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Purpose: In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices' costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures. Design/methodology/approach: A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated. Findings: The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants' comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day. Research limitations/implications: This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope. Originality/value: This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits. [ABSTRACT FROM AUTHOR]

A novel challenge faced by water scientists and water managers today is the efficient management of the available water resources for meeting crucial demands such as drinking water supply, irrigation and hydro-power generation. Optimal operation of reservoirs is of paramount importance for better management of scarce water resources under competing multiple demands such as irrigation, water supply etc., with decreasing reliability of these systems under climate change. This study compares six different state-of-the-art modeling techniques namely; Deterministic Dynamic Programming (DDP), Stochastic Dynamic Programming (SDP), Implicit Stochastic Optimization (ISO), Fitted Q-Iteration (FQI), Sampling Stochastic Dynamic Programming (SSDP), and Model Predictive Control (MPC), in developing pareto-optimal reservoir operation solutions considering two competing operational objectives of irrigation and flood control for the Pong reservoir located in Beas River, India. Set of pareto-optimal (approximate) solutions were derived using the above-mentioned six methods based on different convex combinations of the two objectives and finally the performances of the resulting sets of pareto-optimal solutions were compared. Additionally, key reservoir performance indices including resilience, reliability, vulnerability and sustainability were estimated to study the performance of the current operation of the reservoir. Modeling results indicate that the optimal-operational solution developed by DDP attains the best performance followed by the MPC and FQI. The performance of the Pong reservoir operation assessed by comparing different performance indices suggests that there is high vulnerability (~ 0.65) and low resilience (~ 0.10) in current operations and the development of pareto-optimal operation solutions using multiple state-of-the-art modeling techniques might be crucial for making better reservoir operation decisions. [ABSTRACT FROM AUTHOR]

In the healthcare system, a surgical teamis a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital's patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient. The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient's subgroups. For this end, two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimizationwere proposed. The proposed algorithmswere tested using different datasets and benchmark functions. Furthermore, the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India. The results revealed that the proposed framework was efficient in arranging patients in optimal groups aswell as selecting optimal surgical teams for a given patient. [ABSTRACT FROM AUTHOR]

• A novel design of solar collector combined with biomass-firing heaters is proposed. • Comparative transient modeling is performed to find the best configuration. • Multi-objective optimization is applied to the best configuration. • The optimum domain of major variables is determined through scatter distribution. • The optimum cost and emission of 47.1 $/MWh and 440.62 kg/MWh is obtained. This work proposes a novel hybrid renewable-based cold production system consisting of an innovative yet simple design of evacuated solar collectors integrated with a biomass heater, thermal storage tanks, and an absorption machine. The optimal design, sizing of the components, and operating conditions of the hybrid system are investigated via thorough techno-economic modeling and dual-objective optimizations for a case study in India. In addition, the assessments cover different designs of biomass heaters and various biomass types. Finally, using the coefficient of performance (COP), the levelized cost of cooling (LCOC), and the emission index as the prioritization parameters, the most efficient, the most cost-effective, and the most environmentally-friendly configurations are indicated. The results show that integrating evacuated plate collectors with a specific design of biomass-heater burning sugarcane baggas is the most appropriate option from all aspects. According to the optimization results, at the best solution point, emission index and LCOC are, respectively, 440.62 kg/MWh and 47.1 USD/MWh. Moreover, the scatter distribution of major decision parameters indicates that while the volume of the hot storage tank is not a sensitive parameter, the chiller temperature and volume of the cold storage tank should be kept at their lowest bounds. [ABSTRACT FROM AUTHOR]

Modelling for long-term goals involving multiple factors and criteria often require incorporating decision-makers preferences to realize optimum satisfaction. Goal programming (GP) is an operational research technique that is relevant to analysing decision-making problems with multiple competing and conflicting objectives. Multi-objective goal programming approach takes advantage of striking the trade-off between the overachievement and underachievement of the decision-makers future aspirations. The concept of GP with a satisfaction function integrates the preference of the decision-makers explicitly. In this paper, we proposed a multi-objective optimization model integrating economic growth, electricity consumption, greenhouse gas emission and the number of employees across the primary, secondary and tertiary sectors of Indian economy using the concept of GP with a satisfaction function. The model validated with data from the three economic sectors, and the results provided a quantitative justification for achieving economic growth, electricity consumption, with optimal employment strength across the sectors, for the sustainable development goals of India vision 2030. Also, a strong suggestion for improvement and encouragement in the use of renewable energies such as wind and solar and reduction in fossil fuels utilization to arrest the high emission tendencies shortly was evidence by the solution. [ABSTRACT FROM AUTHOR]

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

In India, many major and medium reservoirs have been constructed to meet the fast-growing demands of irrigation, hydropower generation, drinking and industrial water supply. To achieve social and economic sustainability in water scarce areas, it is vital to efficiently manage available water resources. Indeed, water resources systems are complex and require systematic study to facilitate optimal planning and management decisions. The present study supports the strategies designed to check the deficit in water availability for Indira Sagar Reservoir system in Madhya Pradesh, India, using a novel approach of standard PSO combining two variants, elitism and variational parameters, namely a time-variant elitist mutation multi-objective particle swarm optimization (TV-EMMOPSO). The performance of the proposed algorithm is compared with the optimal operation policies developed by multi-objective particle swarm optimization (MOPSO) and elitistmutation particle swarm optimization (EM-MOPSO). The results obtained can help the managers of Indira Sagar Reservoir plan better strategies in future. [ABSTRACT FROM AUTHOR]

Self-adaptive cuckoo search algorithm is used to optimize the design of water distribution system problems. It is proposed to dynamically adjust the two sensitive parameters of the algorithm, (i) step size control parameter 'α' and (ii) discovering probability parameter 'Pa' which largely govern the exploration and exploitation search strategies of the algorithm. These parameters are essential for enhancing the performance of the algorithm and normally the values of these parameters needs careful selection according to the type of problem. Single objective self-adaptive cuckoo search algorithm (SACSA) and multi-objective self-adaptive cuckoo search algorithm (SAMOSCA) are proposed in this study. Robustness and efficiency of these algorithms in single (minimization of cost) and multi-objective scenarios (minimization of cost and maximization of resilience) is validated using standard water distribution benchmark problems i.e. Two loop and Hanoi network. These are later applied to solve a medium size real-life water distribution system located at Pamapur, Telangana, India. A simulation-optimization based program combining the water distribution network simulation software EPANET 2.2 and MATLAB is used for computation. The proposed methodology has provided better results in terms of computational efficiency as well as found better solutions when compared to the previously reported results in both single and multi-objective scenarios. In the case of multi-objective problems, it has been observed that SAMOCSA has been able to find new points in pareto front when compared to the best-known pareto front reported in the literature. Self-adaptive cuckoo search algorithm has been found to be an attractive alternative in both exploration and exploitation of larger search spaces for finding better optimal solutions. [ABSTRACT FROM AUTHOR]

Maintenance management of a large road network with limited resources is a challenging task in developing countries like India. Road agencies expect that the condition of their road infrastructure should always above the desired level of performance even under restricted resources. This requires an integrated decision support system considering all aspects of maintenance. In this paper, a framework for optimal maintenance decisions by multi-objective approach is formulated for a road network by considering functional condition of the pavement quantified in terms of roughness and structural condition quantified in terms of rebound deflection. A multiobjective optimization model is developed using Integer Linear Programming (ILP). A novel approach to track the age of pavement is applied in the formulation of the mathematical model. The ε-constraint method is adopted to generate non-dominated solutions. Budget bound optimizing model is formulated and implemented for a typical road network of four roads and optimal maintenance scheduling is arrived. [ABSTRACT FROM AUTHOR]

In this study, we explore the possibility of techno-ecological synergy (TES) based sustainable design of hybrid renewable energy systems (HRES) and their surrounding ecosystems to mitigate emissions. We consider an HRES system with a photovoltaic cell, wind turbine, fuel cell, electrolyzer, and diesel generator. The NO 2 and CO 2 emissions from HRES have been conventionally treated using a selective catalytic reactor and monoethanolamine (MEA), respectively. However, a forest ecosystem can naturally uptake NO 2 and CO 2. Therefore, we have considered the forest ecosystem as an additional unit operation in this study and its growth dynamics have been incorporated in the system design. Thus, we analyze two approaches to remediate emissions from the HRES: (1) technological approach — SCR and MEA, (2) TES approach — forest ecosystem in tandem with technology. Unlike previous TES design studies, we focus on cities in India and compare a traditional method for forest restoration and a faster Miyawaki method considering their growth dynamics. Furthermore, we consider the case of social forestry, which is relevant in developing countries. Here, the trees are pruned, which increases the pollution uptake rate. We find that the emission abatement technology cannot be removed from the system as it is needed for seasons wherein trees shed their leaves; therefore a periodic profile of technology utilization for emissions abatement is observed. We find the TES approach to be more economically feasible for pollution uptake than a purely technology based approach for the sustainable operation of HRES at Delhi and Chennai in India, respectively. Based on this, the annualized cost of electricity generation is found to be 15% less than the base case. Moreover, the study reveals that annualized cost of electricity generation associated with the Miyawaki method is 13% less than the traditional reforestation method for attaining TES designs. [Display omitted] • Techno-ecological synergy (TES) of HRES and surrounding ecosystems is explored. • The dynamics of the forest ecosystem are considered in the design. • Traditional methods of forest restoration compared with the faster Miyawaki method. • TES approach is economically more feasible than a purely technology-based approach. [ABSTRACT FROM AUTHOR]

The present study reports the modeling and simulation of methanol production through the gasification of freshly mined three coal samples having different wt.% of ash, viz., low-ash coal (LAC), medium-ash coal (MAC), and high-ash coal (HAC), collected from three different locations in India. The effect of the input parameters such as steam-to-coal mass ratio and gasification temperature on process performance parameters such as stoichiometric number, syngas yield, green house gas emissions from the process stream or net stream carbon dioxide equivalent, and methanol yield was studied through process sensitivity analysis and input parameters are optimized for the maximum methanol yield and minimum green house gas emission. Primary economic investigation at optimized conditions revealed that an internal rate of return of 5.93% is achieved for HAC at minimum plant scale and setting the methanol selling price of 400 USD/ton whereas IRR obtained as 3.20% and 5.87% for LAC and MAC, respectively, at this condition. The coal sample, HAC, performed better due to a high internal rate of return (i.e., 7.53% at base case) and subsequently low production cost. The present study's findings can be decisive for the countries with substantial low-grade high-ash coal reserves concerning coal diversification for its sustainable consumption in methanol production. [Display omitted] • Equilibrium thermodynamic modelling of steam-coal gasification process. • Kinetic modelling of methanol synthesis process utilizing low-rank Indian coal. • Sensitivity analysis to study the influence of operating parameters. • Detailed comparative techno-economic analysis for methanol production. • Environmental analysis for coal to methanol production. [ABSTRACT FROM AUTHOR]

In the last few decades, production and procurement of food grain in India have steadily increased, however, storage capacity has not increased proportionally. The government of India (GOI) is establishing the various capacitated silos across the country to bridge this storage capacity gap. This paper presents a novel integrated multi-objective, multi-modal and multi-period mathematical model for grain silo location-allocation problem with Dwell time to support the decision-making process of GOI. Two conflicting objectives-minimization of total supply chain network cost and total lead time (transit and dwell time) are simultaneously optimized using two Pareto based multi-objective algorithms with calibrated parameters. [ABSTRACT FROM AUTHOR]

• Evaluate the impact of temporal resolution of objective on optimized decisions. • Use three formulations of a multi-objective inter-basin water transfer problem. • Decisions based on annual reliability outperforms seasonal/fortnightly reliability. • Temporal resolution of objective determines the preferred search operators of the BORG MOEA. Large scale water infrastructure serves multiple purposes including provisioning of freshwater, protection from floods, navigation, hydro-electricity, etc. Decision analysis for reservoir systems relies heavily on optimization techniques that identify optimal operational strategies using a dynamic systems model. Optimization requires the analyst to define performance indicators, or, objective functions, that aggregate performance across multiple time periods in a planning horizon. A question thus arises: does the temporal resolution at which objective functions are aggregated have a substantial impact on resultant optimal operational strategy? Here, we investigate this issue using three formulations of a multi-objective optimization problem for a proposed inter-basin water transfer in Southern India. The Borg multi-objective evolutionary algorithm (MOEA) is used to optimize five objectives: the reliability, resilience and vulnerability of demand satisfaction, reliability of maintaining minimum environmental flows, and reliability of preventing high flow exceedances downstream. We alter the temporal resolution at which the reliability of demand satisfaction is calculated by aggregating water deficits at fortnightly, seasonal and annual scale, resulting in three formulations of the decision problem. Pareto approximate strategies resulting from the fortnightly, seasonal, and annual reliability formulations result in annual demand deficits ranging from 82 to 172 Mm3, 79–152 Mm3, and 61–96 Mm3, respectively. This improvement in performance for annual reliability-based strategies exploits greater flexibility within the systems model to store water temporarily in secondary storage structures like lakes, ponds, check dams, in the command area serviced by the project. We also find that the choice of temporal resolution of objective function has a substantial influence on the combination operators preferred during the search phase of the Borg MOEA. Our results highlight the value of exploring objective functions at different temporal resolutions for reservoir optimization. [ABSTRACT FROM AUTHOR]

• We enumerate 25,000 + solutions for the Ganges water sharing treaty, renewed in 2026. • Hierarchical clustering identifies redundant objectives and governing trade offs. • Pareto-optimization & scenario-neutral analysis finds climate-robust treaty solution. Climate change will alter the flow availability and expected water allocations in international river treaties, many of which were designed using historical flow records. Effective transboundary treaties should anticipate these concerns and seek to satisfy the priorities of all riparian countries while being robust to impending changes in climate. This task is complicated by the fact that specific objectives associated with each party's priorities are not necessarily common knowledge (framing uncertainty), and the direction, amplitude and effect of long term changes in hydro-climatic drivers can be highly uncertain (climate uncertainty). We frame the design of a transboundary treaty as a multi-objective optimization problem. We use hierarchical clustering to address problem-framing uncertainty by identifying the subset of objectives associated with the governing trade-offs imposed by the bio-physical characteristics of the shared river system. We then carry out a scenario-neutral climate sensitivity analysis to identify climate-robust Pareto-optimal treaty solutions. We illustrate the approach for the Ganges water agreement, which is due to be renewed in 2026. Based on an enumerated population of 25,121 feasible treaty solutions, we identify governing objectives and 16 treaty solutions that are Pareto optimal under most considered combinations of changes in sea level and dry season flow regime. This work provides a path towards improving transboundary allocations for the Ganges water treaty and, more broadly, a template to support transboundary cooperation over shared international rivers. [ABSTRACT FROM AUTHOR]

The Markowitz portfolio optimization model, popularly known as the Mean-Variance model, assumes that stockreturns follow normal distribution. But when stock returns do not follow normal distribution, this model wouldbe inadequate as it would prescribe sub-optimal portfolios. Stock market literature often deliberates that stock returns are non-normal. In such context the Markowitz model would not be sufficient to estimate the portfolio risks. The purpose of this paper is to expand the original Markowitz portfolio theory (mean-variance) via adding the higher order moments like skewness (third moment about the mean) and kurtosis (fourth moment about the mean) in the return characteristics. The research paper investigates the impact of including higher moments using multi-objective programming model for portfolio stock selection and optimization. The empirical results indicate that the inclusion of higher moments had a considerable impact in estimating the returns behavior of portfolios. The portfolios optimized using all the four moments, generated higher returns for the given level of risk in comparison to the returns of the Markowitz model during the study period 2000-2011. The results of this study would be immensely useful to fund managers, portfolio managers and investors as it would help them in understanding the Indian stock market behavior better and also in selecting alternative portfolio selection models. [ABSTRACT FROM AUTHOR]

Abstract: Transportation of hazardous wastes through a region poses immense threat on the development along its road network. The risk to the population, exposed to such activities, has been documented in the past. However, a comprehensive framework for routing hazardous wastes has often been overlooked. A regional Hazardous Waste Management scheme should incorporate a comprehensive framework for hazardous waste transportation. This framework would incorporate the various stakeholders involved in decision making. Hence, a multi-objective approach is required to safeguard the interest of all the concerned stakeholders. The objective of this study is to design a methodology for routing of hazardous wastes between the generating units and the disposal facilities through a capacity constrained network. The proposed methodology uses posteriori method with multi-objective approach to find non-dominated solutions for the system consisting of multiple origins and destinations. A case study of transportation of hazardous wastes in Kolkata Metropolitan Area has also been provided to elucidate the methodology. [Copyright &y& Elsevier]

The article presents a multi-objective particle swarm optimization method for generating optimal solutions to problems in reservoir operation. Reservoir operation frequently demands that competing objectives be balanced. An approach for optimizing tradeoffs is therefore essential. The authors present a particle swarm algorithm and apply it first against test data, and then to a case study involving the Bhadra reservoir in India. Results in both instances were encouraging, and in the case of Bhadra evinced a superior methodology.

Abstract: The accelerating pace of waste generation from used electrical and electronic equipment is of growing global concern. Within this waste stream, computer hardware is quite significant in terms of both volume and risk to the environment because of the hazardous materials within it. The waste management hierarchy of prevention, reuse, recycle, treatment and disposal in landfill is accepted as a universal guideline for waste management. The contemporary concept of integrated solid waste management is very complex comprising of not only the environmental aspects or the technical aspects of the waste management hierarchy, but also incorporating economic, institutional, perceived risk and social issues in the context of complete life cycle of waste. Moreover, when to shift from one stage of hierarchy to another, is an involved decision warranting inclusion of several case specific issues. This paper presents a life cycle based multi-objective model that can help decision makers in integrated waste management. The proposed model has been applied to a case study of computer waste scenario in Delhi, India, which apart from having computer waste from its native population receives large quantities of imported second hand computers. The model has been used to evaluate management cost and reuse time span or life cycle of various streams of computer waste for different objectives of economy, perceived risk and environmental impact. The model results for different scenarios of waste generation have been analyzed to understand the tradeoffs between cost, perceived risk and environmental impact. The optimum life cycle of a computer desktop was observed to be shorter by 25% while optimizing cost than while optimizing impact to the environment or risk perceived by public. Proposed integrated approach can be useful for determining the optimum life cycle of computer waste, as well as optimum configuration of waste management facilities, for urban centers where computer waste related issues are of growing concern. [Copyright &y& Elsevier]

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multiobjective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

This study to address the issues involved in the planing and design of a computer waste management system in an integrated manner. A decision-support tool is presented for selecting an optimum configuration of computer waste management facilities (segregation, storage, treatment/processing, reuse/recycle and disposal) and allocation of waste to these facilities, The model is based on an integer linear programming method with the objectives of minimizing environmental risk as well as cost. The issue of uncertainty in the estimated waste quantities from multiple sources is addressed using the Monte Carlo simulation technique. An illustrated example of computer waste management in Delhi, India is presented to demonstrate the usefulness of the proposed model and to study tradeoffs between cost and risk. The results of the example problem show that it is possible to reduce the environ- mental risk significantly by a marginal increase in the available cost. The proposed model can serve as a powerful tool to address the environmental problems associated with exponentially growing quantities of computer waste which are presently being managed using rudimentary methods of reuse, recovery and disposal by various small-scale vendors. [ABSTRACT FROM AUTHOR]

The crop growth and crop yield in agriculture depend upon many factors such as weather conditions, soil type, and application of fertilizers. The crop net return can be increased by deciding suitable crops for a particular land depending upon its weather conditions. The application of an appropriate amount of fertilizers also promotes crop growth and yield. On the other hand, the use of fertilizers needs to be minimized to reduce the capital cost as well as to prevent its harmful effect on soil and the environment. This paper models the problem of increasing net crop benefit and reducing the application of fertilizers as multi-objective optimization functions. A hybrid CSA-PSO optimization algorithm is proposed for solving multi-objective problems by combining crow search algorithm (CSA) and particle swarm optimization (PSO). The performance of the proposed algorithm is evaluated against CEC 2009 benchmark functions. This algorithm is implemented for crop pattern optimization in India's Telangana state, which depicts the proposed algorithm's feasibility and effectiveness. • A multi-objective crop model is designed to maximize crop net return. • A new hybrid multi-objective optimization algorithm CSA-PSO is introduced. • The flight length is defined randomly to increase the exploration of the CSA-PSO. • The CSA-PSO is implemented for crop planning in Telangana, India. [ABSTRACT FROM AUTHOR]

This work performs integrated multi-objective optimization of lignocellulosic biorefinery system considering four objectives capturing the three dimensions of sustainability, namely, cost, life cycle greenhouse gas (GHG) emissions, and life cycle water footprint of ethanol and employment generation. Feedstock selection, biorefinery location and size, biorefinery conversion processes, and detailed biomass procurement and processing strategy are the decision variables. The model was applied to a case study of Maharashtra, India, considering 33 districts and five different feedstock to produce ethanol to meet the 10% gasoline blending target. The optimal values for ethanol cost, GHG emission, and water footprint were Rs. 50.39/l ($ 0.69/l), 0.933 kg CO 2 eq./l and 0.271 m3/l, respectively, while a total of 5087 jobs were created in the biorefineries for job maximization. Feedstock selection, biorefinery number and their locations affected the objectives. Multi-objective optimization identified design possibilities with considerable reduction in water footprint at nominal increase in the cost and GHG emissions. The sensitivity of the model with respect to the ethanol demand, biomass availability, and cost of cotton stalk was studied. A reduction in biomass availability to 20% had minimal impact on minimal GHG emissions, but the minimum cost increased by 21% while the minimum water footprint increased by 81%. Sensitivity analysis based on cost of cotton stalk showed that it was a much more important feedstock from the economic perspective than from GHG emissions and water footprint perspectives. The results provide region-specific recommendations for scaling of lignocellulosic biofuel systems. • Optimal design of lignocellulosic biorefinery system considering multiple objectives. • Total cost, GHG emissions, water footprint, and jobs considered as objectives. • Application of the model to biorefinery design problem in Maharashtra, India. • Water footprint changed significantly depending on the objective function. • Cotton stalk was the most critical feedstock, and governed biorefinery location. [ABSTRACT FROM AUTHOR]

• Sustainable roads need integration of factors like economic, environmental, constructability. • Sustainable road designs can be developed by employing multi-objective optimization. • Blend of particle swarm optimization, and crowd distancing is apt for optimization. • K-means clustering can downsize the output from multi-objective optimization. • The proposed optimization models can be used for asset management and decision making. The development of a sustainable road network in Northeast India is challenging due to the rough terrain, inclement weather, budgetary constraints, and lack of skilled workforce, among other factors. Since the sustainable development of roads involves balancing multiple goals and limitations, in this work, a hybrid multi-objective optimization model was developed by integrating the multi-objective particle swarm optimization with the crowd distance technique and the K-means clustering strategy. The application of the proposed approach was demonstrated by designing a road suitable for Northeast India. The proposed model generated sustainable designs by optimizing the budgetary and environmental limits and workforce competence. The results indicated that multiple optimization techniques can be incorporated in the design of sustainable highways in Northeast India. The use of stabilized layers instead of granular layers helped reduce transportation costs and environmental impacts. This study can assist highway engineers in the prompt evaluation of multiple options and decision making. [ABSTRACT FROM AUTHOR]

Improving farming systems in resource-poor contexts is often difficult as farmers face multiple challenges to implement the innovations developed by researchers. Viable solutions may however be present within local communities by positive deviant farmers, i.e. farmers that outperform positively compared to others. This study develops a positive deviance informed methodology to support redesign of farming systems, with the aim to improve farm productive, economic and environmental performances. We tested the methodology in Bihar, India, using survey data from 43 farms and the indicators of operating profit, soil organic matter balance, water use and dietary energy production. Positive deviant farms and practices were first identified and then recombined into a redesigned farm in consultation with farmers. The FarmDESIGN model was used to calculate current farm performance and to explore potential alternative farm configurations in the redesign. We found that outstanding performance on all indicators could only be reached by integrating high livestock density with an optimal combination of crop practices, which confirms the key role of interactions among components in mixed crop-livestock systems to improve all dimensions of farm sustainability. The redesigns outperformed all real farms on the indicators assessed. Farmers confirmed the viability of the redesigns in focus group discussions and their suggestions can serve as useful input for a next cycle of farm redesign. Since all suggestions are locally practiced and have proven to be accessible, affordable and recognizable, we conclude that our methodology based on positive deviant farms and practices yields promising results with a large potential to boost agricultural development for resource-scarce smallholder farmers. • We integrate the positive deviance approach with redesign of farming systems. • Redesign allowed doubling indicator performance relative to the population mean. • Indicators only improved when combining high livestock density and crop practices. • Redesign is based on best practices present within community and therefore viable. • Farmers' feedback was obtained in focus groups to inform a new cycle of redesign. [ABSTRACT FROM AUTHOR]

• Farms with higher resource endowment had larger outcome spaces. • Optimization results highlight the trade-off between profit and water use. • Environmental objectives could be improved with re-allocation of current crops. • Sustainable intensification with mung bean was not beneficial for all farm types. Agriculture in rural Bihar needs to maintain its productivity while enhancing its biophysical sustainability. To sustainably intensify the predominant rice-wheat systems, alternative cropping patterns with short duration legumes, planted in the usually fallow summer season, were developed. The addition of the legume ensures near-permanent soil cover, breaks the cereal-cereal cycle, and aims to improve soil quality while yielding between 0.6 and 1.2 Mg ha-1 of protein-rich dry matter. On-station experiments previously demonstrated the agronomical feasibility of the alternative cropping patterns while this study addresses the implications of such field-level changes at farm-level for different types of smallholders. We used the model FarmDESIGN to 1) assess current farm performance, 2) explore options to rearrange cropping patterns and 3) assess the impact of cropping patterns with mung bean. We found diverse farm performances, indicating a heterogenous farming community. Re-arranging current cropping patterns gave all farms possibilities to save water, increase soil organic matter content and decrease nitrogen losses but showed trade-offs with operating profit. Higher resource endowed farms had most potential to favourably rearrange the farm and improve multiple performance indicators. Two out of the five farms assessed did not benefit from including the alternative cropping patterns. We conclude that the impact of innovations greatly depends on farm type and current farm features and performance, described by a farm typology. [ABSTRACT FROM AUTHOR]

Despite considerable efforts to improve water management, India is becoming increasingly water stressed due to multiple factors, including climate change, increasing population, and urbanization. We address one of the most challenging problems in the design of water treatment plants: how to select a suitable technology for a specific scenario or context. The process of decision making first requires the identification of feasible treatment configurations based on various objectives and criteria. In addition, the multiplicity of water quality parameters and design variables adds further complexity to the process. In this study, we propose a novel Decision Support Tool (DST), designed to address and support the above challenges. In this user-friendly tool, both Multi-Criteria Decision Analysis (MCDA) and Multi-Objective Optimization (MOO) methods are employed. The integration of MCDA with MOO facilitates the generation of feasible drinking water treatment solutions, identifies optimal options, and ultimately, improves the process of decision making. This implemented approach has been tested for different contexts, including for different types of raw water sources and system implementation scales. The results show that this tool can enhance the process of decision making, supporting the user (e.g., stakeholders and decision makers) to implement the most suitable water treatment systems, keeping in view the trade-offs. [ABSTRACT FROM AUTHOR]