Compromise Decision Research Articles

Lower Bounds on Integrated Risk, Subject to Inequality Constraints

SummaryStatistical decision theory can sometimes be used to find, via a least favourable prior distribution, a statistical procedure that attains the minimax risk. This theory also provides, using an ‘unfavourable prior distribution’, a very useful lower bound on the minimax risk. In the late 1980s, Kempthorne showed how, using a least favourable prior distribution, a specified integrated risk can sometimes be minimised, subject to an inequality constraint on a different risk. Specifically, he was concerned with the solution of a minimax‐Bayes compromise problem (‘compromise decision theory’). Using an unfavourable prior distribution, Kabaila & Tuck (), provided a very useful lower bound on an integrated risk, subject to an inequality constraint on a different risk. We extend this result to the case of multiple inequality constraints on specified risk functions and integrated risks. We also describe a new and very effective method for the computation of an unfavourable prior distribution that leads to a very useful lower bound. This method is simply to maximize the lower bound directly with respect to the unfavourable prior distribution. Not only does this method result in a relatively tight lower bound, it is also fast because it avoids the repeated computation of the global maximum of a function with multiple local maxima. The advantages of this computational method are illustrated using the problems of bounding the performance of a point estimator of (i) the multivariate normal mean and (ii) the univariate normal mean.

A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System

To address the decision-making problem for real-time multi-objective flood operations in multi-reservoir system, this paper develops a multi-objective best compromise decision model (MoBCDM). Utility function is used to quantitatively express the preference of decision maker, and also fuzzy analytic hierarchy process (FAHP) and segmentation and averaging (Seg/Ave) are adopted together with the preferences of decision participants (hydrologist and reservoir manager) to convert the problem into a scalar optimization. The differential evolution (DE) algorithm is implemented for obtaining the best compromise solution. The multi-objective flood operation problem in Shiguan River Basin (in China), which contains two reservoirs and three flood control points, is used as a case study. The analyses are performed to compare four historical flood operations scenarios, this model and current operating rules. The results of the analyses show that the MoBCDM outperforms all operational scenarios in terms of peak flow reduction at three flood downstream control points. In addition, the MoBCDM execution is very efficient in real-time implementation, and also weighting coefficients for the use by the MoBCDM can get high resolution calculated by FAHP.

Design Exploration to Determine Process Parameters of Ladle Refining for an Industrial Application

Due to increasing competition and the demand for light-weight steel, manufacturers strive to produce new grades of steel to meet stringent sets of property specifications. The authors are informed by engineers at a major steel-making plant that for them to determine the set-points for the ladle, tundish, and caster to make a new grade of steel it takes between 20 and 25 physical plant trials. These plant trials are expensive and very difficult to schedule. In this paper, the authors focus on explicating the method for one unit operation in the steel-making process, namely, ladle refining, which is a key operation for maintaining cleanliness and controlling composition; these have a significant bearing on the steel's mechanical properties. The challenge is to determine set points for ladle refining using computational models. In this paper, the authors present a method for visualizing and exploring the solution space using the compromise Decision Support Problem (cDSP). To illustrate the efficacy of the method, the authors determine the set points of the ladle operation in an industrial setting. The focus of the paper is to elucidate the utility of method and not on the analysis models used in the framework.

Solving the bilateral group selection problem using optimal stopping options

The group selection problem (GSP) models situations in which a decision maker (DM) is required to select the best from amongst a set of offers that are presented sequentially in a specified number of groups. Such a decision process is a part of everyday organisational life where dynamic decisions have to be taken in real time. However, practical scenarios involve more than one person in making critical decisions. Motivated by these facts, we address in this article the GSP with two DMs. We define the bilateral GSP as follows: two DMs are required to collectively make a choice from alternatives that are presented over time in groups. Every DM has its own evaluation of the inspected offers which may eventually lead to conflicts if the DMs do not agree on a decision. To solve this problem, we propose a dynamic programming approach incorporating conflict management rules to come up with a compromise decision. We test our approach on several data sets and for different DMs' utilities, and we finally show the relevance of our approach via convenient performance measures.

Solving the bilateral group selection problem using optimal stopping options

The group selection problem (GSP) models situations in which a decision maker (DM) is required to select the best from amongst a set of offers that are presented sequentially in a specified number of groups. Such a decision process is a part of everyday organisational life where dynamic decisions have to be taken in real time. However, practical scenarios involve more than one person in making critical decisions. Motivated by these facts, we address in this article the GSP with two DMs. We define the bilateral GSP as follows: two DMs are required to collectively make a choice from alternatives that are presented over time in groups. Every DM has its own evaluation of the inspected offers which may eventually lead to conflicts if the DMs do not agree on a decision. To solve this problem, we propose a dynamic programming approach incorporating conflict management rules to come up with a compromise decision. We test our approach on several data sets and for different DMs' utilities, and we finally show the relevance of our approach via convenient performance measures.

철도구조개혁 과정에서의 이해집단간 갈등탐색에 관한 고찰: 옹호연합모형(ACF)을 중심으로

우리나라의 철도산업 구조개혁 관련 논의는 1998년 철도청 경영진단을 시작으로 2005년 한국철도공사의 출범, 2013년 수서발 KTX 법인 면허 발급과 민영화 논란에 이르기까지 지속적으로 이루어져 왔다. 본 연구에서는 철도산업 구조개혁이 10년 이상의 장기성을 통해 이루어진 점에 비추어, 이러한 정책변동은 여러 외적 변수와 장기적인 정책주체들의 상호작용에 기인하여 이루어졌다고 보고, 옹호연합모형을 적용하여 고찰하고자 하였다. 철도산업 구조개혁은 연합(Coalition)간의 합의점을 도출하지 못한 채 정권교체 등의 시점에 보다 우월한 지배연합에 의해 정책이 추진되었다고 볼 수 있으며, 이러한 정책의 성공적인 집행을 위해서는 깊은 갈등관계 속에서의 상이한 정책선호와 도구들을 중재하고 절충할 수 있는 대안을 도출하는 정책중개자의 역할이 중요할 것으로 판단하였다. The discussion about the structural reformation of Korean rail industry has been consistently conducted from the evaluation on Korean National Railroad's management in 1998, the foundation of KORAIL in 2005 to the approval of Suseo KTX corporation establishment in 2013 and the controversy over its privatization. This research explores the changes of government policies by applying to ACF in the assumption that those changes have been implemented due to the interaction of various external variables and main agents in policy which had existed for a long-term period that the rail industry's structural reformation has been practiced for more than 10 years. The unions failed to reach an agreement for the rail industry's structural reformation, the policies regarding the reformation have been led by superior political unions. And this research concludes that a mediator who can compromise unions' different policy preferences and means and bring up a compromised decision in conflicted situation plays a pivotal role to successfully practice these policies.

The entrepreneurial middle ground: Higher education entry decisions of aspiring entrepreneurs

The growing demand for more entrepreneurs has engendered a proliferation of entrepreneurship education programmes, which, in different ways, aim to assist aspiring entrepreneurs ‘learn’ entrepreneurship. Yet, understanding the higher education entry decisions of aspiring entrepreneurs is a veritable research lacuna, which creates fertile ground for investigation. This paper reports on an exploratory study investigating the entry decisions of first-year participants enrolled on a recently launched degree programme employing a team-based, experiential approach to learning. The analysis uses an interpretive frame to explore why aspiring entrepreneurs opt for formal education in place of, or alongside, other learning and career journeys. Findings indicate that some participants opted for a university education as part of a positive compromised decision, reflecting the reticence of these aspiring entrepreneurs to ‘go it alone’ in the world of business. In this sense, such experiential forms of entrepreneurial education may provide a suitable ‘middle ground’ for some aspiring entrepreneurs.

Design Exploration for Determining the Set Points of Continuous Casting Operation: An Industrial Application

To compete with other materials and/or contribute toward light-weighting of vehicles, newer grades of steel are continuously invented and experimented upon. Due to the costs and time involved in such developments, manufacture of new grades of steel at an industrial scale is difficult. We propose a method that is useful for steel manufacturers interested in producing a steel product mix with new grades of steels by predicting the required change in the operating set points of each unit operation in the manufacturing chain of products with the new grade of steel. Here, we demonstrate a method to determine the set points of one unit operation, continuous casting which is measured in terms of conflicting objectives including productivity, quality, and production costs. These parameters are sensitive to the operating set points of casting speed, superheat, mold oscillation frequency, and secondary cooling conditions. To ensure targeted performance and address the challenges of uncertainty and conflicting objectives, an integrated computational method based on surrogate models and the compromise decision support problem (cDSP) is presented. The method is used to explore the design space available for casting operations and determine operating set points to meet requirements imposed on the caster from subsequent downstream processes. This method is of value to the steel industry and enables the rapid and cost effective production of a product mix with a new grade of steel.

Reconstruction of Pedagogical Education—The Fundamental and the Applied in Teacher Education

Authors propose a new approach for the development of educational sphere, where philosophical categories of fundamental and applied knowledge are considered as two counter processes of reality and pole cognition, between which project-technological activity is carried out. This fact allows to treat any pedagogical project as a compromise decision, middle manifestation between the indicated poles, made by multiple coordinations. Taking into account dual character of education, while performing educational activity it is possible to observe so called «pedagogical swing» to either way, expressed by wide diversity. The principle of necessity and reasonable sufficiency of the categories correlation is based on education and training of the specialists.

Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the “best” solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions.

Design Exploration of Engineered Materials, Products, and Associated Manufacturing Processes

In the past few years, ICME-related research has been directed towards the study of multi-scale materials design. However, relatively little has been reported on model-based methods that are of relevance to industry for the realization of engineered materials, products, and associated industrial manufacturing processes. Computational models used in the realization of engineered materials and products are fraught with uncertainty, have different levels of fidelity, are incomplete and are even likely to be inaccurate. In light of this, we adopt a robust design strategy that facilitates the exploration of the solution space thereby providing decision support to a design engineer. In this paper, we describe a foundational construct embodied in our method for design exploration, namely, the compromise Decision Support Problem. We introduce a problem that we are using to establish the efficacy of our method. It involves the integrated design of steel and gears, traversing the chain of steel making, mill production, and evolution of the material during these processes, and linking this to the mechanical design and manufacture of the gear. We provide an overview of our method to determine the operating set points for the ladle, tundish and caster operations necessary to manufacture steel of a desired set of properties. Finally, we highlight the efficacy of our method.

Analytical approximations for the solution of chance constrained optimization problems

AbstractUncertain influences are inherent in many practical processes, stemming for instance from measurement errors in process optimization or missing knowledge about the future behavior of a given system, e.g., the financial markets. Therefore, it is desirable to explicitly consider uncertainties when optimizing such processes. One possible approach for this task is the usage of chance constrained optimization (CCOPT), which allows that the constraints are only held with a certain probability level. This enables the user to make a compromised decision between reliability and profitability. Solving CCOPT problems usually consists of two steps. First, transforming the chance constraints into deterministic ones and then solving the transformed problem with a standard NLP solver. Depending on the underlying process, several approaches exist for the transformation. Here, we investigate the usage of so called analytical approximations (AAs). In general, AA approaches do not lead to an exact deterministic representation of CCOPT problems. Nonetheless, it can be shown that a solution obtained by AA is always feasible for the original problem. Furthermore, the corresponding optimization problem generated by AA is computationally more tractable than the original formulation, making these approaches interesting for larger scale processes. We propose a new AA method and present a comparison with existing AA approaches, considering efficiency and suitability. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Robust engineering: improved inductive design exploration approach to bionic system

Multidimensional space biomimetic information of space variables is acquired by evaluating bionic degree indices of them. According to bionic degree of each space variable, comprehensive hyperdimensional error margin index in variable space is defined to characterise bionic robustness of discrete points in input space based on inductive design exploration method. Feasible point checking equation is established, which considers differences between space variables, to further reduce target dimensions in optimisation and distinguish variables with different bionic degree. Compromise decision support problem method is used to identify the best solution from the subset using deviation of comprehensive hyperdimensional error margin index in each output space as objective function.

Exploring the geometry and material space in gear design

Gear design is a complex process. When new materials and manufacturing processes are introduced, traditional empirical knowledge is unavailable and considerable effort is required to find starting design concepts. This forces gear designers to go beyond the traditional standards-based design methods. The method presented here, the concept exploration method, is based on the compromise decision support problem and is demonstrated for gear design which requires the simultaneous exploration of geometry, material and manufacturing spaces to exploit synergies. The results obtained are in agreement with existing knowledge. To further develop design guidelines, ternary contour plots of goal achievement are created to show the interdependence among various design goals. These ternary charts help gear designers to visualize and understand the complexity and compromises involved and help them to make trade-offs among individual goals.

Recognizing, Naming, and Measuring a Family Intensive Care Unit Syndrome

Most major decisions in the intensive care unit (ICU) regarding goals of care are shared by clinicians and someone other than the patient. Multicenter clinical trials focusing on improved communication between clinicians and these surrogate decision makers have not reported consistently improved outcomes. We suggest that acquired maladaptive reasoning may contribute importantly to failure of the intervention strategies tested to date. Surrogate decision makers often suffer significant psychological morbidity in the form of stress, anxiety, depression, and post-traumatic stress disorder. Family members in the ICU also suffer cognitive blunting and sleep deprivation. Their decision-making abilities are eroded by anticipatory grief and cognitive biases, while personal and family conflicts further impact their decision making. We propose recognizing a family ICU syndrome to describe the morbidity and associated decision-making impairment experienced by many family members of patients with acute critical illness (in the ICU) and chronic critical illness (in the long-term, acute care hospital). Research rigorously using models of compromised decision making may help elucidate both mechanisms of impairment and targets for intervention. Better quantifying compromised decision making and its relationship to poor outcomes will allow us to formulate and advance useful techniques. The use of decision aids and improving ICU design may provide benefit now and in the near future. In measuring interventions targeting cognitive barriers, clinically significant outcomes, such as time to decision, should be considered. Statistical approaches, such as survival models and rank statistic testing, will increase our power to detect differences in our interventions.

Fuzzy Decision Method Applied in Action of Reactive Power Compensation Devices in Wind System

Frequent occurrence of large-scale cascading trip-off of wind turbine raises the concern about the decision process of ordered control of reactive power compensation devices. The theory of fuzzy multi-attribute decision making is adopted to ascertain the action sequence of reactive power compensation devices. First, a set of evaluation indexes including control sensitivity, regulation margin, response time, response level and cost is set up, and fuzziness of the proposed qualitative indexes is introduced to make them comparable to the proposed quantitative indexes. Then a method to calculate fuzzy weight of each index is put forward for evaluating relative importance of the proposed indexes. Finally, the action sequence of reactive power compensation devices is determined through the theory of fuzzy compromise decision making. The case study shows that the proposed method is effective to obtain the action sequence of reactive power compensation device which correspond to experience.

Optimal Decisions on the Accelerated Degradation Test Plan Under the Wiener Process

The cumulative damage of highly reliable products that subject to multiple loading stresses is investigated under Wiener process. Optimal strategies on the constant-stress accelerated degradation test plan are established to reach a compromised decision between the experiment budget and the estimation precision on the reliability inference. An algorithm is provided to search an optimal strategy for the accelerated degradation test. An example of light emitting diodes is used for illustrating the application of the proposed method.

Making Sense of Doing Wrong: On the Justification of Compromise Decisions

This paper holds that compromises are a kind of agreement and also a kind of decision. The main objectives are: 1) to identify the formal structure of compromise situations, or predicaments where some compromise decision (CD) is unavoidable, including CDs that jeopardize the decision-maker’s integrity; 2) through Amartya Sen’s notions of basic and compulsive judgments, to establish when a CD in a situation of compromise could be morally justified. It concludes that justified CDs involve a rationally justified moral regret which helps to bridge the gap between deontology and consequentialism.

Development and validation of an UPLC method for determination of content uniformity in low-dose solid drugs products using the design space approach

A simple and reproducible UPLC method was developed and validated for the quantitative analysis of finasteride in low-dose drug products. Method validation demonstrated the reliability and consistency of analytical results. Due to the regulatory requirements of pharmaceutical analysis in particular, evaluation of robustness is vital to predict how small variations in operating conditions affect the responses. Response surface methodology as an optimization technique was used to evaluate the robustness. For this, a central composite design was implemented around the nominal conditions. Statistical treatment of the responses (retention factor and drug concentrations expressed as percentage of label claim) showed that methanol content in mobile-phase and flow rate were the most influential factors. In the optimization process, the compromise decision support problem (cDSP) strategy was used. Construction of the robust domain from response-surfaces provided tolerance windows for the factors affecting the effectiveness of the method. The specified limits for the USP uniformity of dosage units assay (98.5–101.5%) and the purely experimental variations based on the repeatability test for center points (nominal conditions repetitions) were used as criteria to establish the tolerance windows, which allowed definition design space (DS) of analytical method. Thus, the acceptance criteria values (AV) proposed by the USP-uniformity of assay only depend on the sampling error. If the variation in the responses corresponded to approximately twice the repeatability standard deviation, individual values for parcentage label claim (%LC) response may lie outside the specified limits; this implies the data are not centered between the specified limits, and that this term plus the sampling error affects the AV value. To avoid this fact, the limits specified by the Uniformity of Dosage Form assay (i.e., 98.5–101.5%) must be taken into consideration to fix the tolerance windows for each factor. All these results were verified by the Monte Carlo simulation.In conclusion, the level of variability for different factors must be calculated for each case, and not arbitrary way, provided a variation is found higher than the repeatability for center points and secondly, the %LC response must lie inside the specified limits i.e., 98.5–101.5%. If not the UPLC method must be re-developed.

Hierarchical product platform design: a domain-independent approach

In this paper, we present a systematic domain-independent approach for realising hierarchical product platforms using numerical taxonomy and technology diffusion based on the compromise Decision Support Problem (cDSP). Hierarchical product platforms present a form of competitive advantage by introducing standardisation on multiple system levels or scales. In this paper, we focus on how to synthesise numerical taxonomy and technology diffusion in a systematic multi-objective decision-making method based on cDSP templates to develop standardisation in platforms. This domain-independent approach to hierarchical product platform design is applied to develop a hierarchical product platform for a family of offshore vessels. Results of the hierarchical offshore vessel product platform design problem and the domain-independent approach to hierarchical product platform design are critically evaluated. The focus of this paper is on the design method rather than on the specific details of this problem.