Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure

Technical and economic feasibility of gradual concentric chambers reactor for sewage treatment in developing countries

A major challenge in developing countries concerning domestic wastewaters is to decrease their treatment costs. In the present study, a new cost-effective reactor called gradual concentric chambers (GCC) was designed and evaluated at lab-scale. The effluent quality of the GCC reactor was compared with that of an upflow anaerobic sludge bed (UASB) reactor. Both reactors showed organic matter removal efficiencies of 90%; however, the elimination of nitrogen was higher in the GCC reactor. The amount of biogas recovered in the GCC and the UASB systems was 50% and 75% of the theoretical amount expected, respectively, and both reactors showed a slightly higher methane production when the feed was supplemented with an additive based on vitamins and minerals. Overall, the economical analysis, the simplicity of design and the performance results revealed that the GCC technology can be of particular interest for sewage treatment in developing countries

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Information systems and healthcare XXIV: Factors affecting the EAI adoption in the healthcare sector

Recent developments in the field of integration technologies like Enterprise Application Integration (EAI) have emerged to support organizations towards improving the quality of services and reducing integration costs. Despite the importance of EAI, there is limited empirical research reported on its adoption in the healthcare sector. Khoumbati et al. [2006] developed a model for the evaluation of EAI in healthcare organizations. In doing so, the causal interrelationship of EAI adoption factors was identified by using fuzzy cognitive mapping. This paper is a progression of previous work in the area and seeks to contribute by validating the model through a different case environment. Thus, this paper contributes by deriving and proposing the MAESTRO model for EAI adoption. MAESTRO identifies a set of factors that influence EAI adoption and it is evaluated through a real-life case study. It provides an understanding of the EAI adoption process through its grounding on empirical data. In doing so, the MAESTRO model supports the management of healthcare organizations during the decision-making process for EAI adoption

Resource preference based improvement heuristics for resource portfolio problem

The multi-project problem environment under consideration involves multiple-projects with activities having alternative execution modes, a general resource budget and a resource management policy that does not allow sharing of resources among projects. The multi-project scheduling model for this problem environment is called Resource Portfolio Problem. There are three basic conceptual problems in RPP: (i) determining the general resource capacities from the given general resource budget (general resource capacities determination); (ii) dedication of the general resource capacities to projects (resource dedication) and finally (iii) scheduling of individual projects with the given resource dedications. In this study, different preference based improvement heuristics are proposed for general resource capacities determination and resource dedication conceptual problems. For general resource capacities determination, the current general resource capacity values are changed according to the resource preferences such that the resulting capacity state would be more preferable. Similarly for resource dedication, resource dedication values of projects are changed according to the preferences of projects for resources such that the resulting resource dedication state would be more preferable. These two improvement heuristics separates and couples the conceptual problems. Different preference calculation methods are proposed employing Lagrangian relaxation and linear relaxation of MRCPSP formulation

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems