Simulation-based Decision Support Research Articles

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

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.

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.

Decision support tool for hospital resource allocation during the COVID-19 pandemic.

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized “what-if” scenarios which are key for real-time experimentation and analysis of current decisions’ down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

Simulation-based decision support for production improvement using integrated ergonomic and productivity performance indicators

Simulation-based decision support for production improvement using integrated ergonomic and productivity performance indicators

Simulation-based decision support for production improvement using integrated ergonomic and productivity performance indicators

Workers in the construction manufacturing industry are exposed to labour-intensive tasks with ergonomic risks such as forceful exertion and repetitive motion. Due to increased productivity and repetitive motions resulting from improvement initiatives implemented in offsite construction manufacturing, the investigation of ergonomic risks associated with these changes is needed. In this context, this paper explores an existing panelised floor production line aiming to minimise its ergonomic risks while improving its current productivity rate. Information pertaining to human body motion and productivity is extracted from video recordings. The ergonomic risks associated with specific tasks are identified using two existing ergonomic risk assessment methods: the rapid entire body assessment and the rapid upper limb assessment. A simulation model is used to evaluate various process improvements from the perspective of both ergonomic risks and productivity to support the decision-making process and the prioritisation of process changes in the factory.

Simulation-based decision support tool for in-house logistics: the basis for a digital twin

In-house logistics activities are an essential part of any organisation as they improve operations productivity and efficiency. In this context, agile decision-making raises the need for planning the overall shop-floor operations and effectively control them. This work tackles this need and proposes a simulation-based decision support tool for in-house logistics that analyses the activities that can occur in both a distribution facility and a production facility towards logistics 4.0. This is done by developing two simulation models using Simio – simulationmodelling framework based on intelligent objects. The resulted models are verified and validated based on two real-world operations. As a conclusion, the developed models are representative of the reality and can be applied in different in-house logistics settings, functioning as digital-twinning tools to operations improvement without real sets disruption.

Design and development of a simulation-based decision support tool to improve the production process at large-scale greenhouses in Japan

Design and development of a simulation-based decision support tool to improve the production process at large-scale greenhouses in Japan

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.

Modeling and implementation of a digital twin of material flows based on physics simulation

Cyber-physical production systems enable adaptivity and flexibility when manufacturing customized products in small batches. Due to varying routes and a high variance of workpieces, material flows in cyber-physical production systems can get highly complex, which can lead to physically induced disturbances that can result in accidents or decreased throughput and high costs. This issue can be addressed by applying a physics engine to simulate the physical interaction between workpieces and the material handling systems during the operation. Connecting such a digital model to a real material handling system in order to derive simulation-based decision support leads to the concept of digital twins. To date, few practical implementations of digital twins in manufacturing outside the machine tool domain were reported. Therefore, this paper describes the modeling and the subsequent implementation of an integrated system that consists of a real material handling system and its digital twin, based on physics simulation. A practical use case demonstrates the versatile advantages of the implemented solution for a manufacturing system with respect to the three digital twin functions prediction, monitoring and diagnosis.

Enhancing enterprise resource planning and manufacturing execution system efficiency: a simulation-based decision support approach

Enhancing enterprise resource planning and manufacturing execution system efficiency: a simulation-based decision support approach

Enhancing enterprise resource planning and manufacturing execution system efficiency: a simulation-based decision support approach

Business units conduct activities based on business processes. Systems currently exists at various levels of the business. Systems, inclusive of enterprise resource planning, manufacturing execution systems and plant systems has been adopted by larger corporates in executing and optimising business functions. These large businesses are described as complex entities with complex business structures inclusive of business processes. The quantification of automation, escalations and critical variables of these business processes has not been effectively conducted. This research focuses on the development, and configuration, of a simulation model for modelling enterprise maturity via business processes. This research approach includes hierarchical layout exploring these enterprise operations adopting business process tools, techniques, and methodologies aligned with a systems thinking approach. A simulation framework is configured and tested. The results indicate that interdependent processes can be modelled together by determining impacts of multiple critical variables in minimising business process execution times.

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.

Towards Circular Water Neighborhoods: Simulation-Based Decision Support for Integrated Decentralized Urban Water Systems

Centralized urban water management currently faces multiple challenges, both at the supply side and the demand side. These challenges underpin the need to progress to the decentralization of urban water, where multiple distributed technologies (water-aware appliances, rainwater harvesting, greywater recycling, sustainable urban drainage) are applied in an integrated fashion and as a supplement to centralized systems to design more resilient neighborhoods. However, the methods and tools to assess the performance of these distributed solutions and provide management support for integrated projects are still few and mostly untested in real, combined cases. This study presents a simulation-based framework for the quantitative performance assessment of decentralized systems at a neighborhood scale, where different technologies can be linked together to provide beneficial effects across multiple urban water cycle domains. This framework links an urban water cycle model, which provides a scenario-based simulation testbed for the response of the whole system, with key performance indicators that evaluate the performance of integrated decentralized solutions at a neighborhood scale. The demonstrated framework is applied to provide an ex ante evaluation of SUPERLOCAL, a newly developed area in Limburg, the Netherlands, designed as a circular, water-wise neighborhood where multiple decentralized technologies are combined.

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.

Data analytics-based decision support workflow for high-mix low-volume production systems

In order to answer the ever-fluctuating demand of high-mix low-volume production environments, reconfiguring the production systems and improving their performance rely heavily on the application of advanced decision support tools. Estimating the expected values of the performance measures (KPIs) in the face of these decisions, however, is even more challenging in such an environment as the complex structure, behavior and input demand creates an enormously large variable domain restraining the analysis. The paper introduces a novel workflow for providing simulation-based decision support for improving KPIs of high-mix low-volume production systems by reducing the size of the input domain with the application of unsupervised machine learning techniques.

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.

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily.