New Strategies for Global Optimization of Chemical Engineering Applications by Differential Evolution

Ph.DDOCTOR OF PHILOSOPH

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions

New strategies for efficient and practical genetic programming.

2006/2007In the last decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand or predict the behavior of many phenomena in science and engineering. Many of these phenomena are translated in mathematical models for convenience and to carry out an easy interpretation. Methods commonly employed for this purpose include, for example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search, and so on. These methods all seek for the optimal or near optimal values of a predefined set of parameters of a model built a priori. But in this case, a suitable model should be known beforehand. When the form of this model cannot be found, the problem can be seen from another level where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. In this thesis, we focus on the Genetic Programming (GP) approach as an automatic method for creating computer programs by means of artificial evolution based upon the original contributions of Darwin and Mendel. While GP has proven to be a powerful means for coping with problems in which finding a solution and its representation is difficult, its practical applicability is still severely limited by several factors. First, the GP approach is inherently a stochastic process. It means there is no guarantee to obtain a satisfactory solution at the end of the evolutionary loop. Second, the performances on a given problem may be strongly dependent on a broad range of parameters, including the number of variables involved, the quantity of data for each variable, the size and composition of the initial population, the number of generations and so on. On the contrary, when one uses Genetic Programming to solve a problem, he has two expectancies: on the one hand, maximize the probability to obtain an acceptable solution, and on the other hand, minimize the amount of computational resources to get this solution. Initially we present innovative and challenging applications related to several fields in science (computer science and mechanical science) which participate greatly in the experience gained in the GP field. Then we propose new strategies for improving the performances of the GP approach in terms of efficiency and accuracy. We probe our approach on a large set of benchmark problems in three different domains. Furthermore we introduce a new approach based on GP dedicated to symbolic regression of multivariate data-sets where the underlying phenomenon is best characterized by a discontinuous function. These contributions aim to provide a better understanding of the key features and the underlying relationships which make enhancements successful in improving the original algorithm.Negli ultimi anni, ingegneri e progettisti hanno espresso un interesse crescente nello sviluppo di nuovi metodi di simulazione e di modellazione per comprendere e predire il comportamento di diversi fenomeni sia in ambito scientifico che ingegneristico. Molti di questi fenomeni vengono descritti attraverso modelli matematici che ne facilitano l'interpretazione. A questo fine, i metodi più comunemente impiegati sono, le tecniche basate sui Reti Neurali, Simulated Annealing, gli Algoritmi Genetici, la ricerca Tabu, ecc. Questi metodi vanno a determinare i valori ottimali o quasi ottimali dei parametri di un modello costruito a priori. E evidente che in tal caso, si dovrebbe conoscere in anticipo un modello idoneo. Quando ciò non è possibile, il problema deve essere considerato da un altro punto di vista: l'obiettivo è trovare un programma o una rappresentazione matematica che possano risolvere il problema. A questo scopo, la fase di modellazione è svolta automaticamente in funzione di un criterio qualitativo che guida il processo di ricerca. Il tema di ricerca di questa tesi è la programmazione genetica (“Genetic Programming” che chiameremo GP) e le sue applicazioni. La programmazione genetica si può definire come un metodo automatico per la generazione di programmi attraverso una simulazione artificiale dei principi relativi all'evoluzione naturale basata sui contributi originali di Darwin e di Mendel. La programmazione genetica ha dimostrato di essere un potente mezzo per affrontare quei problemi in cui trovare una soluzione e la sua rappresentazione è difficile. Però la sua applicabilità rimane severamente limitata da diversi fattori. In primo luogo, il metodo GP è inerentemente un processo stocastico. Ciò significa che non garantisce che una soluzione soddisfacente sarà trovata alla fine del ciclo evolutivo. In secondo luogo, le prestazioni su un dato problema dipendono fortemente da una vasta gamma di parametri, compresi il numero di variabili impiegate, la quantità di dati per ogni variabile, la dimensione e la composizione della popolazione iniziale, il numero di generazioni e così via. Al contrario, un utente della programmazione genetica ha due aspettative: da una parte, massimizzare la probabilità di ottenere una soluzione accettabile, e dall'altra, minimizzare la quantità di risorse di calcolo per ottenerla. Nella fase iniziale di questo lavoro sono state considerate delle applicazioni particolarmente innovative relative a diversi campi della scienza (informatica e meccanica) che hanno contributo notevolmente all'esperienza acquisita nel campo della programmazione genetica. In questa tesi si propone un nuovo procedimento con lo scopo di migliorare le prestazioni della programmazione genetica in termini di efficienza ed accuratezza. Abbiamo testato il nostro approccio su un ampio insieme di benchmarks in tre domini applicativi diversi. Si propone inoltre una tecnica basata sul GP per la regressione simbolica di data-set multivariati dove il fenomeno di fondo è caratterizzato da una funzione discontinua. Questi contributi cercano di fornire una comprensione migliore degli elementi chiave e dei meccanismi interni che hanno consentito il miglioramento dell'algoritmo originale.XX Ciclo198

Proceedings of the inaugural construction management and economics ‘Past, Present and Future’ conference CME25, 16-18 July 2007, University of Reading, UK

This conference was an unusual and interesting event. Celebrating 25 years of Construction Management and Economics provides us with an opportunity to reflect on the research that has been reported over the years, to consider where we are now, and to think about the future of academic research in this area. Hence the sub-title of this conference: “past, present and future”. Looking through these papers, some things are clear. First, the range of topics considered interesting has expanded hugely since the journal was first published. Second, the research methods are also more diverse. Third, the involvement of wider groups of stakeholder is evident. There is a danger that this might lead to dilution of the field. But my instinct has always been to argue against the notion that Construction Management and Economics represents a discipline, as such. Granted, there are plenty of university departments around the world that would justify the idea of a discipline. But the vast majority of academic departments who contribute to the life of this journal carry different names to this. Indeed, the range and breadth of methodological approaches to the research reported in Construction Management and Economics indicates that there are several different academic disciplines being brought to bear on the construction sector. Some papers are based on economics, some on psychology and others on operational research, sociology, law, statistics, information technology, and so on. This is why I maintain that construction management is not an academic discipline, but a field of study to which a range of academic disciplines are applied. This may be why it is so interesting to be involved in this journal. The problems to which the papers are applied develop and grow. But the broad topics of the earliest papers in the journal are still relevant today. What has changed a lot is our interpretation of the problems that confront the construction sector all over the world, and the methodological approaches to resolving them. There is a constant difficulty in dealing with topics as inherently practical as these. While the demands of the academic world are driven by the need for the rigorous application of sound methods, the demands of the practical world are quite different. It can be difficult to meet the needs of both sets of stakeholders at the same time. However, increasing numbers of postgraduate courses in our area result in larger numbers of practitioners with a deeper appreciation of what research is all about, and how to interpret and apply the lessons from research. It also seems that there are contributions coming not just from construction-related university departments, but also from departments with identifiable methodological traditions of their own. I like to think that our authors can publish in journals beyond the construction-related areas, to disseminate their theoretical insights into other disciplines, and to contribute to the strength of this journal by citing our articles in more mono-disciplinary journals. This would contribute to the future of the journal in a very strong and developmental way. The greatest danger we face is in excessive self-citation, i.e. referring only to sources within the CM&E literature or, worse, referring only to other articles in the same journal. The only way to ensure a strong and influential position for journals and university departments like ours is to be sure that our work is informing other academic disciplines. This is what I would see as the future, our logical next step. If, as a community of researchers, we are not producing papers that challenge and inform the fundamentals of research methods and analytical processes, then no matter how practically relevant our output is to the industry, it will remain derivative and secondary, based on the methodological insights of others. The balancing act between methodological rigour and practical relevance is a difficult one, but not, of course, a balance that has to be struck in every single paper

The Largest Unethical Medical Experiment in Human History

This monograph describes the largest unethical medical experiment in human history: the implementation and operation of non-ionizing non-visible EMF radiation (hereafter called wireless radiation) infrastructure for communications, surveillance, weaponry, and other applications. It is unethical because it violates the key ethical medical experiment requirement for “informed consent” by the overwhelming majority of the participants. The monograph provides background on unethical medical research/experimentation, and frames the implementation of wireless radiation within that context. The monograph then identifies a wide spectrum of adverse effects of wireless radiation as reported in the premier biomedical literature for over seven decades. Even though many of these reported adverse effects are extremely severe, the true extent of their severity has been grossly underestimated. Most of the reported laboratory experiments that produced these effects are not reflective of the real-life environment in which wireless radiation operates. Many experiments do not include pulsing and modulation of the carrier signal, and most do not account for synergistic effects of other toxic stimuli acting in concert with the wireless radiation. These two additions greatly exacerbate the severity of the adverse effects from wireless radiation, and their neglect in current (and past) experimentation results in substantial under-estimation of the breadth and severity of adverse effects to be expected in a real-life situation. This lack of credible safety testing, combined with depriving the public of the opportunity to provide informed consent, contextualizes the wireless radiation infrastructure operation as an unethical medical experiment