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In this paper, we simulate allocation policies for a two-stage inventory system that receives perfect advance demand information (ADI) from customers belonging to different demand classes. Demands for each customer class are generated by independent Poisson processes while the processing times are deterministic. All customers in the same class have the same demand lead time (the difference between the due date and the requested date) and back-ordering costs. Each stage in the inventory system follows order-base-stock-policies where the replenishment order is issued upon arrival of a customer order. The problem requires a fast and reliable method that determines the system performance under different policies and ADI. Thus, we employ discrete-event simulation to obtain output parameters such as inventory costs, fill rates, waiting time, and order allocation times. A numerical analysis is conducted to identify a reasonable policy to use in this type of system.

This paper discusses the development of an efficient algorithm that minimizes overproduction in the allocation of wafers to customer orders prior to assembly at a semiconductor production facility. This study is motivated by and tested at Nexperia’s assembly and test facilities, but its potential applications extend to many manufacturers in the semiconductor industry. Inspired by the classic bin covering problem, the wafer allocation problem is formulated as an integer linear program (ILP). A novel heuristic is proposed, referred to as the multi-start swap algorithm, which is compared to current practice, other existing heuristics and benchmarked with a commercial optimization solver. Experiments with real-world data sets show that the proposed solution method significantly outperforms current practice and other existing heuristics, and that the overall performance is generally close to optimal. Furthermore, some data processing steps andheuristics are presented to make the ILP applicable to real-world applications.

The semi-online bin covering problem is a NP-hard problem that occurs in a batching processes in a high-end poultry processing line. The objective is to form batches of items with minimal giveaway, which is the difference between the target and realized batch weight. The items in this process are allocated in the order of arrival, and the weight of the first set of items is assumed to be known. We develop a novel hybrid genetic algorithm, combining a genetic algorithm and several local search methods. Simulation experiments based on real-world data are performed to gain managerial insights. These simulations suggest that the proposed algorithm produces high quality solutions within a reasonable time limit.

The various processes of truck loading at a fuel filling plant are interrelated which makes it complex to analyze and improve the performance of the filling plants. This paper describes these processes and presents a software tool developed in FlexSim for modelling the drivers' activities, analyzing different decision scenarios, and optimizing the filling processes at fuel terminals. The simulation model has been used to identify bottlenecks and evaluating opportunities for performance improvement. This paper reports the application of the model to a lubricant filling plant for analyzing and supporting decision making on the assignment of trucks to bays, and on the availability of the lubricants on the fuel arms at the lading bays.

Background: UK general practice faces a workforce crisis, with general practitioner (GP) shortages, organisational change, substantial pressures across the whole health-care system and an ageing population with increasingly complex health needs. GPs require lengthy training, so retaining the existing workforce is urgent and important. Objectives: (1) To identify the key policies and strategies that might (i) facilitate the retention of experienced GPs in direct patient care or (ii) support the return of GPs following a career break. (2) To consider the feasibility of potentially implementing those policies and strategies. Design: This was a comprehensive, mixed-methods study. Setting: This study took place in primary care in England. Participants: General practitioners registered in south-west England were surveyed. Interviews were with purposively selected GPs and primary care stakeholders. A RAND/UCLA Appropriateness Method (RAM) panel comprised GP partners and GPs working in national stakeholder organisations. Stakeholder consultations included representatives from regional and national groups. Main outcome measures: Systematic review – factors affecting GPs’ decisions to quit and to take career breaks. Survey – proportion of GPs likely to quit, to take career breaks or to reduce hours spent in patient care within 5 years of being surveyed. Interviews – themes relating to GPs’ decision-making. RAM – a set of policies and strategies to support retention, assessed as ‘appropriate’ and ‘feasible’. Predictive risk modelling – predictive model to identify practices in south-west England at risk of workforce undersupply within 5 years. Stakeholder consultation – comments and key actions regarding implementing emergent policies and strategies from the research. Results: Past research identified four job-related ‘push’ factors associated with leaving general practice: (1) workload, (2) job dissatisfaction, (3) work-related stress and (4) work–life balance. The survey, returned by 2248 out of 3370 GPs (67%) in the south-west of England, identified a high likelihood of quitting (37%), taking a career break (36%) or reducing hours (57%) within 5 years. Interviews highlighted three drivers of leaving general practice: (1) professional identity and value of the GP role, (2) fear and risk associated with service delivery and (3) career choices. The RAM panel deemed 24 out of 54 retention policies and strategies to be ‘appropriate’, with most also considered ‘feasible’, including identification of and targeted support for practices ‘at risk’ of workforce undersupply and the provision of formal career options for GPs wishing to undertake portfolio roles. Practices at highest risk of workforce undersupply within 5 years are those that have larger patient list sizes, employ more nurses, serve more deprived and younger populations, or have poor patient experience ratings. Actions for national organisations with an interest in workforce planning were identified. These included collection of data on the current scope of GPs’ portfolio roles, and the need for formal career pathways for key primary care professionals, such as practice managers. Limitations: The survey, qualitative research and modelling were conducted in one UK region. The research took place within a rapidly changing policy environment, providing a challenge in informing emergent policy and practice. Conclusions: This research identifies the basis for current concerns regarding UK GP workforce capacity, drawing on experiences in south-west England. Policies and strategies identified by expert stakeholders after considering these findings are likely to be of relevance in addressing GP retention in the UK. Collaborative, multidisciplinary research partnerships should investigate the effects of rolling out some of the policies and strategies described in this report. Study registration: This study is registered as PROSPERO CRD42016033876 and UKCRN ID number 20700. Funding: The National Institute for Health Research Health Services and Delivery Research programme.

We consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.

In this paper, a general approach for modeling airport operations is presented. Airport operations have been extensively studied in the last decades ranging from airspace, airside and landside operations. Due to the nature of the system, simulation techniques have emerged as a powerful approach for dealing with the variability of these operations. However, in most of the studies, the different elements are studied in an individual fashion. The aim of this paper, is to overcome this limitation by presenting a methodological approach where airport operations are modeled together, such as airspace and airside. The contribution of this approach is that the resolution level for the different elements is similar therefore the interface issues between them is minimized. The framework can be used by practitioners for simulating complex systems like airspace-airside operations or multi-airport systems. The framework is illustrated by presenting a case study analyzed by the authors.

Poultry plants have to operate under challenging deadlines due to recent developments in the industry. They have expressed the need for improved control over their batching process to deal with these changes. In particular, they wish to achieve a target throughput to ensure deadlines are made, while minimizing the giveaway. This paper proposes an algorithm that is capable of controlling such a poultry product batcher, and aims to achieve a target throughput and minimize the giveaway. Using a simulation study we find thatboth objectives can be achieved under a wide range of settings using the proposed algorithm.

Klein Mechanisch Werkplaats Eindhoven (KMWE) is a precision manufacturing company situated in the Netherlands and recently relocated to a new location known as the ‘Brainport Industries Campus’ (BIC). This move allowed KMWE to improve the performance of its manufacturing facility known as the ‘ToolService Center’ (TSC) by investing in vertical automated storage and retrieval systems (AS/RSs). However, these decisions needed to be made under input uncertainties since the move to BIC and modernization of existing equipment would cause changes in operating parameters inside the facility, over which little information was known in advance. In this study, we show how hybrid simulation modelling was used to assess the impact of input uncertainties (such as operator productivity, vertical storage height) on the throughput performance of TSC. Ultimately, the outcomes of this research project were used by KMWE to make an investment decision on new equipment acquisition quantity.

In this work, we propose a re-enactment simulation-based optimization method to determine the minimal total buffer capacity in an assembly line required to meet a target throughput. A distinguishing feature is the use of real-time event traces, in a fast fluid flow simulation model. Employing real-time event traces avoids the necessity to make restrictive modeling assumptions. The fluid simulation is combined with a multi start search algorithm. To demonstrate its effectiveness, the method is applied to a real-world use case in lead frame based semiconductor back-end manufacturing. This use case considers an assembly line consisting of six machines, for which the proposed method determines optimal buffer size configurations within several minutes of computational time.

A commonly used approach to analyzing stochastic differential equations (SDEs) relies on performing Monte Carlo simulation with a discrete-time counterpart. In this paper we study the impact of such a time-discretization when assessing the stationary tail distribution. For a family of semi-implicit Euler discretization schemes with time-step h > 0, we quantify the relative error due to the discretization, as a function of h and the exceedance level x. By studying the existence of certain (polynomial and exponential) moments, using a sequence of prototypical examples, we demonstrate that this error may tend to 0 or ∞. The results show that the original shape of the tail can be heavily affected by the discretization. The cases studied indicate that one has to be very careful when estimating the stationary tail distribution using Euler discretization schemes.

Traditionally, discrete event simulation is associated with logistic system analysis due to its role as an operations research method. In this article, we consider a multiple case study on the alternative use of simulation for operations management education. When stressing the role of the student or trainee instead of operations—as in operations research—alternative demands on model set up and use arise. Identifying such demands and ways to deal with them extends simulation methodology by showing how to exploit existing simulation knowledge and tools for pedagogic purposes. Our multiple case study evaluates four simulation-based games reflecting operations systems in health care and manufacturing. Respective games target learning activities of a different nature. Outcomes of the evaluation provide insights on modelling requirements, and guidelines and good practices that are supportive in meeting these.

Most management problems addressed by simulation studies can be characterized as complex and difficult to analyze. Simplification is instrumental in creating and employing simulation models that are useful – by focusing on those system elements that matter, and feasible – by reducing study efforts. Although simplification is considered a fundamental modelling activity, simulation educational support for mastering associated modelling skills is limited. Main textbooks either do not address this topic or tend to restrict their guidance to a few rules of thumb. This reflects how the topic of simulation model simplification is underdeveloped, despite the field of simulation being around 50 years of age. The purpose of this panel is to initiate a discussion about the way we teach simulation model simplification with the view to identifying improvements. This paper addresses the motivation for the panel, and presents panelists’ “positions” about the way forward for education on simulation model simplification.

Renal failure concerns progressive loss of kidney function. Renal Replacement Therapy (RRT) is a costly, long-running process that includes several decision points in different stages. Small changes in the protocol can impact significantly the expenditures and healthcare outcomes. Unfortunately, policy makers have very little support for benchmarking improvement alternatives. The existing models are designed to fit certain applications with preset parameters and design choices which do not match with the requirements of a policy analysis. A generic approach is required to analyze the effects of different design options adjustable to finer scales. To remedy this, this paper describes a novel toolkit for evaluating renal replacement policies, containing a parametrized colored Petri-Net which can be configured for the specifics of local settings. The model is made available for open access to overcome the non-replicability issue of existing models.

In the poultry processing industry demand and supply are still growing in volume and diversity, which requires more processing capacity, flexibility and smarter control. This paper focuses on the fillet batching process. To minimize the giveaway of fixed-weight fillet batching the right choices on layout, buffer sizes, batch sizes and batch allocation policies are of great importance. We develop a simulation model to support such decisions on design and control. The model is used (i) to determine buffer and grader sizes, (ii) to optimize batch allocation in a dedicated layout, (iii) to compare a dedicated to a flexible layout and (iv) to assess the impact of smart allocation policies. In particular we find that significant reductions in giveaway can be achieved by employing so-called index policies in a flexible layout.

Simplification is considered a fundamental part of modelling and simulation. Model simplification is instrumental in creating models that are useful – by focusing on system elements that matter, and feasible – by reducing study efforts. Despite its widely acknowledged relevance simulation model simplification may still be considered an underdeveloped field. This is mirrored in existing literature, and course books. While the former shows a fragmented landscape in addressing the issue, the latter often offer little guidance for the (future) analyst. To foster further development of the field we assess current progress by providing a literature review. Issues addressed by the review concern: (i) definition and scope of model simplification, (ii) reasons for model simplification, (iii) drivers of inappropriate model complexity, and (iv) approaches for model simplification. The review is meant to provide a useful overview of the work undertaken in this field, aiming to benefit educators, practitioners and researchers.

Probabilistic timed automata are a formal model for real-time systems with discrete probabilistic and nondeterministic choices. To overcome the state space explosion problem of exhaustive verification, a symbolic simulation-based approach that soundly treats nondeterminism to approximate maximum and minimum reachability probabilities has recently become available. Its use of difference-bound matrices to handle continuous real time however leads to poor performance: most operations are cubic or even exponential in the number of clock variables. In this paper, we propose a novel region-based approach and data structure that reduce the complexity of all operations to being linear. It relies on a particular mapping between symbolic regions and concrete representative valuations. Using an implementation within the MODEST TOOLSET, we show that the new approach is not only easier to implement, but indeed significantly outperforms all current alternatives on standard benchmark models.

Commercial-off-the-shelf (COTS) Simulation Packages (CSPs) are widely used in industry primarily due to economic factors associated with developing proprietary software platforms. Regardless of their widespread use, CSPs have yet to operate across organizational boundaries. The limited reuse and interoperability of CSPs are affected by the same semantic issues that restrict the inter-organizational use of software components and web services. The current representations of Web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging Semantic Web. The authors present new research that partially alleviates the problem of limited semantic reuse and interoperability of simulation components in CSPs. Semantic models, in the form of ontologies, utilized by the authors’ Web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through a simulation component ontology that is used to identify required components at varying levels of granularity (i.e. including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The research presented here is based on the development of an ontology, connector software, and a Web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, by adopting a less intrusive interface between participants Although specific to CSPs this work has wider implications for the simulation community. The reason being that the community as a whole stands to benefit through from an increased awareness of the state-of-the-art in Software Engineering (for example, ontology-supported component discovery and reuse, and service-oriented computing), and it is expected that this will eventually lead to the development of a unique Software Engineering-inspired methodology to build simulations in future.

A biological system comprises large numbers of functionally diverse and frequently multifunctional sets of elements that interact selectively and nonlinearly to produce coherent behaviours. Such a system can be anything from an intracellular biological process (such as a biochemical reaction cycle, gene regulatory network or signal transduction pathway) to a cell, tissue, entire organism, or even an ecological web. Biochemical systems are responsible for processing environmental signals, inducing the appropriate cellular responses and sequence of internal events. However, such systems are not fully or even poorly understood. Systems biology is a scientific field that is concerned with the systematic study of biological and biochemical systems in terms of complex interactions rather than their individual molecular components. At the core of systems biology is computational modelling (also called mathematical modelling), which is the process of constructing and simulating an abstract model of a biological system for subsequent analysis. This methodology can be used to test hypotheses via insilico experiments, providing predictions that can be tested by in-vitro and in-vivo studies. For example, the ERbB1-4 receptor tyrosine kinases (RTKs) and the signalling pathways they activate, govern most core cellular processes such as cell division, motility and survival (Citri and Yarden, 2006) and are strongly linked to cancer when they malfunction due to mutations etc. An ODE (ordinary differential equation)-based mass action ErbB model has been constructed and analysed by Chen et al. (2009) in order to depict what roles of each protein plays and ascertain to how sets of proteins coordinate with each other to perform distinct physiological functions. The model comprises 499 species (molecules), 201 parameters and 828 reactions. These in silico experiments can often be computationally very expensive, e.g. when multiple biochemical factors are being considered or a variety of complex networks are being simulated simultaneously. Due to the size and complexity of the models and the requirement to perform comprehensive experiments it is often necessary to use high-performance computing (HPC) to keep the experimental time within tractable bounds. Based on this as part of an EC funded cancer research project, we have developed the SIMAP Utility that allows the SImulation modeling of the MAP kinase pathway (http://www.simap-project.org). In this paper we present experiences with Grid-enabling SIMAP using Condor.

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources).

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.

As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster.

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry.

For many years discrete-event simulation has been used to analyze production and logistics problems in manufactur-ing and defense. Commercial-off-the-shelf Simulation Packages (CSPs), visual interactive modelling environ-ments such as Arena, Anylogic, Flexsim, Simul8, Witness, etc., support the development, experimentation and visua-lization of simulation models. There have been various attempts to create distributed simulations with these CSPs and their tools, some with the High Level Architecture (HLA). These are complex and it is quite difficult to assess how a set of models/CSP are actually interoperating. As the first in a series of standards aimed at standardizing how the HLA is used to support CSP distributed simula-tions, the Simulation Interoperability Standards Organiza-tion’s (SISO) CSP Interoperability Product Development Group (CSPI PDG) has developed and standardized a set of Interoperability Reference Models (IRM) that are in-tended to clearly identify the interoperability capabilities of CSP distributed simulations.

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community.

COTS Simulation Package Interoperability is a problem that has been studied by the Simulation Interoperability Standards Organization’s (SISO) COTS Simulation Package Interoperability Product Development Group (CSPI PDG). The UK National Blood Service maintains the supply chain of blood from donor to hospital. The simulation of this supply chain is vital to better support decisons made for an extremely scarce resource. Such models are very large and can take a very long time to execute. This paper investigates whether or not CSPI PDG standards can be used to create a distributed simulation of this supply chain and if a speed up can be achieved. The results show that for larger blood supply chain models this is the case.

Modern manufacturing supply chains are hugely complex and like all stochastic systems, can benefit from simulation. Unfortunately supply chain systems often result in massively large and complicated models, which even today’s powerful computers cannot run efficiently. This paper presents one possible solution - distributed simulation. This pilot study is implemented in a healthcare setting, the supply chain of blood from donor to recipient.

The vision of grid computing is to make computational power, storage capacity, data and applications available to users as readily as electricity and other utilities. Grid infrastructures and applications have traditionally been geared towards dedicated, centralized, high performance clusters running on UNIX flavour operating systems (commonly referred to as cluster-based grid computing). This can be contrasted with desktop-based grid computing which refers to the aggregation of non-dedicated, de-centralized, commodity PCs connected through a network and running (mostly) the Microsoft Windowstrade operating system. Large scale adoption of such Windowstrade-based grid infrastructure may be facilitated via grid-enabling existing Windows applications. This paper presents the WinGridtrade approach to grid enabling existing Windowstrade based commercial-off-the-shelf (COTS) simulation packages (CSPs). Through the use of a case study developed in conjunction with Ford Motor Company, the paper demonstrates how experimentation with the CSP Witnesstrade and FIRST can achieve a linear speedup when WinGridtrade is used to harness idle PC computing resources. This, combined with the lessons learned from the case study, has encouraged us to develop the Web service extensions to WinGridtrade. It is hoped that this would facilitate wider acceptance of WinGridtrade among enterprises having stringent security policies in place.

Commercial-off-the-shelf simulation packages (CSPs) are used widely in industry. Several research groups are currently working towards the creation of distributed simulation with these CSPs. The motivations to do this are various and are largely unproven as there are very few good examples of this kind of distributed simulation in practice. Our goal is therefore to create a distributed simulation environment using CSPs that will allow end users to make their own decisions as to whether this technology will be useful. This paper presents continuing research in creating such an environment using the CSP Simul8 and the High Level Architecture, the IEEE 1516 standard for distributed simulation. The scope of this paper is limited to the CSPI-PDG Type I Interoperability Reference Model.

Simulation is characterized by the need to run multiple sets of computationally intensive experiments. We argue that Grid computing can reduce the overall execution time of such experiments by tapping into the typically underutilized network of departmental desktop PCs, collectively known as desktop grids. Commercial-off-the-shelf simulation packages (CSPs) are used in industry to simulate models. To investigate if Grid computing can benefit simulation, this paper introduces our desktop grid, WinGrid, and discusses how this can be used to support the processing needs of CSPs. Results indicate a linear speed up and that Grid computing does indeed hold promise for simulation.