Simulation-based Decision Support System Research Articles

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

Leveraging Auto-generative Simulation for Decision Support in Engineer-to-Order Manufacturing

In the sphere of Engineer-to-Order (ETO) manufacturing, the demand for highly customized and rapidly adaptable solutions is escalating, particularly in the context of Industry 4.0 and the burgeoning focus on hyper-customization in Industry 5.0. Traditional, rigid simulation structures often fall short in adapting to these dynamic requirements, creating a need for more flexible simulation frameworks. This paper introduces a novel auto-generative Discrete Event Simulation (DES) system specifically designed to address the unique challenges of ETO manufacturing. Unlike traditional rigid simulation architectures, the proposed system offers substantial flexibility and modularity, significantly easing the development effort. Developed in MATLAB and Simulink, this paper not only presents the foundational logic and architecture of the system but also provides a practical implementation guide. By aligning with the imperatives of Industry 4.0 and 5.0, this research fills a significant gap in the literature and offers a scalable, adaptable simulation-based Decision Support System (DSS) for ETO manufacturing.

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Sensor-fused simulation-based decision support system for earthmoving projects

Discrete Event Simulation (DES) is an effective method to study cyclic and uncertain projects at the operation level. However, qualified DES results highly depend on the input data quality. This paper describes a simulation-enabled context-aware Decision Support System (DSS) based on sensor fusion to model earthwork operations realistically. The DSS backbone is a DES continually updated using real-time data. The information on earthmoving operations is exchanged between reality and DES using GPS and equipment-mounted cameras. The equipment spatiotemporal data and shovel-mounted cameras enhance the DES quality (i.e., locational and contextual awareness) by providing timely information about task durations, load types, and bucket teeth status. Sensor data is fused and fed into the DES model to replicate the earthmoving project. The proposed method is successfully implemented and validated in a large-scale earthmoving case study. The developed DES is proven promising as a reliable DSS for enhancing resource allocation and time-cost reduction.

Building Information Modeling and Building Performance Simulation-Based Decision Support Systems for Improved Built Heritage Operation

Adapting outdated building stocks’ operations to meet current environmental and economic demands poses significant challenges that, to be faced, require a shift toward digitalization in the architecture, engineering, construction, and operation sectors. Digital tools capable of acquiring, structuring, sharing, processing, and visualizing built assets’ data in the form of knowledge need to be conceptualized and developed to inform asset managers in decision-making and strategic planning. This paper explores how building information modeling and building performance simulation technologies can be integrated into digital decision support systems (DSS) to make building data accessible and usable by non-digital expert operators through user-friendly services. The method followed to develop the digital DSS is illustrated and then demonstrated with a simulation-based application conducted on the heritage case study of the Faculty of Engineering in Bologna, Italy. The analysis allows insights into the building’s energy performance at the space and hour scale and explores its relationship with the planned occupancy through a data visualization approach. In addition, the conceptualization of the DSS within a digital twin vision lays the foundations for future extensions to other technologies and data, including, for example, live sensor measurements, occupant feedback, and forecasting algorithms.

Simulation-based decision support system for earthmoving operations using computer vision

A reliable Decision Support System (DSS), particularly in the construction domain, can be driven by quality input information. Although vision-based methods have been widely utilized to retrieve contextual information, their potential is not fully leveraged in construction simulation yet. This study introduces an automated framework that utilizes multi-view video footage for vision-based input modeling within simulation domains. The proposed framework addresses project uncertainties (e.g., equipment performance, operators’ skills, road network, and weather status) using a proactive approach where project task durations are modeled as probabilistic distributions. The modeled distributions are continuously calibrated using the Markov Chain Monte Carlo Bayesian Inference (MCMCBI) approach. A simulation-based Simulated Annealing (SA) optimization is also employed to provide an efficient resource assignment. The extracted vision-based data is validated statistically against actual and spatiotemporal data. The results demonstrate that the suggested vision-based approach can provide qualified DSS input. Statistical analysis also confirms that vision-based data is more consistent with actual data than spatiotemporal data. The presented approach is successfully applied to an actual case study of a large-scale earthmoving project.

A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials.

NeutroMAS4SCRM : a combined multi-agent system with neutrosophic data analytic hierarchy process framework for supply chain risk management

Effective risk management reaction improves the absorption of critical impacts on supply chains. Supply chain risk (SCR) sources, like control, process, demand, and supply, need to be identified, assessed, and mitigated to make rational decisions immediately. Late detection of a disruptive event can cause delays in handling risk. Since SCRs consist of complex, uncertain, and incomplete information, most of the provided risk assessment mechanisms cannot handle it in real-time. Hence, in this paper, we introduce NeutroMAS4SCRM, a framework incorporating real-time Multi-Agent Systems (MAS) with Neutrosophic Data Analytic Hierarchy Processes to best deal with the complexity, uncertainty, and vagueness of SCR management-related issues and which can hence help decision-makers adopt less risky decisions. In addition, the MAS technology contribution to SCR management is outlined through a comparative study among the most recent studies. In contrast, the proposed MAS for the supply chain is implemented under the JADE agent platform, where the FIPA-ACL-based message content is specified using a dedicated ontology. A simulation-based decision support system is used to assess the cost risk and its harmful effects and determine how well the proposed framework can help companies manage risks efficiently. The simulation has proven to reduce risk costs by about 85%.

Simulation-Based Decision Support System for Energy Efficiency in Buildings Retrofitting

The implementation of building retrofitting processes targeting higher energy efficiency is greatly influenced by the investor’s expectations regarding the return on investment. The baseline of this work is the assumption that it is possible to improve the predictability of the post-retrofit scenario, both in energy and financial terms, using data gathered on how a building is being used by its occupants. The proposed approach relies on simulation to estimate the impact of available energy-efficient solutions on future energy consumption, using actual usage data. Data on building usage are collected by a wireless sensor network, installed in the building for a minimum period that is established by the methodology. The energy simulation of several alternative retrofit scenarios is then the basis for the decision support process to help the investor directing the financial resources, based on both tangible and intangible criteria. The overall process is supported by a software platform developed in the scope of the EnPROVE project. The platform includes building audit, energy consumption prediction, and decision support. The decision support follows a benefits, opportunities, costs, and risks (BOCR) analysis based on the analytic hierarchy process (AHP). The proposed methodology and platform were tested and validated in a real business case, also within the scope of the project, demonstrating the expected benefits of alternative retrofit solutions focusing on lighting and thermal comfort.

A simulation-based decision support system to improve the resilience of the food supply chain

ABSTRACT The rapid growth of population since 1950s has resulted in an enormous increase in demand for all kinds of manufactured goods. Manufacturing supply chains are becoming more sophisticated and complex, in particular within global food sector. At the same time, an increasing number of disruptions, ranging from those related to climate change and economic crisis as well as those associated with the socio-political turmoil and modern criminal activities such as cyber-attacks, are threatening the day-to-day operations. Although in the recent years, there have been significant advancements in supply chain management methods and tools, food manufacturers are still struggling to mitigate the possible impacts of these disruptions, leading to questions over the long-term resilience of their supply chains. This paper presents a novel framework for the improvement of food supply chain resilience and proposes a simulation-based decision support approach to aid the manufacturers to mitigate the impact of a range of unanticipated disruptions, specifically those related to climate change. The application of this framework has been demonstrated using activities of a dairy product manufacturer together with other main actors from its supply chain, namely the dairy farmers and wholesalers.

CAL-FERT: a simulation-based decision support system for precision fertilization of vegetable crops

CAL-FERT: a simulation-based decision support system for precision fertilization of vegetable crops

Simulation-Based Decision Support System to Improve Material Flow of a Textile Company

To remain competitive, companies must continuously improve the processes at hand, be they administrative, production, or logistics. The objective of the study described in this paper was to develop a decision-making tool based on a simulation model to support the production of knits and damask fabrics. The tool was used to test different control strategies for material flow, from the raw material warehouse to the finished product warehouse, and thus can also be used to evaluate the impacts of these strategies on the productivity. The data upon which the decision support tool was built were collected from five sectors of the plant: the raw material warehouse, knit production, damask production, finishing work, and the finished product warehouse. The decision support tool met the objectives of the project, with all five strategies developed showing positive results. Knit and damask production rates increased by up to 8% and 44%, respectively, and a reduction of 75% was observed in the waiting time on the point of entry to the finishing work area, compared to the company’s existing system.

Uncertainty Quantification in Vehicle Content Optimization for General Motors

A vehicle content portfolio refers to a complete set of combinations of vehicle features offered while satisfying certain restrictions for the vehicle model. Vehicle Content Optimization (VCO) is a simulation-based decision support system at General Motors (GM) that helps to optimize a vehicle content portfolio to improve GM’s business performance and customers’ satisfaction. VCO has been applied to most major vehicle models at GM. VCO consists of several steps that demand intensive computing power, thus requiring trade-offs between the estimation error of the simulated performance measures and the computation time. Given VCO’s substantial influence on GM’s content decisions, questions were raised regarding the business risk caused by uncertainty in the simulation results. This paper shows how we successfully established an uncertainty quantification procedure for VCO that can be applied to any vehicle model at GM. With this capability, GM can not only quantify the overall uncertainty in its performance measure estimates but also identify the largest source of uncertainty and reduce it by allocating more targeted simulation effort. Moreover, we identified several opportunities to improve the efficiency of VCO by reducing its computational overhead, some of which were adopted in the development of the next generation of VCO.

A simulation model to investigate impacts of facilitating quality data within organic fresh food supply chains

Demand for and production of organic fresh food play an increasing role worldwide. As a result, a growing amount of fresh fruits and vegetables has to be transported from predominantly rural production regions to customers mostly located in urban ones. Specific handling and storage conditions need to be respected along the entire supply chain to maintain high quality and product value. To support organic food logistics operations, this work investigates benefits of facilitating real-time product data along delivery and storage processes. By the development of a simulation-based decision support system, sustainable deliveries of organic food from farms to retail stores are investigated. Generic keeping quality models are integrated to observe impacts of varying storage temperatures on food quality and losses over time. Computational experiments study a regional supply chain of organic strawberries in Lower Austria and Vienna. Results indicate that the consideration of shelf life data in supply chain decisions allow one to reduce food losses and further enables shifting surplus inventory to alternative distribution channels.

A Monte Carlo simulation-based decision support system for reliability analysis of Taiwan’s power system: Framework and empirical study

Taiwan aims to greatly increase renewable energy generation by 2025; as such, an important topic is whether to increase the statutory planning reserve margin (PRM) to overcome high share of Variable Renewable Energy (VRE). Therefore, in this research, the goal is to investigate the use of loss of load expectation (LOLE) as a risk measure to provide insights into how the available power capacity, at a national level, can fail to meet the customer load. A Monte-Carlo-simulation-based framework was proposed to enable fast calculation of the LOLE. Considering a general national power system that consists various sources of renewable and conventional power, the proposed framework allows for a scenario-based calculation under the realistic situation that various conventional energy sources can be ramped up to dynamically meet losses of load. To make the methodology more user-friendly and applicable to power systems, a decision support system was developed. Moreover, a reliability analysis of Taiwan’s power system was conducted to show how to evaluate the impact of energy policy by 2025. Sensitivity analyses on two scenarios (with and without limiting coal-fired power generation) on LOLE were done. Finally, recommendations related to the reliability of the power system under Taiwan’s energy transition were provided.

Sustainability in logistic hubs: a decision support system for investigating green practices at container terminals

Logistic hubs, and especially container terminals, are today responsible for a major percentage of the environmental pollution. Practices such as the onshore power supply (OPS) may have relevant benefits but it has been argued that container terminal operators perceive a paucity of evidence that the benefits derived from the implementation of these practices exceed the investment costs. To this end, an advanced simulation-based decision support system is proposed in this article to represent a reliable test environment to simulate and compare different alternatives in terms of environmental impacts and economic sustainability. The implementation of an OPS system is considered as case study. Results show that an OPS system must consider the source of the electric power needed by ships at berth, i.e., coal, biofuels or renewable energies. The unit costs of 1 m3 of saved CO2 and NOX by implementing an OPS system are also calculated.

Sustainability in logistic hubs: a decision support system for investigating green practices at container terminals

Logistic hubs, and especially container terminals, are today responsible for a major percentage of the environmental pollution. Practices such as the onshore power supply (OPS) may have relevant benefits but it has been argued that container terminal operators perceive a paucity of evidence that the benefits derived from the implementation of these practices exceed the investment costs. To this end, an advanced simulation-based decision support system is proposed in this article to represent a reliable test environment to simulate and compare different alternatives in terms of environmental impacts and economic sustainability. The implementation of an OPS system is considered as case study. Results show that an OPS system must consider the source of the electric power needed by ships at berth, i.e., coal, biofuels or renewable energies. The unit costs of 1 m3 of saved CO2 and NOX by implementing an OPS system are also calculated.

Flood risk management approaches and tools for mitigation strategies of coastal submersions and preparedness of crisis management in France

ABSTRACTAs a consequence of the disastrous Xynthia storm surge on 27–28 February 2010, France has significantly modified the country’s regulations related to flood risk management through implementation of the national plan for mitigation and management of the flash submersions (Plan Submersions Rapides). The paper describes the results of ‘CRISMA - Modelling crisis management for improved action and preparedness’, a collaborative research and development project co-funded by the European Community (2011–2014), which developed a generic framework for implementation of simulation-based decision support systems in different domains of the natural or industrial risks (floods, snowstorms, earthquakes, forest fires, accidental pollutions, mass accidents). In particular, the paper addresses the CRISMA-WAVE application of the framework regarding simulation of submersion effects at a range of temporal and spatial scales, preparedness planning and assessment of impacts depending on scenarios based on mitigation options for managing the inundation risks in the pilot area, Charente-Maritime county. In addition, an example of a complex regulatory study implementing a flood prevention action programme along the Northern Sea coast in order to provide an overall mid- and long-term intervention strategy regarding the combined risk of river floods and marine submersion, making use of CRISMA-WAVE methods and tools, is presented. The simulation capacity of the platform appears to provide a very good analytic support to find negotiated solutions corresponding to a consensus of all the stakeholders. The prevention strategy developed for the area is based on a solidarity principle between coastal areas and land areas. Adaptation of the territory to the increased risks due to the climate change will need tens of years to be effective.

Data-Centered Runtime Verification of Wireless Medical Cyber-Physical System

Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine, where huge amounts of data are sampled by the wireless medical devices and sensors, and is passed to the decision support systems (DSSs). Many text-based guidelines have been encoded for work-flow simulation of DSS to automate health care based on those collected data. But for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex temporal properties encoded in those data, which brings new challenges to current simulation-based DSS with limited support of automatical formal verification and real-time data analysis. In this paper, we conduct the first study on applying runtime verification to cooperate with current DSS based on real-time data. Within the proposed technique, a user-friendly domain specific language, named DRTV, is designed to specify vital real-time data sampled by medical devices and temporal properties originated from clinical guidelines. Some interfaces are developed for data acquisition and communication. Then, for medical practice scenarios described in DRTV model, we will automatically generate event sequences and runtime property verifier automata. If a temporal property violates, real-time warnings will be produced by the formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of medical care scenarios and have applied the proposed technique to assist existing wireless medical cyber-physical system. As presented in experiment results, in terms of warning detection, it outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.

Impact of forest management intensity on mushroom occurrence and yield with a simulation-based decision support system

Well-researched and sound integration of non-wood forest products into a forest management planning process presents an opportunity to understand the fundamental causative basis of forest dynamics through decision support systems (DSS). The aim of this study is to present a simulation-based mushroom integrated decision support system, ETÇAPSimulation model, and forecast the effects of forest management intensity on mushroom productivity and occurrence. This is conducted through five policy sets with different planning scenarios, representing timber-oriented forest management (T1), multipurpose forest management (M1, M4, M6), and no intervention (NI). The study area involves the Kızılcasu Planning unit from the northwestern part of Turkey. The spatial distribution and productivity models of Lactarius delisiosus and L. salmonicolor generated for the Kızılcasu Planning unit were used in the simulation based model. The results indicated that both minimum harvesting ages and the intensity of forest management interventions are very important for increasing mushroom productivity and occurrence in the case study area. Under the T1 forest management scenario, mushroom productivity is estimated to be approximately 12,469 tonnes. When the planning approach changes to NI, M1, M4 and M6, however, mushroom productivity experiences 86%, 28%, 26% and 33% reduction rates, respectively. Despite these reductions, spatial distribution of mushrooms has been positively affected by forest management interventions.Simulations with multipurpose management policy would probably lead lower estimations of spatial distribution areas and productivity of mushroom than timber-oriented forest management policy would. Multiple-use planning approach seems to contribute less to mushroom productivity and the occurrence than does timber-oriented approach. In fact, determining optimal harvesting ages and the intensity of forest management interventions for each forest value presents a great challenge for decision makers. The DSS tools like ETÇAPSimulation are of great importance to understand the long term effects of various management plans on forest dynamics. Active management of forest resources will be important for achieving simultaneous production of timber products and mushrooms.