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

Design of personalized location areas for future Pcs networks

In Global Systems for Mobile Communications (GSM), always-update location strategy is used to keep track of mobile terminals within the network. However future Personal Communication Networks (PCS) will require to serve a wide range of services (digital voice, video, data, and email) and also will have to support a large population of users. Under such demands, determining the exact location of a user by traditional strategies would be difficult and would result in increasing the signaling load imposed by location-update and paging procedures. The problem is not only in increasing cost, but also in non-efficient utilization of a precious resource, i.e., radio bandwidth; In this thesis, personalized Location Areas (PLAs) are formed considering the mobility patterns of individual users in the system such that the signaling due to location update and paging is minimized. We prove that the problem in this formulation is of NP complexity. Therefore we study efficient optimization techniques able to avoid combinatorial search. Three known classes of optimization techniques are studied. They are Simulated Annealing, Tabu Search and Genetic Search. Three algorithms are designed for solving the problem. Modeling does not assume any specific cell structure or network topology that makes the proposal widely applicable. The behavior of mobile terminals in the network is modeled as Random Walk with an absorbing state and the Markov chain is used for cost analysis; Numeric simulation carried out for 25 and 100 hexagonal cell networks have shown that Simulated Annealing based algorithm outperforms other two by indicators of the runtime complexity and signaling cost of location management. The ID\u27s of cells populating the calculated area are provided to the mobile terminal and saved in its local memory every time the mobile subscriber moves out its current location area. Otherwise, no location update is performed, but only paging. Thus, at the expense of small local memory, the location management is carried more efficiently

Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

This review introduces the methods for further enhancing resource assignment in distributed computing situations taking into account QoS restrictions. While resource distribution typically affects the quality of service (QoS) of cloud organizations, QoS constraints such as response time, throughput, hold-up time, and makespan are key factors to take into account. The approach makes use of a methodology from the Capuchin Search Particle Large Number Improvement (CS-PSO) apparatus to smooth out resource designation while taking QoS constraints into account. Throughput, reaction time, makespan, holding time, and resource use are just a few of the objectives the approach works on. The method divides the resources in an optimum way using the K-medoids batching scheme. During batching, projects are divided into two-pack assembles, and the resource segment method is enhanced to obtain the optimal configuration. The exploratory association makes use of the JAVA device and the GWA-T-12 Bitbrains dataset for replication. The outrageous worth advancement problem of the multivariable capacity is addressed using the superior calculation. The simulation findings demonstrate that the core (Cloud Molecule Multitude Improvement, CPSO) computation during 500 ages has not reached assembly repeatedly, repeatedly, repeatedly, and repeatedly, respectively.The connection analysis reveals that the developed model outperforms the state-of-the-art approaches. Generally speaking, this approach provides significant areas of strength for a successful procedure for improving resource designation in distributed processing conditions and can be applied to address a variety of resource segment challenges, such as virtual machine setup, work arranging, and resource allocation. Because of this, the capuchin search molecule enhancement algorithm (CSPSO) ensures the success of the improvement measures, such as minimal streamlined polynomial math, rapid consolidation speed, high productivity, and a wide variety of people

Hybrid Systems of Soft Computing Technologies in Designing Team Decision for Supply Chain Management Systems of Organizations

Abstract—The main objective is development of hybrid systems for adaptive designing and supply chain management / strategizing of team decision “packages” for design work based on the use of soft computing technologies and system-creative thinking (SCT). An algorithm is proposed, and the results of case studies on predicting the effectiveness and optimal organization of team thinking, as well as designing team solutions using the technical package of social technologies are presented. They are exemplified by developing a system of products and marketing channels (points of contact) of an employer brand (EB) of an organization for individual stakeholder groups. An algorithm has been developed for using a system of hybrid “soft computing” technologies and system-creative thinking in supply chain process of project teamwork; practical calculations have been carried out using this algorithm. The algorithm and systems of models for using “soft computing” for supply chain developed allow us to obtain a synergistic effect from controlling a system of hybrid technologies at various stages of teamwork. The package includes a “basic” technology comprising “training teams”, and also the formation of a KPI system that characterize team work (units 1 and 2), “product” technologies comprising analysis of team organization thinking, forecasting team performance, team productivity management, as well as supply chain management of project (units 4, 5, 6), and also “closing” technology being a strategizing (adaptive management) of team work (dynamic control of the algorithm as a whole)

Modeling and analysis of hospital facility layout problem

The optimal solution to any facility layout problem is an important aspect and a major concem as it involves significant material handling and transportation cost. The objective is to arrange the departments within the predefined facility boundaries in the way that the interaction between the functions is efficient and the overall movement cost is minimized. While facility layout problems have traditionally focused on manufacturing facilities, there has been little work on analyzing layouts for hospitals. The thesis focuses on hospital facility layout problems (HLP) to (i) minimize the movements of patients and (ii) minimize the movements of accompanying resources such as doctors, nurses, equipment and paramedical staff. The thesis consists of two sections. In the first section, a model for the multi-floor layout problem is presented based on the minimization of movement cost. The model has travel frequency or number of trips, trip difficulty rating, baseline travel cost and distance as parameters for determining the movement cost. In the second section, some additional parameters and constraints are imposed on the model and it is simulated using Microsoft Excel. Simulations are also run to study the effect of different proposed strategies on movement cost. These proposed strategies show a reduction in movement cost from the sample layout strategy in section one. A representative example is used to illustrate the applicability of the proposed formulation

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers

The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques