Proposed Decision Framework Research Articles

Decision framework for optimal installation of outriggers in tall buildings

Abstract Installation sequence of outrigger system, an important structural component of high-rise buildings, is often determined simply based on engineers' experience, posing a threat to the structural safety and stability. This paper proposes a comprehensive decision framework for developing the optimal installation plan for the outrigger system, in which construction simulation and safety analysis of the overall structural system are well integrated. The proposed framework is applied to a super-tall building with a height of 600 m. First, the finite element method (FEM) model of the skyscraper used for construction simulation is validated by field measurements during Typhoon ‘Nida’. Based on the validated FEM model, the lower limits (earliest) for installing the outrigger system are obtained through the outrigger trusses' safety analysis for the service stage of the building, while the upper limits (latest) are determined through the analysis of structural stiffness and global stability for the construction stage. Thereupon, a rational plan is established for installing the outrigger system into the skyscraper, and the viability and efficiency of the proposed decision framework are examined by analyzing the construction simulation models. The outcomes of this study are expected to be of use and interest for structural engineers and researchers involved in construction management of installing outriggers into high-rise buildings, and therefore provide valuable implications for other similar projects.

Development of a balanced scorecard-based supplier collaborative performance index

PurposeThe purpose of this paper is to design and develop a balanced scorecard (BSC)-based index for quantifying the suitability of suppliers to mature to the next level of collaboration with the buyer company.Design/methodology/approachA BSC-based decision framework was developed using factors and factor indicators affecting collaboration with the supplier, using exploratory research. The proposed decision framework was implemented as a real-world case study in an Indian manufacturing organisation by using the Analytic Hierarchy Process. An 11-step methodology was developed to quantify supplier suitability to mature to the next level of collaboration. This is referred to as the “supplier collaborative performance index” (SCPI).FindingsThe proposed decision-making framework helps quantify the extent of collaboration with each supplier. It serves as an index by using the perspectives that are significant for the strategic performance of the buyer company, the factors that affect the collaboration and their specific factor indicators. The initial results of the implementation of the case study were found to be useful in judging supplier suitability in order to mature in their relationship.Practical implicationsA comprehensive BSC-based framework for enhancing relationships with suppliers, SCPI will be instrumental in deciding, managing and improving the level of collaboration with suppliers in manufacturing companies, depending on priorities.Originality/valueThis approach provides a single index to establish the supplier’s suitability to mature to the next level of collaboration with a buyer company. The higher the value of the Collaboration Index for a supplier, the better is the chance to move to the next level of maturity.

Fuzzification supported spectrum decision framework for cognitive radio networks

AbstractEfficient decision support framework and swift occupation of the available licensed spectrum slot by secondary users (SUs) are the challenging issues in cognitive radio networks. An SU has to sense multiple target frequency spectrum slots in the shortest possible time before deciding to select and occupy the best idle slot for its transmission. A spectrum decision framework for the SUs in cognitive radio networks based on fuzzy logic is proposed. The activity time, possession, and quality of service of the spectrum slot lead to the decision of occupying the available spectrum slot(s). The proposed decision framework ensures that the SU's transmission in the particular spectrum slot does not cause any interference with the licensed user of the slot. The proposed method yields enhanced throughput as compared with the existing channel assignment schemes.

Rational Decision Framework for Designing Pile-Load Test Programs

Abstract There is currently an inconsistency in the recommendations that are available in pile-design codes and practices regarding the required number of proof-load tests and the level of the proof loads for piles. In this paper, a pre-posterior decision-making framework is proposed to allow for selecting the optimal pile-load test program that would result in the maximum expected benefit to a project, while maintaining a target level of reliability in the pile design at the site. This proposed methodology is original, practical, and is based on site-specific information that is unique to any given project. The proposed methodology is based on a robust Bayesian approach that allows for updating the capacity distribution of piles at a site, given the results of the proof-load test program. The efficiency of the proposed decision framework is demonstrated by applying it on a practical design example that involves piles that are driven in a site consisting of medium-dense sand. Results indicate that: (1) the optimum proof-load level that results in the maximum benefit to the example project is 1.5 times the design load, (2) the optimum number of tests is a function of the number of piles (superstructure load) and the costs of the pile construction and testing, (3) as the number of piles in the site increases, the optimal required number of proof-load tests also increase, with the optimum number of pile-load tests being around 1 % to 2 % of the total number of piles at the site, and (4) the optimal number of pile-load tests increases as the cost of pile construction and installation increases and as the cost of implementing the pile test program decreases.

A cloud decision framework in pure 2-tuple linguistic setting and its application for low-speed wind farm site selection

Low-speed wind farm site selection is crucially important for investment returns. However, three great problems reducing the decision-making accuracy and restricting applications exist in the present multiple criteria decision analysis. Firstly, the uncertainty of information fails to be fully described, without considering its randomness. Secondly, during dimensionless treatment and normalizing, some information distortion and loss are caused when evaluating the differences among criteria values just from a mathematical standpoint. Thirdly, the managers are excluded from the decision-making process, which decreases the practicality and operability, and considerably restricts the application of the decision-making methods at the same time. In order to overcome these deficiencies, a cloud-based decision framework under pure 2-tuple linguistic environment is proposed for low-speed wind farm site selection in this paper. First, the criteria values are transformed into 2-tuple linguistic through dimensionless treatment and normalizing; then the extended golden section method is used to transform 2-tuple linguistic into cloud variable. Next, a pure cloud weighted arithmetic averaging operator is constructed to rank the alternatives. After that a case from China is presented to demonstrate the effectiveness. Finally, the comparison analysis and sensitive analysis are conducted, proving the correctness and advantages of the proposed decision framework.

A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty

Wastewater infrastructure management is increasingly important because of urbanization, environmental pollutants, aging infrastructures, and climate change. We propose a scenario-based multi-criteria decision analysis (MCDA) framework to compare different infrastructure alternatives in terms of their sustainability. These range from the current centralized system to semi- and fully decentralized options. Various sources of uncertainty are considered, including external socio-economic uncertainty captured by future scenarios, uncertainty in predicting outcomes of alternatives, and incomplete preferences of stakeholders. Stochastic Multi-criteria Acceptability Analysis (SMAA) with Monte Carlo simulation is performed, and rank acceptability indices help identify robust alternatives. We propose step-wise local sensitivity analysis, which is useful for practitioners to effectively elicit preferences and identify major sources of uncertainty. The approach is demonstrated in a Swiss case study where ten stakeholders are involved throughout. Their preferences are quantitatively elicited by combining an online questionnaire with face-to-face interviews. The trade-off questions reveal a high concern about environmental and an unexpectedly low importance of economic criteria. This results in a surprisingly good ranking of high-tech decentralized wastewater alternatives using urine source separation for most stakeholders in all scenarios. Combining scenario planning and MCDA proves useful, as the performance of wastewater infrastructure systems is indeed sensitive to socio-economic boundary conditions and the other sources of uncertainty. The proposed sensitivity analysis suggests that a simplified elicitation procedure is sufficient in many cases. Elicitation of more information such as detailed marginal value functions should only follow if the sensitivity analysis finds this necessary. Moreover, the uncertainty of rankings can be considerably reduced by better predictions of the outcomes of alternatives. Although the results are case based, the proposed decision framework is generalizable to other decision contexts.

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty’s 1–9 scale, this paper proposes a cross-ratio-based bipolar 0.1–0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.

Decision framework for feasibility analysis of introducing the steam turbine unit to recover industrial waste heat based on economic and environmental assessments

With increasing concern of pollution in manufacturing processes, development of advanced green manufacturing technology has drawn a lot of attention. Due to limited energy efficiency in manufacturing processes, a great sum of thermal energy has been directly released and caused environmental problems. It is of great importance to develop advanced manufacturing technology to recover waste thermal energy for promoting sustainability of manufacturing sector. Steam turbine unit is a common equipment for recovering waste thermal energy. The industrial heat recovery process by steam turbine unit brings both economic and environmental benefits. However, the user or the manufacturing enterprise needs to pay a great cost to purchase, install and maintain the steam turbine unit, and the whole life cycle of the unit casts a considerable environmental load. Due to lack of comprehensive research, it is difficult for the user to decide whether should introduce the steam turbine unit for recovering waste thermal energy or not. This paper aims to establish a decision framework which facilitates this decision making procedure by conducting economic and environmental assessments. First, two working modes of the steam turbine unit are investigated and summarized according to the types of energy output - electricity and mechanical driving forces. Second, the economic and environmental assessing models are developed to estimate the potential profit of waste thermal energy recovery from each working mode based on cost-benefit analysis and life cycle assessment. Third, a decision framework composited with nine possible combinations of economic and environmental assessment results is proposed. Finally, a case study in pure terephthalic acid plant shows that the decision framework can quantify the economic and environmental impact of industrial waste heat recovery. The results indicate that the waste heat recovery for the plant is applicable, and payback period is 2.28 years. The proposed decision framework is proved to be meaningful to the feasibility analysis of the waste thermal energy recovery with steam turbine unit. It is an effective technology for improving energy conservation and emission reduction of manufacturing processes operationally.

Planning for assisted colonization of plants in a warming world.

Assisted colonization is one way of facilitating range shifts for species that are restricted in their ability to move in response to climate change. Here we conceptualize and apply a new decision framework for modelling assisted colonization of plant species prior to in situ realization. Three questions were examined: a) Is species translocation useful in a certain area? b) where, and c) how long will it be successful in the future? Applying our framework to Carex foetida in Italy at the core of its distribution and its southern edge revealed that assisted colonization could be successful in short-term (2010–2039) climate conditions, partially in medium (2040–2069) but not in long-term (2070–2099) scenarios. We show that, for some species, it is likely that assisted colonization would be successful in some portions of the recipient site under current and short-term climate conditions, but over the mid- and long-term, climate changes will make species translocation unsuccessful. The proposed decision framework can help identify species that will need different conservation actions (seed banks and/or botanical gardens) when assisted colonization is unlikely to be successful. Furthermore it has broad applicability, as it can support planning of assisted migration in mountainous areas in the face of climate change.

A decision framework for Integrated Assessment Modelling of air quality at regional and local scale

Decision making in the field of air quality and greenhouses gases reductions can nowadays be supported by a clear overall framework and by computer tools that integrate the most relevant aspects of the problem. This approach is particularly important at local scale since new general rules on emission abatement at European level can only marginally modify the most critical hotspots and may be very costly. This paper adapts the general Drivers, Pressures, State, Impacts, Responses (DPSIR) scheme proposed by the European Environment Agency to the specific case of local air quality policies and shows how the most recent scientific developments in impact evaluation and social acceptance can be integrated. The proposed decision framework represents a general methodology to design Integrated Assessment Modelling (IAM) systems aimed at the implementation of effective Air Quality Plans (AQP). An extensive survey across European countries shows the current degree of adoption of these approaches.

CRITICAL FACTORS IMPORTANT FOR EFFECTIVE INDUSTRY- INSTITUTE INTERACTIONS (III): AN INDIAN PERSPECTIVE

<p>The management of Industry-Institute Interactions (III) has been found to be an important aspect in today’s market. In this paper an attempt has been made to identify Critical Factors (CFs) and rank these for effective III from an Indian perspective. The present paper is based on two research stages. Initially, an extensive literature review was conducted to identify CFs of III. Forty three CFs for effective III were identified and categorized into nine dimensions based on input from experts. In the second stage, the Analytical Hierarchy Process (AHP) was utilized to rank these dimensions and CFs for effective III from an Indian perspective. Paired comparisons in AHP were based on the opinions of experts (selected from academia and industry). The proposed decision framework may offer some valuable guidelines for policy makers to develop their plan of action in terms of design of short and long term policies and strategies to promote effective III in India. A sensitivity analysis was also performed to investigate the robustness and priority ranking stability of CFs in the proposed framework. This paper may help India to achieve and manage effective III leading to potential economic, social, political, cultural and environmental advantages.</p>

A Multi-Criteria Decision Framework to Measure Spiritual Intelligence of University Teachers

Spiritual Intelligence (SI) is an emerging concept in the realm of spirituality. In the current scenario of human development, there is a dire need to integrate spirituality into management, education, research and technology. In fact, a proper convergence of spirituality with the research performance of university teachers may lead to the development of a strong academic learning process. Since SI encompasses a multitude of the dimensions as its construct, a suitable and effective multi-criteria decision framework is proposed to measure SI. The Analytic Hierarchy Process (AHP) has been established as a robust multi-criteria decision-making tool and thus, will be apt for generating SI index which is termed as spiritual quotient (SQ) by considering the SI construct. An illustrative case example is also considered to show the applicability of the proposed decision framework.

Merchant selection and pricing strategy for a platform firm in the online group buying market

The online group-buying market is characterized by intense competition between brokers, called platform firms, which function as intermediaries between merchants and consumers. In an environment of intense competition, merchant selection and pricing strategies are critical for platform firms. This paper employs business analytics to support strategy formulation for these firms by forecasting market demand and analyzing competitive environments. We apply the proposed decision framework, which relies on business analytics, to a study of the online group-buying market in Japan.

Dynamic optimization of maintenance and improvement planning for water main system: Periodic replacement approach

This paper proposes a Markov decision process (MDP) based approach to derive an optimal schedule of maintenance, rehabilitation and replacement of the water main system. The scheduling problem utilizes auxiliary information of a pipe such as the current state, cost, and deterioration model. The objective function and detailed algorithm of dynamic programming are modified to solve the periodic replacement problem. The optimal policy evaluated by the proposed algorithm is compared to several existing policies via Monte Carlo simulations. The proposed decision framework provides a systematic way to obtain an optimal policy.

Concept of decision graphical framework for optimising adaptation of civil infrastructure to a changing climate

This article proposes a framework for decision optimisation for adaptation of civil infrastructure to a changing climate, taking basis in decision graphical representation. The focus of the article is to develop a platform on which multiple climate scenarios and general circulation models, results from probabilistic hazard analysis as well as consequence analysis – together with their uncertainties – are represented in a practically operational manner. The output from the decision framework is the optimal timing and magnitude of the adaptation. First, assumptions underlying the proposed decision framework and types of information required are provided. Thereafter, the proposed decision framework is presented. This work provides the concept only; however, it serves as an exercise and thus provides a research direction on how to collect and summarise information for the purpose to approach to the civil infrastructure adaptation in an emerging climate change.

Optimal Scheduling of the Maintenance and Improvement for Water Main System Using Markov Decision Process

Optimal scheduling of maintenance and improvement on the water main system is a substantial problem since it can greatly reduces the potential threat of failure and the expenses. The Markov decision process (MDP) based methodology to find the optimal schedule which minimizes the cost is proposed. Since it needs the information about the current state of pipe, cost, and deterioration model, the definition and the usage of auxiliary information are also presented. The objective function and detailed algorithm of dynamic programming (DP) are modified due to the difficulty of implementing the conventional DP approaches. The result is compared to two simple policies via Monte Carlo simulation. Validity of the solution and improvement in computational time are proved. Proposed decision framework provides an easy way to obtain the comprehensive and local-specialized policy.

A Decision Framework for Optimal Pairing of Wind and Demand Response Resources

Day-ahead electricity markets do not readily accommodate power from intermittent resources such as wind because of the scheduling difficulties presented by the uncertainty and variability in these resources. Numerous entities have developed methods to improve wind forecasting and thereby reduce the uncertainty in a day-ahead schedule for wind power generation. This paper introduces a decision framework for addressing the inevitable remaining variability resulting from imperfect forecasts. The framework uses a paired resource, such as demand response, gas turbine, or storage, to mitigate the generation scheduling errors due to wind forecast error. The methodology determines the cost-effective percentage, or adjustment factor, of the forecast error to mitigate at each successive market stage, e.g., 1 h and 10 min ahead of dispatch. This framework is applicable to any wind farm in a region with available pairing resources, although the magnitude of adjustment factors will be specific to each region as the factors are related to the statistics of the wind resource and the forecast accuracy at each time period. Historical wind data from New England are used to illustrate and analyze this approach. Results indicate that such resource pairing via the proposed decision framework will significantly reduce the need for an independent system operator to procure additional balancing resources when wind power participates in the markets.

Risk-constrained framework for residential storage space heating load management

Residential demand response based on real time pricing provides a strong incentive for customers to reduce their energy payment. However, acting under an environment with time-varying prices will expose them to uncertain energy bills. This paper presents a risk-constrained framework for residential customers for scheduling the electric storage space heating load. The proposed decision framework attempts to accomplish desired settlement between expected cost minimization and cost deviation without altering the user's thermal comfort. The price and load uncertainty are captured by a scenario based stochastic programming approach. The optimization model is solved using Genetic Algorithm and implemented using a moving-window procedure. The simulation results demonstrate that the proposed framework for scheduling the storage space heating load provides a method to selectively hedge against the price and load uncertainty risk. The optimal framework will result in an improved interaction between the electrical aggregator and its customers under the smart grid paradigm.

Risk-Constrained Operation for Internet Data Centers in Deregulated Electricity Markets

In this paper, we study the problem of achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators. To achieve the target above, we propose a risk-constrained stochastic programming decision framework. Then, we formulate a risk-constrained expected energy cost minimization problem with the uncertainties in spot price and workload. To solve the formulated problem, we use a decomposition-based cutting plane algorithm. Finally, extensive evaluations based on real-life data show the effectiveness of the proposed decision framework.

A decision model for setting target levels in software quality function deployment to respond to rapidly changing customer needs

Responding to the need for enhanced software quality, software manufacturers have recently applied quality improvement techniques to software development process. Software quality function deployment, as a technique for better quality designs that match customer expectations, has been used in software development to maximize customer satisfaction. This article proposes a fuzzy regression-based decision framework that considers future requirements for setting target levels of technical attributes in software quality function deployment. Ordered weighted averaging operators are employed to implement quantifier-guided aggregations that enable to calculate the integrated importance degree for each customer need by combining the current and future voice of customers. Then, a fuzzy optimization model, which considers the integrated importance degrees of customer needs and uses the functional relationships obtained by fuzzy regression, is developed to determine target levels of technical attributes of the new or improved software products that meet customer needs at the time when the product reaches the market. The fuzzy optimization model includes both the center values and the spread values of the parameter estimates and thus circumvents loss of information in determining target levels of technical attributes. A search engine quality improvement problem is presented to illustrate the proposed decision framework.