Model-driven Decision Support Research Articles

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

A model-driven decision support system for the storage location assignment problem in bulk ports

Yard storage management involves tactical and operational decisions affecting bulk port productivity. The main challenge is to ensure that bulk materials are available and stored according to physical and managerial constraints while minimizing storage space utilization. This article studies a new variant of the storage location assignment problem by considering contamination constraints for bulk materials. The problem is inspired by a real case study of Africa's largest fertilizer hub port. To tackle the problem, a model-driven decision support system, optimizing the storage space while satisfying vessels' demands and avoiding contamination from overlapping bulk materials, is developed. A mixed-integer linear programming model is formulated, and an efficient rolling-horizon heuristic is investigated as a solution approach. Extensive numerical experiments are performed, and the results confirm the efficiency of this model-driven support system from both theoretical and practical points of view.

Bayesian Models for Targeted Cyber Deception Strategies (Student Abstract)

We propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to support cyber deception based on a detailed model of attacker beliefs. We discuss this approach using a case study based on passively observed operating system (OS) fingerprinting data. In passive reconnaissance attackers can remain undetected while collecting information to identify systems and plan attacks. Our DSS is intended to support preventative measures to protect the network from successful reconnaissance, such as by modifying features using deception. We validate the prediction accuracy of the model in comparison with a sequential artificial neural network (ANN). We then introduce a deceptive algorithm to select a minimal set of features for OS obfuscation. We show the effectiveness of feature-modification strategies based on our methods using passively collected data to decide what features from a real operating system (OS) to modify to appear as a fake [different] OS.

A decision support system for selection and risk management of sustainability governance approaches in multi-tier supply chain

Lower-tier suppliers' sustainability noncompliance and focal company's failure at meeting the expectations of the stakeholders to extend sustainability towards lower-tier suppliers carry multiple risks, tangible and intangible, to the focal company. It is expected that extending sustainability to suppliers at lower tiers through effective sustainability governance approaches (SGAs) can reduce these risks for focal companies. The existing literature lacks research on decision support tools using management science techniques to help decision makers choose the most suitable SGA/SGAs in a given situation and the risk management of SGAs in multi-tier supply chain. The present study develops a model-driven decision support system (DSS) using Bayesian network (BN) that can assist operations managers in selecting the most effective SGA/SGAs in multi-tier supply chain considering each situation. The developed DSS includes contingency factors and risk variables and their relationships which are identified through a systematic literature review and is applied to the multi-tier, sustainable supply chain of a multinational company operating in China to demonstrate its practical applicability. The DSS is then used in the risk management of the SGAs in multi-tier supply chain, which includes core steps such as identification of the contingency factors and risk variables, the prioritisation of the contingency factors and risk treatment. By Prioritising the basic contingency factors, ‘‘Focal company's sustainability knowledge’’ and ‘‘The specific nature of the materials sourced from lower-tier supplier’’, and ‘‘First-tier supplier's possession of internal resources'’ and ‘‘First-tier supplier's sustainability training’’ were identified as the two most important factors regarding their impact on the effectiveness of the direct and indirect approaches respectively. Detailed managerial implications related to the development and implementation of the DSS and the risk management process are also provided.

OnionAider: A Model Driven Decision Support System for Weather and Pest-Occurrence Prediction in Onion Cultivation

The cultivation of onions is crucial for meeting global demand, as they are widely consumed and used as a seasoning in various dishes worldwide. However, the production of onions is significantly affected by climatic factors and pest infestations. To address these challenges, this study presents OnionAider, a Model Driven Decision Support System that utilizes weather and pest-occurrence prediction models for onion cultivation. The system integrates descriptive, exploratory, historical, and AGILE methodologies to develop a comprehensive mobile application. The effectiveness of OnionAider was evaluated through data retrieval, questionnaires, and interviews with farmers, experts, and relevant stakeholders. The results indicate a strong relationship between climatic data and pest infestation in onions, with temperature and humidity being the primary predictors. The developed mobile application incorporates an algorithm derived from the Proponent-Rete method and offers valuable features for onion farmers. The system complies with ISO 25010 software compliance criteria. This article discusses the methodology, results, and implications of OnionAider in enhancing onion cultivation practices.

Understanding and managing the participation of batteries in reserve electricity markets

The proliferation of variable renewable energy increases the significance of reserve markets, which provide energy flexibility to compensate for supply–demand mismatches on short notice. Accordingly, market regulators in different locations have been continuously adopting different mechanisms to attract flexible sources, such as batteries to participate in reserve markets. While these mechanisms are different and sometimes contradictory, there is no consensus on their effectiveness. Therefore, there is a dire need to examine their effectiveness systematically. This paper proposes a model-driven decision support system, focusing on regulators’ perspectives, assisting them in evaluating the effectiveness of different market mechanisms for attracting batteries in reserve electricity markets. We formulate the problem as a stochastic Mixed-Integer-Linear-Program, incorporating practical restrictions and applicable to many electricity markets. As an example, we apply our model to the case of the German electricity market to evaluate some market mechanisms. We find that although some reserve markets do not allow separate bidding in charging and discharging directions (asymmetric bidding), this seems to be an effective mechanism to attract battery operators. When asymmetric bidding is allowed, battery operators often optimally choose to reserve their capacity only in the charging direction and use the discharging direction in energy markets, taking opposite seller/buyer roles in energy and reserve markets. Our results also advocate mechanisms such as remunerating the activated energy and shifting the gate closure closer to the time of operation. In contrast, some existing rewarding mechanisms designed for flexible resources do not seem to be very effective.

A decision support system for consolidated distribution of a ceramic sanitary ware company

This study proposes a model-driven decision support system (DSS) for a consolidated distribution problem of a ceramic sanitary ware company with two factories producing different types of products. The primary motivation for consolidating loads of the these factories is to better use the empty capacity of the vehicles in terms of weight and volume. For transportation, the company has made contracts for each factory with a third-party logistics provider. In these contracts, there are special conditions according to the characteristics of the products. We suggest two kinds of transportation approaches that determine how to combine loads of two factories and how to solve the routing problem that arises to distribute the remaining loads. The DSS includes a mathematical model that determines full-truck loads and a hybrid genetic algorithm that solves the open vehicle routing problem. Both of these approaches have been developed specifically for the problem within the scope of the study. The mathematical model provides the best use of vehicles in terms of volume and weight. At the same time, the meta-heuristic approach offers the opportunity to address the complex cost structure of the emerging routing problem. The proposed DSS receives all order data from the enterprise resource planning system and constructs a dynamic distribution problem. Then it generates solution alternatives by using the models developed in this study and presents cost-effective and visualizable distribution plans.

DEVELOPMENT OF MODEL-DRIVEN DECISION SUPPORT SYSTEM TO SCHEDULE UNDERWATER HULL CLEANING

Maritime industries are constantly searching for a method to enhance ship efficiency, with increasing concern about the environmental impact and rising fuel prices. Marine biofouling is one of the factors that increase ship fuel consumption. However, removing the fouling of the ship requires effort for hull maintenance. Due to the trade-off between conducting maintenance and performance degradation, this study presents the development of a Model-Driven Decision Support System (MD-DSS) to predict the optimum time for underwater hull cleaning for biofouling management. Five stages (sub-models) are employed to develop a DSS, namely: ship resistance estimation, estimation of additional resistance due to biofouling, an iterative-based method for determining the best time to conduct the hull cleaning, and an analysis report. The implemented algorithm was validated by comparing its result with a manually scheduled maintenance date. The DSS is able to determine the best time (date) for maintenance in all given scenarios. By giving two scenarios of different maintenance costs and different fuel prices, the optimisation results produce the same number of maintenances. Within 60 months, four to five hull cleanings are required. It is also found that when the optimal number of maintenances is known, then increasing this number will not have any impact on reducing the hull cleaning costs because the reduction in fouling does not significantly reduce the costs incurred for maintenance. During several trials of the DSS, it is shown that the system can generate maintenance schedules for different time intervals of ship operation within an acceptable time. It takes approximately 52 minutes, 12 minutes, and 5 minutes consecutively to determine the maintenance schedules for ship operation intervals of 5 years, 2.5 years, and 1 year.

DEVELOPMENT OF MODEL-DRIVEN DECISION SUPPORT SYSTEM TO SCHEDULE UNDERWATER HULL CLEANING

Maritime industries are constantly searching for a method to enhance ship efficiency, with increasing concern about the environmental impact and rising fuel prices. Marine biofouling is one of the factors that increase ship fuel consumption. However, removing the fouling of the ship requires effort for hull maintenance. Due to the trade-off between conducting maintenance and performance degradation, this study presents the development of a Model-Driven Decision Support System (MD-DSS) to predict the optimum time for underwater hull cleaning for biofouling management. Five stages (sub-models) are employed to develop a DSS, namely: ship resistance estimation, estimation of additional resistance due to biofouling, an iterative-based method for determining the best time to conduct the hull cleaning, and an analysis report. The implemented algorithm was validated by comparing its result with a manually scheduled maintenance date. The DSS is able to determine the best time (date) for maintenance in all given scenarios. By giving two scenarios of different maintenance costs and different fuel prices, the optimisation results produce the same number of maintenances. Within 60 months, four to five hull cleanings are required. It is also found that when the optimal number of maintenances is known, then increasing this number will not have any impact on reducing the hull cleaning costs because the reduction in fouling does not significantly reduce the costs incurred for maintenance. During several trials of the DSS, it is shown that the system can generate maintenance schedules for different time intervals of ship operation within an acceptable time. It takes approximately 52 minutes, 12 minutes, and 5 minutes consecutively to determine the maintenance schedules for ship operation intervals of 5 years, 2.5 years, and 1 year.

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.

An interactive decision support system for real-time ambulance relocation with priority guidelines

Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emergency operations. To mitigate such delays, researchers proposed the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. In this study, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The developed mathematical model partitions and prioritizes demand into four categories and continuously updates them over time. Furthermore, it dynamically calculates the number of coverages in different regions based on the current location of idle ambulances. Also, we developed a real-time risk assessment DSS for recommended relocations, which could be utilized as a reference by the EMS user while implementing suggested relocation decisions. A real case study is used to validate the proposed DSS, and its final output is compared to the existing operational policy. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio. Compared to the existing operational policy, the developed decision support system decreased the time to respond to calls, which was deemed to be more than to offsets the increase in travel time due to relocation. Furthermore, the system also reduced the total working time of all ambulances by about 9% per shift.

An Agent-Based Model-Driven Decision Support System for Assessment of Agricultural Vulnerability of Sugarcane Facing Climatic Change

In recent years, there have been significant changes in weather patterns, mainly caused by sharp increases in temperature, increases in carbon dioxide, and fluctuations in precipitation levels, negatively impacting agricultural production. Agricultural systems are characterized by being vulnerable to the variation of biophysical and socioeconomic factors involved in the development of agricultural activities. Agent-based models (ABMs) enable the study, analysis, and management of ecosystems through their ability to represent networks and their spatial nature. In this research, an ABM is developed to evaluate the behavior and determine the vulnerability in the sugarcane agricultural system; allowing the capitalization of knowledge through characteristics such as social ability and autonomy of the modeled agents through fuzzy logic and system dynamics. The methodology used includes information networks for a dynamic assessment of agricultural risk modeled by time series, system dynamics, uncertain parameters, and experience; which are developed in three stages: vulnerability indicators, crop vulnerability, and total system vulnerability. The development of ABM, a greater impact on the environmental contingency is noted due to the increase in greenhouse gas emissions and the exponential increase in extreme meteorological phenomena threatening the cultivation of sugarcane, making the agricultural sector more vulnerable and reducing the yield of the harvest.

Beef Supply Chain Analysis to Improve Availability and Supply Chain Value Using System Dynamics Methodology

The research focuses on beef commodity where beef demands data continue increasing proportionate or in line with increasing population, national per capita income, and the needs of industrial foods process materials. On the other hand, domestic beef supply tends to decrease so it can’t meet domestic demand. Furthermore, there are problems with fulfilling supplies to the end customer. The effort in solving the problems by improving the supply chain process using a model-driven decision support system, to model the existing system and build new scenarios to increase beef supply chain performance. The model-driven decision support system approaches using a system dynamics model to accommodate key variables or parameters that significantly contribute to the continuity strategic beef supply chain. The results showed a policy scenario with success indicators in the form of optimizing average beef weight values to 300 kgs per cattle and with carcass percentage value at 55% life weight, projecting deficit beef values decreasing in each year. At 2027 deficit value becoming small which is 8,520 tons, which ends up becoming surplus in 2028. The results of the model-driven decision support system output scenario can be functioned as input to the decision-maker to elaborate policy process related to the beef's food sector to achieve food self-sufficiency which one of the government missions.

FARMs: A Geospatial Crop Modeling and Agricultural Water Management System

To ensure agricultural sustainability and desirable environmental outcomes, stakeholders need systems-based model-driven decision support tools. The objective of this study was to develop a global scale web-based geospatial crop modeling application called Food, Agriculture, and Resource Management system (FARMs), to simplify the application of the crop simulation model —Decision Support System for Agrotechnology Transfer (DSSAT) without requiring users to create input weather, climate, and soil files. FARMs was built based on open source Geographic Information System (GIS) technologies and DSSAT to allow for adaptive management through its ability to perform in-season yield predictions for alfalfa and maize, currently. Validation of FARMs against variety trial data in California was acceptable between measured and simulated yields for alfalfa. The work done in this study showed how a complex model like DSSAT can be translated into a useable web-based decision support tool for near-real-time simulation with the help of open-source GIS technologies.

IoT-Driven Workflows for Risk Management and Control of Beehives

The internet of things (IoT) and Industry 4.0 technologies are becoming widely used in the field of apiculture to enhance honey production and reduce colony losses using connected scales combined with additional data, such as relative humidity and internal temperature. This paper exploits beehive weight measurements and builds appropriate business rules using two instruments. The first is an IoT fixed scale installed on one hive, taking rich continuous measurements, and used as a reference. The second is a portable nomad scale communicating with a smartphone and used for the remaining hives. A key contribution will be the run and triggering of a business process model based on apicultural business rules learned from experience and system observed events. Later, the evolution of the weight of each individual hive, obtained by either measurement or inference, will be associated with a graphical workflow diagram expressed with the business process model and notation (BPMN) language, and will trigger events that inform beekeepers to initiate relevant action. Finally, the BPMN processes will be transformed into executable models for model driven decision support. This contribution improves amateur and professional user-experience for honeybee keeping and opens the door for interoperability between the suggested model and other available simulations (weather, humidity, bee colony behavior, etc.).

Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy

Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel algorithmic approach which fuses Markov Chain Monte Carlo (MCMC) and Machine Learning methods to accelerate uncertainty quantification for groundwater flow models. We formulate the governing mathematical model as a Bayesian inverse problem, considering model parameters as a random process with an underlying probability distribution. MCMC allows us to sample from this distribution, but it comes with some limitations: it can be prohibitively expensive when dealing with costly likelihood functions, subsequent samples are often highly correlated, and the standard Metropolis–Hastings algorithm suffers from the curse of dimensionality. This paper designs a Metropolis–Hastings proposal which exploits a deep neural network (DNN) approximation of a groundwater flow model, to significantly accelerate MCMC sampling. We modify a delayed acceptance (DA) model hierarchy, whereby proposals are generated by running short subchains using an inexpensive DNN approximation, resulting in a decorrelation of subsequent fine model proposals. Using a simple adaptive error model, we estimate and correct the bias of the DNN approximation with respect to the posterior distribution on-the-fly. The approach is tested on two synthetic examples; a isotropic two-dimensional problem, and an anisotropic three-dimensional problem. The results show that the cost of uncertainty quantification can be reduced by up to 50% compared to single-level MCMC, depending on the precomputation cost and accuracy of the employed DNN.

Food Secure Metropolitan Areas: The Transition Support System Approach

Metropolitan areas are highly complex systems where a transition towards food security encompasses a systematic change in the whole food chain. Existing decision support tools that have been developed to inform policy-makers are mostly data-driven but hardly consider the activities and preferences of different stakeholders in the food system. The aim of this article is to contribute to urban food security by combining stakeholder participation with model-driven decision support. For this purpose, we developed and tested the Transition Support System (TSS) approach. In this TSS approach, decision support tools and participatory processes are mutually employed to promote urban food security in an ongoing, stakeholder-inclusive and reflexive process of governance. Our application of the TSS approach in two contrasting case studies highlights how this can contribute towards the development of new perspectives on urban food security, building a joint agenda towards the future and better (mutual) understanding of the issues at stake. We conclude that promoting a transition in the urban food system demands joint learning and reflexive evaluation in order to adapt governance, requiring researchers to play a supporting role in a broader process of change.

The Model Driven Decision Support System for Predicting the Major Pest in Onion (Allium Cepa L)

The Model Driven Decision Support System for Predicting the Major Pest in Onion (Allium Cepa L)

Enhancement of overall equipment effectiveness (OEE) data by using simulation as decision making tools for line balancing

<span>Nowadays, the digitalization of the production-based industries is driven by emerging technologies tools. The concept of lean manufacturing (LM) towards Industry 4.0 was developed where data analytics of engineering processes are analyzed and connected to reduce wastes. Many authors discuss about the benefits of extending data analytics as a method to support decision. However, the absence of comprehensive framework on how to embed LM and IT tools has existed as a new challenge. The aim of the research is to initiate a framework of model driven decision support system (MD-DSS) where data simulation and communication technologies are accompanied for manufacturing process improvement. In this research, Overall Equipment Effectiveness (OEE) data was captured through internet networking system and simulate to predict the improvement output. The main information flow route within MD-DSS are demonstrated in detail to show how decision-making process. To illustrate the applicability of the MD-DSS, it has been applied at food industry in Malaysia. The results show that the MD-DSS can easily be adopted in factories facilited with internet network to support decision-making of improvement plan activities.</span>