Model-driven Decision Support System Research Articles

Building Model-Driven Decision Support System in Product Redesign Plan

Product recovery strategy requires a thoughtful consideration of environmental implications of operational processes, undergone by a manufactured product in its entire product lifecycle, from stages of material processing, manufacturing, assembly, transportation, product use, product post-use and end-of-life. At the returns stream from product use stage, those parts and/or component assemblies from a used product have several disposition alternatives for recovery, such as direct reuse, remanufacture, recycle or disposal. Due to such complexity of the manufacturing processes in recovery, current decision methodologies focus on the performance measures of cost, time, waste and quality separately. In this article, an integrated decision model for used product returns stream is developed to measure the recovery of utilisation value in the aspects of cost, waste, time, and quality collectively. In addition, we proposed a model-driven decision support system (DSS) that may be useful for manufacturers in making recovery disposition alternatives. A case application was demonstrated with the use of model-driven DSS to measure recovery utilisation value for the used product disposition alternatives. Finally, the future work and contributions of this study are discussed.

New lower bounds for solving a scheduling problem with resource collaboration

The collaboration of robots or other renewable resources for the efficient performance of complex operations is an essential part of modern industrial systems. Here we considered a key feature of the scheduling problem, namely, alternative modes by which to execute an operation. Every operation mode uses a specific set of collaborating resources to perform the operation. This structure allows the scheduling engineer to incorporate in each operation a set of activities using few resources, where each resource performs some of the activities during a portion of the operation's duration. As such, this setup allows the reuse of previously designed solution segments in a manner that conserves engineering efforts and reduces model size while retaining the flexibility conferred by the use of alternative modes. In this paper, we propose new lower bounds for improving the solution process efficiency and we demonstrate a model-driven decision support system for formulating and solving such scheduling problems. Two types of bounds are derived and analyzed, LP relaxation and parameters-based calculation. Both types consider partial solutions at the customized branch and bound solution tree. The dominance relations among the bounds are determined analytically, and the performances of the dominant bounds are compared empirically. The results demonstrate a 70% runtime reduction compared to previously published methods and bounds.

The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective

Managing efficiently the flow of products throughout the supply chain is essential for succeeding in today's marketplace. We consider the Kanban (from Lean Management) and Drum-Buffer-Rope (DBR, from the Theory of Constraints) scheduling mechanisms and evaluate their performance in a four-echelon supply chain operating within a large noise scenario. Through an agent-based system, which is presented as a powerful model-driven decision support system for managers, we show the lower sensitivity against variability and the higher financial performance of DBR, which occurs as this mechanism improves the supply chain robustness due to its bottleneck orientation. Nonetheless, we prove the existence of regions in the decision space where Kanban offers similar performance. This is especially relevant taking into account that Kanban can be implemented at a lower cost, as DBR requires a higher degree of information transparency and a solid contract between partners to align incentives. In this sense, we offer decision makers a methodological approach to reach an agreement when the partners decide to move from Kanban to DBR in a bid to increase the overall net profit in supply chains operating in a challenging noise scenario.

Risk stratification in deep brain stimulation surgery: Development of an algorithm to predict patient discharge disposition with 91.9% accuracy

Clinical decision making is susceptible to biases and can be improved with the application of predictive models and decision support systems (DSS). The purpose of this study was to develop a predictive risk stratification model and DSS that could accurately predict whether a patient was likely to be of high- or low-acuity discharge disposition (DD) status subsequent to DBS surgery. Data were collected for 135 DBS patients by reviewing medical records. Multivariate logistic regression was applied to develop the predictive algorithm. The two significant predictive models showed good fit and were calibrated by using AUROC curve analysis. The model predicting DD in all patients (n = 135) yielded a predictive accuracy of 91.9% (AUROC = 0.825, p < .001). The model predicting DD in Parkinson’s Disease patients (n = 91) yielded a predictive accuracy of 89.0% (AUROC = 0.853, p < .001). Age was a significant predictor of DD across all patients (OR = 1.11, p < .05) and BMI was a significant predictor of DD amongst Parkinson’s Disease patients (OR = 1.16, p < .05). It is possible to accurately predict the DD of DBS patients using routinely collected preoperative variables. The predictive algorithms were applied in the form of a model-driven DSS to assist in improving clinical decision making and patient safety.

Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach

Against the backdrop of a changing political, economic and ecological environment, energy utilities are facing several challenges in many countries. Due to an increasing decentralization of energy systems, the conventional business could be undermined. Yet the reliable integration of small-scale renewable technologies and associated system transformations could represent an opportunity as well. Municipal energy utilities might play a decisive role regarding successful transition. For better decision-making, they need to investigate under which conditions certain novel business cases can become a sustainable part of their future strategy. The development of the strategy is a challenging task which needs to consider different conditions as business portfolio, the customer base, the regulatory framework as well as the market environment. Integrated Multi-Modal Energy System (IMMES) models are able to capture necessary interactions. This research introduces a model-driven decision support system called Integrated Resource Planning and Optimization (IRPopt). Major aim is to provide managerial guidance by simulating the impact of business models considering various market actors. The mixed-integer linear programming approach exhibits a novel formal interface between supply and demand side which merges technical and commercial aspects. This is achieved by explicit modeling of municipal market actors on one layer and state-of-the-art technology components on another layer as well as resource flow relations and service agreements mechanism among and between the different layers. While this optimization framework provides a dynamic and flexible policy-oriented, technology-based and actor-related assessment of multi-sectoral business cases, the encapsulation in a generic software system supports the facilitation. Based on the actor-oriented dispatch strategy, flexibility potential of community energy storage systems is provided to demonstrate a real application.

A decision support system for integrated container handling in a transshipment hub

The productivity of a maritime container terminal can be improved through a model-driven decision support system (DSS) focused on a better integration among container handling operations occurring across the quay, transfer and yard areas. Integration is pursued to minimize the blocking, locking and other queuing phenomena which are unavoidable, especially when human-operated equipment is shared in a real environment subjected to several random events and activities. An integrated queuing network is proposed in this paper as the natural modeling paradigm for a DSS aimed to highlight and quantify the blocking, locking and other queuing phenomena experienced in real practice. After an in-depth discussion of the limitations of solving the queuing network model analytically, discrete-event simulation is adopted as solution method. Numerical examples referred to a case study for a real transshipment hub return reliable estimates for the above queuing phenomena. They illustrate how the queuing-based DSS may effectively support the operations manager in determining the proper operational policies and equipment management with respect to a proficient integration of container handling operations.

Design and evaluation of a model-driven decision support system for repurposing electric vehicle batteries

The diffusion of electric vehicles suffers from immature and expensive battery technologies. Repurposing electric vehicle batteries for second-life application scenarios may lower the vehicles’ total costs of ownership and increases their ecologic sustainability. However, identifying the best – or even a feasible – scenario for which to repurpose a battery is a complex and unresolved decision problem. In this exaptation research, we set out to design, implement, and evaluate the first decision support system that aids decision-makers in the automobile industry with repurposing electric vehicle batteries. The exaptation is done by classifying decisions on repurposing products as bipartite matching problems and designing two binary integer linear programs that identify (a) all technical feasible assignments and (b) optimal assignments of products and scenarios. Based on an empirical study and expert interviews, we parameterize both binary integer linear programs for repurposing electric vehicle batteries. In a field experiment, we show that our decision support system considerably increases the decision quality in terms of hit rate, miss rate, precision, fallout, and accuracy. While practitioners can use the implemented decision support system when repurposing electric vehicle batteries, other researchers can build on our results to design decision support systems for repurposing further products.

Desalination network model driven decision support system: A case study of Saudi Arabia

This study aims to develop a network model driven platform that supports decision-makers to make well-informed decisions for the efficient water supplies, taking Saudi Arabia as a case study. The water/energy network analysis should be able to identify optimal locations for sustainable desalination infrastructure investments, accounting for the existing assets and the current investment plans. The geographical aspect of individual resource production and distribution can be quantitatively handled by a graph-theoretic approach. This study employs a new multicommodity network flow model called the INFINIT (interdependent network flows with induced internal transformation) model, which enables to address water-energy nexus issues and to optimize the flow of multiple resources as well as placement of new water/energy facilities at the individual facility level. The INFINIT model in this study formulates and solves mixed-integer linear programming (MILP) problems to minimize the designated multi-objective functions of the total cost and CO2 emission. As a result of optimization, the Pareto-optimal solutions with different network flow topology and the downselected potential locations for new facilities are obtained. To effectively visualize alternative design and policy scenarios, two ways of visualization of the results are developed: a MATLAB-based graphical user interface and tabletop 3D map projection.

Holism versus reductionism in supply chain management: An economic analysis

Since supply chains are increasingly built on complex interdependences, concerns to adopt new managerial approaches based on collaboration have surged. Nonetheless, implementing an efficient collaborative solution is a wide process where several obstacles must be faced. This work explores the key role of experimentation as a model-driven decision support system for managers in the convoluted decision-making process required to evolve from a reductionist approach (where the overall strategy is the sum of individual strategies) to a holistic approach (where global optimization is sought through collaboration). We simulate a four-echelon supply chain within a large noise scenario, while a fractional factorial design of experiments (DoE) with eleven factors was used to explore cause-effect relationships. By providing evidence in a wide range of conditions of the superiority of the holistic approach, supply chain participants can be certain to move away from their natural reductionist behavior. Thereupon, practitioners focus on implementing the solution. The theory of constraints (TOC) defines an appropriate framework, where the Drum–Buffer–Rope (DBR) method integrates supply chain processes and synchronizes decisions. In addition, this work provides evidence of the need for aligning incentives in order to eliminate the risk to deviate. Modeling and simulation, especially agent-based techniques, allows practitioners to develop awareness of complex organizational problems. Hence, these prototypes can be interpreted as forceful laboratories for decision making and business transformation.

A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES

In ceramic companies, uncertainty in the tone and gage obtained in first quality units of the same finished good (FG) entails frequent discrepancies between planned homogeneous quantities and real ones. This fact can lead to a shortage situation in which certain previously committed customer orders cannot be served because there are not enough homogeneous units of a specific FG (i.e., with the same tone and gage). In this paper, a Model-Driven Decision Support System (DSS) is proposed to reassign the actual homogeneous stock and the planned homogeneous sublots to already committed orders under uncertainty by means of a mathematical programming model (SP-Model). The DSS functionalities enable ceramic decision makers to generate different solutions by changing model options. Uncertainty in the planned homogeneous quantities, and any other type of uncertainty, is managed via scenarios. The robustness of each solution is tested in planned and real situations with another DSS functionality based on another mathematical programming model (ASP-Model). With these DSS features, the ceramic decision maker can choose in a friendly fashion the orders to be served with the current homogeneous stock and the future uncertainty homogeneous supply to better achieve a balance between the maximisation of multiple objectives and robustness.

Efficient GIS-based model-driven method for flood risk management and its application in central China

Abstract. In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.

Steps towards a high-frequency financial decision support system to pricing options on currency futures with neural networks

In this paper, we present steps towards a model-driven financial decision support system (FDSS) to pricing options on currency futures, which can be embedded in a high-frequency trading process. Due to the difficulty of option valuation, we provide an alternative heuristic option pricing approach with neural networks. We show that the use of neural networks is not only suitable in generating accurate trading signals, but also in generating automated fast run-time trading signals for the decision taker. To achieve this, we conduct an experiment with an empirical tick data set of EUR/USD options on currency futures of four weeks. An essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of input variables. Nevertheless, we also have to take particular limitations into account, which give us useful hints for further research and steps.

A simulation-based decision support system for industrial field service network planning

Technical field services for industrial machinery and equipment have become increasingly important for original equipment manufacturers. To deliver services to their customers, companies have to build up new core competencies and infrastructure, a challenge due to the high complexity and dynamics of this business. To assist companies in the strategic design of their network and the planning of resources for delivering industrial field services, we present a model-driven decision support system that uses discrete event simulation to support decision makers in various aspects of strategic design and tactical planning. The benefits of the decision support system include the creation of a generic framework that makes it possible to create simulation models of different field service networks for multiple purposes. Specifically, the system can be used to support various tactical planning and strategic design decisions while keeping investments low in terms of time consumption and money spending. In addition, the paper closes an identified gap involving a lack of decision support for the management of field service networks. An application of the decision support system in an exemplary case is used to illustrate potential applications and benefits.

Contribution of Decision Support System in Enhancing Productivity and Profitability of the Firm

The business paradigms are changing amidst changing business environment. There are newer technologies at disposal, rising customer awareness and their expectations from the need to attain efficiency and effectiveness in business processes for survival and competitive advantage. This paper provides insights on the significance of business decision making and present state of organizations that are striving to achieve optimal utilization of limited business resources. The paper highlights the nature of linear programming model and its importance in effective business decision making. The business impact of model is illustrated in the case using the linear programming model and transportation method through excel solver in computer manufacturing firms to help in deciding optimum quantity to produce within limited resources and how the computers manufactured can be distributed to market places at minimal cost. The paper elicits the effectiveness of the linear programming model to realize good decision making in business by meeting the business objectives through optimal utilization of resources. It concludes that model driven decision support system enhances the productivity and profitability of the firm in a constrained environment and is a highly effective model for solving business problems.

A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods

This work has been carried out as part of the project “DPI2008-06788-C02-01 (PERMACASI)” funded by Ministerio de Ciencia e Innovacion of the Spanish Government and as part of the project “PAID-06-10-2396 (NegoSol-MAS)” funded by Vicerrectorado de Investigacion of Universidat Politecnica de Valencia.

A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products

Uniform product deliveries are required in the ceramic, horticulture and leather sectors because customers require product homogeneity to use, present or consume them together. Some industries cannot prevent the lack of homogeneity in products in their manufacturing processes; hence, they cannot avoid non-uniform finished products arriving at their warehouses and, consequently, fragmentation of their stocks. Therefore, final uniform product amounts do not match planned production ones, which frequently makes serving previous committed orders with homogeneous quantities impossible. This paper proposes a model-driven decision support system (DSS) to help the person in charge of delivery management to reallocate the available real inventory to orders to satisfy homogenous customer requirements in a collaborative supply chain (SC). The DSS has been validated in a ceramic tile collaborative SC.

Sediment Toxicity Testing for Prospective Risk Assessment—A New Framework and How to Establish It

ABSTRACT There is a recognized need to design a new framework for sediment toxicity testing that meets current scientific standards and regulatory requirements, such as reliable assessment of toxicity, which prevents any harmful effects on biodiversity, a strong capability to predict population- and community-level effects, and applicability of the results to decision-making. We propose a new framework for prospective sediment toxicity testing, and suggest solutions to the key methodological challenges that hinder establishment of this framework (comparison of sensitivities, design of test batteries, consideration of different exposure routes, extrapolations to population and community levels, use of test results for decision-making). The proposed framework consists of the following three units: test-battery system, higher-tier testing systems and additional ecological modeling, and a decision support system. The key methodologies proposed to establish this framework are compound-tailored test-battery use approach, relative sensitivity distribution analysis, toxicity tests that combine bacteria and arthropods, micro- and mesocosms studies, population and community models, and model-driven decision support systems. The proposed framework, as well as the key methods mentioned above, has the potential to improve not only prospective toxicity testing for sediments, but also ecological risk assessment in general.

A DSS generator for multiobjective optimisation of spreadsheet-based models

Water management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support applications. GANetXL’s utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.

Optimization of copper treatments in organic viticulture by using a web-based decision support system

This paper presents Coptimizer, a model-driven decision support system designed to help growers to optimize and track the use of copper-based fungicides against grapevine downy mildew in European organic viticulture, where the use of only a fixed amount of copper per year per hectare is permitted. Copper is a preventive fungicide allowed in organic agriculture that is active only at the application site (i.e. it is non-systemic), so that new plant growth results in unprotected tissues. In areas where disease incidence is high, organic farmers usually apply weekly copper treatments at the risk of exceeding the fixed threshold. The capability of Coptimizer for not exceeding the copper threshold was evaluated in simulation using historical data and tested in two experiments under field conditions during 2008. The results show that by using Coptimizer growers could be able to maintain the same level of protection as that gained by traditional application schedule while applying only half the amount of the fungicide.

Dynamic optimisation of price, warranty length and production rate

This study investigates a marketing and production problem that uses price, warranty length and production rate as simultaneous dynamic decision variables. Furthermore, this study was conducted under a policy of free replacement of defective items; and under conditions where demand was dynamic and dependent on price, warranty and cumulative sales. A continuous profit maximisation model was formulated, which first considers the expected warranty cost per item. Then, it considers the steps for dynamic optimisation, which eventually derive the optimal price, warranty length and production rate. Discretisation was then applied to the profit maximisation model and a digital computer was used to identify the optimal control paths, obtaining a finite solution that is a set of real numbers for practical application. A model-driven Decision Support System is finally established, which provides a graphical user interface for overcoming the complexity of the analytical process. Subsequently, the proposed system was tested and the analytical solution was verified using several demand functions for additive lifetime distributions, thereby demonstrating the system's effectiveness.