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Assignment Problem corresponds with the product distribution between demand points and supply points. Many algorithms were suggested to find the optimal result. The purpose of this study is to propose an appropriate model to explore the solution to the assignment problem. This paper focuses on Hungarian Method. This study has conducted a detailed case study to find a feasible solution to the assignment problem. The computational outcomes suggested that Hungarian Method provides an optimal solution and can handle any complex scenario. The findings of this study can be used as a reference for decision-makers to mitigate production-related risk and adapted to sustainable market changes. [ABSTRACT FROM AUTHOR]

Operation Research (OR) is techniques and methods use to make better decision. Operation research is use to find optimal or near-optimal solutions to complex decision making problem. Linear Programming is a mathematical technique for maximizing or minimizing a linear function of several variables, such as output or cost. This paper demonstrates the application area of operation research and different methodologies used to solve linear programming problem (LPP). Linear programming are basically used to optimize the solution. Linear programming is one of the most popular techniques of operation research. It is a mathematical technique for allotting limited recourses of a firm in an optimum manner. The technique embraces almost every functional area of the business-production, finance, marketing, distribution etc. – in every type of industry. Operation research uses different methods to solve the complex business problem like graphical method, simplex method, transportation method, assignment method and game theory. [ABSTRACT FROM AUTHOR]

The multi-objective assignment problem is basically the N men -N tasks problem, where a single task has to be assigned to an individual with a view of optimizing the outcomes. A common challenge is to address the conflicting objectives which produce Pareto-optimal solutions. The main feature of the work is- normalizing all the criteria into a single scale regardless of their measurement units and their demand of minimum or maximum, which reliefs us from careful attention in quantifying the quality criteria. The methodology also included the decision maker's preferences regarding the objectives. While solving the problem through a genetic algorithm, a new encoding scheme is used together with a partially matched crossover (PMX). The working principle of the proposed algorithm is illustrated with a numerical example and its effectiveness has been compared with some well-established methodologies. It is found that the proposed algorithm provides a better solution with minimal computational effort. [ABSTRACT FROM AUTHOR]

We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomized mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomized mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of e/e-1 . The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of 18/13 on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. In the case that the mechanism must additionally be non-bossy, an improved lower bound of e/e-1 holds. This lower bound is tight given that RSDM for strict preference lists is non-bossy. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomized strategy of the administrator who interviews the applicants. [ABSTRACT FROM AUTHOR]

Context: Personnel assignment (PA) is an important problem in industry. In general it is about assigning the right people to the right tasks. Operations research plays a big role in solving such problems. Objective: In this paper, we study the personnel assignment problem (PAP) and the proposed solutions to solve it. In addition to that, we aim to identify promising future works from the study results. Methods: We take a systematic approach towards studying the literature of the PAP. A general systematic review method, which has been recently used by a number of researchers in the field of software engineering, was modified and deployed in this study. Results: The analysis results reveal potential solution approaches, the trends in application of existing solution methods, and some potential future research areas. The review process is based on our variation of an existing literature review method. This variation is also presented in the paper. Conclusions: Although a concern in industry, PAP has not been widely studied when compared to other similar fields of research. It has been mainly studied in operations research and in the context of military personnel assignments. It seems that artificial intelligence and machine learning still have a good potential to contribute to this field of research in different applications. Application of PAP in software engineering (SE) is an open area of research. For instance, it looks promising for developer or bug assignments in software development projects. [ABSTRACT FROM AUTHOR]

Cluster ensembles can improve single clusters performance effectively, but the cluster labels are not fused directly due to lack of prior information supervising. Based on analysis and research on existing methods, label aligning was convert to the assign problem. And two cluster label aligning algorithms were proposed, which use overlap similarity rate as the coefficient matrix, Hungary and implicit enumeration algorithm as basic algorithms. Lastly, experimental simulation and capability analysis were completed, and results prove the availability and applicability of the new algorithms. [ABSTRACT FROM PUBLISHER]

This paper discusses a various customer heterogeneous server (VCHS) queuing system strategy for the optimal assignment of mobile users to wireless LAN access points (APs) to optimize user quality of service (QoS). The users and APs have heterogeneous types of service requests and services, such as types of service providers, respectively. It is assumed that users arrive in a stochastic manner and that the proportions of user traffic volumes to each service request are provided. The user only connects to an AP that matches the service with the user request. Then, the user leaves after downloading a specific volume of data. A “group” assignment strategy is proposed to match the service resources to the service request demands. The strategy includes how to make a group from a number of APs proportional to the volume of user traffic to each service request. The evaluations show that the “group” assignment strategy is significantly better than a “random” assignment strategy in terms of mean waiting time. The proposed strategy reduces the mean waiting time by 25%, with more significant decreases as the number of APs with more types of services increases. [ABSTRACT FROM PUBLISHER]

Acheving maximal overall efficiency is a priority in all companies. This problem is formulated as a knap-sack problem and afterwards as a linear assignment problem. An example for a real company is solved and discussed. [ABSTRACT FROM AUTHOR]

The maximal common efficiency is the purpose in the team-games. This problem is formulated as a linear assignment problem. An example in baseball is solved and discussed. [ABSTRACT FROM AUTHOR]