Dynamic Decision Support System Research Articles

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.

Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials.

The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals' evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials. We used the MIMIC-III database for development and AKIKI trials for validation. Participants were adult ICU patients with severe AKI receiving mechanical ventilation or catecholamine infusion. We used a doubly robust estimator to learn when to start RRT after the occurrence of severe AKI for three days in a row. We developed a "crude strategy" maximizing the population-level hospital-free days at day 60 (HFD60) and a "stringent strategy" recommending RRT when there is significant evidence of benefit for an individual. For validation, we evaluated the causal effects of implementing our learned strategies versus following current best practices on HFD60. We included 3748 patients in the development set and 1068 in the validation set. Through external validation, the crude and stringent strategies yielded an average difference of 13.7 [95% CI -5.3 to 35.7] and 14.9 [95% CI -3.2 to 39.2] HFD60, respectively, compared to current best practices. The stringent strategy led to initiating RRT within 3 days in 14% of patients versus 38% under best practices. Implementing our strategies could improve the average number of days that ICU patients spend alive and outside the hospital while sparing RRT for many. We developed and validated a practical and interpretable dynamic decision support system for RRT initiation in the ICU.

User-Centered Development of an Online Dashboard Tool for Economic Sustainability for Small and Medium Enterprises

This study aimed to improve an existing static benchmarking tool for the economic sustainability of small and medium enterprises (SMEs) in the wine sector to develop and elevate it into a dynamic online dashboard decision support system (DSS) for economic sustainability. Development was conducted in line with a user-centered-development process in four main steps. In the first step, producers’ expectations for an optimal tool were elicited using a qualitative approach of 24 in-depth interviews with long-term users of the existing PDF reports. Among the 10 requirements, producers requested an overall summary assessment of the most important KPIs of their business (including the provision of ideal values), intuitive visual presentations, long-term time developments, flexible reference groups, and short comments. Based on the wine producers’ systemized responses, the first version of the online benchmarking tool was designed and implemented in cooperation with experienced online designers and programmers. In the third step, a usability test was conducted to elicit options for further improvements that were implemented before the fourth step, the release of the final version to the industry. The systematic development process outlined and established here and the released DSS tool that is publicly available with open access provide valuable insights for institutions planning similar future dashboards for other sectors, particularly for SMEs. This constitutes an important step toward the development of more holistic support tools for sustainability performance measurement across all pillars of sustainability moving forward. To the best of the authors’ knowledge, the DSS developed represents the first online dashboard tool for economic sustainability for SMEs globally and in the wine sector.

Сonceptual basis for building a base of models for determining the value of a construction company's information assets

An actual area of research in the field of increasing the effectiveness of the functioning of modern information and communication technologies (ICT) is the principles and methods of establishing the value of information assets (IA) in order to effectively choose means of their protection.
 The object of research of this work is the decision support system (DSPR) for establishing the value of the information asset of the information and communication technologies of the construction company.
 The purpose of the study is to determine the conceptual foundations of building a base of system models, which would allow creating a flexible dynamic decision support system for the implementation of decision-making processes, having a flexible structure that provides for the possibility of implementing alternative scenarios (options) for establishing the value of information assets.
 The principles and conceptual foundations of building a base of models, a decision-making support system with a flexible structure, configured for the possibility of implementing alternative scenarios of expert evaluation, depending on the degree of certainty and availability of data, are proposed. As a conceptual basis, the structure of the model base, alternative and scenario modeling of decision-making processes, a classifier of scenario formation conditions, a database of models in the form of a class diagram, a multivariate process model of information technology for establishing the value of information assets in the form of a set of hierarchical process models of alternative network modeling are considered.
 In the proposed approach, increasing the probability of the obtained results is achieved due to the fact that the user is given the opportunity to choose the most appropriate and effective evaluation scenario, taking into account the goals and objectives of the evaluation, the availability and degree of certainty of the basic data, the existing resource, time and information limitations when making decisions.

Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs.

Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye

ABSTRACT The most important key to determining sustainable energy policies is reliable and accurate energy demand forecasting. Grey prediction, one of the demand forecasting methods, makes successful forecasts with limited data and without the need for any prior knowledge. In this study, GM (1,1) and Grey Verhulst from time series models and GM (1, N) model based on cause–effect relationship were used to forecast Türkiye’s natural gas demand. Dynamic grey forecasting models have been developed in order to obtain robust and reliable predictions. All developed models were evaluated according to performance criteria, and the superior model was obtained to be GM (1,5). Within the scope of the study, it was determined that models based on cause–effect relationship should be preferred instead of time series models. For this, population, the amount of electricity generated from natural gas, industrial production index, and building area are determined as independent variables. Three different scenarios were created taking into account the specified independent variables, and natural gas demand until 2025 was obtained. According to the forecasting results, Türkiye’s natural gas demand for 2025 will happen as an interval between 29.61 and 53.62 mtoe based on the low and high scenarios, respectively. According to the expected scenario, this demand would be realized around 40 mtoe for Türkiye in the year 2025. Finally, Grey Dynamic Decision Support System was designed and introduced to easily apply the dynamic grey models by end-users.

Evaluation of Dynamic Flashing Yellow Arrow Decision Support System in the Field Using Before and After Real-Time Data

A permissive left turn is one of the most dangerous movements a driver can make because the driver travels across opposing traffic lanes. In recent years, flashing yellow arrow (FYA) signals have been proven to help drivers make safer left turns. The four-section signal display with FYA permissive left-turn indication creates an opportunity to enhance the left-turn phase with a variable mode that can change on demand. A challenge to using this system is knowing the best times of day to apply the FYA indication. The authors developed an exclusive hardware platform: first, to provide a generic device compatible with the different controller types used by different jurisdictions and, second, to automate selection of the FYA left-turn modes based on available gaps in the opposing traffic at intersections acquired in real time from existing sensors in the field. Phase IV provided conclusive field testing and evaluation of the decision support system (DSS), by switching between red and FYA modes in a rational manner consistent with driver expectations and left-turning gap acceptance thresholds. It was also concluded that coordinated signals with long cycle lengths, 3 min and longer, help provide adequate gaps even in heavy traffic patterns since most of the vehicle arrivals are in platoons and at the beginning of the cycle. The analysis showed that the utilization factor for the DSS recommendations for all the intersections ranged between 65% and 75% during peak conditions on weekdays and between 90% and 95% during off-peak conditions and weekends. The total delay in the before study for all the intersections amounted to 737 vehicle-hours and in the after-study was 440 vehicle-hours of delay. An overall reduction of about 40% in delay which confirms the operational benefit of the dynamic FYA DSS. The developed platform is applicable at any four-section head configuration to maximize safety and efficiency of the intersections.

SD-DSS model of sustainable groundwater resources management using the water-food-energy security Nexus in Alborz Province

Unbalanced development of Alborz Province in recent years has amplified groundwater security crisis as well as water stress. This study conducted dynamic decision support system modelling of sustainable groundwater resources management based on water-food-energy security nexus (WFESN-SD-DSS). Using the results, the scenarios were designed in three categories of solutions: 1- Groundwater resources security; 2- Groundwater resources security; and 3- Groundwater resources security. The WFEN-SD-DSS model findings implied that combined policies represented the best solution and these included: Aquifer artificial recharge of 10 million cubic meters per year; control of well water usage; exploitation and reuse of graywater to supply 10% of agricultural water; 10% development of irrigation efficiency using novel irrigation systems; 10% reduction of agricultural waste; 10% development of the performance of agricultural products by modifying seeds, conserving plants; 5% reduction of electrical energy dissipation in combined cycle power plants; and development of solar water pump systems in agriculture sector.

Perspective: Phosphorus monitoring must be rooted in sustainability frameworks spanning material scale to human scale

Phosphorus (P) is a finite resource, and its environmental fate and transport is complex. With fertilizer prices expected to remain high for years and disruption to supply chains, there is a pressing need to recover and reuse P (primarily as fertilizer). Whether recovery is to occur from urban systems (e.g., human urine), agricultural soil (e.g., legacy P), or from contaminated surface waters, quantification of P in various forms is vital. Monitoring systems with embedded near real time decision support, so called cyber physical systems, are likely to play a major role in the management of P throughout agro-ecosystems. Data on P flow(s) connects the environmental, economic, and social pillars of the triple bottom line (TBL) sustainabilty framework. Emerging monitoring systems must account for complex interactions in the sample, and interface with a dynamic decision support system that considers adaptive dynamics to societal needs. It is known from decades of study that P is ubiquitous, yet without quantitative tools for studying the dynamic nature of P in the environment, the details may remain elusive. If new monitoring systems (including CPS and mobile sensors) are informed by sustainability frameworks, data-informed decision making may foster resource recovery and environmental stewardship from technology users to policymakers.

Investigating the effects of various control measures on economy and spread of COVID-19 in Turkey: a system dynamics approach.

The coronavirus disease 2019 (COVID-19) which began in Wuhan in December 2019 has permeated all over the world in such a short time and was declared as a pandemic by World Health Organization (WHO). The pandemic that is erupting all of a sudden attracts the researchers to examine the spread and effects of the disease as well as the possible treatments and vaccine developments. In addition to the analytical models, such as compartmental modeling, Markov decision process, and so on, simulation and system dynamics (SD) are also widely applied in this field. In this study, we adopt the compartmental modeling stages to build an SD approach for the spread of the disease. A dynamic control measure decision support system (DSS) that varies depending on the number of daily cases is incorporated to the model. Furthermore, the economic loss in the gross domestic product (GDP) and workforce due to hospital stay and death caused by the COVID-19 are also investigated. The model is tested with various numerical parameters and the results are presented. The results on the spread of the disease and the associated economic loss provide meaningful insights into when control measures need to be imposed at which level. We also provide some policy insights, including some alternative policies, such as increasing awareness of people and vaccination in addition to control measures. The results reveal that the total number of cases and deaths is approximately 37% higher in the absence of dynamic DSS. However, everything comes at a price and applying such control measures brings about an increase in the economic loss about 47%.

A Dynamic Decision Support System for Sustainable Supplier Selection under Fuzzy Environment

The plastics business has grown rapidly in recent years, with annual growth rates ranging from 16% to 18% each year (second only to the telecommunications and textile industries). The plastic industry is regarded as a dynamic industry in the Vietnamese economy due to its rapid development rate. Its high growth stems from a big market with significant expansion potential, since Vietnam’s plastic sector is still in its early stages in comparison to the rest of the globe and plastic goods are pushed and utilized in many aspects of life. In order to ensure sustainable development and comply with the provisions of the Law on Environment Protection, plastic manufactures and importers must ensure to fulfill two responsibilities: (1) proper product and packaging recycling—applicable to products and packaging with recyclable value; and (2) the collection and treatment of waste—applicable to products and packaging containing hazardous substances, difficult to recycle, causing difficulties for waste collection and treatment. Therefore, raw materials supplier selection in the plastic industry is a complex decision, and decision makers must consider many qualitative factors, quantitative factors and environmental attributes during the decision making. As a result, the goal of this study is to present an integrated multicriteria decision making model (MCDM) strategy for sustainable supplier selection in the plastics sector under fuzzy environment circumstances. This paper makes a contribution by proposing a hybrid fuzzy analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) approach model for raw material supplier selection in the plastics sector. This research also provided a useful guideline for supplier selection in other industries.

Comparative Analysis of SAW and TOPSIS on Best Employee Decision Support System

The decision-making process has many assessment criteria needed as the basis for its assessment. A large number of problems regarding the length of time required in the decision-making process require decision-makers to find solutions. Decision Support System is one option that can be developed by decision makers because it can help improve efficiency and accuracy in the decision-making process.
 The process of developing decision support requires certain calculation methods as part of the processing. The methods that are quite widely used to build a decision support system include the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This research aims to analyze the accuracy of the cases raised as solutions to decision-making problems. A dynamic decision support system has been successfully created to design dynamics in the calculation of the SAW method and the TOPSIS method. The system is evaluated and analyzed for its accuracy level based on manual calculations. The results obtained are the SAW system has an accuracy value of 65% and the TOPSIS system is 100%. Furthermore, the calculation of the accuracy value of the SAW and TOPSIS methods in order to find out the best method to use by taking parameters in the form of the same value results generated from the calculations of the two methods. The results obtained are the accuracy value of the SAW method of 40% and the TOPSIS method of 100% based on testing using 60 employee data and 8 criteria used.

A Dynamic Decision Support System for Selection of Cloud Storage Provider

In recent years, the cloud computing model has gained increasing attention and popularity in the field of information technology. For this reason, people are migrating their applications to public, private, or hybrid cloud environments. Many cloud vendors offer similar features with varying costs, so an appropriate choice will be the key to guaranteeing comparatively low operational costs for an organization. The motivation for this work is the necessity to select an appropriate cloud storage provider offering for the migration of applications with less cost and high performance. However, the selection of a suitable cloud storage provider is a complex problem that entails various technical and organizational aspects. In this research, a dynamic Decision Support System (DSS) for selection of an appropriate cloud storage provider is proposed. A web-based application is implemented using PHP and MySQL to facilitate decision makers. The proposed mechanism has been optimized in a way that enables the system to address static database issues for which a user might not acquire the best solution. It focuses on comparing and ranking cloud storage providers by using two modules: scraping and parsing. The evaluation of the proposed system is carried out with appropriate test cases and compared with existing tools and frameworks.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI PEMBUKAAN CABANG USAHA ONLINE BICYCLE INDONESIA DENGAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) DAN TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)

The choice of business location will affect the risks and benefits of the company as a whole. This condition occurs because the location greatly affects the fixed costs and variable costs, both in the medium term and long term. The location of the business should be taken into account at the time of planning, so that the business to be run can be organized implementation in the future. Online Bicycle Indonesia is a startup engaged in goods and food delivery services by using online-based bike, will open branches in several cities and districts in Tangerang Raya area so that the result of choosing the right branch location, required a dynamic decision support system that can later be used as consideration of managers in the process of selecting branch locations. Problems in selecting branch of this company can be used Decision Support System using AHP-TOPSIS method. Analytical Hierarchy Process (AHP) method has advantages based on pair comparison matrix and perform consistency analysis, while Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method can solve practical decision making, because the concept is simple and easy to understand, and have the ability to measure the relative performance of decision alternatives. The results of the study show Kota Tangerang Selatan is superior to the weight of 0.824, the two Kota Tangerang with a weight of 0.732 and three Kabupaten Tangerang with a weight of 0.0, The decision taken can be accounted for by the calculation of AHP-TOPSIS as a model in the decision support system

TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System

Mechanization in agriculture involves all stages of cultivating and preparing innovations, from basic and essential hand devices and tools to more complex and mechanized implements. Fundamentally, such devices and tools enable agricultural activities to initiate different crop yields in many different neighborhoods in the world's ecological system. Due to the unstable nature of fuel prices, market demands, and damaging effects of tillage machinery vibrations on the operator, exploring efficient methods of preventing these situations has become indispensable. There are many existing methods related to tillage optimization, including fast, high quality, and cost-effective tilling. However, none of the existing methods perform real-time and dynamic recommendations due to the inability to provide real-time tillage data. Another issue is the unavailability of a tillage recommendation model that acts based on the changes of a fixed set of tillage parameters and provides the necessary guidance to the tractor driver during the tillage operation. This paper proposes a Tillage Operations Quality Optimization (TOQO) model that aims to improve the efficiency of tillage operations. The model provides real-time tillage data through integrating the Internet of Things (IoT) technology and a dynamic Decision Support System (DSS) for tillage machinery operation and process optimization. The proposed model has been successfully implemented, tested, and evaluated in real-world tillage operations. The results of this TOQO study clearly show improvements in the tillage operations. This is because the TOQO gives online recommendations in real-time based on dynamic measurements of six tillage performance evaluation parameters. The parameters are vibration, bulk density, slippage ratio, fuel consumption, real tillage depth, and field efficiency. The TOQO model successfully handles more than one tillage machinery tractor by using cloud computing services. Vibration increases when using the system on the X-axis at 46.63843 rms, Y-axis at 23.23612 rms, and the Z-axis at 51.47240 rms. Vibration fractures the soil, reducing soil cohesiveness when using an oscillating tillage tool and producing smaller soil aggregates than a non-oscillating one. When using the system, the value of the virtual density increases by 0.23821 g/cm3. Most of the bulk density values for the optimized phase are within the recommended or required growth range. There is a slight increase in depth by 0.4279 cm and a decrease in slippage ratio by 0.64364%. Tractor fuel consumption decreases by 1.2169 L per hour, indicating a reduction in tractor wheel slippage. Reduced fuel consumption and wheel slippage of tillage operation are essential parameters to increase field efficiency and reduce tillage operation costs.

Project scheduling and performance prediction: a fuzzy-Bayesian network approach

PurposeDespite the extensive research in project risk management and availability of several techniques and tools, quantifying uncertainty in project schedules remains a challenge. Current risk analysis models suffer from several shortcomings that need to be addressed to provide more reliable and valid schedules. This paper aims to present a dynamic decision support system with the purpose of providing project managers with necessary tool for making real-time informed decisions.Design/methodology/approachThe proposed approach incorporates the widely accepted critical path method (CPM) calculations in a Bayesian network (BN). BN is employed to conduct inferencing and causal analysis and provide probabilistic results, which can improve the decision-making process. Time parameters of each activity in the CPM network is modeled by a set of simulation nodes in the BN. Prior probability distribution of activities duration is extracted from experts using a fuzzy analytical solution.FindingsThe model proposed in this paper is able to address some key outstanding issues of current project scheduling techniques through: (1) modeling the causality among different sources of schedule uncertainty, (2) minimizing uncertainty in experts' evaluations, (3) assessing effects of unknown risk factors and (4) using actual activity data for learning the behavior of project and predicting crew productivity.Originality/valueThe purposed methodology provides a framework for the new generation of project schedule analysis tools that are better informed by available knowledge and data, and hence, more reliable and useful.

A Dynamic Decision Support System for Sustainable Supplier Selection in Circular Economy

Supplier selection is an important and challenging problem in sustainable supply chain management. We propose a dynamic decision support system (DSS) for sustainable supplier selection in circular supply chains. Unlike the linear take-make-waste-dispose production systems, circular supply chains are nonlinear make-waste-recycle production systems with zero-waste vision. The proposed DSS allows users to customize and weight their economic, social, and circular criteria with a fuzzy best-worst method (BWM) and select the most suitable supplier with the fuzzy inference system (FIS). Machine learning is used to maintain and synthesize the criteria scores for the suppliers after each supplier selection engagement. We present a case study at a petrochemical holding company with a controlling interest over several subsidiary companies to demonstrate the applicability of the proposed approach.

A dynamic decision support system for evaluating peer-to-peer rental accommodations in the sharing economy

Abstract Accommodation accounts for one of the biggest expenses while traveling, and deciding where to stay is often confusing and time-consuming. Fortunately, travelers have more options than ever before because of the substantial growth of the peer-to-peer (P2P) short-term rentals in the sharing economy. Designing a user-friendly system that considers travelers’ preferences in choosing the right accommodation can enhance customer satisfaction and increase profitability. We propose a dynamic decision support system based on the theory of multi-criteria decision making to assist travelers in personalizing their preferences and finding quality accommodations in the dominant P2P market that aligns with those preferences. We use the fuzzy best-worst method to measure the intensity of the user’s preferences and the fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) to score and evaluate alternative P2P rental properties. We present a case study in the P2P rental accommodations industry to demonstrate the applicability of the method proposed in this study.

PENERAPAN DECISION SUPPORT SYSTEM DYNAMIC MENGGUNAKAN SIMPLE ADDICTIVE WEIGHTING DALAM PENENTUAN PEGAWAI TERBAIK

Lots of applications or programs that are very useful to simplify human work. This includes applications that are made within a company. A company needs an intelligent system or an agent that controls the company's system. In a company has employees who work. This research will discuss the Dynamic Decision Support System in determining the best employees using one of the web-based Multi-Criteria Decision Making methods, which is Simple Additive Weighting (SAW). By using 2 types of data namely pattern data and test data. The data inputted were 15 data consisting of 10 test data and 5 pattern data. Then a confusion matrix can be obtained in the form of an accuracy value of 25%, a precision of 100%, a recall of 14%, and an F Measure of 24.5%.

Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation

A dynamic optimized production scheduling which takes into account demand fluctuation and uncertainty is very critical for the efficient performance of Prefabricated Component Supply Chain. Previous studies consider only the conditions in the production factory and develop corresponding models, ignoring the dynamic demand fluctuation that often occurs at the construction site and its impact on the entire lifecycle of prefabricated construction project. This paper proposes a dynamic flow shop scheduling model for prefabricated components production, which incorporates demand fluctuation such as the advance of due date, insertion of urgent component and order cancellation. An actual prefabrication construction project has been used to validate the proposed multi-objective genetic algorithm model. The experimental results show that the proposed model can achieve a cost saving of up to 43.2%, which shows that the proposed model can cope well with the occurrence of demand fluctuation. This research contributes to the dynamic decision support system for managing prefabricated components.