Application of Discrete Event Simulation in Industrial Sectors: A Case Study

Discrete Event Simulation (DES) has become a useful tool in the evaluation of changes that may bring positivity to manufacturing and process organizations for both goods and services provision. The main focus of any business entails the reduction of cost and lead time while increasing profits and this is why refining of production processes is essential. This paper reports the application of DES in two case studies. The case studies selected for the implementation of Discrete Event Simulation are a packaging company and a local mobile phone service provider using the software FlexSim. The implementation aims at showcasing the versatility and its ability to provide the relevant data to make more informed decision while optimizing the entire processes involved in production

A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study

Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limitations come up when commercial discrete event simulation software are used for modeling complex real world-systems (i.e. supply chains, industrial plants). The objective of this paper is twofold: first a state of the art on commercial discrete event simulation software and an overview on discrete event simulation models development by using general purpose programming languages are presented; then a Supply Chain Order Performance Simulator (SCOPS, developed in C++) for investigating the inventory management problem along the supply chain under different supply chain scenarios is proposed to readers.Comment: International Journal of Computer Science Issues online at http://ijcsi.org/articles/A-General-Simulation-Framework-for-Supply-Chain-Modeling-State-of-the-Art-and-Case-Study.ph

Enabling Cross-Event Optimization in Discrete-Event Simulation Through Compile-Time Event Batching

A discrete-event simulation (DES) involves the execution of a sequence of event handlers dynamically scheduled at runtime. As a consequence, a priori knowledge of the control flow of the overall simulation program is limited. In particular, powerful optimizations supported by modern compilers can only be applied on the scope of individual event handlers, which frequently involve only a few lines of code. We propose a method that extends the scope for compiler optimizations in discrete-event simulations by generating batches of multiple events that are subjected to compiler optimizations as contiguous procedures. A runtime mechanism executes suitable batches at negligible overhead. Our method does not require any compiler extensions and introduces only minor additional effort during model development. The feasibility and potential performance gains of the approach are illustrated on the example of an idealized proof-ofconcept model. We believe that the applicability of the approach extends to general event-driven programs

Modeling emergency departments using discrete event simulation techniques

This paper discusses the application of Discrete Event Simulation (DES) for modeling the operations of an Emer-gency Department (ED). The model was developed to help the ED managers understand the behavior of the system with regards to the hidden causes of excessive waiting times. It served as a tool for assessing the impact of major departmental resources on Key Performance Indicators (KPIs), and was also used as a cost effective method for testing various what-if scenarios for possible system im-provement. The study greatly enhanced managers’ under-standing of the system and how patient flow is influenced by process changes and resource availability. The results of this work also helped managers to either reverse or modify some proposed changes to the system that were previously being considered. The results also show a possible reduc-tion of more than 20% in patients waiting times

Discrete-event simulation unmasks the quantum Cheshire Cat

It is shown that discrete-event simulation accurately reproduces the experimental data of a single-neutron interferometry experiment [T. Denkmayr {\sl et al.}, Nat. Commun. 5, 4492 (2014)] and provides a logically consistent, paradox-free, cause-and-effect explanation of the quantum Cheshire cat effect without invoking the notion that the neutron and its magnetic moment separate. Describing the experimental neutron data using weak-measurement theory is shown to be useless for unravelling the quantum Cheshire cat effect

A general framework for statistical inference on discrete event systems.

We present a framework for statistical analysis of discrete event systems which combines tools such as simulation of marked point processes, likelihood methods, kernel density estimation and stochastic approximation to enable statistical analysis of the discrete event system, even if conventional approaches fail due to the mathematical intractability of the model.The approach is illustrated with an application to modelling and estimating corrosion of steel gates in the Dutch Haringvliet storm surge barrier.discrete event systems;kernel density estimation;optimization via simulation;parameter estimation;stochastic approximation;likelihood methods;market point process

Parallel discrete event simulation: A shared memory approach

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models

Implementing system simulation of C3 systems using autonomous objects

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems

Parallel Discrete Event Simulation with Erlang

Discrete Event Simulation (DES) is a widely used technique in which the state of the simulator is updated by events happening at discrete points in time (hence the name). DES is used to model and analyze many kinds of systems, including computer architectures, communication networks, street traffic, and others. Parallel and Distributed Simulation (PADS) aims at improving the efficiency of DES by partitioning the simulation model across multiple processing elements, in order to enabling larger and/or more detailed studies to be carried out. The interest on PADS is increasing since the widespread availability of multicore processors and affordable high performance computing clusters. However, designing parallel simulation models requires considerable expertise, the result being that PADS techniques are not as widespread as they could be. In this paper we describe ErlangTW, a parallel simulation middleware based on the Time Warp synchronization protocol. ErlangTW is entirely written in Erlang, a concurrent, functional programming language specifically targeted at building distributed systems. We argue that writing parallel simulation models in Erlang is considerably easier than using conventional programming languages. Moreover, ErlangTW allows simulation models to be executed either on single-core, multicore and distributed computing architectures. We describe the design and prototype implementation of ErlangTW, and report some preliminary performance results on multicore and distributed architectures using the well known PHOLD benchmark.Comment: Proceedings of ACM SIGPLAN Workshop on Functional High-Performance Computing (FHPC 2012) in conjunction with ICFP 2012. ISBN: 978-1-4503-1577-