Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

Motivated by a cutting edge problem related to the shape of ?-helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a "kink," which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight ?-helix and derive the maximum likelihood estimation procedure. The cylinder is an accepted geometric model for ?-helices, but our statistical formulation, for the first time, quantifies the uncertainty in atom positions around the cylinder. We propose a change point technique "Kink-Detector" to detect a kink location along the helix. Unlike classical change point problems, the change in direction of a helix depends on a simultaneous shift of multiple data points rather than a single data point, and is less straightforward. Our biological building block is crowdsourced data on straight and kinked helices; which has set a gold standard. We use this data to identify salient features to construct Kink-detector, test its performance and gain some insights. We find the performance of Kink-detector comparable to its computational competitor called "Kink-Finder." We highlight that identification of kinks by visual assessment can have limitations and Kink-detector may help in such cases. Further, an analysis of crowdsourced curved ?-helices finds that Kink-detector is also effective in detecting moderate changes in axial directions.

Pursuit of contradictory goals is inherent in the activities of any typical public-sector organization. Bulk of the existing literature on how contradictory goals can be pursued successfully focuses on a single pair of contradictory goals, largely pursued by private sector organizations. This empirical study identifies a system of multiple pairs of contradictory goals associated with structural separation of subunits of a public-sector organization in India, where the organization is a municipal corporation and two of its subunits engaged in the business of Public Urban Transport are involved. Emergence of contradictory goals was observed to be linked with the dynamics of key stakeholder interests. The study revealed that the top and middle managers may respond differently to a pair of contradictory goals, and the same manager also responds differently to different pairs of contradictory goals. Three pairs of contradictory goals were identified. These goals involved subunit identity and shared identity, contractor compliance and maintaining relationship with the contractor, and performance and learning. Realization of organizational ambidexterity appeared linked to how well the simultaneity and variety of contradictory goals are matched by simultaneity and variety of responses, namely a combination of structural, contextual and behavioral arrangements by the organization and individuals.

In this paper we propose an optimization model for scheduling electrical appliances for an individual household. Here, customers are offered dynamic prices which are a function of that household's planned consumption and forecasted grid load. We consider a grid connected system with a battery and an in-house renewable energy generator in the proposed scheduling model. This model minimizes the customer's electricity bill subject to different constraints. We analyze our model with various pricing policies, price ranges and appliance operation windows. We find that the expenditure of the consumer decreases considerably in our model when shifting from flat prices to dynamic prices based on the forecasted grid load and the consumer's individual planned consumption. Considerable expenditure reduction and individual load flattening is achieved with the use of a battery and an in-house renewable energy generator. Also, larger the price range, higher is the load flattening and lower is the expenditure. We show that our proposed pricing policy is beneficial to both consumers and suppliers.

Insincere respondents can have an adverse impact on the validity of substantive inferences arising from self-administered questionnaires (SAQs). The current study introduces a new method for identifying potentially invalid respondents from their atypical response patterns. The two-step procedure involves generating a response inconsistency (RI) score for each participant and scale on the SAQ and subjecting the resulting scores to latent profile analysis to identify classes of atypical RI respondent profiles. The procedure can be implemented post-data collection and is illustrated through a survey of school climate that was administered to N = 52,102 high school students. Results of this screening procedure revealed high levels of specificity and expected levels of concordance when contrasted with the results of traditionally used methods of screening items and response time. Contrasts between valid and invalid respondents revealed similar patterns across the three screening procedures when compared across external measures of academics and risk behaviors.

Voting Advice Applications (VAAs) are online decision support systems that try to match voters with political parties or candidates in elections, typically based on how each responds to a number of policy issue statements. Such VAAs play a major role in many countries. In this paper, we describe the development and large-scale application of a new innovative matching algorithm for the most widely used VAA in Finland. We worked closely with the owner of the VAA, the largest daily newspaper in Finland, Helsingin Sanomat. Their previous algorithm, which one might call a "naive" approach, was improved by including measures of candidate's political power and influence, using proxy variables of media visibility and incumbency status. The VAA was implemented for the 2015 Parliamentary Election in Finland; our matching algorithm was used by 140,000 voters (26.7% of the electorate) in the Helsinki election district. The innovative algorithm generated recommendations that many voters were happy about, followed by users incidental comments that this was the first time the VAA recommended candidates they wanted to vote for. This showed the importance of catering to different kinds of voters with a model not previously considered by any VAA in any country.

This study examines the role of information constraints in the adoption of the System of Rice Intensification (SRI) in India by explicitly incorporating information in the adoption model. The results showed that effective information along with other factors such as membership in a farmer organisation, availability of labourers, irrigation facility and so forth were important in determining the SRI adoption. The results also revealed that the Government of India's National Food Security Mission programme did not have significant impact in promoting greater dissemination and adoption of SRI.

[No abstract available]

This study examines three important research questions. First, is there a latitude of acceptance with respect to small quantity changes? Second, is there an asymmetric effect of quantity changes? Third, is there a differential effect between high-equity and low-equity brands in response to quantity changes which is acceptable to customers? The effect of quantity change on purchase intention was examined through the decrease (increase) in original quantity of high-equity brands (low-equity brands), keeping price constant. ANOVA and ANCOVA were used to estimate the main and interaction effects. Empirical results show that: (a) there is evidence for LQA; (b) the effect of quantity change is asymmetric; and (c) the LQA range is larger for low-equity brands. A lower range of LQA for high-equity brands limits quantity reduction choice as a firm strategy and lowering price by a small percentage is unlikely to be successful for a low-equity brand.

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.

Purpose: Knowledge is considered as a strategic asset for the organizations, especially for knowledge-intensive firms. Research and development (R&D) is a significant unit in organizations, as it is devoted to knowledge creation and transfer. The success of any R&D project in an organization depends on its innovative value and the transfer of knowledge to the employees. This study aims to focus on factors triggering knowledge-hiding behavior among R&D employees, thus disrupting the knowledge creation in the organization. Design/methodology/approach: The grounded theory approach has been used to analyze qualitative data collected from 19 in-depth interviews of R&D professionals (middle and junior level) working in Indian pharmaceutical firms. Findings: The study identified factors that triggered knowledge-hiding behavior among employees. These factors include distrust, competitive work environment, perceived career insecurity, lack of recognition, lack of reciprocation and lack of confidence in own knowledge. In addition, four hiding strategies used by employees to hide their knowledge from their fellow members were explored and identified: playing innocent, being misleader/evasive hiding, rationalized hiding and counter-questioning. Research limitations/implications: Besides improving the understanding of knowledge-hiding behavior, particularly in the Indian context, this study has implications for both managerial practices and organizational policies. Originality/value: This paper highlights the factors influencing knowledge-hiding behavior among R&D employees. Knowledge-hiding construct has not been adequately studied; however, it prevails in the organization and has potential to influence various individual- and organizational-level outcomes. In addition, ways of hiding knowledge used by employees were identified and new forms of strategies named counter-questioning were found.

Big Data Analytics is an important and flexible tool available for data analysis and informed decision making. In this paper, we look at the use of Big Data Analytics in asset liability management and asset allocation in uncertain economic situations using stochastic linear programming (SLP). In particular, this paper is an extension of our earlier work and we contribute to the existing literature by conducting experiments on the stochastic model through DSS. In particular, for this SLP based DSS, we address issues like the optimal number of scenarios required for good results, and the impact of the change in the number of scenarios on the stability of the model. The paper also addresses the impact of the change in the number of scenarios on the policy holders' as well as shareholders' reserves. In particular, we show the relevance of employing a larger number of scenarios and also present the experimental design developed to test the relevance of this model. We also show that a stochastic model employing fewer scenarios produced marked improvements in both return side measures as well as risk side measures compared to a mean value model or a partial mean value model. copyright @ 2018 Elsevier Ltd

The compensation capabilities of Choquet integral are augmented to consider the complex attitudinal character of a decision maker. The resulting operator is termed as attitudinal Choquet integral (ACI). The proposed ACI is further extended as induced ACI. The special cases of ACI are investigated. The usefulness of ACI is shown through a case study. copyright @ 2018 Wiley Periodicals, Inc.

Biogas has emerged as a promising renewable technology to convert agricultural, animal, industrial and municipal wastes into energy. Biogas development can be integrated with strategies to improve sanitation as well as reduce indoor air pollution and greenhouse gases. Currently, the total biogas production in India is 2.07 billion m3/year. This is quite low compared to its potential, which is estimated to be in the range of 29-48 billion m3/year. Hence, this study aims to identify both technical and non-technological barriers impending biogas dissemination in India. Biogas dissemination is affected by various waste, renewable energy, and urban policies. Barriers were therefore identified individually for rural and urban biogas systems existing in India using decomposition analysis. The results show that type and importance of barriers vary strongly between biogas systems due to the difference in technology maturity, feedstock availability and quality, supply chain, awareness level and policy support. copyright @ 2017 The Author(s)

India has embarked on an ambitious highway development program to significantly improve interstate road transport connectivity. Between 2000 and 2015, total length of India's national highway network has nearly doubled and 4 + lane share of the national highways has increased from 2 to 20%, along with associated improvements in safety and surface quality. National highway development is considered as a tool to promote employment growth by stimulating economic activities and attracting foreign investments. As India's central government continues to increase budget allocations for national highway projects in anticipation of generating more jobs, this study empirically investigates whether past investments can be linked to employment growth. I analyze changes in non-agricultural private sector employment over a 10-year period (2003-2012) across 25 states in response to changes in the density (lane-km per unit area) of national highways, controlling for other factors affecting employment. Using a series of static (pooled ordinary least squares, random-effects and fixed-effects) and dynamic (random- and fixed-effects with first-order autoregressive or AR(1) disturbances, and system GMM or generalized method of moments) panel regressions, I find that 10% increase in national highway density in India is associated with 1-6% (depending on model specification and estimation approach) increase in private sector employment, all else equal. This paper provides first empirical evidence suggesting that India's national highway development efforts have produced positive employment benefits in the past. In addition to contributing to transportation planning scholarship, the findings are expected to inform policy-makers in India as they develop future highway investment plans aimed, in part, at economic development. This paper will also be useful to decision-makers in other developing countries with comparable policy environments. copyright @ 2018 Elsevier Ltd

The increased role of market actors in the provision of drinking water has implied a greater salience to issues of affordability. With its emphasis of increasing affordability without compromising on quality, the idea of frugal innovation holds out particular promise in resource constrained environments. In this paper, we study the case of low cost household water filters using reverse osmosis (RO) technology in the Indian city of Ahmedabad. Using mixed methods, we find the citizen to be an active participant - a co-producer - in the frugal innovation process that allows small scale, local entrepreneurs to customize their products and services to the needs and price expectations of their customers. This co-production process, that has been facilitated by the proximity of the user with the local entrepreneur, has not only reduced private costs borne by users but also been as successful as their more expensive counterparts in meeting the diverse needs of end users. Further, we find that the context facilitating such service co-production is characterized by institutional co-production, with actions of both the state and citizen creating the demand for innovation around more affordable, private solutions. While there is much to celebrate in the value created by the frugal innovation, we believe the implications of the contested and unregulated water filter technology suggest reasons for caution. copyright @ 2016 Elsevier Ltd

The impact of the local contextualisation of successive rounds of educational decentralisation reform on organisational learning and capacities of rural educational governance structures is examined. From locating schools in local self-government in the mid-1990s, the focus shifted in the 2000s to school accountability. This shift induced a reconstruction of the "risk" posed by the earlier round of reform and the identification of aspects of organisational learning to be retained or discarded. Such an ability to choose is an important indicator of organisational capacity for reform. The next round of reforms should include academic supervision in the accountability mandate. copyright @ 2018 Economic and Political Weekly. All rights reserved.

Many objective optimization problems have turned out to be a considerable challenge for evolutionary algorithms due to the difficulty of finding and visualizing high-dimensional Pareto frontiers. Fortunately, however, the task can be simplified whenever an interaction with a human decision maker is possible. Instead of finding the entire Pareto frontier, the evolutionary search can be guided to the parts of the space that are most relevant for the decision maker. In this paper, we propose an interactive method for solving many objective optimization problems. Drawing on the recent developments in multiple criteria decision making, we introduce an effective strategy for leveraging polyhedral preference cones within an evolutionary algorithm. The approach is mathematically motivated and is applicable to situations, where the user's preferences can be assumed to follow an unknown quasi-concave and increasing utility function. In addition to considering the preference cones as a tool for eliminating non-preferred solution candidates, we also present how the the cones can be leveraged in approximating the directions of steepest ascent to guide the subsequent search done by the evolutionary algorithm through a proposed merit function. To evaluate the performance of the algorithm, we consider well known test problems as well as a practical facility location problem. copyright @ 2018 Elsevier Ltd

Since the official submission of the National Determined Contributions (NDCs) under the Paris agreement framework, a set of studies attempted to assess the energy and economic impacts on NDCs by using large-scale energy-economy models, both at the national and global level. This paper presents a novel approach to quantify policy impacts of NDCs, by combining a set of technology-rich country-level models for major economies with an economy-wide global CGE model. The methodology enhances the credibility of global model-based scenarios, by complementing it with detailed representation of country-level policy priorities and structural heterogeneities, captured by national-level models. It also improves the consistency between national short-to-medium term policy plans with global long-term climate objectives. Results indicate that energy efficiency improvements and increased penetration of RES are the main contributors to emission reduction induced by NDCs. The role of emission reduction options differentiates by country, depending on national socio-economic objectives, energy resource endowment and climate policy ambition. The Paris pledges are found to be consistent with robust economic growth both in developed and in emerging economies. NDCs result in a more labour-intensive economy driven by RES expansion, while major clean energy manufacturers (EU, China) have the potential to improve their balance of trade. copyright @ 2018 Elsevier Ltd