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Construction‐oriented discrete‐event simulation often faces the problem of defining uncertain information input, such as subjectivity in selecting probability distributions that result from insufficient or lack of site productivity data. This paper proposes incorporation of fuzzy set theory with discrete‐event simulation to handle the vagueness, imprecision and subjectivity in the estimation of activity duration, especially when insufficient or no sample data are available. Based upon an improved activity scanning simulation algorithm, a fuzzy distance ranking measure is adopted in fuzzy simulation time advancement and event selection for simulation experimentation. The uses of the fuzzy activity duration and the probability distribution‐modeled duration are compared through a series of simulation experiments. It is observed that the fuzzy simulation outputs are arrived at through only one cycle of fuzzy discrete‐event simulation, still they contain all the statistical information that are produced through multiple cycles of simulation experiments when the probability distribution approach is adopted.

This paper aims to address a central concern in modeling and simulating electric grids and the information infrastructure that monitors and controls them. The paper discusses the need for and methods to construct simulation models that include important interactions between the physical and computational elements of a large power system.

The paper offers a particular approach to modeling and simulation of hybrid systems as an enabling technology for analysis (via simulation) of modern electric power grids. The approach, based on the discrete event system specification, integrates existing simulation tools into a unified simulation scheme. The paper demonstrates this approach with an integrated information and electric grid model of a distributed, automatic frequency maintenance activity.

Power grid modernization efforts need powerful modeling and simulation tools for hybrid systems.

The main limitation of this approach is a lack of advanced simulation tools that support it. Existing commercial offerings are not designed to support integration with other simulation software products. The approach to integrating continuous and discrete event simulation models can overcome this problem by allowing specific tools to focus on continuous or discrete event dynamics. This will require, however, adjustments to the underlying simulation technology.

This paper demonstrates an approach to simulating complex hybrid systems that can, in principle, be supported by existing simulation tools. It also indicates how existing tools must be modified to support our approach.

To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties.

The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead‐time determination. The developed methodology employs bi‐directional feedback between the non‐stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined.

An example of determining realistic production lead‐time data in the ERP system is presented to illustrate how such a marriage can be achieved.

The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations.

The organizations who have been using the discrete event simulation in their planning and decision‐making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models.

The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

A discrete‐event simulation of a utility trouser manufacturing plant is described. The simulation model, written in the GPSS/PC language, was validated with operating data from a large plant with a nominal production capacity of 40,000 pairs of men's denim trousers per week. Specifically, the simulation results closely agreed with key plant operating figures, such as production rate, number of work stations, work‐in‐process inventory and residence time in production.

Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.

This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.

The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.

This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.

This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.

This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.

This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

The purpose of this paper is to investigate the integration of discrete event simulation (DES) and value stream mapping (VSM) to enhance the productivity of road surfacing operations by achieving high production rates and minimum road closure times. Highway infrastructure is one of the most valuable assets owned by the public sector. The success of national and local economies as well as quality of life of the general public depend on the efficient operations of highways. Ensuring smooth traffic operations requires maintenance and improvements of the highest standard.

Research approach involved the use of primary data collected from direct observation, interviews, review of archival records and productivity databases. Based on this, process maps and value stream maps were developed which were subsequently used to produce discrete event simulation models for the exploration of different optimisation scenarios.

This research highlights the synergistic relationship between VSM and DES in driving innovation in construction processes. Identified factors that affect roadworks process productivity include machine, manpower, material, information, environment and method-related factors. A DES model is presented to optimise the process and increase the production rates. A hybrid DES-VSM approach ensures an integrated approach to process optimisation.

This study is an application of hybrid version of previously published DES-VSM framework in the manufacturing sector. The present study has extended and tested its applicability within road surfacing operations. The different what-if scenarios presented in this paper might not be applicable to other parts of the world owing to various constraints. The study has focused on addressing the waste production inherent in pavement laying process. Even though external variables could possibly influence pavement process, those were ignored to allow for in-depth focus on the process under consideration.

Road users are one of the most important stakeholders that will benefit from the positive implications of this study. Private resurfacing companies and transport departments can optimise their overall process and style of working by comparing their end-to-end process and work plans with the ones mentioned in this paper. It will boost the productivity of equipment like planners, pavers and other machines used for resurfacing operations.

Existing approaches to process modelling such as VSM and process diagrams are constrained by their effectiveness in the analysis of dynamic and complex processes. This study presents a DES-based approach to validate targeted improvements of the current state of road surfacing processes and in exploration of different optimisation scenarios.

The purpose of this paper is to develop a generic framework for hybrid (integrated deployment of system dynamics and discrete event simulation) simulation which can be applied in the healthcare domain.

As hybrid simulation in an organisational context is a new topic with limited available data on deployment of hybrid simulation in organisational context, an inductive approach has been applied. On the basis of knowledge induced from literature, a generic conceptual framework for hybrid simulation has been developed. The proposed framework is demonstrated using an explanatory case study comprising an accident and emergency (A&E) department.

The framework provided detailed guidance for the development of a hybrid model of an A&E case study. Findings of this case study suggest that the hybrid model was more efficient in capturing behavioural impact on operational performances.

The framework is limited to only SD and DES; as agent‐based is another simulation method which is emerging as a promising tool for analysing problems such as spread of infectious diseases in healthcare context, inclusion of this into the framework will enhance the utility of the framework.

This framework will aid in the development of hybrid models capable of comprehending both detail as well as dynamic complexity, which will contribute towards a deeper understanding of the problems, resulting in more effective decision making.

It is expected that this research will encourage those engaged in simulation (e.g. researchers, practitioners, decision makers) to realise the potential of cross‐fertilisation of the two simulation paradigms.

Currently, there is no conceptual framework which provides guidance for developing hybrid models. In order to address this gap, this paper contributes by proposing a conceptual framework for hybrid simulation for the healthcare domain.

Focuses on the integrated use of simulation tools, particularly discrete‐event simulation, in the design and development of manufacturing systems in Japanese industry. The results are based on questionnaires and visits to seven large Japanese manufacturers and show that most of the visited companies do not use simulation to any large extent, particularly not discrete‐event simulation. Some of the reasons for this are general, while others are specific for Japan. However, the use of simulation is believed to increase in Japanese industry. Furthermore, argues that there is a large potential for increased use of advanced simulation techniques in Japanese manufacturing companies, mainly for two reasons. This would result in improved communication, reduced time‐to‐market and higher flexibility in volume and product‐mix.

Continuous process flow is a prerequisite of lean systems as it helps to reduce throughput times, improve quality, minimize operational costs, and shorten delivery times. The purpose of this paper is to empirically demonstrate the application of a methodology that combines a time‐based study, discrete‐event simulation and the trial and error method to enable a leaner process through more efficient line balancing and more effective flow for a park homes production process. This method is replicable across other contexts and industry settings.

The paper reviews the UK park homes production industry and, specifically, a major factory that builds these homes. It compares the factory method to traditional on‐site construction methods. An empirical study of production times was carried out to collect data in order to analyse the current workload distribution and the process flow performance of the park homes production process. Finally, seven discrete‐event simulation models were developed in order to test different scenarios and define the optimum line balance for every section of the production process.

By combining time study, discrete‐event simulation and trial and error methods, the workload distribution and process flow performance of the park homes production line were analysed and improved. A reduction of between 1.82 and 36.32 percent in balancing losses in some sections of the process was achieved.

This paper supports current knowledge on process flow improvement and line balancing by exploring and analysing these issues in a real‐life context. It can be used to guide production management practitioners in their selection of methods and demonstrates how they are exploited when seeking to improve process flow, efficiency and line balancing of production operations.

The study uses a real industrial application to demonstrate how the methodological combination and deployment of process flow improvement strategies, such as time study, simulation, and trial and error, can help organisations achieve process flow improvements and, as a consequence, a leaner production process.

To evaluate and optimize asphalt paving operations taking place in the reconstruction project of Interstate 74 in the USA using simulation.

Data collected during construction was used to determine useful probabilistic density functions for the activities duration and to validate a developed simulation model. Upon validation, the developed model was used to study the impact of resources on the flow of operations, and on the cost‐effectiveness of the construction process.

The developed simulation model was successful in simulating the paving operation and its accuracy was acceptable compared to field measurements. Based on the results of a sensitivity analysis of the critical resources, multiple factors were identified in the decision‐making process to ensure that all aspects of the operation were considered. For the conditions pertinent to this construction site, 30 trucks, one paver, and two rollers are recommended. Using this set of resources results in a prompt and effective execution of the construction process.

The developed model is applicable only to asphalt paving operations. The predictive capability of the developed model has not yet been investigated.

Implementation of the presented model in practical applications can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing project‐specifics conditions and limitations. If this approach is not feasible, production charts may be developed to easily determine the optimum resources that should be used given specific time constraints and quantity, issues.

The presented methodology has great potential to optimize resources and production rates in similar asphalt paving operations. Moreover, benefits of simulation would be maximized if it was used during the planning phase, as it will affect equipment orders and material shipments.