Optimisation of piping network design for district cooling system

A district cooling system (DeS) is a.scheme for centralised cooling energy distribution which takes advantage of economies of scale and load diversity. . A cooling medium (chilled water) is generated at a central refrigeration plant and then supplied to a district area, comprising multiple buildings, through a closed-loop piping circuit. Because of the substantial capital investment involved, an optimal design of the distribution piping . configuration is one of the crucial factors for successful implementation of a district 1'. cooling scheme. Since there. exists an enormous number of different combinations of the piping configuration, it is not feasible to evaluate each individual case using an exhaustive approach. This thesis exammes the problem of determining an optimal distribution piping configuration using a genetic algorithm (GA). In order to estimate the spatial and temporal distribution of cooling loads; the climatic conditions of Hong Kong were investigated and a weather database in the form of a typical meteorological year (TMY) was developed. Detailed thermal modelling of a number of prototypical buildings was carried out to determine benchmark cooling loads. A novel Local Search/Looped Local Search algorithm was developed for finding optimal/near-optimal distribution piping configurations. By means of computational . experiments, it was demonstrated that there is a promising improvement to GA performance by including the Local Search/Looped Local Search algorithm, in terms of both solution quality and computational efficiency. The effects on the search performance of a number of parameters were systematically investigated to establish the most effective settings. In order to illustrate the effectiveness of the Local Search/Looped Local Search algorithm, a benchmark problem - the optimal communication,spanning tree (OCST) was used for comparison. The results showed that the Looped Local Search method developed in this work was an effective tool for optimal network design of the distribution piping system in DCS, as well as for optimising the OCST problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Análisis del problema de elección del ganador en subastas combinatorias. Aplicaciones a problemas de Ingeniería de Organización.

En la amplia literatura referida a las subastas combinatorias, las características y los mecanismos asociados a estas, destaca el hincapié a la hora de mencionar la resolución de un importante problema asociado a estas subastas. Este es el problema de determinación del ganador, “Winner Determination Problem” (WDP). Debido a ese gran interés, en el presente trabajo se ha realizado una extensa búsqueda referida a las subastas combinatorias y a los distintos algoritmos utilizados para la resolución del Winner Determination Problem, particularmente en problemas relacionados con el ámbito de la ingeniería. El fin de este trabajo es acercarnos un poco más a este problema e intentar encontrar una metodología para la óptima ejecución de este tipo de subastas. Por otro lado, se comparan distintos mecanismos de subastas combinatorias, creando para ello un marco analítico que nos ayude a entender mejor sus numerosas características y nos permita identificar sus fortalezas y debilidades.In the extensive literature referring to combinatorial auctions, their characteristics and mechanisms associated with them, emphasis is placed on the resolution of an important problem associated with these auctions. This is the problem of determining the winner, “Winner Determination Problem” (WDP). Due to this great interest, in the present work an extensive search has been made regarding combinatorial auctions and the different algorithms used for the resolution of the Winner Determination Problem, particularly in problems related to the engineering field. The aim of this work is to get a little closer to this problem and try to find a methodology for the optimal execution of this type of auctions. On the other hand, different combinatorial auction mechanisms are compared, and an analytical framework is built with the aim of helping us to better understand their many characteristics and to allow us to identify their strengths and weaknesses.Departamento de Organización de Empresas y Comercialización e Investigación de MercadosGrado en Ingeniería en Organización Industria

The scheduling of manufacturing systems using Artificial Intelligence (AI) techniques in order to find optimal/near-optimal solutions.

This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.Board of Trustees, Endowment Fund Project, KPK University of Engineering and Technology (UET), Peshawar and Higher Education Commission (HEC), Pakista