Recently, technological developments have affected most lifestyles, especially with the growth in Internet usage. Internet applications highlight the E-commerce capabilities and applications which are now available everywhere; they receive a great number of users on a 24-7 basis because online services are easy to use, faster and cheaper to acquire. Thus E-commerce web sites have become crucial for companies to increase their revenues. This importance has identified certain effective requirements needed from the performance of these applications. In particular, if the web server is overloaded, poor performance can result, due to either a huge rate of requests being generated which are beyond the server¿s capacity, or due to saturation of the communication links capacity which connects the web server to the network. Recent researches consider the overload issue and explore different mechanisms for managing the performance of E-commerce applications under overload condition. This thesis proposes a formal approach in order to investigate the effects of the extreme load and the number of dropped requests on the performance of E- III commerce web servers. The proposed approach is based on the class-based priority scheme that classifies E-commerce requests into different classes. Because no single technique can solve all aspects of overload problems, this research combines several techniques including: admission control mechanism, session-based admission control, service differentiation, request scheduling and queuing model-based approach. Request classification is based on the premise that some requests (e.g. buy) are generally considered more important than others (e.g. browse or search). Moreover, this research considers the extended models from Priority Scheduling Mechanism (PSM). These models add a new parameter, such as a review model or modify the basic PSM to low priority fair model, after the discovery of ineffectiveness with low priority customers or to add new features such as portal models. The proposed model is formally specified using the ¿ -calculus in early stage of models design and a multi-actor simulation was developed to reflect the target models as accurately as possible and is implemented as a Java-based prototype system. A formal specification that captures the essential PSM features while keeping the performance model sufficiently simple is presented. Furthermore, the simplicity of the UML bridges the gap between ¿-calculus and Java programming language. IV There are many metrics for measuring the performance of E-commerce web servers. This research focuses on the performance of E-commerce web servers that refer to the throughput, utilisation, average response time, dropped requests and arrival rate. A number of experiments are conducted in order to test the performance management of the proposed approaches.

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Organizations providing home care services are inclined to optimize their activities in order to meet the constantly increasing demand for home care. In this context, home care providers are confronted with multiple, often conflicting, objectives such as minimizing their operating costs while maximizing the service level offered to their clients by taking into account their preferences. This paper is the first to shed some light on the trade-off relationship between these two objectives by modeling the home care routing and scheduling problem as a bi-objective problem. The proposed model accounts for qualifications, working regulations and overtime costs of the nurses, travel costs depending on the mode of transportation, hard time windows, and client preferences on visit times and nurses. A distinguishing characteristic of the problem is that the scheduling problem for a single route is a biobjective problem in itself, thereby complicating the problem considerably. A metaheuristic algorithm, embedding a large neighborhood search heuristic in a multi-directional local search framework, is proposed to solve the problem. Computational experiments on a set of benchmark instances based on reallife data are presented. A comparison with exact solutions on small instances shows that the algorithm performs well. An analysis of the results reveals that service providers face a considerable trade-off between costs and client convenience. However, starting from a minimum cost solution, the average service level offered to the clients may already be improved drastically with limited additional costs. (authors' abstract)

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / One approach to improving spectrum usage efficiency is to manage the scheduling of frequencies more effectively. The use of metrics to analyze frequency scheduling could aid frequency managers in a variety of ways. However, the basic question of what is a good metric for representing and analyzing spectral usage remains unanswered. Some metrics capture spectral occupancy. This paper introduces metrics that change the focus from occupancy to availability. Just because spectrum is not in use does not mean it is available for use. A significant factor in creating unused but unusable spectrum is fragmentation. A mission profile for spectrum usage can be considered a rectangle in a standard time versus frequency grid. Even intelligent placement of these rectangles (i.e., the scheduling of a missions spectrum usage) can not always utilize all portions of the spectrum. The average typical mission availability (ATMA) metric provides a way of numerically answering the question: Could we have scheduled another typical mission? This is a much more practical question than: Did we occupy the entire spectrum? If another mission couldn’t have been scheduled, then the entire spectrum was effectively used, even if the entire spectrum wasn’t occupied.

The vast majority of researches in the scheduling context focused on finding optimal or near-optimal predictive schedules under different scheduling problem characteristics. In the construction industry, predictive schedules are often produced in advance in order to direct construction operations and to support other planning activities. However, construction projects operate in dynamic environments subject to various real-time events, which usually disrupt the predictive optimal schedules, leading to schedules neither feasible nor optimal. Accordingly, the development of a dynamic scheduling model which can accommodate these real-time events would be of great importance for the successful implementation of construction scheduling systems. This research sought to develop a dynamic scheduling based solution which can be practically used for real time analysis and scheduling of construction projects, in addition to resources optimization for construction enterprises. The literature reviews for scheduling, dynamic scheduling, and optimization showed that despite the numerous researches presented and application performed in the dynamic scheduling field within manufacturing and other industries, there was dearth in dynamic scheduling literature in relation to the construction industry. The research followed two main interacting research paths, a path related to the development of the practical solution, and another path related to the core model development. The aim of the first path (or the proposed practical solution path) was to develop a computer-based dynamic scheduling framework which can be used in practical applications within the construction industry. Following the scheduling literature review, the construction project management community s opinions about the problem under study and the user requirements for the proposed solution were collected from 364 construction project management practitioners from 52 countries via a questionnaire survey and were used to form the basis for the functional specifications of a dynamic scheduling framework. The framework was in the form of a software tool fully integrated with current planning/scheduling practices with all core modelling which can support the integration of the dynamic scheduling processes to the current planning/scheduling process with minimal experience requirement from users about optimization. The second research path, or the dynamic scheduling core model development path, started with the development of a mathematical model based on the scheduling models in literature, with several extensions according to the practical considerations related to the construction industry, as investigated in the questionnaire survey. Scheduling problems are complex from operational research perspective; so, for the proposed solution to be functional in optimizing construction schedules, an optimization algorithm was developed to suit the problem's characteristics and to be used as part of the dynamic scheduling model's core. The developed algorithm contained few contributions to the scheduling context (such as schedule justification heuristics, and rectification to schedule generation schemes), as well as suggested modifications to the formulation and process of the adopted optimization technique (particle swarm optimization) leading to considerable improvement to this techniques outputs with respect to schedules quality. After the completion of the model development path, the first research path was concluded by combining the gathered solution's functional specifications and the developed dynamic scheduling model into a software tool, which was developed to verify & validate the proposed model s functionalities and the overall solution s practicality and scalability. The verification process started with an extensive testing of the model s static functionality using several well recognized scheduling problem sets available in literature, and the results showed that the developed algorithm can be ranked as one of the best state-of-the-art algorithms for solving resource-constrained project scheduling problems. To verify the software tool and the dynamic features of the developed model (or the formulation of data transfers from one optimization stage to the next), a case study was implemented on a construction entity in the Arabian Gulf area, having a mega project under construction, with all aspects to resemble an enterprise structure. The case study results showed that the proposed solution reasonably performed under large scale practical application (where all optimization targets were met in reasonable time) for all designed schedule preparation processes (baseline, progress updates, look-ahead schedules, and what-if schedules). Finally, to confirm and validate the effectiveness and practicality of the proposed solution, the solution's framework and the verification results were presented to field experts, and their opinions were collected through validation forms. The feedbacks received were very positive, where field experts/practitioners confirmed that the proposed solution achieved the main functionalities as designed in the solution s framework, and performed efficiently under the complexity of the applied case study.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / An operational concept for how a Demand Access Multiple Assignment (DAMA) system could be configured for the NASA Space network is examined. Unique aspects of this concept definition are the use of the Multiple Access system within the Space Network to define an order wire channel that continuously scans the Low Earth Orbit space for potential users and the use of advanced digital signal processing technology to look for the Doppler-shifted carrier signal from the requesting satellite. After the reception of the signal, validation and processing of the request is completed. This paper outlines the concept and the ways in which the system could work.

In this thesis we address capacity planning problems with different demand and service characteristics, motivated by healthcare applications. In the first application, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a two-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair while considering staff overtime and pooling policies and other staff planning constraints. We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model. In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between each order's arrival and fulfillment, with the goal of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operational practices. Hence, solving this problem requires simultaneously scheduling the staff and the forecasted demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision-support tool. We implement this model in a company that offers document processing and other back-office services to healthcare providers. We provide details on the model validation, implementation, and results, including a 25\% increase in the company's staff productivity. Finally, we provide insights on the effects of some of the model's parameters and settings, and assess the performance of a proposed heuristic to solve this problem. In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs, each job has a scheduled start time and duration, and belongs to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected,' and the service level is measured by the (weighted) percentage of accepted jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to global and demand-class-based service level constraints. We first analyze the complexity of this problem and several of its special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning decisions for surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy