Fuzzy Simulation Techniques in Construction Engineering and Management

In every production plant, it is necessary to have an estimation of production level. Sometimes there are many parameters affective in this estimation. In this paper, it tried to find an appropriate estimation of production level for an industrial factory called Barez in an uncertain environment. We have considered a part of production line, which has different production time for different kind of products, which means both environmental and system uncertainty. To solve the problem we have simulated the line and because of the uncertainty in the times, fuzzy simulation is considered. Required fuzzy numbers are estimated by the use of bootstrap technique. The results are used in production planning process by factory experts and have had satisfying consequences. Opinions of these experts about the efficiency of using this methodology, has been attached.

MO3 COMPARING METHODS OF DATA SYNTHESIS: UPDATING PARAMETERS OF AN EXISTING PROBABILISTIC COST-EFFECTIVENESS MODEL

MO1 ADVANTAGES AND DISADVANTAGES OF MARKOV MODELS VERSUS DISCRETE EVENT MODELS FOR SIMULATING PATIENT FLOWS

Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

Construction companies are increasingly utilizing sensing technologies to automatically record different steps of the construction process in detail for effective monitoring and control. This generates a significant amount of event data that can be used to learn the underlying behavior of agents in a construction site using process mining. While process mining can be used to discover the real process and identify and analyze deviations and bottlenecks in operations, it is a backward-looking approach. On the other hand, discrete event simulation (DES) provides a means to forecast future performance from historical data to enable proactive decision-making by operation managers relating to their projects. However, this method is largely unused by the industry due to the specialized knowledge required to create the DES models. This paper thus proposes a framework that extends the utility of collecting event data and their process models, by transforming them into DES models for forecasting future performance. This framework also addresses another challenge of using DES relating to its inability to update itself as the project progresses. This challenge is addressed by using the Bayesian updating technique to continuously update the input parameters of the simulation model for the most up-to-date estimation based on data collected from the field. The proposed framework was validated on a real-world case study of an earthmoving operation. The results show that the process mining techniques could accurately discover the process model from the event data collected from the field. Furthermore, it was noted that continuous updating of DES model input parameters can provide accurate and reliable productivity estimates based on the actual data generated from the field. The proposed framework can help stakeholders to discover the underlying sequence of their operations, and enable timely, data-driven decisions regarding operations control.

The main idea of the presented new approach is to join a discrete event simulation (DES) and mathematical programming techniques (i.e. mixed integer programming, MIP) for optimization of complex manufacturing processes. Thereby, a DES model allows a detailed problem description. For a target oriented optimization several capacity allocation problems are solved by a MIP solver, reducing the degrees of freedom in the DES model. As an example a typical parallel machine scheduling problem arising in semiconductor industry was chosen. Different process constraints like machine dedications, setups, auxiliary resources and processing time dependences are discussed - advantages and disadvantages of simulation-based and exact scheduling approaches are drafted. The investigated optimization goals comprise the reduction of total tardiness and setups efforts as well as a balanced machine utilization. Based on real manufacturing data of a wafer test area this approach is evaluated.

Discrete event simulation (DES) techniques cover a broad collection of methods and applications that allows imitating, assessing, predicting and enhancing the behavior of large and complex real-world processes. This work introduces a modern DES framework, developed with SIMIO simulation software, to optimize both the design and operation of a complex beer packaging system. The proposed simulation model provides a 3D user-friendly graphical interface which allows evaluating the dynamic operation of the system over time. In turn, the simulation model has been used to perform a comprehensive sensitive analysis over the main process variables. In this way, several alternative scenarios have been assessed in order to achieve remarkable performance improvements. Alternative heuristics and optimization by simulation can be easily embedded into the proposed simulation environment. Numerical results generated by the DES model clearly show that production and efficiency can be significantly enhanced when the packaging line is properly set up.

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

Agent-based modeling (ABM) has gained great popularity in recent years, especially in application areas where human behavior is important, because it opens up the possibility of capturing such behavior in great detail. Hybrid models which combine ABM with discrete-event simulation (DES) are particularly appealing in service industry applications. However in this paper we argue that many of the so-called distinctions between agents in an ABM and entities in a DES are artificial, and we describe several DES models which use standard entities to represent agent-like behaviors.

In this paper fuzzy set theory was applied to discrete event simulation to model uncertainty in input data. Various approaches to fuzzy simulation have been proposed in the literature, even if many of the problems are still to be solved. The key points are how to manage the simulation event list and how to update the fuzzy simulation clock. These two tasks are mainly based on the ranking algorithm. In the following, the classical algorithms were applied to rank temporal fuzzy sets and the results obtained were compared with the ones obtained by the proposed ranking algorithm. The comparison was performed by analyzing a simple case study in the manufacturing field. The results show how the new ranking algorithm can be very useful in a fuzzy simulation environment.

Fuzzy programming with fuzzy decisions and fuzzy simulation-based genetic algorithm

Geographic Service Delivery for Endovascular Clot Retrieval: Using Discrete Event Simulation to Optimize Resources

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

PurposeThe 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.Design/methodology/approachResearch 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.FindingsThis 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.Research limitations/implicationsThis 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.Practical implicationsRoad 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.Originality/valueExisting 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.

BackgroundThe World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru.MethodsUsing data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with “what-if” scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system.ResultsOur simulations show how much the screening system’s capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general “rules” for resource and operational planning.ConclusionsMultilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions—how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.