paper aij has been considered to be trapezoidal and triangular numbers denoted by aij which are more realistic and general in nature. Robust’s ranking method [10] has been used for ranking the fuzzy numbers. The fuzzy assignment problem has been transformed into crisp assignment problem in the linear programming problem form and solved by using Hungarian method; Numerical examples show that the fuzzy ranking method offers an effective tool for handling the fuzzy assignment problem.