Optimal sequencing of a set of positive numbers with the variance of the sequence's partial sums maximized

We consider the problem of sequencing a set of positive numbers. We try to find the optimal sequence to maximize the variance of its partial sums. The optimal sequence is shown to have a beautiful structure. It is interesting to note that the symmetric problem which aims at minimizing the variance of the same partial sums is proved to be NP-complete in the literature.Comment: 12 pages;Accepted for publication in Optimization Lette

Understanding coping with cancer: How can qualitative research help?

Research in psycho-oncology investigates the psycho-social and emotional aspects of cancer and how this is related to health, well-being and overall patient care. Coping with cancer is a prime focus for researchers owing to its impact on patients' psychological processing and life in general. Research so far has focused mainly on quantitative study designs such as questionnaires to examine the coping strategies used by cancer patients. However, in order to gain a rich and deep understanding of the reasons, processes and types of strategies that patients use to deal with cancer, qualitative study designs are necessary. Few studies have used qualitative designs such as semi-structured interviews to explore coping with cancer. The current paper aims to review the suitability and benefits of using qualitative research designs to understand coping with cancer with the help of some key literature in psycho-oncology research

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses

The completion time variance problem and its extensions.

Ng Chi To.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 169-173).Acknowledgements --- p.iAbstract --- p.iiChapter Chapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Problem Formulation and Motivation --- p.1Chapter 1.2 --- Past Research Works --- p.3Chapter 1.3 --- Results of the Study --- p.5Chapter 1.4 --- Organization of the Thesis --- p.7Chapter Part I --- THE CTV PROBLEM --- p.9Chapter Chapter 2 --- A GENERALIZATION OF SCHRAGE'S CONJEC- TURE --- p.10Chapter 2.1 --- Schrage's Conjecture --- p.10Chapter 2.2 --- Generalization --- p.13Chapter Chapter 3 --- ASYMPTOTIC OPTIMALITY --- p.15Chapter 3.1 --- Optimal Sequences under a Symmetric Structure --- p.17Chapter 3.2 --- An Upper Bound for the Relative Error --- p.21Chapter 3.3 --- Asymptotical Probabilistic Analysis --- p.25Chapter Chapter 4 --- ADDITIONAL FINDINGS --- p.37Chapter Chapter 5 --- THE BEST V-SHAPED SEQUENCE --- p.46Chapter 5.1 --- Transformation of the CTV Problem to a Boolean Optimization Problem --- p.47Chapter 5.2 --- Minimization of the Expected CTV among All the V-shaped Fixed Sequences --- p.48Chapter Chapter 6 --- THE WORST CASE ANALYSIS --- p.65Chapter 6.1 --- A Lower Bound for the CTV Problem --- p.66Chapter 6.2 --- A Worst Case Bound --- p.71Chapter Part II --- EXTENSIONS --- p.75Chapter Chapter 7 --- A MORE GENERAL MODEL --- p.76Chapter 7.1 --- Some Basic Concepts --- p.76Chapter 7.2 --- Problem Description --- p.78Chapter 7.3 --- Applications and Difficulties --- p.80Chapter Chapter 8 --- THE ZERO STARTING PROBLEM --- p.83Chapter 8.1 --- Problem Transformation --- p.85Chapter 8.2 --- Properties --- p.88Chapter 8.3 --- Algorithm A and Promising Solutions --- p.93Chapter 8.4 --- Time Complexity of Algorithm A --- p.94Chapter Chapter 9 --- PROBABILISTIC ANALYSIS OF PROMISING SO- LUTIONS --- p.95Chapter 9.1 --- Promising Solutions under a Symmetric Structure --- p.95Chapter 9.2 --- An Upper Bound for the Relative Error of Promising Solutions --- p.100Chapter 9.3 --- Probabilistic Analysis on the Relative Error of Promising Solutions --- p.106Chapter Chapter 10 --- CONCLUDING REMARKS AND FUTURE RESEARCH WORK --- p.118Appendix A Preliminary Results for Analysis --- p.122Appendix B Proofs of Some Lemmas --- p.127Appendix C Proofs of Some Theorems --- p.149Appendix D Proofs of Some Properties --- p.160Appendix E An Alternative to Completion Time Variance --- p.167Bibliography --- p.16