Modelling an End to End Supply Chain system Using Simulation

Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Ontology-driven conceptual modeling: A'systematic literature mapping and review

All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research

Building Information Modelling [BIM] for energy efficiency in housing refurbishments

Building Information modelling offers potential process and delivery improvements throughout the lifecycle of built assets. However, there is limited research in the use of BIM for energy efficiency in housing refurbishments. The UK has over 300,000 solid wall homes with very poor energy efficiency. A BIM based solution for the retrofit of solid wall housing using lean and collaborative improvement techniques will offer a cost effective, comprehensive solution that is less disruptive, reduces waste and increases accuracy, leading to high quality outcomes. The aim of this research is to develop a BIM based protocol supporting development of 'what if' scenarios in housing retrofits for high efficiency thermal improvements, aiming to reduce costs and disruption for users. The paper presents a literature review on the topic and discusses the research method for the research project (S-IMPLER)

Advanced inference in fuzzy systems by rule base compression

This paper describes a method for rule base compression of fuzzy systems. The method compresses a fuzzy system with an arbitrarily large number of rules into a smaller fuzzy system by removing the redundancy in the fuzzy rule base. As a result of this compression, the number of on-line operations during the fuzzy inference process is significantly reduced without compromising the solution. This rule base compression method outperforms significantly other known methods for fuzzy rule base reduction.Peer Reviewe

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions