A multi-agent system using iterative bidding mechanism to enhance manufacturing agility

The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed. © 2012 Elsevier Ltd. All rights reserved.

An iterative agent bidding mechanism for responsive manufacturing

In today's market, the global competition has put manufacturing businesses in great pressures to respond rapidly to dynamic variations in demand patterns across products and changing product mixes. To achieve substantial responsiveness, the manufacturing activities associated with production planning and control must be integrated dynamically, efficiently and cost-effectively. This paper presents an iterative agent bidding mechanism, which performs dynamic integration of process planning and production scheduling to generate optimised process plans and schedules in response to dynamic changes in the market and production environment. The iterative bidding procedure is carried out based on currency-like metrics in which all operations (e.g. machining processes) to be performed are assigned with virtual currency values, and resource agents bid for the operations if the costs incurred for performing them are lower than the currency values. The currency values are adjusted iteratively and resource agents re-bid for the operations based on the new set of currency values until the total production cost is minimised. A simulated annealing optimisation technique is employed to optimise the currency values iteratively. The feasibility of the proposed methodology has been validated using a test case and results obtained have proven the method outperforming non-agent-based methods.

Handling varying amounts of missing data when classifying mental-health risk levels

One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.

To link or not to link: a test of the case linkage principles using serial car theft data

The purpose of the present study is to test the case linkage principles of behavioural consistency and behavioural distinctiveness using serial vehicle theft data. Data from 386 solved vehicle thefts committed by 193 offenders were analysed using Jaccard's, regression and Receiver Operating Characteristic analyses to determine whether objectively observable aspects of crime scene behaviour could be used to distinguish crimes committed by the same offender from those committed by different offenders. The findings indicate that spatial behaviour, specifically the distance between theft locations and between dump locations, is a highly consistent and distinctive aspect of vehicle theft behaviour; thus, intercrime and interdump distance represent the most useful aspects of vehicle theft for the purpose of case linkage analysis. The findings have theoretical and practical implications for understanding of criminal behaviour and for the development of decision-support tools to assist police investigation and apprehension of serial vehicle theft offenders.

Modelling the dynamics of genetic algorithms using statistical mechanics

A formalism for modelling the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics, originally due to Prugel-Bennett and Shapiro, is reviewed, generalized and improved upon. This formalism can be used to predict the averaged trajectory of macroscopic statistics describing the GA's population. These macroscopics are chosen to average well between runs, so that fluctuations from mean behaviour can often be neglected. Where necessary, non-trivial terms are determined by assuming maximum entropy with constraints on known macroscopics. Problems of realistic size are described in compact form and finite population effects are included, often proving to be of fundamental importance. The macroscopics used here are cumulants of an appropriate quantity within the population and the mean correlation (Hamming distance) within the population. Including the correlation as an explicit macroscopic provides a significant improvement over the original formulation. The formalism is applied to a number of simple optimization problems in order to determine its predictive power and to gain insight into GA dynamics. Problems which are most amenable to analysis come from the class where alleles within the genotype contribute additively to the phenotype. This class can be treated with some generality, including problems with inhomogeneous contributions from each site, non-linear or noisy fitness measures, simple diploid representations and temporally varying fitness. The results can also be applied to a simple learning problem, generalization in a binary perceptron, and a limit is identified for which the optimal training batch size can be determined for this problem. The theory is compared to averaged results from a real GA in each case, showing excellent agreement if the maximum entropy principle holds. Some situations where this approximation brakes down are identified. In order to fully test the formalism, an attempt is made on the strong sc np-hard problem of storing random patterns in a binary perceptron. Here, the relationship between the genotype and phenotype (training error) is strongly non-linear. Mutation is modelled under the assumption that perceptron configurations are typical of perceptrons with a given training error. Unfortunately, this assumption does not provide a good approximation in general. It is conjectured that perceptron configurations would have to be constrained by other statistics in order to accurately model mutation for this problem. Issues arising from this study are discussed in conclusion and some possible areas of further research are outlined.

Transaction attributes and buyer-supplier relationships in AMT acquisition and implementation:the case of Malaysia

This paper explores how transaction attributes of technology affect differences in the relationship between technology buyers and suppliers. It also examines the impact on performance of different patterns of relationship between technology buyers and suppliers. Data obtained from 147 manufacturing firms in Malaysia are used to test several hypotheses, which were derived from a review of the literature on technology, transaction cost theory and buyer–supplier relationships (BSR). The research results indicate that the higher the level of technological complexity, specificity and uncertainty, the more firms are likely to engage in a closer relationship with technology suppliers. Even though the majority of firms reported improvements in their performance, results indicate that firms demonstrating a closer relationship with technology suppliers are more likely to achieve higher levels of performance than those that do not. It is also shown that with high levels of transaction attribute, implementation performance suffers more when firms have weak relationships with technology suppliers than with moderate and low levels of transaction attribute.

Noise and the trading mechanism:the case of SETS

On 20 October 1997 the London Stock Exchange introduced a new trading system called SETS. This system was to replace the dealer system SEAQ, which had been in operation since 1986. Using the iterative sum of squares test introduced by Inclan and Tiao (1994), we investigate whether there was a change in the unconditional variance of opening and closing returns, at the time SETS was introduced. We show that for the FTSE-100 stocks traded on SETS, on the days following its introduction, there was a widespread increase in the volatility of both opening and closing returns. However, no synchronous volatility changes were found to be associated with the FTSE-100 index or FTSE-250 stocks. We conclude therefore that the introduction of the SETS trading mechanism caused an increase in noise at the time the system was introduced.

Optimization of facility location-allocation problem in customer-driven supply chain

This paper develops and applies an integrated multiple criteria decision making approach to optimize the facility location-allocation problem in the contemporary customer-driven supply chain. Unlike the traditional optimization techniques, the proposed approach, combining the analytic hierarchy process (AHP) and the goal programming (GP) model, considers both quantitative and qualitative factors, and also aims at maximizing the benefits of deliverer and customers. In the integrated approach, the AHP is used first to determine the relative importance weightings or priorities of alternative locations with respect to both deliverer oriented and customer oriented criteria. Then, the GP model, incorporating the constraints of system, resource, and AHP priority is formulated to select the best locations for setting up the warehouses without exceeding the limited available resources. In this paper, a real case study is used to demonstrate how the integrated approach can be applied to deal with the facility location-allocation problem, and it is proved that the integrated approach outperforms the traditional costbased approach.

Retail multinational learning:a case study of Tesco

Purpose - This article examines the internationalisation of Tesco and extracts the salient lessons learned from this process. Design/methodology/ approach - This research draws on a dataset of 62 in-depth interviews with key executives, sell- and buy-side analysts and corporate advisers at the leading investment banks in the City of London to detail the experiences of Tesco's European expansion. Findings - The case study of Tesco illuminates a number of different dimensions of the company's international experience. It offers some new insights into learning in international distribution environments such as the idea that learning is facilitated by uncertainty or "shocks" in the international retail marketplace; the size of the domestic market may inhibit change and so disable international learning; and learning is not necessarily facilitated by step-by-step incremental approaches to expansion. Research limitations/implications - The paper explores learning from a rather broad perspective, although it is hoped that these parameters can be used to raise a new set of more detailed priorities for future research on international retail learning. It is also recognised that the data gathered for this case study focus on Tesco's European operations. Practical implications - This paper raises a number of interesting issues such as whether the extremities of the business may be a more appropriate place for management to experiment and test new retail innovations, and the extent to which retailers take self-reflection seriously. Originality/value - The paper applies a new theoretical learning perspective to capture the variety of experiences during the internationalisation process, thus addressing a major gap in our understanding of the whole internationalisation process. © Emerald Group Publishing Limited.

Workers' understanding of chemical risks: electroplating case study

BACKGROUND: There is limited research concerning how small companies in particular, respond to health and safety messages. AIMS: To understand individuals' knowledge and beliefs about chemical risks and to compare these with those of experts. METHODS: The use of chromic acid in particular, and also other chemicals associated with chrome plating were studied. All chromium plating firms were based in the West Midlands. The methodology involved initial face to face interviews (n = 21) with chromium platers, structured questionnaires (n = 84) to test the prevalence of beliefs identified in the interviews, an expert questionnaire, and a workshop to discuss findings. The responses of platers were compared with those of occupational health and safety experts. RESULTS: Although chromium platers appeared to understand the short term adverse effects of the chemicals to which they are exposed, their understanding of long term, or chronic effects appeared to be incomplete. They had good knowledge of acute effects based primarily on experience. Platers were aware of the hazardous nature of the chemicals with which they work, but did not draw distinction between the terms "hazards" and "risks". They had difficulties articulating the effects of the chemicals and how exposure might occur; although it is inappropriate to equate this with lack of knowledge. A significant minority of platers displayed deficiencies in understanding key technical terms used in Safety Data Sheets. CONCLUSIONS: This study provides a method which can be used to gain some understanding of workers' knowledge and beliefs about risks that they are exposed to in the workplace. The study also identifies gaps between the platers' knowledge and beliefs and those of experts. New risk information needs to be designed which addresses the information needs of platers using language that they understand.

A configuration framework of manufacturing strategy paradigm in state owned enterprises within China's petrochemical industry:building context-embedded configurations for the paradigm of strategic manufacturing management from exploratory case studies

The role of the production system as a key determinant of competitive performance of business operations- has long been the subject of industrial organization research, even predating the .explicit conceptua1isation of manufacturing, strategy in the literature. Particular emergent production issues such as the globalisation of production, global supply chain management, management of integrated manufacturing and a growing e~busjness environment are expected to critically influence the overall competitive performance and therefore the strategic success of the organization. More than ever, there is a critical need to configure and improve production system and operations competence in a strategic way so as to contribute to the long-term competitiveness of the organization. In order to operate competitively and profitably, manufacturing companies, no matter how well managed, all need a long-term 'strategic direction' for the development of operations competence in order to consistently produce more market value with less cost towards a leadership position. As to the long-term competitiveness, it is more important to establish a dynamic 'strategic perspective' for continuous operational improvements in pursuit of this direction, as well as ongoing reviews of the direction in relation to the overall operating context. However, it also clear that the 'existing paradigm of manufacturing strategy development' is incapable of adequately responding to the increasing complexities and variations of contemporary business operations. This has been factually reflected as many manufacturing companies are finding that methodologies advocated in the existing paradigm for developing manufacturing strategy have very limited scale and scope for contextual contingency in empirical application. More importantly, there has also emerged a deficiency in the multidimensional and integrative profile from a theoretical perspective when operationalising the underlying concept of strategic manufacturing management established in the literature. The point of departure for this study was a recognition of such contextual and unitary limitations in the existing paradigm of manufacturing strategy development when applied to contemporary industrial organizations in general, and Chinese State Owned Enterprises (SOEs) in particular. As China gradually becomes integrated into the world economy, the relevance of Western management theory and its paradigm becomes a practical matter as much as a theoretical issue. Since China markedly differs from Western countries in terms of culture, society, and political and economic systems, it presents promising grounds to test and refine existing management theories and paradigms with greater contextual contingency and wider theoretical perspective. Under China's ongoing programmes of SOE reform, there has been an increased recognition that strategy development is the very essence of the management task for managers of manufacturing companies in the same way as it is for their counterparts in Western economies. However, the Western paradigm often displays a rather naive and unitary perspective of the nature of strategic management decision-making, one which largely overlooks context-embedded factors and social/political influences on the development of manufacturing strategy. This thesis studies the successful experiences of developing manufacturing strategy from five high-performing large-scale SOEs within China’s petrochemical industry. China’s petrochemical industry constitutes a basic heavy industrial sector, which has always been a strategic focus for reform and development by the Chinese government. Using a confirmation approach, the study has focused on exploring and conceptualising the empirical paradigm of manufacturing strategy development practiced by management. That is examining the ‘empirical specifics’ and surfacing the ‘managerial perceptions’ of content configuration, context of consideration, and process organization for developing a manufacturing strategy during the practice. The research investigation adopts a qualitative exploratory case study methodology with a semi-structural front-end research design. Data collection follows a longitudinal and multiple-case design and triangulates case evidence from sources including qualitative interviews, direct observation, and a search of documentations and archival records. Data analysis follows an investigative progression from a within-case preliminary interpretation of facts to a cross-case search for patterns through theoretical comparison and analytical generalization. The underlying conceptions in both the literature of manufacturing strategy and related studies in business strategy were used to develop theoretical framework and analytical templates applied during data collection and analysis. The thesis makes both empirical and theoretical contributions to our understanding of 'contemporary management paradigm of manufacturing strategy development'. First, it provides a valuable contextual contingency of the 'subject' using the business setting of China's SOEs in petrochemical industry. This has been unpacked into empirical configurations developed for its context of consideration, its content and process respectively. Of special note, a lean paradigm of business operations and production management discovered at case companies has significant implications as an emerging alternative for high-volume capital intensive state manufacturing in China. Second, it provides a multidimensional and integrative theoretical profile of the 'subject' based upon managerial perspectives conceptualised at case companies when operationalising manufacturing strategy. This has been unpacked into conceptual frameworks developed for its context of consideration, its content constructs, and its process patterns respectively. Notably, a synergies perspective towards the operating context, competitive priorities and competence development of business operations and production management has significant implications for implementing a lean manufacturing paradigm. As a whole, in so doing, the thesis established a theoretical platform for future refinement and development of context-specific methodologies for developing manufacturing strategy.

Model evaluation and case series data

We discuss aggregation of data from neuropsychological patients and the process of evaluating models using data from a series of patients. We argue that aggregation can be misleading but not aggregating can also result in information loss. The basis for combining data needs to be theoretically defined, and the particular method of aggregation depends on the theoretical question and characteristics of the data. We present examples, often drawn from our own research, to illustrate these points. We also argue that statistical models and formal methods of model selection are a useful way to test theoretical accounts using data from several patients in multiple-case studies or case series. Statistical models can often measure fit in a way that explicitly captures what a theory allows; the parameter values that result from model fitting often measure theoretically important dimensions and can lead to more constrained theories or new predictions; and model selection allows the strength of evidence for models to be quantified without forcing this into the artificial binary choice that characterizes hypothesis testing methods. Methods that aggregate and then formally model patient data, however, are not automatically preferred to other methods. Which method is preferred depends on the question to be addressed, characteristics of the data, and practical issues like availability of suitable patients, but case series, multiple-case studies, single-case studies, statistical models, and process models should be complementary methods when guided by theory development.

Efficiency analysis of Islamic banks:a case of Gulf Cooperation Council (GCC)

Although considerable effort has been invested in the measurement of banking efficiency using Data Envelopment Analysis, hardly any empirical research has focused on comparison of banks in Gulf States Countries This paper employs data on Gulf States banking sector for the period 2000-2002 to develop efficiency scores and rankings for both Islamic and conventional banks. We then investigate the productivity change using Malmquist Index and decompose the productivity into technical change and efficiency change. Further, hypothesis testing and statistical precision in the context of nonparametric efficiency and productivity measurement have been used. Specially, cross-country analysis of efficiency and comparisons of efficiencies between Islamic banks and conventional banks have been investigated using Mann-Whitney test.

Mobile case-based decision support for intelligent patient knowledge management

Hospitals everywhere are integrating health data using electronic health record (EHR) systems, and disparate and multimedia patient data can be input by different caregivers at different locations as encapsulated patient profiles. Healthcare institutions are also using the flexibility and speed of wireless computing to improve quality and reduce costs. We are developing a mobile application that allows doctors to efficiently record and access complete and accurate real-time patient information. The system integrates medical imagery with textual patient profiles as well as expert interactions by healthcare personnel using knowledge management and case-based reasoning techniques. The application can assist other caregivers in searching large repositories of previous patient cases. Patients' symptoms can be input to a portable device and the application can quickly retrieve similar profiles which can be used to support effective diagnoses and prognoses by comparing symptoms, treatments, diagnosis, test results and other patient information. © 2007 Sage Publications.

Hierarchical approach to project planning:the case of a petroleum pipeline construction

Projects exposed to an uncertain environment must be adapted to deal with the effective integration of various planning elements and the optimization of project parameters. Time, cost, and quality are the prime objectives of a project that need to be optimized to fulfill the owner's goal. In an uncertain environment, there exist many other conflicting objectives that may also need to be optimized. These objectives are characterized by varying degrees of conflict. Moreover, an uncertain environment also causes several changes in the project plan throughout its life, demanding that the project plan be totally flexible. Goal programming (GP), a multiple criteria decision making technique, offers a good solution for this project planning problem. There the planning problem is considered from the owner's perspective, which leads to classifying the project up to the activity level. GP is applied separately at each level, and the formulated models are integrated through information flow. The flexibility and adaptability of the models lies in the ease of updating the model parameters at the required level through changing priorities and/or constraints and transmitting the information to other levels. The hierarchical model automatically provides integration among various element of planning. The proposed methodology is applied in this paper to plan a petroleum pipeline construction project, and its effectiveness is demonstrated.