Zugl.: Karlsruhe, Univ., Diss., 2009 / Hergestellt on demand

Chemnitz, Techn. Univ., Diplomarb., 2001.

Zugl.: Karlsruhe, Univ., Diss., 2009 / Hergestellt on demand. - Zusätzliches Online-Angebot unter http://uvka.ubka.uni-karlsruhe.de/shop/isbn/978-3-86644-395-2

Diplomarbeit Humboldt-Univ. Berlin, 1996.

Facility Location problem refers to placing facilities (mostly vehicles) in appropriate locations to yield the best coverage with respect to other important factors which are specific to the problem. For instance in a fire station some of the important factors are traffic time, distribution of stations, time of the service and so on. Furthermore, budget limitation, time constraints and the great importance of the operation, make the optimum allocation very complex. In the past few years, several research in this area have been done to help managers by designing some effective algorithm to allocating facilities in the best way possible. Most early proposed models were focused on static and deterministic methods. In static models, once a facility assigns to a location, it will not relocate anymore. Although these methods could be utilized in some simple settings, there are so many factors in real world that make a static model of limited application. The demands may change over time or facilities may be dropped or added. In these cases a more flexible model is desirable, thus dynamic models are proposed to be used in such cases. Facilities can be located and relocated based on the situations. More recently, dynamic models became more popular but there were still many aspects of facility allocation problems which were challenging and would require more complex solutions. The importance of facility location problem becomes significantly more relevant when it relates to hospitals and emergency responders. Even one second of improvement in response time is important in this area. For this reason, we selected ambulance facility allocation problem as a case study to analyze this problem domain. Much research has been done on ambulances allocation. We will review some of these models and their advantages and disadvantages. One of the best model in this areas introduced by Rajagopalan. In this work, his model is analyzed and its major drawback is addressed by applying some modifications to its methodology. Genetic Algorithm is utilized in this study as a heuristic method to solve the allocation model.

<p> The development of a unified system for the automatic neutron activation analysis of large numbers of samples is described. The realization of the system entailed the automation of a gamma ray spectrometer system by means of a data and control link to a small computer (PDP-15) and the development of a reliable and fast data reduction algorithm suited to the small computer system. A detailed study of the algorithm and the errors associated with it has been included. </p> / Thesis / Master of Science (MSc)

No description available.

We consider the lower-bounded facility location (LBFL) problem (, also known as load-balanced facility location), which is a generalization of uncapacitated facility location (UFL) problem where each open facility is required to serve a minimum number of clients. More formally, in the LBFL problem, we are given a set of clients Ɗ , a set of facilities Ƒ, a non-negative facility-opening cost f_i for each i ∈ Ƒ, a lower bound M, and a distance metric c(i,j) on the set Ɗ ∪ Ƒ, where c(i,j) denotes the cost of assigning client j to facility i. A feasible solution S specifies the set of open facilities F_S ⊆ Ƒ and the assignment of each client j to an open facility i(j) such that each open facility serves at least M clients. Our goal is to find feasible solution S that minimizes ∑_{i ∈ F_S} f_i + ∑_j c(i,j). The current best approximation ratio for LBFL is 550. We substantially advance the state-of-the-art for LBFL by devising an approximation algorithm for LBFL that achieves a significantly-improved approximation guarantee of 83.

We consider the lower-bounded facility location (LBFL) problem (, also known as load-balanced facility location), which is a generalization of uncapacitated facility location (UFL) problem where each open facility is required to serve a minimum number of clients. More formally, in the LBFL problem, we are given a set of clients Ɗ , a set of facilities Ƒ, a non-negative facility-opening cost f_i for each i ∈ Ƒ, a lower bound M, and a distance metric c(i,j) on the set Ɗ ∪ Ƒ, where c(i,j) denotes the cost of assigning client j to facility i. A feasible solution S specifies the set of open facilities F_S ⊆ Ƒ and the assignment of each client j to an open facility i(j) such that each open facility serves at least M clients. Our goal is to find feasible solution S that minimizes ∑_{i ∈ F_S} f_i + ∑_j c(i,j). The current best approximation ratio for LBFL is 550. We substantially advance the state-of-the-art for LBFL by devising an approximation algorithm for LBFL that achieves a significantly-improved approximation guarantee of 83.

Anotace práce v anglickém jazyce The aim of the work is to describe and explain the concept of Facility management as a complex of service activities in the management of assets. Furthermore, i want to see how it is applied in the administration of the assets of FM Technical University in Brno. thesis is divided into a theoretical part, which is contained in the first chapter, and part practical, which i paid in the second chapter. While ensuring the handouts for the practical part of the thesis i used the option of personal visits to each of the faculties. Here i carried out in the form of dotazníkovou and personal interview research on responsible management personnel of buildings – internal facility managers. The result of the activities are an indication of how the management of support activities at individual faculties, what is the distribution of own and external activities. At the conclusion of their work i want to do an evaluation approach of the individual faculties on matters of management and try on the recommendation of the possible changes.