A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning

In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. However, there are a number of deficiencies associated with this approach. Firstly, in a real-life scenario, the medical team considers multiple factors rather than just the similarity between two cases and not always the most similar case provides with the most appropriate solution. Thus, in this thesis, the cases with high similarity to a new case have been evaluated with the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach takes into account multiple criteria besides similarity to prescribe a final solution. Moreover, the obtained dose plans were optimised through a Goal Programming mathematical model to improve the results. By incorporating oncologists’ experiences about violating the conventionally available dose limits a system was devised to manage the trade-off between treatment risk for sensitive organs and necessary actions to effectively eradicate cancer cells. Additionally, the success rate of the treatment, the 2-years cancer free possibility, has a vital role in the efficiency of the prescribed solutions. To consider the success rate, as well as uncertainty involved in human judgment about the values of different features of radiotherapy Data Envelopment Analysis (DEA) based on grey numbers, was used to assess the efficiency of different treatment plans on an input and output based approach. In order to deal with limitations involved in DEA regarding the number of inputs and outputs, we presented an approach for Factor Analysis based on Principal Components to utilize the grey numbers. Finally, to improve the CBR base of the system, we applied Grey Relational Analysis and Gaussian distant based CBR along with features weight selection through Genetic Algorithm to better handle the non-linearity exists within the problem features and the high number of features. Finally, the efficiency of each system has been validated through leave-one-out strategy and the real dataset. The results demonstrated the efficiency of the proposed approaches and capability of the system to assist the medical planning team. Furthermore, the integrated approaches developed within this thesis can be also applied to solve other real-life problems in various domains other than healthcare such as supply chain management, manufacturing, business success prediction and performance evaluation

Sustainable resource allocation for power generation: The role of big data in enabling interindustry architectural innovation

Economic, social and environmental requirements make planning for a sustainable electricity generation mix a demanding endeavour. Technological innovation offers a range of renewable generation and energy management options which require fine tuning and accurate control to be successful, which calls for the use of large-scale, detailed datasets. In this paper, we focus on the UK and use Multi-Criteria Decision Making (MCDM) to evaluate electricity generation options against technical, environmental and social criteria. Data incompleteness and redundancy, usual in large-scale datasets, as well as expert opinion ambiguity are dealt with using a comprehensive grey TOPSIS model. We used evaluation scores to develop a multi-objective optimization model to maximize the technical, environmental and social utility of the electricity generation mix and to enable a larger role for innovative technologies. Demand uncertainty was handled with an interval range and we developed our problem with multi-objective grey linear programming (MOGLP). Solving the mathematical model provided us with the electricity generation mix for every 5 min of the period under study. Our results indicate that nuclear and renewable energy options, specifically wind, solar, and hydro, but not biomass energy, perform better against all criteria indicating that interindustry architectural innovation in the power generation mix is key to sustainable UK electricity production and supply

Knowledge management for food supply chain synergies – A maturity level analysis of SME companies

Despite the considerable number of papers addressing knowledge management (KM) aspects in supply chains, many research issues in the area are still neglected. One of the main research gaps in this field concerns the maturity level of KM practices adoption by small and medium enterprises (SMEs). This paper addresses this research gap by developing a framework to support the analysis of the maturity level of KM adoption in an SME context. The framework is applied in a multiple case study developed to investigate the extent to which SMEs operating in the food sector are deploying KM practices to support more sustainable initiatives. By relating KM maturity levels, perspectives and processes to sustainable practices concerning food waste and by-product synergies, the paper makes an original contribution to advance theory and practice in the area. The paper also points out potential barriers that companies face to implement sustainability related KM practices

A hybrid approach of VIKOR and bi-objective integer linear programming for electrification planning in a disaster relief camp

In this paper, we provide a model which optimizes the allocation of electricity generation systems, in terms of their number and location, in a disaster relief camp. The objectives that this model takes into account are minimization of the total cost of the project and prioritization of those generation systems that perform favourably. Energy and specifically electricity plays an important role in the provision of essential needs like lighting, water purification, heating, ventilation and medical care for displaced people. Disaster relief camps are commonly considered as off-grid projects, so individual generation and control systems are the main means of electrification. To support decision makers in electrification planning for temporary and semi-temporary camps, we propose a bi-objective integer linear programming model. The performance evaluation of technologies such as fuel generators, wind turbines and solar panels is conducted with an MCDM (VIKOR) approach. The model is applied on a hypothetical but realistic map site with data regarding commercially available equipment. The better performance of solar panels regarding the evaluation criteria have made them the dominant applied source of renewable electricity generation system and together with application of micro-grids in the model they have proven to reduce the cost of generation significantly. However, installing fuel generators have been found necessary for facilities which can cause a remarkable damage in case of electricity interruption. The model is promising in helping relief aid agencies to design an electrification project with minimum cost and maximum utility

Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning

Sustainable energy generation is a key feature in sustainable development and among various sources of energy electricity due to some unique characteristics seems particularly important. Optimising electricity generation mix is a highly complex task and requires consideration of numerous conflicting criteria. To deal with uncertainty of experts’ opinions, inaccuracy of the available data and including more factors, some of which are difficult to quantify, in particular for environmental and social criteria, we applied grey relational analysis (GRA) with grey linguistic, and grey interval values to obtain the rank of each system. Then the obtained ranking were used as coefficients for a multi objective decision making problem, aimed to minimize the cost, import dependencies and emissions as well as maximizing the share of generation sources with better ranking. Due to existence of interval variables multi objective grey linear programming (MOGLP) method was used to solve the problem. Our results for the UK as a case study suggest increased role for all low carbon energy technologies and sharp reduction in the use of coal and oil. We argue that the integrated GRA–MOGLP approach provides an effective tool for the evaluation and optimisation of complex sustainable electricity generation planning. It is particularly promising in dealing with uncertainty and imprecisions, which reflect real-life scenarios in planning processes

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Big data cloud computing framework for low carbon supplier selection in the beef supply chain

Purpose With the rapid economic development of nations across the globe, there is proportionate increment in corresponding carbon footprint. There are numerous counter measures proposed to mitigate it in terms of legislation and policy framing. However, they have a short-sighted vision of predominantly focusing on manufacturing and transportation industry thereby neglecting one of the significant contributor of global emissions- agricultural industry. Among all the agri-food products, beef has the highest carbon footprint and majority of its emission are generated in beef farms. The issue is more intensive in developing nations where most of global cattle are raised and simultaneously farmers are less informed and aware of resources/technology to address emissions from their farms. Therefore, there is need to raise awareness among farmers and thereby incorporate carbon footprint as a major cattle supplier selection attribute by abattoir and processor and integrate it as a standard practice in procurement of cattle. Design/methodology A novel framework based on big data cloud computing technology is developed for eco-friendly cattle supplier selection. It is capable of measuring greenhouse gas emissions in farms and assimilate into the cattle supplier selection process. Fuzzy AHP, DEMATEL and TOPSIS method is employed to make an optimum tradeoff between conventional quality attributes and carbon footprint generated in farms to select the most appropriate supplier. Findings The proposed framework would assist in shedding the environmental burden of beef supply chain as the majority of carbon footprint is generated in beef farms. Moreover, the vertical coordination in the supply chain among farmers and abattoir, processor would be strengthened. The execution of the framework is depicted in case study section. Originality The literature is deficient of eco-friendly supplier selection in the agri-food sector particularly in developing countries. This study bridges the gap in the literature by proposing a novel framework to incorporate carbon footprint into traditional supplier selection process via an amalgamation of big data, ICT and Operations Research. The proposed framework would assist in mitigating the carbon footprint of beef products as they have highest emissions among all agri-food products. This framework is generic in nature and can be implemented in any food supply chain

An efficient approach to radiotherapy dose planning problem:a TOPSIS case-based reasoning approach

© 2016, © 2016 Informa UK Limited, trading as Taylor & Francis Group. Dose planning of prostate cancer is a complex and time-consuming process. Usually, oncologists use past experience and spend a large amount of time to determine the optimal combination of dose in phase I and II of treatment. In this article, a novel TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) case-based reasoning (CBR) approach is proposed to capture the past experience and expertise of oncologists. Initially, cases that resemble new case are extracted from database. Thereafter, inferred cases are evaluated using TOPSIS, a multi-criteria decision-making approach to prescribe an optimal dose plan. Robustness of the proposed method is validated on data sets collected from the City Hospital Campus, Nottingham University Hospitals, NHS, UK, using leave-one-out strategy. In experiment, the proposed methodology outperformed CBR approach. It also endorses the suitability of multi-criteria decision-making approach. This method will help oncologists to make a better trade-off between similarity measures, success rate and side effects of treatment. The methodology is generic in nature and can help oncologists both new and experienced in dose planning process

Knowledge management for food supply chain synergies – A maturity level analysis of SME companies

Despite the considerable number of papers addressing knowledge management (KM) aspects in supply chains, many research issues in the area are still neglected. One of the main research gaps in this field concerns the maturity level of KM practices adoption by small and medium enterprises (SMEs). This paper addresses this research gap by developing a framework to support the analysis of the maturity level of KM adoption in an SME context. The framework is applied in a multiple case study developed to investigate the extent to which SMEs operating in the food sector are deploying KM practices to support more sustainable initiatives. By relating KM maturity levels, perspectives and processes to sustainable practices concerning food waste and by-product synergies, the paper makes an original contribution to advance theory and practice in the area. The paper also points out potential barriers that companies face to implement sustainability related KM practices

Knowledge management for food supply chain synergies – A maturity level analysis of SME companies

Despite the considerable number of papers addressing knowledge management (KM) aspects in supply chains, many research issues in the area are still neglected. One of the main research gaps in this field concerns the maturity level of KM practices adoption by small and medium enterprises (SMEs). This paper addresses this research gap by developing a framework to support the analysis of the maturity level of KM adoption in an SME context. The framework is applied in a multiple case study developed to investigate the extent to which SMEs operating in the food sector are deploying KM practices to support more sustainable initiatives. By relating KM maturity levels, perspectives and processes to sustainable practices concerning food waste and by-product synergies, the paper makes an original contribution to advance theory and practice in the area. The paper also points out potential barriers that companies face to implement sustainability-related KM practices