Operational Decision Support Research Articles

Decision support for energy-saving idle production facility operations based on M2M environment

Decision support for energy-saving idle production facility operations based on M2M environment

Decision support for energy-saving idle production facility operations based on M2M environment

Decision support for energy-saving idle production facility operations based on M2M environment

Decision support for energy-saving idle production facility operations (Decision support in production line)

Decision support for energy-saving idle production facility operations (Decision support in production line)

Intellectual decision-making system in the context of potentially dangerous nuclear power facilities

The article deals with intelligent operation decision support system under condition of potentially hazardous nuclear facilities. The proposed system is referred to the class of advising systems and does not make final decisions in case of deviations of parameters to be analyzed, but generates general ways to solve an encountered problem and issues a set of recommendations for the plant personnel. In the article a fuzzy logic tool is used as mathematic tool. Lessons learnt from operation of nuclear facilities demonstrate that existing critical components (parts, areas, welding joints) are subject to increased failure under conditions of high operational loads, including beyond design loads and negative environmental impact. Usually in that situation there is probability of equipment integrity failure, when the unit is at power, with severe defect downing. For instance, the coolant leak and potential development of initial penetration defect to critical dimensions. In other words, in fact, the final observable result is always one – formation and development of operational crack which jeopardizes design integrity of the component and, accordingly, seriously compromises the nuclear power unit operation. The proposed situational model is linked with real knowledge data base where generated situational pairs are stored. The expert system is used for knowledge data base formation. Actually the proposed system consists of two independent fuzzy systems. From mathematical tool point of view, the advantage of such systems combination is lack of defuzzification unit in the first system and fuzzification unit in the second one.

Scientific, technical and institutional challenges towards next-generation operational flood risk management decision support systems

ABSTRACTThe global dissemination of operational systems for flood forecasting, early warning and risk management is much more heterogeneous than comparable forecasting services found in the meteorological community. This results primarily from the need for local ‘on-the-ground’ knowledge, such as addressing regional hydrological phenomena or the impact of local water resources management, and the challenge for small or weak institutions to integrate and operate such sophisticated systems. In this paper, we summarize and discuss the state-of-the-art of this kind of systems from a scientific, technical and institutional perspective and provide some recent applications. This leads to the development of an improved integration approach of existing building blocks and cross-organizational collaboration aimed at defining a next-generation flood risk management approach. The approach is implemented through the combination of a general platform with a community-driven effort, which substantially relies on local expertise.

When and what wholesale and retail prices should be set in multi-channel supply chains?

This paper investigates the optimal timing and level of wholesale and retail prices set in multi-channel supply chains, where a manufacturer produces and sells products to retailers that compete to resell the products, by applying the framework of an observable delay game devised in noncooperative game theory. We assume that one manufacturer and two retailers, which constitute a two-echelon supply chain, can select not only the levels of wholesale and retail prices, respectively, but also the timing of pricing. Our analysis of a dynamic game composed of discrete periods provides two useful conclusions for operational decision support. First, the manufacturer must simultaneously set its wholesale prices for products that are sold to separate retailers at the same time. Second, in contrast to the simultaneous price setting by the manufacturer, the retailers must sequentially set respective retail prices at different times; thus, the retailers should stagger their timings for setting retail prices. We formally demonstrate that these timings of pricing decisions by the manufacturer and the retailers constitute a subgame perfect Nash equilibrium in the dynamic noncooperative game played by the three supply chain parties. Consequently, these conclusions can be used as practical guidelines for supply chain members choosing optimal timing of pricing.

A bi-objective model for vessel emergency maintenance under a condition-based maintenance strategy

When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship’s machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as ‘vessel emergency maintenance’. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship’s main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model

AbstractHydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high‐fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high‐fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high‐fidelity hydrodynamic and water quality model CE‐QUAL‐W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high‐fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high‐fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

Thermodynamic Performance Simulation and Modeling of Saturated Steam Turbines

The thermal load performance optimization and control is an important means for performance analysis, control research and operation optimization of steam turbine. It has great significance for operator training, parameter adjustment and decision support. In saturated steam turbine, the rapid expansion of steam will lead to non-equilibrium two-phase condensation flow, and the model establishment is more complicated. In order to study the thermal characteristics of saturated steam turbine during load changing process, the non-equilibrium box model of two phase flow was used to simulate the process of vapor-liquid two phase flow in the saturated steam turbine cascade, analysed the energy and mass exchange process in cascade passage of saturated steam turbine, on the basis of these theories, a segemented lumped parameter model that could reflect parameter distribution between cascade passage of saturated steam turbine was established.

Embedded analytics: improving decision support for humanitarian logistics operations

Analytical techniques continue to advance in efficacy, as well as complexity. However, it is sometimes unrealistic to employ complex analyses during time-constrained humanitarian disaster operations. We propose that simple, embedded analytics tools can provide an effective and practical means toward managing humanitarian operations. In this paper, we demonstrate a real-world application of our technique in a patient evacuation context. This paper contributes to literature and practice by showing how simple analytic methods and open-source imagery tools can offer significant value to the humanitarian operations literature. The application also highlights some challenges to drawing a clear picture from disparate data sources in the humanitarian operations domain.

Dynamic risk analysis for operational decision support

Quantitative risk assessments for offshore oil and gas installations have been developed and used to support decision making about major hazards risk for more than 30 years. Initially, these studies were used to support the design process, aiming to develop installations that could be operated safely throughout their lifetime. As installations were put into operation, the studies were updated with as-built and operational information to provide a basis for making decisions also in the operational phase. This was however only partially successful, and the general impression has been that the studies have not been very actively used in operations. Many explanations have been given, the most common being that the reports were too complicated and written for risk analysis experts, not operations personnel on offshore installations and that the results could not be updated sufficiently often to reflect changes in risk on a day-by-day basis. This may be a part of the explanation, but in this paper, we have looked into the decision context and the types of decisions made in operation, compared to those in the design phase. Based on this, it is concluded that the focus of existing models need to be extended to cover activity risk in a more detailed way, as well as the risk associated with the technical systems. Instead, a revised methodology for developing quantitative risk assessments is proposed, focusing on the parameters and activities that change during operation. The methodology has also been tested on an offshore installation, to investigate the feasibility in practice.

Operational Decision Support for Material Management in Continuous Mining Systems: From Simulation Concept to Practical Full-Scale Implementations

Material management in opencast mines is concerned with planning, organizing, and control of the flow of materials from their extraction points to destinations. It can be strongly affected by operational decisions that have to be made during the production process. To date, little research has focused on the application of simulation modeling as a powerful supportive tool for decision making in such systems. Practical experiences from implementing a simulation model of a mine for the operational support on an industrial scale are not known to the authors. This paper presents the extension of a developed stochastic simulation model by the authors from a conceptual stage (TRL4) to a new Technology Readiness Level (TRL 6) by implementing it in an industrially relevant environment. A framework for modeling, simulation, and validation of the simulation model applied to two large opencast lignite mines is presented in detail. Operational implementation issues, experiences, and challenges in practical applications are discussed. Furthermore, the strength of applying the simulation modeling as an operational decision support for material management in coal mining is demonstrated. Results of the case studies are used to describe the details of the framework, and to illustrate the strength and limitations of its application.

Planning Dragline Positioning Sequence with A * Search Algorithm

Dragline excavators are among the largest earthmoving machines in surface mining, where they are used to remove the waste material that sits above a target mineral or coal reserve. The effectiveness of their operation is highly dependent on the sequence of positions at which they operate and the material movement during the excavation at each position. This paper addresses the problem of how to position a dragline in order to efficiently finish an excavation task with a specified digging and dumping strategy at each position. The dragline positioning sequence is computed among a range of candidate positions using the A* search algorithm. The algorithm is applied to two simulated dragline excavation scenarios that are defined based on actual terrain data. We show that the proposed A* algorithm is able to produce the optimal positioning sequences in a reasonable amount of computation time, allowing it to be applied in real-time decision support for the dragline operators.

A Spatial-Based Decision Support System for wood harvesting management in mountain areas

In this paper, a spatial-based economic model is proposed with the aim of estimating the most likely harvest cost of a forest block in relation to its particular morphological and operating features. This work, which is based on the classical stumpage price assessment method, presents an economic balance of a forest cut, attained by conducting a cost analysis of each logging phase of the different standard harvesting strategies. The study area is in the North-West of Italy, in the Mount Cotolivier forest compartment, in Oulx, Piedmont. The map of the stand structure, which is included in the Oulx Forest Management Plan, was used to locate blocks (areas considered homogeneous according to the stand structure and forest typology) where silvicultural cuts could be scheduled. The feasibility of the selected logging strategies was mapped considering six conditioning factors, of both a topological and a topographic nature. Their influence was weighted by means of a score assignation and integrated in a Multi-Criteria Decision Making procedure. The scores were mathematically combined to calculate a spatial dependent cost-function (Block Exploitation Aptitude, BEA) in which the suitability of each block to be harvested was mapped through a specific strategy. The obtained BEA was then used to estimate the most suitable productivity rate of the harvests of each block. The unitary costs of the strategies were estimated and then compared to find the most profitable one for each block.This model has proved to be effective in generating objective economic results concerning harvest cuts in productive stands in mountainous areas. The proposed methodology simultaneously takes into account different factors and generates feasibility scenarios, in the space domain, for the considered harvesting strategies. The proposed model represents a prototype on which an operational Decision Support System could be based to assist forest managers over the short-medium term.

Why Sample? - Understanding Common Sampling Objectives for Oil Spill Response

ABSTRACT #2017-204: There is a growing recognition of the role science plays in supporting oil spill response coupled with increasing reliance on data-driven management and decision-making approaches. Collecting samples for analysis of hydrocarbons and other chemicals potentially used during oil spill response (e.g., dispersants) has become common place on many spills. While the rationale and approaches for oil spill sampling may be well known to experienced chemists and environmental scientists, the response community is still gaining experience in integrating sampling programs into dynamic oil spill response and decision-making. This paper reviews common sampling objectives for three key aspects of spill response: operational decision-support, environmental impact assessment (including natural resource damage assessment), and source identification. These broad categories span a range of interrelated sub-topics including, among others, public/worker health and safety; understanding how physical and chemical properties of oil influence selection of response options; monitoring cleanup effectiveness, especially for alternative response technologies such as dispersants; identifying and differentiating between spill and non-spill pollution sources; and evaluating potential impacts to resources at risk. Methods for achieving sampling objectives, including development of Sampling and Analysis Plans, are discussed with the goal of increasing awareness among response managers and improving response capability among staff who may be tasked with sampling support during training exercises or actual incidents. Relevant considerations for study design, collection methods, and analytical parameters are also reviewed.

Solving Practical Railway Crew Scheduling Problems with Attendance Rates

Arising from a practical problem in German rail passenger transport, a prototype for a multi-period railway crew scheduling problem with attendance rates for conductors is developed and evaluated in this paper. The consideration of attendance rates is of increasing importance in regional transport networks and requires decision support. For this purpose business analytics is applied in order to offer an approach to transform real-world data to concrete operational decision support (action). The focus here is on the analysis step using a new set covering model with several essential restrictions integrated for the first time. A hybrid column generation approach is applied, which solves the pricing problem by means of a genetic algorithm. The artifact is evaluated with the help of a case study of three real-world transport networks. It is shown that the hybrid solution approach is able to solve the problem more effectively and efficiently compared to conventional approaches used in practice.

Editorial: Special Section on Harnessing Flexible Transmission Assets for Power System Optimization

The role of power flow control is increasing in importance due to variable resources and the difficulty of siting new transmission. While HVDC, flexible AC transmission systems, and transmission switching are utilized today, their capabilities have been incorporated into existing operations decision support tools, such as energy management systems, only to a very limited extent. As a result, such flexible transmission assets are underutilized. This special section of the IEEE T ransactions on Power Systems is focused on software development, modeling, and algorithms that address practical challenges on real-world test cases in order to achieve near-term industry adoption.

RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in agricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers' loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation (EO) data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS), for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

ИНТЕЛЛЕКТУАЛИЗАЦИЯ ПРОЦЕССА ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ ПРИ ЭКСПЛУАТАЦИИ ЭНЕРГЕТИЧЕСКИХ КОТЕЛЬНЫХ УСТАНОВОК

ИНТЕЛЛЕКТУАЛИЗАЦИЯ ПРОЦЕССА ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ ПРИ ЭКСПЛУАТАЦИИ ЭНЕРГЕТИЧЕСКИХ КОТЕЛЬНЫХ УСТАНОВОК

M2M 環境下の省エネルギアイ ドル状態付き生産設備の運用方法の研究

M2M 環境下の省エネルギアイ ドル状態付き生産設備の運用方法の研究