Joint Requirement of Two Multi-skill Resource Types in Multi-period Multi-site Assignment Problem

A classic assignment problem determines how to assign resources to tasks in the best possible way. Over the past years, the classic assignment problem has been extended and more complicated assignment models have been proposed. A multi-period multi-site assignment problem is one extension of the classic assignment problem. The number of site and period are increased to more than one and the decision is extended to consider assigning resources to site while concerning tasks in each site and period. Most multi-period multi-site assignment models do not concern joint of resources for operation; however, in some real-life problems, there is a case in which joint of resources for doing tasks is required. In this study, we consider joint requirement of two multi-skill resource types in the multi-period multi-site assignment problem and propose the mathematical model and heuristic. The developed heuristic is separated into two parts. The first part is to create an initial solution by CPLEX In the second part, to improve solution, algorithms for allocating resources to sites and assigning resources to tasks are developed. The computational experiment is done for studying the characteristic of the proposed problem when joint requirement is added and also evaluating the efficiency of the developed algorithm. The result shows that the complexity of the problem highly depends on the ratio of task requiring one and two resource types and while other parameters are fixed except the number of resource, there is only one range of the number of resource that makes the problem complex. For the efficiency of the algorithm, the developed heuristic can find good solutions in a short time in all ranges of the number of resource in all test problems (average optimal gap of all test problems is 7.25%)

An Algorithm for the Generalized Quadratic Assignment Problem

This paper reports on a new algorithm for the Generalized Quadratic Assignment problem (GQAP). The GQAP describes a broad class of quadratic integer programming problems, wherein M pair-wise related entities are assigned to N destinations constrained by the destinations’ ability to accommodate them. This new algorithm is based on a Reformulation Linearization Technique (RLT) dual ascent procedure. Experimental results show that the runtime of this algorithm is as good or better than other known exact solution methods for problems as large as M=20 and N=15

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Algoritmi per la soluzione del problema dell'assegnamento generalizzato

La tesi analizza gli algoritmi per risolvere il problema dell'assegnamento generalizzato, e valuta in particolare le prestazioni dell'algoritmo di Posta ed altri

Determining efficient scheduling approach of doctors for operating rooms: An analysis on Al-Shahid Ghazi Al-Hariri hospital in Baghdad

Government hospitals in Iraq have long been suffering from overcrowded patients, and shortages of doctors and nurses. Unstable environment with occurrences of random warrelated incidents has put further burden on hospitals’ limited resources particularly the surgical department. Large number of pre-scheduled elective surgeries has occasionally been interrupted by the incoming war-related incidents patients. This in turn has put tremendous pressure on the hospital management to maximize utilization of its operating rooms’ resources including surgeons and nurses, whilst simultaneously minimizing idle time. Al-Shahid Ghazi Al-Hariri hospital in Baghdad is presently experiencing these issues. Therefore, this study has been undertaken with the aims to identify efficient scheduling approach for elective surgeries for operating rooms in Al-Shahid Ghazi Al-Hariri hospital while considering interruptions from non-elective surgery (incoming patients from warrelated incidents). Specifically, this study intends to develop a Mixed Integer Linear Programming (MILP) model to maximize the utilizations of operating rooms, availability of surgeons as well as to minimize potential idle time. A meta-heuristic approach in the form of a Tabu Search is then employed to generate an acceptable solution and utilizing time more efficiently. Real data was collected from the hospital in the form of interviews, observations and secondary reports. The initial MILP computational results show that the proposed model has successfully produced optimal solutions by improving the utilization of operating rooms. Notwithstanding, the difficulty to produce results in reasonable time for larger problem instances has led to the application of a more efficient meta-heuristic approach. The Tabu Search results indicated better performance of the model with good quality solutions in fewer computation times. The finding is important as it determines the feasibility of the proposed model and its potential benefit to all relevant stakeholders

End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions

El problema de asignación generalizado dinámico: modelación y estructura

Los problemas de asignación (de ahora en adelante AP, por sus siglas en inglés) han jugado un papel muy importante dentro de la optimización de problemas de la vida real, ya que son múltiples las situaciones que pueden modelarse de este modo. Son muchas las variantes del AP, dependiendo de las características del problema bajo consideración; sin embargo, al pertenecer estas variantes a la clase NP, resulta difícil abordarlos como un problema de optimización (o de valor óptimo), ya que es improbable encontrar la mejor solución en tiempo polinomial; así, en muchas ocasiones, basta con encontrar una respuesta que sea lo "suficientemente cercana al óptimo", en un tiempo de cómputo razonable. En este trabajo se define una nueva variante del AP, denominada Problema de Asignación Generalizado Dinámico (de ahora en adelante DGAP). Se deducen algunas propiedades matemáticas del mismo, que luego son usadas en la elaboración de un algoritmo genético empleado para su solución.MaestríaMagister en Ingeniería Industria