Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Development of optimal location and design capacity of wastewater treatment plants for urban areas: a case study in Samawah city

Water, and related wastewater structures, are critical factors in the existence and the improvement of civilizations. Wastewater gathering and management has a considerable effect on the climate and economy at both regional and global level, and, accordingly, it is appropriate to advance actions that guarantee effective management for wastewater, particularly in urban areas. This research thus examined the environmental and economic aspects of proposed locations for wastewater treatment plants. Samawah city, located in the southern part of Iraq, was selected as a case study for the research methodology, and for research purposes, the studied city was divided into three main zones (1, 2, and 3) of sixteen areas. The Google Earth tool was used to calculate the lowest elevations in the studied zones in order to assess the suggested positions of treatment plants. Additionally, the WinQSB program was utilised to select the most appropriate positions for treatment plants based on data obtained from local government departments. These data include population, water consumption, and required lengths and subsequent cost of pipes. This research thus developed a new strategy for assigning the locations of wastewater treatment plants

Optimization of selection response under restricted inbreeding

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Improved genetic algorithms by means of fuzzy crossover operators for revenue management in airlines

Abstract: Revenue Management is an economic policy that increases the earned profit by adjusting the service demand and inventory. Revenue Management in airlines correlates with inventory control and price levels in different fare classes. We focus on pricing and seat allocation problems in airlines by introducing a constrained optimization problem in Binary Integer Programming (BIP) formulation. Two BIP problems are represented. Moreover, some improved Genetic Algorithms (GAs) approaches are used to solve these problems. We introduce new crossover operators that assign a Fuzzy Membership Function to each parent in GAs. We achieve better outputs with new methods that take lower calculation times and earn higher profits. Three different test problems in different scales are selected to evaluate the effectiveness of each algorithm. This paper defines new crossover operators that help to reach better solutions that take lower calculation times and more earned profits

Generation expansion planning optimisation with renewable energy integration: A review

Generation expansion planning consists of finding the optimal long-term plan for the construction of new generation capacity subject to various economic and technical constraints. It usually involves solving a large-scale, non-linear discrete and dynamic optimisation problem in a highly constrained and uncertain environment. Traditional approaches to capacity planning have focused on achieving a least-cost plan. During the last two decades however, new paradigms for expansion planning have emerged that are driven by environmental and political factors. This has resulted in the formulation of multi-criteria approaches that enable power system planners to simultaneously consider multiple and conflicting objectives in the decision-making process. More recently, the increasing integration of intermittent renewable energy sources in the grid to sustain power system decarbonisation and energy security has introduced new challenges. Such a transition spawns new dynamics pertaining to the variability and uncertainty of these generation resources in determining the best mix. In addition to ensuring adequacy of generation capacity, it is essential to consider the operational characteristics of the generation sources in the planning process. In this paper, we first review the evolution of generation expansion planning techniques in the face of more stringent environmental policies and growing uncertainty. More importantly, we highlight the emerging challenges presented by the intermittent nature of some renewable energy sources. In particular, we discuss the power supply adequacy and operational flexibility issues introduced by variable renewable sources as well as the attempts made to address them. Finally, we identify important future research directions

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government

Optimal design and operation of livestock breeding programmes with restrictions in inbreeding

Modem breeding programmes of livestock species have successfully led to increased genetic merit in traits of economic relevance through accurate and intense selection. However, concomitant increased levels of inbreeding have been also observed. Quadratic optimisation constitutes a general approach to the joint management of the rates of genetic gain (ΔG) and inbreeding (ΔF) in selected populations. The rate of inbreeding can be used as a measure of risk in the breeding programme. The method optimises the genetic contributions of selection candidates for maximising ΔG while restricting ΔF to a pre-defined value. The ΔF restriction is achieved by applying a quadratic constraint on the average co-ancestry of selection candidates weighted by their projected use. The general objectives of this thesis were: i) to implement and evaluate the potential benefits of quadratic optimisation in real livestock populations; ii) to develop a deterministic framework for predicting ΔG under constrained ΔF and iii) to evaluate the benefits of quadratic optimisation in multiple trait scenarios under mixed inheritance modelsThe application of quadratic optimisation in two populations of beef cattle (Aberdeen Angus) and sheep (Meatlinc) led to important increases in the expected AG. At the observed ΔF in each population, increments per year in ΔG of 17% for Meatlinc and 30% for Aberdeen Angus were found in comparison to the ΔG expected from conventional truncation BLUP selection. More relaxed constraints on ΔF allowed even higher increases in expected ΔG in both populations.Stochastic simulations have revealed that under quadratic optimisation the selective advantage of the candidates for selection is primarily their Mendelian sampling terms rather than their breeding values as under truncation selection. Thus, under quadratic optimisation, the contribution of candidates to the future genetic pool is decided upon the best information on their unique superiority or inferiority with respect to the parental mean.A self-contained and accurate deterministic approach for predicting ΔG for pre-defined ΔF has been developed. It requires only specification of the trait heritability, the number of selection candidates and the target ΔFBenefits from quadratic optimisation were also evaluated in a two-trait scenario where the trait with lower heritability was affected by an identified quantitative trait loci (QTL). Extra gains in the breeding goal were observed throughout the whole selection process from the combined use of both optimised contributions and QTL information. In contrast, this scheme was not the most effective for improving each of the traits in the breeding objective. The design and operational tools developed in this thesis constitute a general framework for the evaluation and realisation of the benefits from quadratic optimisation tools in practical livestock breeding programmes

Performance Modelling, and Adaptive Control for Linked Sequential Systems

This thesis investigates the dynamics of linked sequential systems of machines in industrial laundries. Two aspects are considered: firstly the control of such systems and in particular the decision making point when a batch to be processed can be sent to one of many identical machines, and secondly the modelling of the whole system of linked machines. The decision making point in the control of these systems is frequently implemented in a sub-optimal manner, or a manner which becomes sub-optimal as conditions change. An adaptive system is preferable and an Evolutionary Artificial Neural Network approach (EANN) is proposed. The EANN is tested on simulations of real laundry systems and shown to be effective. Then it is applied to two abstract game playing problems in order to better understand its limitations. Limitations are found to include the fact that if learning does not appear to take place, it is not possible to determine if this is a failure of the Evolutionary approach or the Artificial Neural Network parameters. The dynamics and performance of Linked Sequential Systems in Industrial Laundries are not well understood or covered by theory in the literature. The theory of the performance of these systems is outlined, and an Agent Based Model (ABM) simulation presented. The ABM simulation is explained and then the simulation is compared to a real world system in an existing laundry. The performance of the existing system is measured and compared to the prediction of the ABM simulation. The ABM simulation is shown to offer a better understanding of the system than the previous static calculation. Finally the ABM is used in a design exercise to show how it could be used to specify a system more accurately than the static calculation at design stage

Forecasting inflation with thick models and neural networks

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models