Discrete Event Simulation Algorithm Research Articles

An estimation of distribution algorithm for hybrid flow shop scheduling under stochastic processing times

The estimation of distribution algorithm (EDA) has recently emerged as a promising alternative to traditional evolutionary algorithms for solving combinatorial optimisation problems. This paper presents a novel two-phase simulation-based EDA (TPSB-EDA) for minimising the makespan of a hybrid flow shop under stochastic processing times. To address the stochastic scheduling problem efficiently, the proposed TPSB-EDA incorporates a two-phase simulation model to estimate the performance of candidate solutions. In this model, an optimal back propagation network is firstly applied to identify a set of roughly good solutions, and then the selected solutions are further evaluated by a discrete-event simulation algorithm. Moreover, an annealing selection mechanism (ASM) is adopted to preserve the population diversity of EDA. Different from the selection operators of common EDAs, the ASM uses Boltzmann probability in the annealing algorithm to select part of population to establish the probabilistic model. Computation results indicate that the TPSB-EDA provides good solutions in the aspects of solution quality and computational efficiency.

A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients

With the pressing demand for improving patient accessibility, the traditional scheduling system may not be effective for mitigating the adverse effects caused by no-shows, appointment cancellations and late arrivals. For this reason, open access scheduling, which specifies that a portion of clinic appointment slots be reserved for short-notice appointments, was proposed and adopted in recent years. In literature, many studies have developed a variety of approaches and models to optimize the open access scheduling systems, while few considers the inclusion of walk-in patients and the optimal allocation of reserved slots on the scheduling template under the open access configuration. In this paper, we propose a Discrete Event Simulation and Genetic Algorithm (DES–GA) approach to find the heuristic optimal scheduling template under the clinic setting that allows both open access and walk-in patients. The solution can provide scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient. In this approach, the cost is measured by the weighted summation of patient waiting time, provider idle time, and provider overtime. A case study and sensitivity analysis are conducted to show how the heuristic optimal scheduling template generated from the proposed approach could vary under different scenarios. This also illustrates the viability of our model. The results show that the heuristic optimal scheduling templates are significantly affected by the patient attendance rate, level of demands of same-day appointment and walk-in admissions, as well as the cost coefficients associated with patient waiting time, provider idle time and provider overtime.

Optimization of a complex urban intersection using discrete event simulation and evolutionary algorithms

Dealing with traffic management for complex crossroads is a challenging problem for traffic control planners. As a contribution to solve this problem, the present paper develops a mesoscopic simulation model for detecting the most suited fire plan for a complex road intersection, using a discrete event simulation tool and an evolutionary algorithm optimization. The modeling goal is to eliminate congestion by choosing an appropriate fire plan which will be adapted to the actual configuration of the intersection, as well as to a future reconfiguration meant to accept a higher inflow of vehicles. The proposed model is applied to a down-town crossroads from Nancy, France. Four different configurations of the input data flow were studied under the proposed simulation-optimization approach, and an optimal fire plan is proposed.

Mathematical programming time-based decomposition algorithm for discrete event simulation

Mathematical programming has been proposed in the literature as an alternative technique to simulating a special class of Discrete Event Systems. There are several benefits to using mathematical programs for simulation, such as the possibility of performing sensitivity analysis and the ease of better integrating the simulation and optimisation. However, applications are limited by the usually long computational times. This paper proposes a time-based decomposition algorithm that splits the mathematical programming model into a number of submodels that can be solved sequentially to make the mathematical programming approach viable for long running simulations. The number of required submodels is the solution of an optimisation problem that minimises the expected time for solving all of the submodels. In this way, the solution time becomes a linear function of the number of simulated entities.

Stochastic flux analysis of chemical reaction networks

BackgroundChemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes.ResultsWe describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology.ConclusionsWe provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

An extensible object-oriented and agent-based framework for modelling and simulating supply chains

We propose an extensible object-oriented agent-based framework for modelling and simulating supply chains. A problem with existing supply-chain analysis tools is that most of them are designed only for specific configurations of supply chains. The primary goal of this work is to provide an open and extensible framework for analysing supply chains with heterogeneous elements and structures. Our framework incorporates the following features: 1 It adopts an agent-based approach to handle interactions among elements of a supply chain, and an analyst can introduce new types of elements for a specific application; 2 To promote the design reusability, we propose an object-oriented type system that supports behaviour inheritance; 3 The framework formally defines a meta-model for elements of a supply chain; 4 The framework includes a discrete-event simulation algorithm, which defines interactions among elements via messages and deliveries. We also discuss SIMRISK, our Java implementation of the framework.

An [formula omitted] Energetic Retrial Queue with Vacations and it's Control

In this note, we present some results about the MX/G/1 retrial queue with vacations. Retrial times are governed by an arbitrary probability law which is independent of the number of customers in the retrial group. We consider an energetic interpretation in the sense that the service of a customer requires not only a random time, but also a random amount of energy with arbitrarily probability distribution. The server is turned off and takes a vacation when the system becomes empty. The random energy required for each vacation is also arbitrary distributed. We derive a stochastically recursive relation which can be used as a discrete-event simulation algorithm for our queue. Next, we give an explicit formula for the generating function of the number of customers in orbit in steady state and exhibit explicit forms of stochastic decomposition property. Finally, we show how to obtain performance measures of interest and optimal control parameters for vacation and retrial policies.

Hybrid algorithm for discrete event simulation based supply chain optimization

Supply chain optimization, as a key determinant of strategic resources mobility along the value-added chain, allows each participant in the global network to capitalize on its particular strategic competency. Simulation is widely used to test the impact on supply chain performance for the strategic level decisions, such as the number of plants, the modes of transport, or the relocation of warehouses. However, the complexity of supply chain optimization problem and the stochastic nature of simulation cause the unaffordable computational load; the evaluation of a large number of alternatives for supply chain optimization is in a class of NP-hard problem and the number of simulation replications is required for accurately evaluating the performance of each alternative. The objective of the present work is to propose hybrid algorithm with the application of the nested partitioning (NP) method and the optimal computing budget allocation (OCBA) method to reduce the computational load, hence, to improve the efficiency of supply chain optimization via discrete event simulation. The NP method is a global sampling strategy that is continuously adapted via a partitioning of the feasible solution region. The number of candidate alternatives to be evaluated can be reduced by the application of NP. The OCBA method minimizes the number of samples (simulation replications) required to evaluate a particular alternative by allocating computing resources to potentially critical alternative. Carefully designed experiments show extensive numerical result to illustrate the benefits of the proposed approach.

Individual‐based and stochastic modeling of cell population dynamics considering substrate dependency

In this article, we propose an individual-based and stochastic modeling approach that is capable of describing the bacterial cell population dynamics during a batch culture. All stochastic nature inherent in intracellular molecular level reactions and cell division processes were considered in a single model framework by embedding a sub-model describing individual cell's growth kinetics in a discrete event simulation algorithm. The resultant unique feature of the model is that the effects of the stochasticities on the cell population dynamics can be investigated for different substrate-dependent cell growth kinetics. When Monod kinetics was used as the sub-model, the stochasticities only slightly affected the cell mass increase and substrate consumption profiles during the batch culture although they were still important in describing the changes of cell population distributions. When Andrews substrate inhibition kinetics was used, however, it was revealed that the overall cell population dynamics could be seriously influenced by the stochasticities. Under a critical initial substrate level, the cell population could proliferate against the substrate inhibition only when the stochasticities were considered.

Analysing probabilistically constrained optimism

AbstractIn previous work we presented the DTRD algorithm, an optimistic synchronization algorithm for parallel discrete event simulation of multi‐agent systems, and showed that it outperforms Time Warp and time windows on a range of test cases. DTRD uses a decision‐theoretic model of rollback to derive an optimal time to delay read event so as to maximize the rate of LVT progression. The algorithm assumes that the inter‐arrival times (both virtual and real) of events are normally distributed. In this paper we present a more detailed evaluation of the DTRD algorithm, and specifically how the performance of the algorithm is affected when the inter‐arrival times do not follow the assumed distributions. Our analysis suggests that the performance of the algorithm is relatively insensitive to events whose inter‐arrival times are not normally distributed. However, as the variance of event inter‐arrival times increases, its performance degrades to that of Time Warp. The evaluation approach we present is generally applicable, and we sketch how a similar analysis may be performed for two other decision‐theoretic optimistic synchronization algorithms. Copyright © 2009 John Wiley & Sons, Ltd.

Preventive maintenance optimisation of multi-equipment manufacturing systems by combining discrete event simulation and multi-objective evolutionary algorithms

This paper is focused on preventive maintenance optimisation in manufacturing environments, with the objective of determining the optimal preventive maintenance frequencies for multi-equipment systems under cost and profit criteria. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multi-objective evolutionary algorithms have shown to deal with multi-objective problems to develop a maintenance management and optimisation approach. An application case is performed where the activities applied on a system that produces hubcaps for the car making industry, showing the quantitative benefits of adopting the detailed approach.

Predicting availability functions in time-dependent complex systems with SAEDES simulation algorithms

In this paper, we propose the use of discrete-event simulation (DES) as an efficient methodology to obtain estimates of both survival and availability functions in time-dependent real systems—such as telecommunication networks or distributed computer systems. We discuss the use of DES in reliability and availability studies, not only as an alternative to the use of analytical and probabilistic methods, but also as a complementary way to: (i) achieve a better understanding of the system internal behavior and (ii) find out the relevance of each component under reliability/availability considerations. Specifically, this paper describes a general methodology and two DES algorithms, called SAEDES, which can be used to analyze a wide range of time-dependent complex systems, including those presenting multiple states, dependencies among failure/repair times or non-perfect maintenance policies. These algorithms can provide valuable information, specially during the design stages, where different scenarios can be compared in order to select a system design offering adequate reliability and availability levels. Two case studies are discussed, using a C/C++ implementation of the SAEDES algorithms, to show some potential applications of our approach.

Real-time decision support for planning concrete plant operations enabled by integrating vehicle tracking technology, simulation, and optimization algorithms

By integrating the vehicle tracking system, discrete-event simulation algorithm, and evolutionary optimization algorithm, we developed HKCONSIM-Realtime, a decision-support platform created specifically for handling ready-mixed concrete operations. This platform is capable of (1) tracking the positions of concrete trucks and monitoring the motion and status of concrete deliveries in real time, (2) transforming the tracking records into data that provide updated input to simulation, and (3) optimizing the operations and logistics of concrete production based on simulation of the production system using the most current data. This paper presents an overview of the design and development of (1) the hardware and software modules, (2) the data flow and processing throughout the system, and (3) the role of the system in providing interactive, effective support for the human operator to attain cost efficiency. Case studies are given to demonstrate the functionality and application of the prototype system. Key words: simulation, optimization, vehicle tracking, construction planning, ready-mixed concrete, Hong Kong.

Parallel discrete event simulation algorithm for manufacturing supply chains

Parallel discrete event simulation (PDES) is concerned with the distributed execution of large-scale system models on multiple processors. It is an enabler in the implementation of the virtual enterprise concept, integrating semi-autonomous models of production cells, factories, or units of a supply chain. The key issue in PDES is to maintain causality relationships between system events, while maximizing parallelism in their execution. Events can be executed conservatively only when it is safe to do so, sacrificing the extent to which potential parallelism of the system can be exploited. Alternatively, they can be processed optimistically without guarantee of correctness, but incurring the overhead of a rollback to an earlier saved state when causality error is detected. The paper proposes a modified optimistic scheme for distributed simulation of constituent models of a supply chain in manufacturing, which exploits the inherent operating characteristics of its domain.

P/sup 2/EDAS: asynchronous, distributed event driven simulation algorithm with inconsistent event preemption for accurate execution of VHDL descriptions on parallel processors

The DoD's very high speed integrated circuit Hardware Description Language, VHDL, is aimed at allowing hardware designers to accurately describe, simulate, and validate combinational and sequential hardware designs on computers prior to building a prototype. Since its conception in the early 1980s, an important goal of VHDL has been to execute complex models concurrently and efficiently on parallel processors. To achieve this objective while exploiting the maximum theoretical parallelism, one requires a deadlock-free, asynchronous, distributed, discrete event simulation algorithm. The YADDES approach, proposed in the literature, achieves deadlock-free, distributed, discrete event simulation, but suffers from an important limitation. YADDES assumes that every entity is characterized by a single propagation delay. In contrast, for hardware entities such as gates, flip-flops, ALUs, and microprocessors, two or more propagation delay values are used corresponding to every path between the input and output. While the use of multiple delays aims at accurate representation of reality, often inconsistent events may be generated that must be detected and preempted. This paper describes a new asynchronous, parallel, event driven simulation algorithm with inconsistent event preemption, P/sup 2/EDAS. P/sup 2/EDAS represents a significant advancement in that it permits the use of any number of propagation delays for every path between the input and output of every hardware entity.

A stochastic model of chromosome segregation errors with reference to cancer cells

A majority of tumor cells have an abnormal number of chromosomes, and this number varies even among the cells of clonal origin. Recent publications suggest that the acquisition of chromosome numerical abnormalities is an early event in a carcinogenesis pathway. Chromosomal instability may also play an important role in tumor progression. However, the mechanisms that result in abnormal number of chromosomes are poorly understood. One of the possible sources of chromosomal instability is a failure of chromosomes to segregate to the proper cells during cell division. We now propose a stochastic model that describes the evolution of chromosome number in a population of dividing cells as a result of such chromosome segregation errors (CSE). Assuming the independence of life cycles for all chromosomes within a cell, our model describes a process of evolution of cell karyotypes as a random branching walk in a K-dimensional nonnegative integer space ( K = 23 for human cells). The conditions of cell death define K absorption boundaries for this random walk. Our basic model of human cells has all absorption boundaries set to zero. In the model, we have introduced a single parameter, a probability of segregation errors, which reflects an average rate of segregation errors per cell division. Using this model, we have examined the possible impact of CSE on chromosome numerical changes in cell populations. The model was implemented in a C++ program based on a discrete event simulation algorithm. Using computer simulation experiments, we have tested the sensitivity of our model to the initial conditions and parameter values. We have examined the effects of CSE on survival of cells and clones as well as on the dynamics of population growth and chromosome number distributions. Our modeling results suggest that the long-term dynamics of cell population growth depends upon the rate of segregation errors. The fraction of clones surviving was estimated as a function of the probability of CSE, and critical values for the probability of CSE were approximated which separates qualitatively different patterns of model behavior. The model suggests a theoretical limit for the rate of CSE that would allow a diploid population to survive without a complete loss of any chromosome type. It also suggests a minimal interval of probability of CSE values for which 100% survival of clones is possible. Our modeling results allow comparisons to be

Rollback overhead reduction methods for time warp distributed simulation

Parallel discrete event simulation is a useful technique to improve performance of sequential discrete event simulation. We consider the time warp algorithm for asynchronous distributed discrete event simulation. Time warp is an optimistic synchronization mechanism for asynchronous distributed systems that allows a system to violate the synchronization constraint and, in this case, make the system rollback to a correct state. We focus on the kernel of the time warp algorithm, that is the rollback operation, and we propose some techniques to reduce the overhead due to this operation. In particular, we propose a method to reduce the overhead involved in state saving operation, two methods to reduce the overhead of a single rollback operation and a method to reduce the overall number of rollbacks. These methods have been implemented in a distributed simulation environment on a distributed memory system. Some experimental results show the effectiveness of the proposed techniques.

Dag consistent parallel simulation

We present a novel approach to parallel discrete event simulation based on the Cilk model of multithreaded computation. Cilk's runtime system not only manages the low-level aspects of program execution, but also provides the user with an algorithmic model of performance which can be used to predict the execution time of a parallel simulation. Moreover, a Cilk application can ``scale down'' to run on a single processor with nearly the same performance as that of serial code.A conservative parallel discrete event simulation algorithm has been developed in which communication between logical processes is achieved using Cilk's virtual memory model, dag consistent shared memory. The simulation executes in cycles, where each cycle involves a divide and conquer computation. Although local lookahead information can be exploited, the algorithm is robust in that it also calculates a global simulation time for each cycle. It can therefore be used for applications where zero lookahead may occur.

BULK SYNCHRONOUS PARALLEL ALGORITHMS FOR CONSERVATIVE DISCRETE EVENT SIMULATION∗

All the parallel discrete event simulation algorithms developed so far have been designed to suit a specific parallel model (e.g., a PRAM model, a MP-RAM model, etc.). This paper presents several versions of conservative parallel discrete event simulation algorithms developed around a unifying model for general purpose parallel computer design and programming, namely around the Bulk Synchronous Parallel (BSP) model. The new algorithms are analysed in terms of the BSP model parameters and the effectiveness of simulators based on these algorithms is evaluated. The performance achieved even for a loosely coupled distributed system is comparable with that reported in previous research work, while the generality of the BSP model provides portability to the new approaches.

On the proof of correctness of "Yet another asynchronous distributed discrete event simulation algorithm (YADDES)"

The discipline of discrete event simulation may be utilized to model many physical systems such as digital hardware, queueing networks, telephone networks, simulated warfare, and banking transactions. Where the entities of a physical system execute independently and interact asynchronously, an asynchronous distributed event-driven simulation algorithm may enable the simulation of the system to execute on a parallel processor. This has the potential to significantly reduce the total simulation time. YADDES is the first algorithm that is characterized by: (1) acceptable performance, (2) freedom from deadlock, and (3) probably correct, for circuits where the interactions between the entities constitute a cyclic dependence. Given their complex, asynchronous nature, an important issue associated with all asynchronous distributed algorithms is their correctness, i.e., the generation of accurate output for given input stimulus, under all possible conditions. This paper presents a mathematical proof of correctness and reports the performance of YADDES on the Armstrong parallel processor at Brown University.