Simulation-based Decision Support Tool Research Articles

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.

Examining Patterns of Persistent Listeria Contamination in Packinghouses Using Agent-Based Models

Persistent Listeria monocytogenes contamination may occur in a packinghouse if the pathogen successfully infiltrates the facility and reaches a harborage site, where it may be difficult to remove and may contaminate produce within the facility. There is a need for simulation-based decision support tools that can predict which equipment sites are more likely to undergo persistent contamination and simulate potential corrective actions to prevent this contamination. Thus, we adapted for longer term simulation two existing applications of an agent-based model of Listeria spp. hourly contamination dynamics in produce packinghouses. Next, we developed a novel approach to identify and analyze persistent and transient Listeria contamination patterns on simulated agents representing equipment sites and employees. Testing of corrective actions showed that methods that involved targeted, facility-specific, risk-based sanitation were the most effective in reducing both the likelihood and duration of persistent contamination. Generic approaches to controlling Listeria (e.g., more concentrated sanitizers) are unlikely to be successful and suggest that use of sanitation schedules produced through facility-specific root cause analysis and hygienic design are key in reducing persistence. Hourly Listeria contamination patterns also suggest that transient contamination may be mistaken for persistent contamination, depending on the frequency of environmental sampling. Likewise, as concentrations of Listeria on most contaminated agents were predicted to be very low, there is also a possibility to mistake persistence for transient contamination of sites, or even miss it outright, due to false-negative environmental Listeria monitoring results. These findings support that agent-based models may be valuable decision support tools, aiding in the identification of contamination patterns within packinghouses and assessing the viability of specific corrective actions.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

Assessing the impact of COVID-19 measures on COPD management and patients: a simulation-based decision support tool for COPD services in the UK

ObjectivesTo develop a computer-based decision support tool (DST) for key decision makers to safely explore the impact on chronic obstructive pulmonary disease (COPD) care of service changes driven by restrictions...

Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses.

The complex environment of a produce packinghouse can facilitate the spread of pathogens such as Listeria monocytogenes in unexpected ways. This can lead to finished product contamination and potential foodborne disease cases. There is a need for simulation-based decision support tools that can test different corrective actions and are able to account for a facility’s interior cross-contamination dynamics. Thus, we developed agent-based models of Listeria contamination dynamics for two produce packinghouse facilities; agents in the models represented equipment surfaces and employees, and models were parameterized using observations, values from published literature and expert opinion. Once validated with historical data from Listeria environmental sampling, each model’s baseline conditions were investigated and used to determine the effectiveness of corrective actions in reducing prevalence of agents contaminated with Listeria and concentration of Listeria on contaminated agents. Evaluated corrective actions included reducing incoming Listeria, modifying cleaning and sanitation strategies, and reducing transmission pathways, and combinations thereof. Analysis of Listeria contamination predictions revealed differences between the facilities despite their functional similarities, highlighting that one-size-fits-all approaches may not always be the most effective means for selection of corrective actions in fresh produce packinghouses. Corrective actions targeting Listeria introduced in the facility on raw materials, implementing risk-based cleaning and sanitation, and modifying equipment connectivity were shown to be most effective in reducing Listeria contamination prevalence. Overall, our results suggest that a well-designed cleaning and sanitation schedule, coupled with good manufacturing practices can be effective in controlling contamination, even if incoming Listeria spp. on raw materials cannot be reduced. The presence of water within specific areas was also shown to influence corrective action performance. Our findings support that agent-based models can serve as effective decision support tools in identifying Listeria-specific vulnerabilities within individual packinghouses and hence may help reduce risks of food contamination and potential human exposure.

A simulation-based decision support tool for dynamic post-disaster pedestrian evacuation

Taiwan, located in the Pacific Ring of Fire, is highly vulnerable to the destructive impact of powerful earthquakes. When a massive earthquake occurs, failure to take proper measures can lead to massive casualties, collapse of public facilities, and disruption to traffic networks. Among the many post-disaster management strategies, efficient evacuation is absolutely critical as it can potentially reduce the number of casualties substantially. At present, most post-disaster evacuation decisions are made based on past experience, but the rarity of high-magnitude earthquakes impedes the rate at which the quality of the decision-making process can improve. As such, this study primarily aims to develop an efficient and credible evacuation simulation modeling framework which can be used as a powerful tool to bolster evacuation strategies. The simulation evacuation framework we propose is known as the Stochastic Pedestrian Cell Transmission Model (SPCTM). The model is built based on real data of the mapping of roads and emergency shelters in an urban environment, as well as predictive models of pedestrian flow, the number of people needed to be evacuated after an earthquake, and the impact of earthquakes on urban infrastructure. Much of the data is provided by the Taiwan Earthquake Impact Research and Information Application platform (TERIA) developed by the National Science and Technology Center for Disaster Reduction (NCDR). Applying this data, the model divides the traffic network into a cellular structure, applies the concepts of inflow and outflow to represent population movement and uses random variables to simulate the selection behavior of pedestrian agents. In addition to SPCTM, we propose an alternative model which makes slight modifications to SPCTM to offer users a complementary framework in which evacuees are armed with less ideal information about the evacuation situation. An empirical study is run in a district of Taipei, Taiwan based on a simulated earthquake of magnitude 6.5 in which the overall and moment-by-moment simulation evacuation process is presented. The use of SPCTM as a simulation evacuation model is compared to the current government evacuation policy in Taiwan. Further potential benefits of using the proposed simulation evacuation tool are numerous. For example, the frameworks proposed in this study can offer valuable insights to policy-makers on infrastructure design decisions and evacuation strategies before an earthquake occurs, including the best placement of relief centers, to potentially significantly reduce the number of casualties. Also, simulation results can be utilized after a disaster to assist in crucial decisions related to road restoration priorities, distribution of supplies to shelters, etc.

A simulation-based decision support tool for integrating site layout and construction planning of tunneling projects

PurposeIntegrating construction and site layout planning in mechanized tunnel infrastructure projects is essential due to the mutual impacts of construction planning and site layout decisions. Simulation can incorporate site layout planning and construction planning of tunneling projects in a unified environment. However, simulation adoption by industry practitioners has remained relatively limited due to the special skills required for building and using simulation models. Therefore, this paper aims to create a simple-to-use simulation tool that supports site layout and construction operation planning of tunneling projects. This tool intends to promote the simulation application in site layout planning.Design/methodology/approachThe current paper proposes simulation as a decision support tool (DST) to provide an integrated environment for modeling tunnel construction operations, site layout and capturing the mutual impacts. A special purpose simulation (SPS) tool was customized and developed for typical mechanized tunneling projects, by tunnel boring machines, to facilitate building the model and allow access to users with limited simulation knowledge.FindingsThe results show that the developed SPS tool is of great assistance to construction industry practitioners to analyze a variety of site layout and construction plan scenarios and make informed decisions based on its comprehensive and intuitive outputs.Originality/valueThe main contribution of this research is to promote simulation application in site layout planning of tunneling projects through the development of a simple-to-use tool, which has sufficient details for site layout planning and constraints. The developed DST enables planners to make decisions simultaneously on the site layout, other construction planning variables and identify the most efficient plan.

Construction Site Layout Planning Using a Simulation-Based Decision Support Tool

Background: A site layout plan is one of the important decisions to be made in the planning phase of each construction project as it can significantly impact on-site transportation, construction logistics, and safety. This decision could be complicated owing to the uncertainties inherent in construction projects and the complex relationships between the influencing factors and decision variables. Methods: To improve site layout planning, this study aims to develop a simulation-based decision support tool (DST) that enables planners to consider the following: (1) construction uncertainties, (2) construction resources (i.e., material, equipment, and workers), (3) site layout constraints, and (4) mutual impacts between site layout and construction plan variables, for site layout planning of construction projects. Results: The developed DST visualizes the site layout plan within a simulation environment and provides seamless interactions between the site layout model and the simulation model. These capabilities facilitate planning construction site layout using simulation by establishing two-way information flows between the site layout and simulation components, which can further promote application of simulation in construction site layout planning. Usefulness and practicality of the proposed DST is demonstrated in site layout planning of a steel erection project. Conclusions: Using this DST can reduce some common wastes in construction projects and the cost associated with them, including on-site transportation, material handling and storage, and waiting time for the material arrival.

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 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

Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review

Early simulation work in the decision-making stage faces several challenges, including, for example, rapid changes of design, input variable uncertainties, and the lack of design information, although early design work represents a large percentage of energy saving potential. The availability of simulation tools for early design stages can help the architect analyze more alternatives. In this study, the existing simulation tools were explored and classified into three categories: simulation plugins based on the design software, geometry user interfaces for a simulation engine, and self-governing simulation tools. Each category’s typical tools were illustrated with their use, and a uniform standard comparison was conducted to screen tools that are available in the early design stages. The future trends of simulation tools are discussed in the second part: building databases based on existing knowledge, uncertainty and sensitivity analyses, and optimization. Time-consuming simulation is a problem in the use of simulation tools in early design stages. Advanced techniques were developed in this part for fast computing, i.e., cloud computing, parallel computing, meta-models, and more statistical methods. This paper illustrates the practical application of particular simulation tools in the early design stage, presents their limitations, and discusses decision-support tools for specific building design activities.

Design and development of a simulation-based decision support tool to improve the production process at an engineered wood products mill

The demand for engineered wood products has been increasing due to their advantages over solid wood in many applications and the need for efficient and sustainable use of natural resources. In order to meet the growing demand of products, managers seek continuous improvement in the production process, which involves complex and interrelated decisions. Simulation-based decision support tools are proven beneficial when dealing with complex managerial decisions, especially in manufacturing systems. In this research, real industrial data are used and a decision support tool is developed to assist the managers of an engineered wood products mill in Canada to analyze the production process, and evaluate alternative configurations and workers assignment to increase the throughput. This paper presents the first attempt in the forest industry simulation literature that incorporates the interactions between the operators and multiple machines. An important outcome of this research was to prevent substantial monetary implications of replacing a piece of equipment. While managers believed that replacing a piece of equipment (sizer) with a more modern one would eliminate the production bottleneck and increase the throughput, the results of the simulation model showed that it would only shift the bottleneck in the mill without any meaningful benefits. The evaluation of worker-machine interactions within the entire mill provided another important finding. It indicated that the addition of a worker and the automation of the conveyor could increase the throughput by almost 21%.

Use of a simulation-based decision support tool to improve emergency department throughput

A simulation based decision support model was used in the redesign of an emergency department (ED) with close to 180,000 visits per year. In order to accommodate high patient volumes at a single site ED, improving patient throughput time is necessary to maintain operational efficiency and to provide high quality patient care. A throughput time goal of arrival to departure under 3 h for 80% of ED patients was selected as the redesign project objective. Using discrete event simulation modeling, target areas for improvement are identified including optimized process flow, resource allocation and operational policies. Simulation modeling allows ED leadership to make decisions on operational changes using quantitative information of the impact of what-if scenarios on key performance measures. Based on simulation data results, changes in ED processes were implemented that resulted in 81% of patients having a length of stay in the ED of less than 3 h; a 30% improvement in average patient length of stay.

Assessment approaches to logistics for offshore wind energy installation

Offshore wind farm installation planning is highly complex, due to the high dependency on weather and the oversized components that impose specific constraints in areas such as transportation and lifting. Currently, there is very little transparency vis-à-vis the logistics challenges in the industry. We extend the literature by creating an overall view of the coherency between logistical methods and project performance. We develop knowledge about how to use the various approaches by analyzing different logistical solutions. A holistic view of the coherency between the approaches in terms of logistics and project performance, taking into consideration the external influence of weather, is provided through analysis of actual projects in the North Sea region. Case study findings reveal the major factors to be pre-assembly, vessel load, and the distance to shore. We suggest a pre-assembly strategy comprised of a minimum number of components for installation onsite and a maximum number of turbines to be loaded on a vessel. These findings are especially important for the new wind farms being positioned further offshore. We show by means of a case study, with specific characteristics and weather conditions, that the appropriate strategies can be arrived at by using a simulation-based decision-support tool we developed.

A simulation-based decision support tool for informing the management of patients with Parkinson’s disease

We describe a decision support toolkit that was developed with the aim of assisting those responsible with the management and treatment of Parkinson’s disease (PD) in the UK. Having created a baseline model and established its face validity, the toolkit captures the complexity of PD services at a sufficient level and operates within a user-friendly environment; that is, an interface was built to allow users to specify their own local PD service and input their own estimates or data of service demands and capacities. The main strength of this decision support tool is the adoption of a team approach to studying the system, involving six PD specialist nurses across the country, ensuring that variety of views and suggestions are taken as well as systems modelling and simulations. The tool enables key decision-makers to estimate the likely impact of changes, such as increased use of community services on activity, cost, staffing levels, skill-mix and utilisation of resources. Such previously unobtainable quantitative information can be used to support business cases for changes in the increased use of community services and its impact on clinical outcomes (disease progression), nurse visits and costing.

Simulation-based Decision Support Tools for Demand Disaggregation in a Supply Chain Characterised by High Routing Flexibility

Routing flexibility is a key process feature in supply chains characterised by complex hierarchy at different manufacturing levels. When routing alternatives exist, controlling the supply chain to ensure uniformity and quality of the production outcome becomes a significant challenge, also from a demand planning perspective. In this paper, the demand planning problem at a supply chain operating in the semiconductor industry is investigated. Special attention is paid to mid-term demand planning when production orders are not yet finalised and aggregated demand forecast is considered. Within this planning frame, the demand planners face the difficult task of disaggregating aggregated demand into finer granularity products in order to generate provisional production plans that will be used to foresee potential capacity adjustment requirements. The demand disaggregation process entails routing decisions that also incorporate restrictions occasionally imparted by final customers on eligible routes for a specific product type. Historical demand patterns and routing constraints currently constitute the main decision drivers in the demand disaggregation process; likewise, the only objective accounted for is the timely satisfaction of customers’ orders. However, disregarding capacity constraints and ignoring incoming future demand characterised by more stringent routing requirements leads to uncapacitated production plans which might cause significant lateness in the orders’ production. In this study, simulation-based solution approaches able to facilitate the demand planners in the complex task of disaggregating demand forecast are developed. It is shown how either analytical simulation algorithms or discrete event simulation can be used to quantify the lateness deriving from the allocation of different demand profiles and predict the impact of future demand on the optimal disaggregation logic. For analysis purposes, the supply chain will be modelled as a serial-parallel multistage manufacturing system for which the facilities operating in parallel at each stage are similar from a process viewpoint but present different capacity.

Evaluation of carsharing network’s growth strategies through discrete event simulation

Carsharing organizations are nowadays faced with the emergence of new markets due to the growing popularity of their services. To keep up with the growing demand, they have to constantly adapt their network and balance their stations’ capacities by implementing new strategies. These strategies involve creation of new carsharing stations, increasing the capacity of stations, merging or demerging carsharing stations etc. Currently, the decision makers rely on an intuitive strategy selection process which often results in inadequate decisions being made representing an immediate loss in resources, time and market penetration. This paper presents a discrete event simulation based decision support tool that assists the decision makers in selecting best network growth strategies to implement for meeting adequately the demand growth while maximizing the members’ satisfaction level and minimizing the number of vehicles used. Our discrete event simulation model allows modeling the activities at any given set of carsharing stations, regardless of their number and capacities. A benchmarking comparison of different potential strategies is done. An application of the proposed model on a region of Communauto’s Montréal (Québec, Canada) carsharing network is provided.

Improving Managerial Decision Making through the Use of Automatically Developed Graphical Simulation Models

ABSTRACT Modern organisations increasingly rely on management science techniques and decision support systems (DSS) to enhance problem solving and decision making. Simulation modelling is one of the most widely applied management science techniques. However, constructing a valid model of a complex real—life system can be a tedious, lengthy and error—prone task, even when powerful model specification techniques, based on computer graphics or natural language processing (i. e. on graphical or textual descriptions of models), are incorporated. This is particulary true when attempting to model systems consisting of vast numbers of interacting elements. The paper proposes an alternative methodology for developing simulation—based decision support tools, wich is based on a DSS combining computer graphics with automatic model generation techniques. A case—study from the railway transportation domain is presented to demonstrate the main features of the system and its impact on decision making and organisational p...