Including the environmental criteria when selecting a wastewater treatment plant

Life Cycle Assessment (LCA) is a methodology to generate environmental impact estimates associated with the life cycle stages of a product or process. The approach facilitates a more comprehensive outlook of the end-of-pipe process impacts, in which wastewater treatment plants (WWTPs) are included. Here we describe the implementation of the LCA methodology within a knowledge-based Decision support system (DSS) in order to include the environmental criteria to the decision making process when selecting the most appropriate process flow diagrams for specific scenarios. A sample group of 22 actual operating facilities in Spain, corresponding to five different typologies were assessed by two relevant impact categories within the system: Eutrophication Potential (EP) and Global Warming Potential (GWP). DSS includes useful tools that support a user in choosing a consistent, near optimum solution for an environmental impact specific problem in a reduced time frame. The synergistic combination of the two methodologies to address the design and assessment of treatment facilities can serve to identify the most sustainable options, embracing simultaneously a wide variety of analysis criteria, and enhancing the calculation of environmental savings. Results of averaged paired-comparison ratios between DSS estimates and facilities operations empirical data showed up to 70% and 95% EP and GWP, respectively. Interestingly, when unbiased operational efficiencies for existing facilities were discarded, the matching ratios increased substantially, up to 99% in both cases. The in-depth analysis of different output data gathered during the conceptual design and simulation of operating facilities using DSS identified the best performing facilities; and was used to improve the environmental performance of WWTPs, even during preliminary design of new facilities. Results demonstrated that combined LCA and DSS implementation is a suitable tool to assess WWTP design during the decision-making process. Following this procedure, a reliable interpretation and discussion of the results can be performed.     

一个新的支持多学科模型集成的流域决策支持系统框架研究

传统流域决策支持系统往往使用一两个模型来解决某些特定目标,限于系统结构而不能综合考虑多领域问题。提出了一种新的支持多学科模型集成的流域决策支持系统原型,在一个系统中实现多种学科模型的跨时空尺度集成,为流域综合管理提供全面科学支持。系统采用开放式多学科模型管理方法,其脚本扩展机制使得系统可以容纳几乎所有模型,模型因子方法使得形式多样的科学模型模拟结果可以应用到决策方法中,使系统在多学科模型支持下提供科学合理的决策方案。情景驱动的决策流程、向导式交互界面及GIS等图形化工具支持使得系统用户界面友好\,易用性强。     

Development of optimal water supply plan using integrated fuzzy Delphi and fuzzy ELECTRE III methods—Case study of the Gamasiab basin

This paper presents a novel method for the development of an optimal water supply plan showcased using data from the Gamasiab basin, located in Kermanshah province, Iran, concerning new dams that are being constructed in this semi-arid region. In this paper, a new group multi-criteria decision-making (MCDM) plan is proposed by combining two MCDM methods based on the fuzzy Delphi and fuzzy ELECTRE III methods that convert the experts' opinions to triangular fuzzy numbers based on the level of uncertainty associated with various quantitative and qualitative criteria. Considering the opinions of four non-stakeholder experts and data analysis using the fuzzy Delphi method, the criteria were evaluated. Then, by analysing the results using the fuzzy ELECTRE III method, the final ranking of scenarios is obtained. A sensitivity analysis was conducted to assess the effect of uncertainty on the performance of the decision-making system in scenarios ranking. The total expense, flood control, reservoir capacity and diversion and water transfer played a significant role in selecting the optimal scenario. Additionally, a hydrologic model was developed to evaluate the performance of the optimal scenario in terms of qualitative criteria. The data indicated that there was a good agreement between the results obtained from the hydrological model and the scenario ranking by the employed method. Altogether, a comparison of the proposed method with other MCDM methods, including fuzzy analytic hierarchy process and fuzzy technique for order preference by simulation of ideal solution, indicated that the results of the employed method matched more closely to the local experts' opinion.     

Automatic generation of water distribution systems based on GIS data

In the field of water distribution system (WDS) analysis, case study research is needed for testing or benchmarking optimisation strategies and newly developed software. However, data availability for the investigation of real cases is limited due to time and cost needed for data collection and model setup. We present a new algorithm that addresses this problem by generating WDSs from GIS using population density, housing density and elevation as input data. We show that the resulting WDSs are comparable to actual systems in terms of network properties and hydraulic performance. For example, comparing the pressure heads for an actual and a generated WDS results in pressure head differences of ±4 m or less for 75% of the supply area. Although elements like valves and pumps are not included, the new methodology can provide water distribution systems of varying levels of complexity (e.g., network layouts, connectivity, etc.) to allow testing design/optimisation algorithms on a large number of networks. The new approach can be used to estimate the construction costs of planned WDSs aimed at addressing population growth or at comparisons of different expansion strategies in growth corridors.     

Robust optimal decisions with imprecise forecasts

A robust minimax approach for optimal investment decisions with imprecise return forecasts and risk estimations in financial portfolio management is considered. Single-period and multi-period mean-variance optimization models are extended to worst-case design with multiple rival risk estimations and return forecasts. In multi-period stochastic formulation of classical mean-variance portfolio optimization problem, an investor makes an investment decision based on expectations and/or scenarios up to some intermediate times prior to the horizon and, consequently, rebalances or restructures the portfolio. Multi-period portfolio optimization entails the construction of a scenario tree representing a discretized estimate of uncertainties and associated probabilities in future stages. It is well known that return forecasts and risk estimations are inherently inaccurate and there are different rival estimates, or scenario trees. Robust optimization models are presented and imprecise nature of moment forecasts to reduce the risk of making a decision based on the wrong scenario is addressed. The worst-case performance is guaranteed in view of all rival risk and return scenarios and will only improve when any scenario other than the worst-case is realized. The ex-ante performance of minimax models is tested using historical data and backtesting results are presented.     

Data envelopment analysis of reservoir system performance

In long-term performance analyses of water systems with surface reservoirs for different operating scenarios, the analyst (or decision maker) is faced with two connected problems: (1) how to handle the extensive output of the simulation model and derive information on the scenarios scores for a prescribed set of performance criteria, and (2) how to compare scenarios in a multi-criterial sense while identifying the most desired. The data sets may overburden the analyst, while an evaluating procedure may be subjective due to personal preferences, attitudes, knowledge and miscellaneous factors. The data envelopment analysis (DEA) approach proposed here seems to be reliable in treating these situations, and sufficiently objective in evaluating and ranking the scenarios. Certain performance indices are defined as evaluating criteria in a standard multi-criterial sense, and then virtually divided into scenarios' output and input measures. By considering scenarios as product units, the DEA optimizes the weights of inputs and outputs, computes productivity efficiency for each unit, and rank them appropriately. Omitting the analyst's personal judgment on the technical parameters that describe system's performance restricts, in this way, the influence of the decision maker. A case study application on the reservoir system in Brazil proved that a methodological connection for solving decision problems with discrete alternatives really exists between the DEA and standard multi-criteria methods.     

Development of an environmental flows decision support system

The Murray–Darling Basin in Australia is severely environmentally degraded as a result of a range of anthropogenic changes, most notably the regulation and extraction of surface water resources for irrigated agriculture. Environmental problems include eutrophication of rivers and storages, elevated salinity levels, widespread blooms of toxic blue–green algae, decline of native fish and bird populations, and reduction of area of riverine wetlands. Both the community and the government are committed to improving the state of the environment in the Basin, both for it's intrinsic ecological values, and to ensure the sustainability of production in what is Australia's most economically important agricultural region. To facilitate the on-going trade-off process between competing users of this resource, a decision support system (DSS) is being developed which will enable explicit prediction of the likely response of key features of the riverine environment to proposed flow management scenarios. The DSS is being developed using the RAISON shell (Lam, D.C.L., Mayfield, C.I., Swayne, D.A., Hopkins, K., 1994. A prototype information system for watershed management and planning. Journal of Biological Systems 2 (4), 499–517), and will integrate a range of simple models of riverine ecology which are being developed. These models will include qualitative and quantitative models representing the response of different aspects of the instream and floodplain ecology dependent upon the river flow regime. The DSS will not include a detailed model of river hydrology or hydraulics, but rather, will use the output from the range of such models currently in use in the Basin as inputs to the ecological models. The DSS will also provide a range of tools to allow user-defined evaluation of scenario results, as well as explanations and supporting information to elucidate the ecological modelling.     

Impact of Radio Frequency Identification Technology on Environmental Sustainability

This study is the first empirical investigation of the impact of Radio Frequency Identification (RFID) technology utilization on a manufacturing organization’s ability to implement environmental sustainability practices and the organization’s level of environmental performance. The purpose of the study is to investigate the relationships among RFID technology utilization, environmental sustainability practices, and environmental performance. A structural model depicting the impact (direct and indirect through green supply chain management practices) on environmental performance is theorized. The efficacy of the model is assessed using a Partial-Least-Squares structural equation modeling methodology based on data from a sample of manufacturing managers. The results suggest that RFID technology utilization is positively associated with green supply chain management practices and environmental performance. This study contributes to the understanding of the impact of a specific technology, RFID, on environmental sustainability. RFID technology utilization is established as a viable component of a manufacturing organization’s environmental sustainability strategy.     

Use of modelling error dynamics for the calibration of water distribution systems

The calibration of a water distribution system remains a complex task. Leakages create additional difficulties if their proportion is high. Not all leakages are real. A part of them are apparent leakages. An optimization procedure is proposed to obtain the proportions of real and apparent leakages. The distribution of the leakages over a WDS may be made easier by analyzing the dependence of modelling errors on water flow. It is suggested that pressure measurements should be ranked according to the closeness of measurement to the water source and that modelling errors should be analysed separately for the measurements of different order (from the first to the highest). The advantage of this approach is that the analysis is confined within points of measurements of one order and it allows to redistribute fluxes of leakages (real and apparent) more reliably. Nonlinear optimization with linear constraints is used for the redistribution of leakages over a WDS. The methodology proposed allows to exclude the influence of systematic errors on the result of calibration.     

Tracking a benchmark index – using a spreadsheet‐based decision support system as the driver

This paper presents a two‐phase quantitative approach for enhanced index investing based on the mean‐variance model and the goal programming method. In the first stage, we use the mean‐variance theory to select better performing stocks for an investment pool. Then, in the second stage, we use a goal programming method to weight the selected stocks by balancing both the tracking error and the rate of return. In addition to the theoretical formulation, we construct a spreadsheet‐based decision support system (DSS) based on the transaction data to help resolve the index tracking problem. The paper contributes to the literature in two ways. For academics, we present original discussions on combining an interdisciplinary mean‐variance model and a goal programming method. Unlike the conventional approach used for enhanced index investing that requires a fund manager to actively buy and sell stocks to improve returns, our approach is based on historical data and deduces subjective judgments. Meanwhile, for practitioners, we present an original discussion on using a DSS to support index investing. The results of an empirical survey of the Taiwan stock market are also presented.     

A method for a robust optimization of joint product and supply chain design

This paper proposes a method for finding a robust solution to the problem of joint product family and supply chain design. Optimizing product design and the supply chain network at the same time brings substantial benefits. However, this approach involves decisions that can generate uncertainties in the long term. The challenge is to come up with a method that can adapt to most possible environments without straying too far from the optimal solution. Our approach is based on the generation of scenarios that correspond to combinations of uncertain parameters within the model. The performance of designs resulting from these scenario optimizations are compared to the performance of each of the other design scenarios, based on their probability of occurrence. The proposed methodology will allow practitioners to choose a suitable design, from the most profitable to the most reliable.     

Modelling uncertainty in the sustainability of Intelligent Transport Systems for highways using probabilistic data fusion

The implementation of ITS to increase the efficiency of saturated highways has become increasingly prevalent. It is a high level objective for many international governments and operators that highways should be managed in a way that is both sustainable i.e. environmental, social and economically sound and supportive of a Low-Carbon-Energy Future. Some clarity is therefore needed to understand how Intelligent Transport Systems perform within the constraints of that objective. The paper describes the development of performance criteria that reflect the contributions of Information Communication Technology (ICT) emissions, vehicle emissions and the embedded carbon within the physical transport infrastructure that typically comprises one type of Intelligent Transport System i.e. Active Traffic Management – a scheme that is used to reduce inter-urban congestion. The performance criteria form part of a new framework methodology ‘EnvFUSION’ (Environmental Fusion for ITS) outlined here. This is illustrated using a case study where environmental performance and pollution baselines (collected from independent experts, academic, governmental sources and suppliers) are processed using an attributional Lifecycle Assessment tool. The tool assesses the production and operational processes of the physical infrastructure of Active Traffic Management using inputs from the ‘Ecoinvent’ database. The ICT component (responsible for data links) is assessed using direct observation, whilst vehicle emissions are estimated using data from a National Atmospheric Emissions Laboratory. Analytical Hierarchy Process and Dempster–Shafer theory are used to create a prioritised performance hierarchy: the Intelligent Transport Sustainability Index, which includes weighted criteria based on stakeholder expertise. A synthesis of the individual criteria is then used to reflect the overall performance of the Active Traffic Management scheme in terms of sustainability (low-carbon-energy and socio-economic) objectives.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.     

Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1 h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement.     

Decision making under uncertainty in a decision support system for the Red River

Decision support systems (DSSs) are increasingly being used in water management for the evaluation of impacts of policy measures under different scenarios. The exact impacts generally are unknown and surrounded with considerable uncertainties. It may therefore be difficult to make a selection of measures relevant for a particular water management problem. In order to support policy makers to make a strategic selection between different measures in a DSS while taking uncertainty into account, a methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin. The methodology consists of a Monte Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the significance of the difference between output distributions for different measures. The mean flood damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the construction of a retention basin reduce the flood damage by about 5, 55 and 300 million US$, respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly.     

Robust multi-objective optimization for water distribution system design using a meta-metaheuristic

The design of an urban water distribution system (WDS) is a challenging problem involving multiple objectives. The goal of robust multi-objective optimization for WDS design is to find the set of solutions which embodies an acceptable trade-off between system cost and reliability, so that the ideal solution may be selected for a given budget. In addition to satisfying consumer needs, a system must be built to accommodate multiple demand loading conditions, withstand component failures and allow surplus capacity for growth. In a developmental setting, WDS robustness becomes even more crucial, owing to the limited availability of resources, especially for maintenance. Recent optimization studies have achieved success using multi-objective evolutionary algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II). However, the multi-objective design of a large WDS within a reasonable timeframe remains a formidable problem, owing to the extremely high computational complexity of the problem. In this paper, a meta-algorithm called AMALGAM is applied for the first time to WDS design. AMALGAM uses multiple metaheuristics simultaneously in an attempt to improve optimization performance. Additionally, a Jumping-gene Genetic Algorithm (NSGA-II-JG) is also applied for the first time to WDS design. These two algorithms were tested against some other metaheuristics (including NSGA-II and a new greedy algorithm) with respect to a number of benchmark systems documented in the literature, and AMALGAM demonstrated the best performance overall, while NSGA-II-JG fared worse than the ordinary NSGA-II. Large cost savings and reliability improvements are demonstrated for a real WDS developmental case study in South Africa.     

Hybrid intelligent scenario generator for business strategic planning by using ANFIS

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of experts in scenario planning. The proposed hybrid intelligent scenario generator uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to deal with uncertain inputs. In this methodology, the strengths of expert systems, fuzzy logic and Artificial Neural Networks (ANNs) are joined to generate possible future scenarios. The proposed methodology includes four steps: step 1 defines the scope and internal and external variables and step 2 determines rules from experts. Then, step 3 prepares ANFIS system which is conducted by computer programming in Matlab environment. The Last step is sensitivity analysis to study the effects of variation of inputs on outputs. The applicability of the proposed method has been tested against two different case studies.     

基于场景变化的传输控制协议拥塞控制切换方案

针对轻量级基于学习的拥塞控制算法在某些场景下性能表现会出现断崖式下滑的问题,提出了一种基于场景变化的传输控制协议拥塞控制切换方案。首先,该方案模拟实时的网络环境;然后,根据实时的环境参数来识别场景;最后,将当前的拥塞控制算法切换至该场景下相对最优的轻量级基于学习的拥塞控制算法。实验结果表明,所提方案相较于原来使用单个拥塞控制算法的方案,例如测量瓶颈链路带宽和时延的拥塞控制（BBR）方案、面向性能的拥塞控制（PCC）方案等,可以使不同场景下的网络性能得到显著提升,总吞吐量增幅达到5%以上,总时延降幅达到10%以上。     

A population dynamics approach for the water distribution problem

The water distribution system (WDS) is composed of several elements, where flow control is one of the most important components needed in order to provide a satisfactory level of service. In order to achieve an adequate level of water in the distribution tanks, we need to dynamically control the flow. Here, we propose a population dynamics approach in order to control tanks, by allocating in them the maximum uniform volume. The feedback interconnected systems reach a stable equilibrium point for both approaches presented (i.e. replicator and supply dynamics), and more specific an asymptotically stable equilibrium point for the replicator dynamics case. The stability analysis uses some passivity concepts and classic Lyapunov theory for a closed-loop system that combines the population dynamics (controller), and the WDS (process). We show via simulations the operation under different scenarios.     

Processing online analytics with classification and association rule mining

Business performance measurements, decision support systems (DSS) and online analytical processing (OLAP) have a common goal i.e., to assist decision-makers during the decision-making process. Integrating DSS and OLAP into existing business performance measurements hopes to improve the accuracy of analysis and provide in-depth, multi-angle view of data. This paper describes a decision support system containing our methodology, Weighted and Layered workflow evaluation (WaLwFA), extended to incorporate business intelligence using C4.5 and association rule algorithms. C4.5 produces more comprehensible decision trees by showing only important attributes. Furthermore, C4.5 can be transformed into IF-THEN rules. However, association rules are preferred as data can be described in rules of multiple granularities. Sorting rules based on rules’ complexities permits OLAP to navigate through layers of complexities to extract rules of relevant sizes and to view data from multidimensional perspectives in each layer. Experimental results on an airline domain are presented.