Discrete Event Simulation Algorithm Research Articles

Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm

AbstractPowder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.

A Tactical Planning Approach using Genetic Algorithms and Process Chain Simulation for Closed-Loop Production Systems for high-value components

The implementation of closed-loop production systems in manufacturing is a promising approach to mitigate environmental impacts and supply chain disruptions, while increasing the value and lifespan of products and materials. These systems integrate forward and reverse material flow activities such as repair, remanufacturing, and recycling within a single facility. However, a major challenge in planning and operating closed-loop production systems is the high uncertainty surrounding material availability and condition. Different process chains may be required depending on the extent and type of wear present in the used products. During the tactical planning phase, factory planners play a crucial role in evaluating and economically assessing various system and process chain configurations. Multiple technology chain and manufacturing equipment options provide different solutions for managing forward and reverse material flows. To address this, this paper introduces a metaheuristic framework based on discrete event simulation and genetic algorithms to evaluate these system configurations. The framework serves as decision-support for factory planners, whether they are planning a greenfield production system or undertaking a brownfield project aimed at transforming linear into circular production systems with both forward and reverse flows.The presented framework is built on data models that incorporate different technology and process chain options for manufacturing and repair, considering the condition of the cores. A manufacturing equipment database contains predefined data models describing machine skills and load profiles, which are utilized to generate various simulation models using different production system configurations. These models are then used to execute predefined scenarios. The main objective is to identify configurations that achieve a beneficial fitness value balancing technical, economic, and environmental key performance indicators for closed-loop production systems.

Efficient Simulation of Sparse Graphs of Point Processes

We derive new discrete event simulation algorithms for marked time point processes. The main idea is to couple a special structure, namely the associated local independence graph, as defined by Didelez, with the activity tracking algorithm of Muzy for achieving high-performance asynchronous simulations. With respect to classical algorithms, this allows us to drastically reduce the computational complexity, especially when the graph is sparse.

Имитационная модель беспроводной ad-hoc сети для исследования алгоритмов маршрутизации трафика

Communication network simulators are software designed to model, explore, test and debug network technologies, including wireless decentralized self-organizing networks or ad-hoc networks. They greatly simplify the research, development and optimization of routing protocols in these networks. However, the well-known simulators have a number of disadvantages, including the difficulty of adding custom extensions to ad-hoc network routing protocols, the lack of the necessary network stack, the lack of routing algorithm visualization modes, low performance, and difficulty in debugging communication protocols. The purpose of this work is to create a simulation model of a wireless network that would allow us to explore, debug and evaluate the developed algorithms and routing protocols for ad-hoc networks. At the same time, the requirements for interface ergonomics and the ability to visualize the operation of algorithms, ensure the collection of statistics, and create various scenarios for the operation of the network come to the fore. The article proposes the structure of the simulation model, which includes the modules of the network subscriber, application software, network layer of the OSI data transmission model, radio module, radio transmission environment, statistics collection, visualization and scenario management. To solve the tasks set, the approach of discrete-event modeling was used. To create a simulator of wireless decentralized networks and routing algorithms, a set of classes was developed that implement the modules of the simulation model. Based on the proposed structure, module classes and discrete event simulation algorithm, a software implementation of the simulation model was created using the C++ programming language and the Qt framework. The developed simulation model was used in the course of an experimental study of the effectiveness of the network routing algorithm. The proposed software will simplify the development and debugging of algorithms and routing protocols for ad-hoc networks.

Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm

Introduction:Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed to minimize patients’ waiting time and the percentage of units’ engagement to improve the emergency department (ED) efficiency.Methods:A comprehensive combination method involving Discrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA) was used in this study. After simulating the case and making sure about the validity of the model, experiments were designed to study the effects of change in individuals and equipment on the average time that patients wait, as well as units’ engagement in ED. Objective functions determined using Artificial Neural Network algorithm and MATLAB software were used to train it. Finally, after estimating objective functions and adding related constraints to the problem, a fractional Genetic Algorithm was used to solve the model.Results:According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units’ doctors, were in an optimized condition, but the triage unit, as well as the fast track units’ doctors, should be optimized. After experiments in which the average waiting time in the triage section reached near zero, the average waiting time in the screening section was reduced to 158.97 minutes and also the coefficient of units’ engagement in both sections were 69% and 84%, respectively.Conclusions:Using the service optimization method creates a significant improvement in patient’s waiting time and stream at emergency departments, which is made possible through appropriate allocation of the human and material resources.

Use of discrete event simulation and genetic algorithms to estimate the necessary resources to respond in a timely manner in the Medical Emergency System in Bogotá.

Bogotá has a Medical Emergency System of public and private ambulances that respond to health incidents. However, its sufficiency in quantity, type and location of the resources demanded is not known. Based on the data from the Medical Emergency System of Bogotá, Colombia, we first sought to characterize the prehospital re- sponse in cardiac arrest and determine with the model which is the least number of resources necessary to respond within eight minutes, taking into account their location, number, and type. A database of incidents reported in administrative records of the district health authority of Bogotá (2014 to 2017) was obtained. Based on this information, a hybrid model based on discrete event simulation and genetic algorithms was designed to establish the amount, type and geographic location of resources according to the frequencies and typology of the events. From the database, Bogotá presented 938 671 ambulances dispatches in the period. 47.4% high priority, 18.9% medium and 33.74% low. 92% of these corresponded to 15 of 43 medical emergency codes. The response times recorded were longer than expected, especially in out-of-hospital cardiac arrest (median 19 minutes). In the proposed model, the best scenario required at least 281 ambulances, medicalized and basic in a 3:1 ratio, respectively, to respond in adequate time. Results suggest the need for an increase in the resources that respond to these incidents to bring these response times to the needs of our population.

Haul truck fuel consumption modeling under random operating conditions: A case study

This research study evaluates kinematic fuel consumption factors of haul trucks employed simultaneously in a multi-route operation network under stochastic payload and precipitation conditions. First, a discrete-event simulation algorithm was introduced, and significant parameters available in a material haulage system were correlated with time and location-based fuel usage behavior. Then, the model was validated with a large-scale cement production network covering two separate mines and one processing plant where fifteen different routes and twenty-nine trucks were available. The simulation results showed that precipitation conditions might lead to a variation in fuel consumption by 15–25 percent. Besides, the same-capacity trucks employed in the clay mine were detected to consume 40 percent more fuel in loaded travel than the limestone mine trucks due to the higher frequency of uphill loaded travels. The clay mine trucks also released 1.48 kg/km carbon dioxide in a complete production cycle, which is 17.5 percent more comparatively.

Parallel algorithm development and testing using Petri-object simulation

Parallel algorithms are problematic to develop because of the negative influence of synchronisation, complicated behaviour of threads’ capturing computing resources. Experimental results show performance time’s strong dependence on algorithm parameters, such as the number of subtasks and the complexity of each task. The optimal value of subtask complexity is revealed for the particular algorithm. It is the same for different complexity of the parallelised task (with the same computing resource). To guarantee algorithm speed-up it is important to have a method for investigating the efficiency of parallel algorithm before its implementation on specified computing resources. Stochastic Petri net potentially could be a high accuracy tool for investigating the efficiency of a parallel algorithm. However, a huge number of elements are needed to compose a model of non-trivial algorithm that limits the application of this tool in practice. Petri-object simulation method allows replication of Petri nets with specified parameters and model creation of a list of linked Petri-objects. Basic templates for the model creation of a multithreaded algorithm are developed. Applying these templates, the model of the parallel discrete event simulation algorithm is developed and investigated. By the model results, the algorithm parameters providing the least performance time can be determined.

Analysis on Hull Block Erection Process Considering Variability

The hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.

Empirical Assessment Method of Ultimate Capability of Steel Stiffened Panel under Longitudinal Compressive Load

The hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.

Integrating discrete event simulation and genetic algorithm optimization for bridge maintenance planning

To minimize agency and user costs in a bridge repair project, a bridge maintenance manager should develop an appropriate project schedule considering real-world constraints such as resource limitations (e.g., workspace and crew). This paper presents a new framework called Simulation-based Bridge Maintenance Optimization (SiBMO) by integrating Genetic Algorithm (GA) and Discrete Event Simulation (DES) to identify the optimum maintenance plan taking into account crew limitations. The framework optimizes the sequence of repair-activities in the repair interventions considering workspace limitations and predecessor relationships. SiBMO also develops a high-level schedule of the interventions regarding the project calendar and the Traffic Management Plan (TMP). The Bridge Information Model (BrIM) based user interface developed in this study visualizes the high-level schedule. The results of applying SiBMO on a real case study demonstrates its capability in finding the optimum maintenance plan, its efficiency in optimizing the high-level schedule, and its accuracy in estimating user costs.

A discrete-event simulation algorithm for the optimization of multi-scenario maintenance policies

Maintenance policies that may incorporate alternative work packages with a different intention of implementation have an explicit effect on the availabilities and the operating costs of the production systems. These work packages can be classified under corrective and preventive actions, which offer run-to-failure and failure defeating approaches, respectively. Sub-classes of these actions show a variation by implementation interval, technology and materials used, and the point of application. Corrective maintenance, preventive maintenance by inspections, and opportunistic maintenance are the most frequently observed application types in the production industries. Their financial and functional effectiveness in a maintenance policy may vary depending on the operational substantiality and complexity of the system on target. Therefore, the system operability needs to be ensured with a proper maintenance policy where the redundant contents in the policy should be removed to mitigate the production loss. On this basis, the current study introduces a discrete-event simulation algorithm that is capable of simultaneous evaluation and comparison of multiple maintenance scenarios for a production system. The optimization criterion in the algorithm is interchangeable that can be achieved in two ways as maximization of system availability and minimization of total maintenance cost, based on the available maintenance and cost datasets. For a clear illustration of the algorithm, two case studies with different optimization criteria were provided for multi- and single-system earthmoving operations. The systems were selected from the mining industry, where the high uncertainties in production and machine performance are observed. As well as production and cost outputs, the sensitivity of the failure profiles of subsystems and components to the introduced maintenance scenarios were also discussed in the case studies to reveal the vulnerability zones in the systems.

A dynamic Quantized State System execution framework for Hybrid Automata

Cyber–physical Systems (CPS) involve a network of discrete controllers that control physical processes, e.g., pacemakers controlling the heart rhythm. The intuitive and formal Hybrid Automata (HA) based modelling approach for CPS is gaining momentum. HA combines the model of the plant along with its discrete controller resulting in a piece-wise continuous system with discontinuities. Accurate detection of these discontinuities, using appropriate level crossing detectors, is a key challenge to simulation of CPS modelled in HA. Existing techniques employ time discrete numerical integration with bracketing for level crossing detection. These techniques involve back-tracking and are highly non-deterministic and hence error prone. As level crossings happen based on the values of continuous variables, Quantized State System (QSS)-integration may be more suitable.We propose a dynamic quanta based execution framework for HA. A CPS modelled as a HA is first translated into the proposed formal intermediate format called the Quantized State Hybrid Automata (QSHA). The QSHA is then executed using a discrete event simulation algorithm based on quantized state semantics. The developed formal model and the associated simulation framework guarantees that: ① all level crossings are accurately detected and ② the time of the level crossing is also accurate within floating point error bounds. Interestingly, benchmark results reveal that the proposed simulation technique takes 720, 2.17, 1.33 and 4.41 times fewer simulation steps compared to QSS-1, QSS-3, Runge–Kutta (RK)-45, and Differential Algebraic System Solver (DASSL) integration based techniques, respectively.

Efficient modelling of solute transport in heterogeneous media with discrete event simulation

To address the problem of different time scales present in the simulation of solute transport through systems with a complex permeability structure such as fractured porous rocks, we propose a parallel discrete event simulation (DES) algorithm based on local time stepping criteria, specifically developed for the hybrid finite-element node-centred finite volume (FV) framework. A preemptive-event-processing (PEP) approach is applied to synchronise discrete events with sufficiently close time stamps, thereby facilitating the parallelisation for shared memory architectures. The accuracy of the presented DES-PEP scheme is first verified against the analytical solution of a 1D advection equation with spatially variable coefficients. The DES scheme is then applied to simulate tracer advection through a 3D model of highly heterogeneous fractured rock represented by an unstructured adaptively refined mesh with over 1 million elements. DES produces results comparable to those of a conventional time-driven simulation (TDS), but uses less than 1% of the execution time. Analysis of event distributions shows that updates occur almost exclusively in a small number of FV cells marked by order-of-magnitudes faster fluid flow and advection-dominated transfer, while slow-flowing cells remain inactive and excluded from computations. This focusing of the computational effort leads to high simulation efficiency while simultaneously diminishing round-off errors. Scalability tests with a parallel version of DES on shared memory demonstrate further computational speedups mirroring the increased number of threads. With the use of 20 threads, execution time is reduced from 42.5 days (with TDS) to only 1.5 hours, equivalent to a speedup of over 670. This parallel DES algorithm therefore enables efficient multi-core simulation of solute transport in heterogeneous geologically realistic systems.

Production and preventive maintenance rates control in a failure-prone manufacturing system using discrete event simulation and simulated annealing algorithm

Production and preventive maintenance rates control in a failure-prone manufacturing system (FPMS) was studied in the current survey. One of the most important parameters is to determine the inventory level of the buffer, because if the inventory level is low, the system is faced with shortage in time of breakdown; otherwise, holding cost will be increased. Another effective parameter is determining the time for preventive maintenance which is important to discover breakdowns before their occurrence and thus minimise the cost of corrective maintenance. The purpose of this paper was to determine the optimal production rate and time of preventive maintenance for a single-machine FPMS to minimise sum of costs of holding, shortage, corrective and preventive maintenance. The repair time and time between breakdowns have general distribution. Since the inventory level and time of preventive maintenance should be optimised simultaneously in order to obtain an optimum point, discrete event simulation and simulated annealing were used.

Production and preventive maintenance rates control in a failure-prone manufacturing system using discrete event simulation and simulated annealing algorithm

Production and preventive maintenance rates control in a failure-prone manufacturing system (FPMS) was studied in the current survey. One of the most important parameters is to determine the inventory level of the buffer, because if the inventory level is low, the system is faced with shortage in time of breakdown; otherwise, holding cost will be increased. Another effective parameter is determining the time for preventive maintenance which is important to discover breakdowns before their occurrence and thus minimise the cost of corrective maintenance. The purpose of this paper was to determine the optimal production rate and time of preventive maintenance for a single-machine FPMS to minimise sum of costs of holding, shortage, corrective and preventive maintenance. The repair time and time between breakdowns have general distribution. Since the inventory level and time of preventive maintenance should be optimised simultaneously in order to obtain an optimum point, discrete event simulation and simulated annealing were used.

Simulation of virtual time profile in conservative parallel discrete event simulation algorithm for small-world network

We simulate model for evolution of local virtual time profile in conservative parallel discrete event the simulation (PDES) algorithm with long-range communication links. The main findings of simulation are that i) growth exponent depends logarithmically on the concentration p of long-range links; ii) utilisation of processing elements time decreases slowly with p. Thismeans that the conservative PDES with long-range communication links is fully scalable.

New advances in large‐scale distributed simulation and real‐time applications

New advances in large‐scale distributed simulation and real‐time applications

Relation of Parallel Discrete Event Simulation algorithms with physical models

We extend concept of local simulation times in parallel discrete event simulation (PDES) in order to take into account architecture of the current hardware and software in high-performance computing. We shortly review previous research on the mapping of PDES on physical problems, and emphasise how physical results may help to predict parallel algorithms behaviour.

OPTIMUM CONSTRUCTION EQUIPMENT FLEETS FOR ROAD SURFACE OPERATIONS

Poor subgrade soil condition which is very sensitive to moisture presents a construction challenge for all pavement engineers. One of the case histories which became a dilemma for the Azadegan freeway in Tehran is the presence of waste material in subgrade soil. This paper discusses the site investigation to gather some useful information about the principal causes of these problems and deals with some in-depth Laboratory tests which have been done in the soil and asphalt laboratory of Tehran municipality. The pavement condition index known as PCI has been calculated to address all the deteriorations and causes to make a good decision. After analyzing all the gathered data, several methods has been discussed, for example, deep mixing, short concrete piles, shredded tires, and injection. Finally based on the lack of facilities and budget shortage, a retaining wall with geogrids has been chosen for this very special case which is right now under traffic flow. The optimum selection of equipment fleets in surface work operations is a key element to the success of any road construction project. For years, computer simulations have been used to predict the performance of construction operations based on process flows and resources utilized. However, simulations in essence are not a resource optimization platform, since all possible resource combinations should be examined within the simulation process itself. This paper proposes a hybrid mechanism that integrates discrete event simulation and genetic algorithms to efficiently determine the best resource combination for the surface work operations in road construction. The paper employs genetic algorithms (GAs) for minimizing the total cost of surface work operations examined. A dynamic link utilizes a simulation engine, which models a road’s surface work operations, to calculate the fitness of the generated chromosomes. An actual case study is further utilized to illustrate the effectiveness and performance of this hybrid mechanism.