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.

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.

Optimal Selection of CPU Speed, Device Capacities, and File Assignments

Abstract: This paper presents a computer system configuration design problem in which the objective is to select the CPU speed, the capacities of secondary storage devices, and the allocation of a set of files across the secondary storage devices so as to maximize the system throughput subject to a cost constraint. It is shown that any relative maximum of this complex nonlinear programming problem is also a global maximum. A technique to significantly reduce the dimensionality of the optimization problem is presented along.with an example to illustrate the model's usefulness. The well-known file assignment problem is shown to be a subproblem of this model, and an example is given which demonstrates this fact. Finally, the errors introduced by the conversion of an essentially discrete problem into a continuous one are estimated and bounded.

Analogue neural network for solving the assignment problem

Abstract: An analogue neural network for solving the assignment problem is presented. The proposed analogue neural network is asymptotically stable and able to generate optimal solutions to the assignment problem. An opamp-based circuit realisation of the analogue neural network is described. An illustrative example is also discussed to demonstrate the performance and operating characteristics of the analogue neural network.

3D Path Planning for Multiple UAVs for Maximum Information Collection

Abstract: This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of "which DR should be visited by which UAV". It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section 6. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions.

Fleet Assignment Using Collective Intelligence

Abstract: Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).