This thesis is concerned with the implementation of a proposed Energy Hub Management System (EHMS) mathematical model for residential appliances under Time of Use (TOU) based electricity rate structure. The objective is to shift the residential electrical energy consumption during periods of high grid energy demand to low demand periods subject to operational constraints. The customer benefits from reduced daily energy consumption and consequent reduction to its cost with minimal effect on the comfort level. Two scheduling periods are considered simultaneously. The first is a 24-interval schedule with one-hour time steps for appliances such as a dishwasher, clothes washer and dryer. The second is a 96-interval schedule with 15-minute time steps for other appliances such as refrigerator, freezer and water heater. Each appliance has been modeled as a discrete time linear dynamic system and the objective of this thesis is to make these models work in a real world situation by determining realistic estimations of the model parameters and constants. It is vital to properly calculate the mathematical model parameters as they have direct impact on the results. Minor modifications to some domestic appliance models were proposed to make the practical implementation easier. It was found that while some parameters in the mathematical model can be easily calculated based on thermodynamic equations, other parameters are hard to be calculated; therefore, a practical procedure was proposed to measure these parameters. An experiment on a small refrigerator was carried out to validate the refrigerator mathematical model and parameters measurement procedure. The resulting model is a mixed integer linear problem (MILP) and was solved using GNU Linear Programming Kit (GLPK) freeware solver. The performance of GLPK was found to be satisfactory as compared to the commercial solver CPLEX, and was particularly suitable for practical and commercial implementations.

This thesis is concerned with the implementation of a proposed Energy Hub Management System (EHMS) mathematical model for residential appliances under Time of Use (TOU) based electricity rate structure. The objective is to shift the residential electrical energy consumption during periods of high grid energy demand to low demand periods subject to operational constraints. The customer benefits from reduced daily energy consumption and consequent reduction to its cost with minimal effect on the comfort level. Two scheduling periods are considered simultaneously. The first is a 24-interval schedule with one-hour time steps for appliances such as a dishwasher, clothes washer and dryer. The second is a 96-interval schedule with 15-minute time steps for other appliances such as refrigerator, freezer and water heater. Each appliance has been modeled as a discrete time linear dynamic system and the objective of this thesis is to make these models work in a real world situation by determining realistic estimations of the model parameters and constants. It is vital to properly calculate the mathematical model parameters as they have direct impact on the results. Minor modifications to some domestic appliance models were proposed to make the practical implementation easier. It was found that while some parameters in the mathematical model can be easily calculated based on thermodynamic equations, other parameters are hard to be calculated; therefore, a practical procedure was proposed to measure these parameters. An experiment on a small refrigerator was carried out to validate the refrigerator mathematical model and parameters measurement procedure. The resulting model is a mixed integer linear problem (MILP) and was solved using GNU Linear Programming Kit (GLPK) freeware solver. The performance of GLPK was found to be satisfactory as compared to the commercial solver CPLEX, and was particularly suitable for practical and commercial implementations.

This paper presents a method that uses designed experiments and statistical models to extract information about how solver parameter settings perform for classes of mixed integer programs. The use of experimental design facilitates fitting a model that describes the response surface across all combinations of parameter settings, even those not explicitly tested, allowing identification of both desirable and poor settings. Identifying parameter settings that give the best expected performance for a specific class of instances and a specific solver can be used to more efficiently solve a large set of similar instances, or to ensure solvers are being compared at their best.

Home schooling has steadily been increasing in the past decade. According to a survey in 2007, about 2.5 million children were being home schooled in the US. Typically, parents provide education at the convenience of their home and in some cases an instructor is appointed for the same. The Home School Timetabling problem (HSTP) deals with assigning subjects, timeslots and rooms to every student. In doing so, there are certain hard and specialty constraints that are to be satisfied. Integer programming (IP) has been used in solving the HSTP as it has the advantage of being able to provide information about the relative significance of each constraint with respect to the objective. A prototype in the form of a GUI has been built such that the parent can enter each student’s name, his/her subjects, duration, days and time for each subject, availability times of the parent etc. This data is then fed into the IP model so that it can generate a feasible timetable satisfying all of the constraints. When a solution is found it is formatted to provide the weekly timetable for each student, individually, as well as a complete timetable for all students each day.

The goal of this Master's thesis was to solve resource allocation problems in wireless networks through the implementation of a lightweight simulation platform. The spectrum and power resources of wireless networks have to be efficiently used to accommodate the growing number of wireless terminals and the massive increase of data transferred by their applications. The major problem that needs to be tackled is interference, which significantly limits the performance of wireless systems. In this thesis, the resource allocation of interest was the joint problem of scheduling and power control with Quality of Service (QoS) constraints. The Signal-to-Interference-plus-Noise Ratio (SINR) was used to quantify QoS. This thesis studied the recently proposed mixed-integer linear programming (MILP) formulation of the problem. Due to the scheduling component, the problem is inherently combinatorial and NP-hard, therefore computationally expensive and difficult to solve in tractable time. A simulation platform was implemented in order to automate and facilitate the solving process.As a starting point, wireless channels and channel modeling issues were studied. Then, the platform was implemented to simulate random instances of multi-cellular wireless networks, with several mobile stations per cell, and generate the corresponding channels. Finally, the platform was extended to use the GNU Linear Programming Kit (GLPK) API in order to optimally solve the aforementioned formulated problem for various inputs of generated channels.Tests of the simulation platform were performed to check the consistency of the results. Indeed, the output results satisfied the initial expectations regarding the SINR constraints and the formulation. Moreover, they were produced in reasonable time. An analysis of the output results was presented.This thesis resulted in a configurable and lightweight simulation platform which is able to solve the MILP-formulated resource allocation problem. The simulation platform is basic and does not cover all the aspects of multi-cellular wireless networks and wireless channels. Due to its modularity, it can be extended in a future project.