On-Line Decision Support System for Finished Lamb Production

This paper describes the design and application of a decision support system to assist sheep farmers, allowing them to assess the impact of lambing date, feeding system and the price of concentrate feed on their enterprise efficiency. Industry partners Hybu Cig Cymru (HCC) and farmers have been used as sources of expert data. To validate the system three informed examples are used to measure how minor changes in a farm's production system can make relatively large changes in enterprise profitability. The result is a decision support system for sheep farmers that is user friendly and provides data and visual feedback; it also allows the service provider to update market prices on an annual basis. Limitations of the current decision support system are its inability to consider scenarios which involve the diverse weather patterns which have implications for production systems and long term market trends

A novel case-based reasoning approach to radiotherapy dose planning

In this thesis, novel Case-Based Reasoning (CBR) methods were developed to be included in CBRDP (Case-Based Reasoning Dose Planner) -an adaptive decision support system for radiotherapy dose planning. CBR is an artificial intelligence methodology which solves new problems by retrieving solutions to previously solved similar problems stored in a case base. The focus of this research is on dose planning for prostate cancer patients. The records of patients successfully treated in the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK, were stored in a case base and were exploited using case-based reasoning for future decision making. After each successful run of the system, a group based Simulated Annealing (SA) algorithm automatically searches for an optimal/near optimal combination of feature weights to be used in the future retrieval process of CBR. A number of research issues associated with the prostate cancer dose planning problem and the use of CBR are addressed including: (a) trade-off between the benefit of delivering a higher dose of radiation to cancer cells and the risk to damage surrounding organs, (b) deciding when and how much to violate the limitations of dose limits imposed to surrounding organs, (c) fusion of knowledge and experience gained over time in treating patients similar to the new one, (d) incorporation of the 5 years Progression Free Probability and success rate in the decision making process and (e) hybridisation of CBR with a novel group based simulated annealing algorithm to update knowledge/experience gained in treating patients over time. The efficiency of the proposed system was validated using real data sets collected from the Nottingham University Hospitals. Experiments based on a leave-one-out strategy demonstrated that for most of the patients, the dose plans generated by our approach are coherent with the dose plans prescribed by an experienced oncologist or even better. This system may play a vital role to assist the oncologist in making a better decision in less time; it incorporates the success rate of previously treated similar patients in the dose planning for a new patient and it can also be used in teaching and training processes. In addition, the developed method is generic in nature and can be used to solve similar non-linear real world complex problems

Industry 4.0 and circular economy in an era of global value chains: What have we learned and what is still to be explored?

This article reviews the industry 4.0 (I4.0) and circular economy (CE) literature from a global value chain (GVC) perspective. More specifically, it (1) summarizes the empirical findings on the applications of I4.0 and CE practices; (2) explores the previous literature and identifies several future research directions to advance the existing literature. In this respect, the interface between I4.0 and CE research is a relatively young field of inquiry that has been little concerned with developments in GVCs. We systematically review 112 peer-reviewed papers in the field of I4.0 and CE to distill key future research opportunities and trends in the GVC field. We develop three specific conclusions from our literature review. First, GVCs can vary widely within the various forms of I4.0 technologies with the various CE practices. Second, GVC research is underdeveloped with regard to I4.0 and CE. Third, our findings are congruent with previously published studies, which recognize the importance of GVC research that has generated a rich body of knowledge, mainly from a governance perspective in operations management, supply chain management, and international business. Likewise, our study offers promising avenues for future research studies at the intersection of I4.0, CE, and GVCs. Our systematic literature review suggests that there are many opportunities to advance the I4.0 and CE debates in the burgeoning field of GVC

A novel case-based reasoning approach to radiotherapy dose planning

In this thesis, novel Case-Based Reasoning (CBR) methods were developed to be included in CBRDP (Case-Based Reasoning Dose Planner) -an adaptive decision support system for radiotherapy dose planning. CBR is an artificial intelligence methodology which solves new problems by retrieving solutions to previously solved similar problems stored in a case base. The focus of this research is on dose planning for prostate cancer patients. The records of patients successfully treated in the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK, were stored in a case base and were exploited using case-based reasoning for future decision making. After each successful run of the system, a group based Simulated Annealing (SA) algorithm automatically searches for an optimal/near optimal combination of feature weights to be used in the future retrieval process of CBR. A number of research issues associated with the prostate cancer dose planning problem and the use of CBR are addressed including: (a) trade-off between the benefit of delivering a higher dose of radiation to cancer cells and the risk to damage surrounding organs, (b) deciding when and how much to violate the limitations of dose limits imposed to surrounding organs, (c) fusion of knowledge and experience gained over time in treating patients similar to the new one, (d) incorporation of the 5 years Progression Free Probability and success rate in the decision making process and (e) hybridisation of CBR with a novel group based simulated annealing algorithm to update knowledge/experience gained in treating patients over time. The efficiency of the proposed system was validated using real data sets collected from the Nottingham University Hospitals. Experiments based on a leave-one-out strategy demonstrated that for most of the patients, the dose plans generated by our approach are coherent with the dose plans prescribed by an experienced oncologist or even better. This system may play a vital role to assist the oncologist in making a better decision in less time; it incorporates the success rate of previously treated similar patients in the dose planning for a new patient and it can also be used in teaching and training processes. In addition, the developed method is generic in nature and can be used to solve similar non-linear real world complex problems

Big data analytics and application for logistics and supply chain management

This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods

Market segmentation and industry overcapacity considering input resources and environmental costs through the lens of governmental intervention

The problems with China’s regional industrial overcapacity are often influenced by local governments. This study constructs a framework that includes the resource and environmental costs to analyze overcapacity using the non-radial direction distance function and the price method to measure industrial capacity utilization and market segmentation in 29 provinces in China from 2002 to 2014. The empirical analysis of the spatial panel econometric model shows that (1) the industrial capacity utilization in China’s provinces has a ladder-type distribution with a gradual decrease from east to west and there is a severe overcapacity in the traditional heavy industry areas; (2) local government intervention has serious negative effects on regional industry utilization and factor market segmentation more significantly inhibits the utilization rate of regional industry than commodity market segmentation; (3) economic openness improves the utilization rate of industrial capacity while the internet penetration rate and regional environmental management investment have no significant impact; and(4) a higher degree of openness and active private economic development have a positive spatial spillover effect, while there is a significant negative spatial spillover effect from local government intervention and industrial structure sophistication. This paper includes the impact of resources and the environment in overcapacity evaluations, which should guide sustainable development in emerging economies

Pre-positioning inventory and service outsourcing of relief material supply chain

Service outsourcing is very common in a commercial supply chain, and in humanitarian relief area the transportation service is usually outsourced. To practice relief supply more effectively, it seems essential to enlarge outsourcing from shipping to more areas, and private enterprises could play a vital role. This paper examines the optimal pre-disaster order quantity of a certain relief commodity, based on a two-stage coordinated approach. Our findings show that the delay cost, shortage penalty cost, risk of supply shortage, salvage value, expected perishable rate, unit inventory cost and reactive price have significant impacts on the optimal amount of propositioned inventory. Moreover, the outsourcing strategies differ by types of relief commodities. For perishable supplies, proactive or reactive outsourcing would improve the benefits of buyer and supplier simultaneously. As for imperishable supplies, it is better to combine proactive insourcing approach and reactive outsourcing strategy. In view of some supplies whose monitoring cost is high, the insourcing approach is much better than outsourcing approach

Modelling the factors that influence the acceptance of digital technologies in e-government services in the UAE: a PLS-SEM approach

The digital technologies such as internet play a crucial role in the management of operations of organizations in both public and private sectors. Such technologies support the implementation of effective digital business strategies. By reviewing the extant literature, this paper aims to identify factors that influence the intention to use digital technologies in order to develop a theoretical model which is then tested empirically using the PLS-SEM approach. While many studies have focused solely on the importance of social influence, perceived usefulness, perceived ease of use, awareness, perceived trust in technology, perceived trust in government, perceived cost, and perceived risk, this article brings them together to explain their linkage, and quantifies the relationship. This study is the first empirical attempt to explore the factors influence e-government services adoption in the UAE. Most specifically, this article emphasizes the role of social influence, perceived ease to use, and perceived trust in technology as the important determinants of the intention to digital technology adoption. The paper expands the traditional discussion by incorporating six variables, in addition to Davis’s (1989) the perceived ease to digital technologies use and perceived usefulness, in a model that acts as facilitator or barrier in the intention to use digital technologies. This article helps practitioners to understand of which factors should be given emphasis in enhancing the intention to use digital technologies. The model developed in this paper is not only a response to the need to understand what causes the variation in the intention to use digital technologies from the operation management perspective, it is also a response to practitioner needs to use an appropriate construct to ensure the effective operation and use of the digital technologies in e-government services. The paper will help to identify the key issues surrounding the digital technologies adoption that may lead the successful operations of e-government

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

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences