Performance improvement of remanufacturing systems operating under N-policy

This thesis deals with N-policy M/G/1 queueing remanufacturing system with general server breakdown and start-up time, where the value of returned products exponentially deteriorates since received. The server will instantly turn on the system, but the system requires a start-up period to prepare for remanufacturing when returned products in the queue reach the value of N. Otherwise, the system keeps in turn-off status. During the remanufacturing process, the machines may break down and will return back to service immediately after repairing. The procedures that will be used to achieve the target are as follows. Firstly, the expression of cost function will be derived and solved. Next, the simulation software ProModel will be used to simulate this problem. Finally, a sensitivity analysis is used on a numerical example to show the applicability of the methodology and quality of results

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Decision Support System Approach for the Management of Complex Systems in Transportation and Logistics

L'analisi e la gestione dei sistemi complessi e delle loro ripercussioni in diversi aspetti della vita quotidiana sono tematiche che continuano ad attrarre molta attenzione nella letteratura scientifica. Si considerino, ad esempio, il trasporto marittimo e su strada, i moderni sistemi di assistenza sanitaria, le catene di distribuzione integrate, i processi industriali o, ancora, il nuovo paradigma di citt\ue0 intelligente: \ue8 evidente come in tutti questi contesti vi sia sempre pi\uf9 la necessit\ue0 di analizzare e gestire elementi eterogenei, collegati tra loro al fine di raggiungere un obiettivo comune altrimenti non realizzabile. Tuttavia, il processo decisionale in tali ambiti richiede competenze trasversali che abbracciano svariate discipline, rendendo la gestione di questi sistemi molto complessa e, spesso, inefficace. I Sistemi di Supporto alle Decisioni (DSS) ben si adattano alla previsione ed al controllo dei sistemi complessi grazie a: la loro capacit\ue0 di integrare varie fonti di dati ed informazioni; l'applicazione di modelli formali tipici di diverse discipline; la possibilit\ue0 di interagire costantemente con il sistema considerato. L'obiettivo di questo lavoro di tesi \ue8 quello di definire un approccio generale basato sul concetto di DSS per la gestione di sistemi complessi nel settore dei trasporti e della logistica, e di applicare tale approccio a tre problemi di grande interesse oggigiorno: 1) il problema della ricollocazione dei veicoli nei servizi di car sharing, 2) la gestione intelligente delle operazioni di carica dei veicoli elettrici presso le infrastrutture pubbliche e 3) l'ottimizzazione delle operazioni di drayage nel trasporto container. In particolare, il focus della ricerca \ue8 rivolto al cuore del DSS, ovvero alla parte che direttamente supporta il processo decisionale: i moduli di ottimizzazione e simulazione e le loro interazioni. Vengono considerati diversi approcci di modellazione, simulazione ed ottimizzazione, evidenziando il carattere totalmente generale dell' approccio considerato. I risultati ottenuti nelle diverse applicazioni sottolineano l'efficacia dei DSS nel migliorare il processo decisionale, portando ad un miglioramento generale delle prestazioni dei sistemi in esame. In particolare: 1) l'applicazione del DSS permette di ottimizzare i set-point per l'introduzione di un sistema di incentivi economici atto a risolvere il problema di ricollocazione dei veicoli nei servizi di car sharing, garantendo un miglioramento delle prestazioni del sistema, anche in condizioni di quasi saturazione; 2) il DSS permette la formalizzazione di un approccio leader-follower per il coordinamento delle operazioni di ricarica di veicoli elettrici che tenga conto contemporaneamente sia dei requisiti dell'utente che quelli della rete elettrica; infine, 3) il DSS consente di migliorare l'efficienza delle operazioni di drayage nel trasporto containter, riducendo i costi di trasporto.In recent years, the analysis and management of complex systems and their impacts in many aspects of the every-day life are topics that attract a lot of attention in the scientific literature. Consider for instance road and maritime transportation, modern healthcare systems, integrated supply chains, industrial processes or the new paradigm of smart cities: it is apparent that in all these contexts there is an increasing need of analysing and managing heterogeneous elements, networked together in order to reach a common goal otherwise not achievable. However, making decisions concerning such systems requires specific competences from many disciplines, leading to a very complex and often ineffective management process. Decision Support Systems (DSSs) can strengthen the capacity of predicting and controlling complex systems by integrating various sources of data and information, applying formal models typical of diverse and isolated disciplines and constantly interacting with the considered system. The goal of this work is to define a general approach based on the DSS concept for the management of complex systems in transportation and logistics and to apply it to three problems of great interest nowadays: 1) the user-based vehicle relocation problem} in car sharing services, 2) the smart management of electric vehicles charging operations and 3) the container drayage problem. In particular, the focus of the research is on the core of the DSS, i.e., on the part that directly supports the decision making process: optimization modules, simulation modules and their interactions. Different modelling, simulation and optimization approaches are applied, highlighting the generality of the considered approach regardless the specific context analysed. Results show the ability of DSSs to enhance the effectiveness of the decision process, thus leading to an improvement of the considered systems performance. In particular: 1) the application of the DSS allows to optimize the set-points of an incentive policy designed to solve the vehicle relocation problem in car sharing services, guaranteeing an effective relocation and improving the system performance even in the case of nearly saturated offer; 2) the DSS allows the formalization of a leader-follower approach for the coordination of electric vehicles charging operations which takes into account simultaneously electric grid and drivers requirements; finally, 3) the DSS allows to improve the efficiency of drayage operations in container transportation, reducing total transportation costs

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success