Decision Support System For Operations Research Articles

A decision support system for operations in a container terminal

We describe a variety of inter-related decisions made during daily operations at a container terminal. The ultimate goal of these decisions is to minimize the berthing time of vessels, the resources needed for handling the workload, the waiting time of customer trucks, and the congestion on the roads and at the storage blocks and docks inside the terminal; and to make the best use of the storage space. Given the scale and complexity of these decisions, it is essential to use decision support tools to make them. This paper reports on work to develop such a decision support system (DSS). We discuss the mathematical models and algorithms used in designing the DSS, the reasons for using these approaches, and some experimental results.

Decision support system for reservoir operation considering rotational supply over irrigation blocks

This study aims not only to evaluate agricultural drought in paddy using the storage ratio of the corresponding reservoir, but also to develop a rotational irrigation scheduling system (RISS) for supporting both water supply scheduling and on–off control works for automated irrigation. From the present storage ratio of reservoir and an operation rule curve (ORC) as a guideline for releasing irrigation water, reservoir operators could determine the appropriate time to restrict the irrigation water supply, and calculate the deficient amount of irrigation water. The RISS based on the ORC and weekly rainfall forecasting information has been developed for decision support to minimize drought damage in irrigation districts during the irrigation period. The system was also applied to a test irrigation district with the Yedang irrigation reservoir, which is located in the mid-west of Korea. The RISS could be utilized as a water management program for irrigation reservoirs by interfacing with telemetering and telecontrol (TM/TC) technology.

Air traffic knowledge management policy

A new air traffic knowledge management system is vitally important for the safety and efficiency of future air travel. It can be accomplished by developing a new-generation integrated operational decision support for pilots and controllers. At present the knowledge for decision making is only available when the latter parties share it and negotiate it in real-time. A new policy is required with a new management system and an automated planning of conflict-free air space–time allocation accomplished by an agent-based architecture of a global network of integrated operational decision support systems for airports, airlines and air traffic control. This aims at acquiring and monitoring a flow of air space–time knowledge-and-data structures of conflict-free and optimised flights given the available resources, and ensuring their long-term efficiency by an agent-based planning of flight-plan alterations off-line before signs of air space–time conflicts can develop in real-time. The Agents managing parallel knowledge acquisition and reasoning processes allocate air space–time resources according to the technical capabilities of aircraft in response to flight-plan clearance requests. They ensure decision support for efficient flights and use of air space–time, and conflict-free planning for air traffic management.

Meta web service: building web-based open decision support system based on web services

Abstract Web services are currently one of the trends in network-based business services, which intuitively will be applied to build a semantic web-based decision support system (DSS). Since web services are self-contained, modular business process applications, based on open standards, enable integration models for facilitating program-to-program interactions. Decision modules in a semantic web-based DSS can be viewed as a web service. However, according to the current features, web services know only about themselves, they are neither autonomous, nor are they designed to use ontologies; they are passive until invoked, and they do not provide for composing functionalities. These lead to the motivation on building a sophisticated web service to contain these features and to utilize web services on behalf of the user. This paper aims to propose a new concept of Meta Web Service, a web service-based DSS. The meta web service understands the user's problem statement with ontology, performs web service discovery, web service composition, and automatically generates codes for composite web service execution. Case-based reasoning is applied to quickly find past histories of successful service compositions. A prototype of research web service has been developed to show the feasibility of the proposed idea.

Delivering a decision support system tool to a river contract: a way to implement the participatory approach principle at the catchment scale?

The MULINO project (MULti-sectoral, Integrated and Operational decision support system (DSS) for sustainable use of water resources at the catchment scale), funded by the Environment and Climate Programme of the European Union (EU), aims to support the scientific basis for integrated water management. The purpose of the project is to provide a tool to improve the integrated management of water resources at the catchment scale, based on the requirements of the EU Water Framework Directive (WFD). This paper presents the methodology aspects of the project. The design of the MULINO_DSS is based on the European Environment Agency DPSIR framework of environmental cause-effect relationships. D represents the driving forces, P, the pressures on the environment caused by human activities, S, the state of the environment, I, the impact on the environment and R, the human activities and desirable societal responses. This DPSIR chain provides the end-user of the DSS with an integrated view of complex, interacting issues. The first step in the MULINO project has been the analysis of local decision networks and the identification of an end-user to whom the DSS will be delivered. The importance of this step is illustrated by the results of the analysis for the Belgian case study: the Walloon part of the Dyle river catchment. The design of the DSS is made more complicated in this catchment by the fragmented nature of water management decision making. However, to overcome this problem, the design of the DSS was targeted at the river contract (RC) of the catchment. The coordinator of the RC is a focal point for a range of end-users and stakeholders with responsibility for water management in this catchment. This organisational structure was originally put in place to find a consensus when solving conflicting water management issues. Thus, the concept for the DSS development and delivery fits with the participatory approach principle of the WFD and builds on existing local networks of integrated water management.

The MPC elucidator: a case study in the design for human-automation interaction

In this paper, we describe the design of a decision support system for operators of model-based predictive controllers (MPC). MPC is a form of advanced automatic control that is increasingly common in process operations due to its ability to control and optimize large sections of a process. A cognitive task analysis revealed that current operating displays, which rely on displaying tables of numeric information across several display pages, do not effectively support human operator monitoring, diagnosis, and control of MPC. This case study shows how we applied representation aiding and workspace management design principles to better support the human-automation interaction requirements of monitoring, understanding, and adjusting these complex, semi-autonomous; process controllers. We show how effective user interface design can significantly reduce the complexity of operating with advanced automation, and can lead to improved understanding of how the automation works.

Goal Programming Decision Support System for Multiobjective Operation of Reservoir Systems

Many authors have documented the minimal use of optimization in practical day-to-day multipurpose reservoir operations. The RiverWare decision support system is a flexible general river basin modeling tool that allows water resources engineers to both simulate and optimize the management of multipurpose reservoir systems for daily operations. This paper describes RiverWare's optimization capabilities and its use by Tennessee Valley Authority operations schedulers. Input data requirements include (1) physical and economic characteristics of the system; (2) prioritized policy goals; and (3) parameters for automatic linearization. RiverWare automatically generates and efficiently solves a multiobjective, preemptive linear goal programming formulation of a reservoir system. An advanced feature of RiverWare is that both the physical model of the river basin and the operating policy are defined and easily modified by the modeler through an interactive graphical user interface. Any modifications are automaticall...

DECISION SUPPORT SYSTEM FOR HYDRO-POWER DAM OPERATION DURING FLOOD BY SYMBOLIC REASONING AND ARTFICIAL NEURAL NETWORK

A decision support system for hydropower dam operation is designed in this study based on crisp reasoning by production system and on artificial neural network. At the first step of the designing, symbolic reasoning is adopted to express the provisions of dam operational regulations and related manuals; each provision is translated into computer compatible rule in if-then form and stored in rule bases. Then an inference engine is designed to extract appropriate rules and apply them. Object-oriented designing using C++ is adopted for the inference. Artificial neural network is introduced to extract and express the operators' knowledge and policy. Based on several records of actual operation, the network structure is defined and its parameters are determined.

FEED‐BACK QUALITY CONTROL IN THE BAKING INDUSTRY USING FUZZY SETS

ABSTRACTA methodology for the closed loop control of biscuit quality during baking is proposed and was validated during several experiments. This methodology involves expert knowledge which is captured in a modular way. Three modules are involved in the control strategy of the process. The first module reproduces sensory evaluation by the operator. The second module diagnoses the state of the process with respect to quality state of biscuits. Finally, a decision module supervises the actuators of the process. Different fuzzy concepts are applied within these modules and fuzzy information is processed through the control chain. Results show that using only 11 rules, all components of quality are controlled with accuracy and robustness. This is particularly interesting in the light of the complexity of the process. Such a modular approach makes it possible to follow the evolution of quality during the process, in successive steps which are easily recorded and interpreted by experts. This approach could be envisaged as an operator decision support system.

A methodology for the quantitative evaluation of NPP fault diagnostic systems' dynamic aspects

A fault diagnostic system (FDS) is an operator decision support system which is implemented both to increase NPP efficiency as well as to reduce human error and cognitive workload that may cause nuclear power plant (NPP) accidents. Evaluation is an indispensable activity in constructing a reliable FDS. We first define the dynamic aspects of fault diagnostic systems (FDSs) for evaluation in this work. The dynamic aspect is concerned with the way a FDS responds to input. Next, we present a hierarchical structure in the evaluation for the dynamic aspects of FDSs. Dynamic aspects include both what a FDS provides and how a FDS operates. We define the former as content and the latter as behavior. Content and behavior contain two elements and six elements in the lower hierarchies, respectively. Content is a criterion for evaluating the integrity of a FDS, the problem types which a FDS deals with, along with the level of information. Behavior contains robustness, understandability, timeliness, transparency, effectiveness, and communicativeness of FDSs. On the other hand, the static aspects are concerned with the hardware and the software of the system. For quantitative evaluation, the method used to gain and aggregate the priorities of the criteria in this work is the analytic hierarchy process (AHP). The criteria at the lowest level are quantified through simple numerical expressions and questionnaires developed in this work. these well describe the characteristics of the criteria and appropriately use subjective, empirical, and technical methods. Finally, in order to demonstrate the feasibility of our evaluation method, we have performed one case study for the fault diagnosis module of OASYS™ (On-Line Operator Aid SYStem for Nuclear Power Plant), which is an operator support system developed at the Korea Advanced Institute of Science and Technology (KAIST).

Methodological framework for developing decision support systems (DSS) for hazardous materials emergency response operations

The production, storage, and transportation of hazardous materials are processes of vital economic importance for any advanced and technologically complex society. Although the production and distribution of hazardous materials is associated with economic development, there is a significant potential danger to the natural and social environment in the event of their accidental release, a fact that prompts for the development and implementation of methods and techniques that aim to improve hazardous materials risk management decisions. The objective of this paper is to present a unified framework for developing a Decision Support System (DSS) for supporting a vital function of risk management, namely the management of emergency response operations. The proposed framework recognizes the peculiarities of the hazardous materials decision-making environment which is characterized by: (i) multiple stakeholders, i.e., persons and organizations involved in and affected by hazardous materials risk management decisions; (ii) lack of a formal management structure for monitoring and controlling in a unified manner all Emergency Response Resources; (iii) lack of clear distinction and fragmentation of responsibilities of the actors involved in risk management operations; and (iv) dynamic/real-time decisions, i.e., risk determinants change over time. The proposed framework was used in order to develop a DSS for managing emergency response operations for large scale industrial accidents in Western Attica, Greece.

Distributed planning and control systems for the virtual enterprise: organizational requirements and development life-cycle

This paper describes the requirements analysis and system specification of an Order Promise module to be used as part of a broader Decision Support System for production and operations planning of a Virtual Enterprise. This work is part of a broader project with a particular focus on the microelectronics industry which is a good example of Virtual Enterprise, and where a quick response to the customers needs and to unpredictable changes in production conditions is considered a major factor for success. First, the analysis and specification are presented within a development framework that involves the study of organizational issues of semiconductor enterprises. The use of ontological engineering for supporting the communication and shared understanding of the system concepts is explained and a virtual enterprise ontology is outlined. Following the clarification of the concept of virtual enterprise, the generic techno-organizational requirements for the information system are derived. Finally, a specification of the global planning module and a more detailed one regarding the order promise module is presented.

Use of fuzzy neural-net model for rule generation of activated sludge process

Activated sludge plant is usually difficult to operate and control because of its complex operational behaviour and usual significant process disturbances. To increase safety and improve operating performance of this biological wastewater treatment process, it is important to develop computer operational decision support systems. This intelligent computing system is able to assist ordinary operators to work at the level of a domain expert in daily operation. Neural network techniques and fuzzy logic methods have become important and effective tools to help build such intelligent system. The artificial neural network technique is powerful because it can learn to represent complicated data patterns or data relationships between input and output variables of the system being studied. Nevertheless, it has limitations in performing heuristic reasoning of the domain problem. On the other hand, expert systems are good at performing heuristic reasoning by making use of logic rules. It is, however, generally weak for knowledge acquisition. In this study, a fuzzy neural model is developed for addressing the operating problems of activated sludge processes, relating to prediction and heuristic understanding of the sludge age. Neural network techniques and fuzzy logic are used in model development. Simulation studies show that this fuzzy-neural network model obtained is able to extract fuzzy rules from a set of numerical data that can be used to carry out heuristic reasoning.

An intelligent operator decision support system for abnormal situation management

Abnormal situation management (ASM) involves timely identification and mitigation of any significant departure of the process from an acceptable normal range of operation. Ineffective ASM has significant safety and economic impact on the industry. In this work, an intelligent operator decision support system, called Op-Aide, has been developed to assist the operator in quantitative diagnosis and assessment of abnormal situations. Op-Aide has been developed based on an open, modular, blackboard-based architecture. In this approach, independent modules provide data acquisition, process monitoring, fault diagnosis, and situation assessment capabilities. The implementation of Op-Aide is done in G2, MATLAB and C. Its application is illustrated on the simulation of Amoco Model IV fluidized catalytic cracking unit (FCCU).

An operational decision support system for flood risk mapping, forecasting and management

The paper describes the development of an integrated tool for planning and management that, taking advantage of available high performance computer platforms, allows location of areas at risk and estimation of expected damages (1); to forecast floods and inundation phenomena on the basis of real-time analysis of the present meteorological situation and forecasts available at different time and space scales (2); to evaluate the effects of decisions aimed at reducing social, economical and environmental damages on the basis of planned or real-time forecasted scenarios (3); to allow continuous training of personnel (4).

On the impact of optimisation models in maintenance decision making: the state of the art

In this paper we discuss the state of the art in applications of maintenance optimisation models. After giving a short introduction to the ama, we consider several ways in which models may be used to optimise maintenance, such as case studies, operational and strategic decision support systems, and give examples of each of them. Next we discuss several areas where the models have been applied successfully. These include civil structure and aeroplane maintenance. From a comparative point of view, we discuss future prospects. 8 1998 Elsevier Science Limited.

Decision support for real-time telemarketing operations through Bayesian network learning

Many knowledge discovery systems have been developed in diverse areas, but few systems address the use of knowledge in decision problems explicitly. This paper presents a decision support system for real-time telemarketing operations using the information extracted from the Bayesian network learning model. A prototype decision support system was developed for AT&T customer-contact employees to provide a recommendation regarding the promotion of a telephone discount plan. The system integrated a Bayesian network learning model (knowledge discovery process) and decision-making technique (influence diagram) to provide real-time decision support. A Bayesian network learning model was used to predict a probability of the customer's response from the previous promotion/response history. The influence diagram framework was used to integrate the predicted probability with the cost and benefit related to the possible actions. It was demonstrated that decision support by the Bayesian network learning model itself can be misleading. However, by linking the Bayesian network learning model with rigorous decision-making techniques such as influence diagrams, the decision support system developed in this paper was shown to provide an intelligent decision advice.

AUSTRAL, A Multi-Platform System for Alarm Processing and Decision Support System for Distribution Network Operation

To improve distribution network efficiency, EDF is developing AUSTRAL, a package of real-time functions for alarm processing, diagnosis and service restoration. AUSTRAL will become operational at the beginning of 1998 in three French control centers (Lyon, Nîmes and Versailles). The main aim of the AUSTRAL system is to cut overall outage time. AUSTRAL is a fully integrated system that shares data and MMI (Man-Machine Interface) with the telecontrol system (SCADA). It operates directly on real time data. AUSTRAL is a multi-platform package that may run on several telecontrol systems. The connexion is made via an explicit interface (A.P.I. for Application Program Interface) provided as part of the basic SCADA.

Process Supervision and Diagnosis of Multiple Faults in Chemical Processes

Abstract This article discusses the diagnosis system shell VADIAG which is part of a decision support system (DSS) for chemical plant operators. The shell integrates signal based, model based and knowledge based methods for detection and diagnosis of single and multiple faults. A dedicated analysis and design method is proposed for knowledge engineering for developement of an application specific shell configuration. Several prototype applications have demonstrated the diagnosis system's advanced capabilties.

Fuzzy rule generation from data for process operational decision support

Neural networks and expert systems are now beginning to realise their potential in developing intelligent operational decision support systems. While neural networks are able to learn complex nonlinear functional relations between multiple inputs and outputs, there remains the important limitation that knowledge embedded in the neural network is opaque. This image of a black-box technology is a major factor influencing the acceptability of this approach because it does not improve the heuristic understanding of the domain problem. On the other hand, expert systems make use of logic rules to carry out heuristic reasoning. Knowledge used to reach a conclusion is transparent and can be displayed through HOW and WHY explanation facilities which are an integral part of well-founded expert systems. However, a critical problem with this approach is knowledge acquisition. This contribution introduces a fuzzy neural network to extract fuzzy rules automatically from numerical data. By changing fuzzy membership functions, three types of rules can be extracted, i.e. rules with and without fuzzy membership values and neuro-expert systems which overcome the problems described above.