Special Session on Industry 4.0

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A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Blurring the boundaries between synthesis and evaluation. A customized realist evaluative synthesis into adolescent risk behavior prevention

Realist methodologies have been increasingly advocated for the investigation of complex social issues. Public health programs, such as those designed to prevent adolescent risk behavior, are typically considered complex. In conducting a realist review of the empirical literature relating to such programs, we encountered several challenges, including (a) an overabundance of empirical evidence, (b) a problematic level of heterogeneity within and between methodological approaches, (c) discrepancies between theoretical underpinnings and program operationalization, (d) homogeneity of program outcomes, with very little variation in program effectiveness, and (d) a paucity of description relating to content and process. To overcome these challenges, we developed a customized approach to realist evidence synthesis, drawing on the VICTORE (Volition, Implementation, Contexts, Time, Outcomes, Rivalry, and Emergence) complexity checklist and incorporating stakeholder engagement as primary data to achieve greater depth of understanding relating to contextual and mechanistic factors, and the complex interactions between them. Here we discuss the benefits of this adapted methodology alongside an overview of the research through which the methodology was developed. A key finding from this research was that combining the complexity checklist with primary data from stakeholder engagement enabled us to systematically interrogate the data across data sources, uncovering and evidencing mechanisms which may otherwise have remained hidden, giving greater ontological depth to our research findings. This paper builds on key methodological developments in realist research, demonstrating how realist methodologies can be customized to overcome challenges in developing and refining program theory from the literature, and contributes to the broader literature of innovative approaches to realist research

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

An Agent-Based Decision Support Model for the Development of E-Services in the Tourist Sector

This paper regards cultural heritage as a strategic development tool for urban tourist policy. It highlights the use of e-services as a central instrument in a competitive tourist sector. The appropriate choice of e-services - and packages thereof - depends on the various strategic considerations of urban stakeholders (agents) and may differ for each individual city. The paper offers a systematic analysis framework for supporting these choices and deploys multi-criteria analysis as a systematic evaluation methodology, in particular the Regime method. The evaluation framework is exemplified through an application to three field cases in Europe, viz. the cities of Amsterdam, Genoa and Leipzig. Our analysis concludes that tailor-made packages of e-services that serve the needs of the stakeholders can be made with the help of our evaluation tools.cultural heritage, e-services, city marketing, agent-based decision support model