A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

Fuzzy logic based decision support for mass evacuations of cities prone to coastal or river floods

Facing climate change and more frequent extreme weather conditions, coastal floods and inundations will become more severe. Evacuation can be an efficient solution to secure people's safety in a major disaster. The main difficulty in making an evacuation decision is the imprecise, incomplete and spatially varying nature of the crisis information. In this paper, a fuzzy-logic based method combined with Geographic Information System is proposed to analyze evacuation decision making scenarios. The method can handle qualitative and quantitative data at the same time, avoid sudden changes of decisions affected by uncertainties, and evaluate the spatial necessity to evacuate to support evacuation decision making. The method has been tested at the city of Bordeaux in France. The maps produced representing the need to evacuate can help decision makers better understand evacuation decision situation in terms of local impacts and crisis management anticipation.     

不确定条件下生产调度问题的三阶段决策方法

提出了一种可综合考虑多种不确定因素的生产调度问题三阶段决策方法. 首先分析了不确定条件下的生产调度的决策过程, 根据过程的特点将其分为基本调度、在线调整和补偿三个阶段; 对变量进行分类, 从而与3个阶段相对应; 分别建立了基于情景分析以及子问题最(近)优解的两种三阶段调度数学模型, 并给出了变量的处理方法和阶段性的模型求解方法, 来作为不同阶段的决策依据; 最后以化工批处理过程的短期调度为例, 说明了三阶段调度建模和决策过程的实现方法.     

综合集成与决策

在探索“从定性到定量的综合集成法”提出的动机与基本原理的基础上，讨论了综合集成与决策之间的关系，认为从定性到定量的综合集成体系的研究不能局限于决策与决策支持系统，而应将其视为科学创新的方法论．面对当前众多复杂的重大决策问题，应以综合集成理论作为决策研究的指导，建立综合集成型决策支持系统．     

PS转炉造渣过程的动态优化控制

建立了Pierce-Smith(PS)转炉造渣过程的非线性状态空问模型,并提出了基于生产质量指标反馈校正的铜锍造渣过程的动态优化控制方案.该方案首先基于最优模型求得最优控制律.为消除吹炼过程中的扰动以及其他不确定因素所带来的影响,再引入基于生产质量指标的反馈调整机制.其中反馈信息由软测量模犁根据进出转炉的物料计算得到,智能控制单元根据反馈的质量信息和期望的质量目标间的偏差对最优控制律进行补偿修正.并在该动态优化控制方案基础上,设计并开发了铜锍吹炼过程的优化控制指导决策系统.实际运行结果表明该系统优化了产品的质量,同时也实现了铜锍生产过程的节能.     

基于区间联系数理论的网络安全风险评估方法及应用

针对网络安全风险评估问题,结合区间数理论和联系数理论,提出了一种基于区间联系数理论的网络安全风险评估定量方法模型。区间联系数是一种处理定性与定量相结合的不确定性多属性决策方法,该方法能降低对网络安全风险评估中人为因素或不确定性因素的干扰。并通过实例说明该方法在网络安全风险评估中的具体应用及其评估方法。     

Weapon System Capability Assessment under uncertainty based on the evidential reasoning approach

Weapon System Capability Assessment (WSCA) is the initial point of quantification of capabilities in the military capability planning (MCP). WSCA is often a multiple criteria decision making (MCDM) problem with both quantitative and qualitative information under uncertain environment. In this paper, the analysis process and algorithm for WSCA problem is proposed on the basis of belief structure (BS) model and evidential reasoning (ER) approach which were developed to deal with various types of uncertainties such as ignorance and subjectiveness. First of all, the WSCA criteria hierarchy is built by analyzing how the capability is measured. Secondly, a weapon system capability model is formulated using BS. Thirdly, both qualitative and quantitative information involved in capability measure are transformed into BSs by the data transformation algorithm based on rules. Then, the analytical ER approach is used to aggregate the capability measurement information from sub-capability criteria to top-capability criterion, and the assessed weapon systems are ranked and analyzed according to utility intervals. Finally, a case study of real Main Battle Tank capability assessment is explored to show the proposed process for WSCA.     

Quantitative decision making for cued land reconnaissance: a defence case study

Helping commanders make more informed decisions is one of the most important roles played by defence operations analysts. This paper presents a defence case study of quantitative decision making. It develops a decision model for cued land reconnaissance missions that treats detecting, identifying, and destroying a hostile target to protect an asset as an integral process. The paper investigates issues such as whether an information assessment system, which provides improved knowledge about the nature of the cue, is necessary and what kind of reconnaissance platforms should be used under various conditions. Using a decision tree and evaluating all possible outcomes, the model derives the best course of action based on expected values. Then serials of sensitivity analysis are carried out to account for uncertainties of the scenarios, and their implications are explored. This illustrates that decision tree methodology can be used to analyse complex sequential decision problems in real military situations, and uncertainties can be taken into account in the decision making process.     

Multicriteria decision making under uncertainty: An advanced ordered weighted averaging operator for planning electric power systems

In this study, an advanced ordered weighted averaging (AOWA) operator is proposed for tackling multicriteria decision making (MCDM) problems under uncertainties. The AOWA incorporates techniques of interval theory and a center of gravity (COG) method within a traditional ordered weighted averaging (OWA) operator. It can deal with the uncertain inputs under optimistic and pessimistic conditions without knowing their distribution information and linguistic important degrees of all inputs in MCDM systems. The results obtained help decision makers select the optimal alternative according to their optimism degrees. A case study of planning electric power problems is provided for demonstrating the applicability of the proposed method. The results indicate that reasonable solutions have been generated for both discrete intervals and linguistic inputs. For all criteria under consideration, corrective alternatives can be undertaken sensitively under various optimism degrees and thus can help resolve the conflicts in electric power systems under uncertainties.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

A new integrated methodology using modified Delphi-fuzzy AHP-PROMETHEE for Geospatial Business Intelligence selection

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.     

An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter-varying MPC computes the optimal trajectory in closed-loop by iteratively linearizing the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon, while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.     

A new deterministic approach in a decision support system for ship’s trajectory planning

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.     

Multiattribute decision making based on power operators for linguistic intuitionistic fuzzy set using set pair analysis

The aim of the presented paper is to give a multiattribute decision making (MADM) method under the linguistic intuitionistic fuzzy (LIF) environment using the set pair analysis (SPA) theory. LIF set can express the qualitative information in terms of linguistic variables, whereas the connection number (CN) based on the “identity,” “discrepancy,” and “contrary” degrees of the SPA theory handles the uncertainties and certainties systems. On the basis of these features, we develop some series of linguistic CN (LCN) power weighted and ordered weighted geometric aggregation operator to aggregate the different LCNs. Several properties of the operators are also studied. Afterward, we present a novel MADM method to solve decision‐making problems under LIF set environment and illustrate with several examples to validate it. A comparative analysis is also presented to show the results.     

区间模糊语言多属性群决策方法及应用研究

基于我国银行业所面临的数据匮乏、信息披露制度不完善等问题,国际上先进的统计量化方法并不能完全适用于对我国操作风险的评价,从定性和定量相结合的角度出发,针对操作风险评价中的多属性群决策问题,提出一种在区间模糊语言信息下基于不确定的拓展有序加权平均算子（UEOWA）和不确定语言混合集结算子（ULHA）的多专家多属性商业银行操作风险评价方法,构建基于区间模糊语言的评价集来描述专家不精确、难量化的意见,梳理评价思路,设计评价步骤,通过实例验证了该方法的可操作性和有效性。     

多段决策优化问题的摹矩阵方法及其程序实现

解决最短路优化问题的摹矩阵方法可用于解决一类离散控制系统的最优控制问题。通过将系统控制问题转变成一个多段决策问题,则系统的最优控制问题就变成了多段决策的最短路求解问题。通过一个例子探讨了摹矩阵方法在最优控制应用中的特点,即对比动态规划方法,该方法可以同时完成最短路径和最优策略的寻找,而且易于软件实现。本文最后介绍了利用VC编写摹矩阵方法用于最优控制的一个编程实例。     

Three-Way Behavioral Decision Making With Hesitant Fuzzy Information Systems: Survey and Challenges

Three-way decision (T-WD) theory is about thinking, problem solving, and computing in threes. Behavioral decision making (BDM) focuses on effective, cognitive, and social processes employed by humans for choosing the optimal object, of which prospect theory and regret theory are two widely used tools. The hesitant fuzzy set (HFS) captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades. Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together, this paper reviews and examines advances in three-way behavioral decision making (TW-BDM) with hesitant fuzzy information systems (HFIS) from the perspective of the past, present, and future. First, we provide a brief historical account of the three topics and present basic formulations. Second, we summarize the latest development trends and examine a number of basic issues, such as one-sidedness of reference points and subjective randomness for result values, and then report the results of a comparative analysis of existing methods. Finally, we point out key challenges and future research directions.      

Digital Twin Based Real-time Production Logistics Synchronization System in a Multi-level Computing Architecture

The increasing demands for customized products have brought a lot of inevitable operational dynamics to the production logistics system. How to systematically monitor and evaluate its overall real-time operation status, and to invoke the most appropriate decision-making level, use the most economical computational resource, and conduct a synchronized decision-making and control to the most accurate operation scope to address a randomly occurred dynamics has remained a long-term challenge for researchers. This paper proposes a multi-level cloud computing enabled digital twin system for the real-time monitor, decision and control of a synchronized production logistics system. In the IoT-driven production logistics synchronization (PLS) system with complete real-time information, the dynamics that occurred in the physical layer could be accurately and real-timely captured and its negative effects on the system’s overall operation state could be effectively evaluated in the digital layer. For slight, moderate and severe dynamics, edge computing, fog computing and cloud computing and their dynamically formed multi-level distributed decision-making system will be used to deal with the dynamics in the most effective and economical mode. Finally, the PLS optimization model of production and storage is presented with an industrial case, and the effectiveness is also demonstrated and analyzed.     

目标威胁评估的一种改进影响网络方法

目标威胁评估是在进行作战决策时需要解决的关键问题。针对传统影响网络只能描述二元状态事件的局限性,对其进行了扩展,建立了描述多元状态事件的改进影响网络模型,并推导了影响参数应满足的约束条件及条件概率的计算方法。以防空系统目标为例,在分析目标威胁属性的基础上,基于云模型进行属性值的定性与定量转化,并运用改进的影响网络方法进行目标威胁评估。最后给出仿真实验,实验结果验证了改进影响网络方法的有效性和可行性。     

Optimal feedback control of oil reservoir waterflooding processes

Waterflooding is a process where water is injected into an oil reservoir to supplement its natural pressure for increment in productivity. The reservoir properties are highly heterogeneous, its states change as production progresses which require varying injection and production settings for economic recovery. As water is injected into the reservoir, more oil is expected to be produced. There is also likelihood that water is produced in association with the oil. The worst case is when the injected water meanders through the reservoir, it bypasses pools of oil and gets produced. Therefore, any effort geared toward finding the optimal settings to maximize the value of this venture can never be over emphasized. Waterflooding can be formulated as an optimal control problem. However, traditional optimal control is an open-loop solution, hence cannot cope with various uncertainties inevitably existing in any practical systems. Reservoir models are highly uncertain. Its properties are known with some degrees of certainty near the well-bore region only. In this work, a novel data-driven approach for control variable (CV) selection was proposed and applied to reservoir waterflooding process for a feedback strategy resulting in optimal or near optimal operation. The results indicated that the feedback control method was close to optimal in the absence of uncertainty. The loss recorded in the value of performance index, net present value (NPV) was only 0.26%. Furthermore, the new strategy performs better than the open-loop optimal control solution when system/model mismatch was considered. The performance depends on the scale of the uncertainty introduced. A gain in NPV as high as 30.04% was obtained.