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Adequate disposal of municipal solid waste (MSW) is one of Serbia's most complex environmental challenges. The problem is more serious in urban areas, since large amounts of waste are disposed of in locations that do not comply with environmental, technical, and socio-economic standards. Such is the case for the city of Kraljevo, where about 116,000 inhabitants do not have a sanitary landfill facility. This research includes a multi-criteria analysis, conducted with the help of geographic information systems, to find a suitable landfill site location. After data collection, the first step was to process 15 environmental and socio-economic factors utilizing the fuzzy analytic-hierarchy process method. The second step comprised the visual analysis and selection of the ten most suitable locations from the synthetic convenience map. The third step involved the final ranking of sites by means of the fuzzy multi-objective analysis by ratio, plus the full multiplicative form method, based on four additional beneficial and non-beneficial criteria. The results show that sanitary landfill candidate site A4 is the most suitable location for constructing a sanitary landfill site due to its large area (569 ha) and relatively short distance from the urban zone (8 km). This study is the first to integrate geographic information systems and the fuzzy analytic-hierarchy process, multi-objective analysis by ratio, and the full multiplicative form algorithm for sanitary landfill selection. The results of the research can be used as a reference for safe waste disposal in the city of Kraljevo. [ABSTRACT FROM AUTHOR]

Time cost and quality are three important factors in planning and controlling construction projects. Getting these factors in balance, which minimizes the project duration, the total project cost, and maximizes the total quality could define the success of a construction project. Aside from planning and estimating the project properly, a consideration of uncertainty while implementing the project is needed to represent a more realistic outcome for time, cost, and quality trade-off (TCQT) problem in construction projects. In this paper, fuzzy logic is utilized to model the uncertainty embedding α-cut approach to see the effect of the uncertainty on the time, cost, and quality of the project. Then multi-objective Symbiotic Organism Search (SOS) algorithm is applied to find a set of the optimal solution in different uncertainty levels and provide the project manager several possible actions to implement the project. Two numerical case studies of a repetitive construction project were analyzed to see the effectiveness of the model and its capability to solve the TCQT problem in the construction project. The results showed that the proposed model is powerful to explore the solution for the shortest project duration with minimum incurred cost and high overall quality in the construction project. In comparison to the other widely used methods and other algorithms, the proposed model is proven to be effective and competitive in solving the TCQT problem.

Time cost and quality are three important factors in planning and controlling construction projects. Getting these factors in balance, which minimizes the project duration, the total project cost, and maximizes the total quality could define the success of a construction project. Aside from planning and estimating the project properly, a consideration of uncertainty while implementing the project is needed to represent a more realistic outcome for time, cost, and quality trade-off (TCQT) problem in construction projects. In this paper, fuzzy logic is utilized to model the uncertainty embedding α-cut approach to see the effect of the uncertainty on the time, cost, and quality of the project. Then multi-objective Symbiotic Organism Search (SOS) algorithm is applied to find a set of the optimal solution in different uncertainty levels and provide the project manager several possible actions to implement the project. Two numerical case studies of a repetitive construction project were analyzed to see the effectiveness of the model and its capability to solve the TCQT problem in the construction project. The results showed that the proposed model is powerful to explore the solution for the shortest project duration with minimum incurred cost and high overall quality in the construction project. In comparison to the other widely used methods and other algorithms, the proposed model is proven to be effective and competitive in solving the TCQT problem. [ABSTRACT FROM AUTHOR]

Labor mobility and employment in energy enterprises can not only create wealth for society, but also inject vitality into urban development. In order to improve the accuracy of multi-objective analysis of labor mobility behavior, a multi-objective analysis model of labor mobility behavior in energy enterprises based on point-to-point network is designed. Firstly, the paper analyzes the influencing factors of employment decision-making of energy enterprises. Then, based on the main statistical indicators of the point-to-point network, the labor flow analysis network is established, and the solution method of the model is proposed. Finally, the labor mobility rate is calculated according to the established model, and the multi-objective analysis of labor mobility behavior of energy enterprises in the point-to-point network is completed. The experimental results show that applying the multi-objective analysis model of labor flow behavior in point-to-point network, the measurement results of the proportion of migrant workers in urban labor force are not different from the actual value, and the multi-objective analysis time under multi-project comparison is short, which can be applied to the actual situation. [ABSTRACT FROM AUTHOR]

This study presents a novel approach named as "multiple objective social group optimization" (MOSGO) to tradeoff time, cost, quality, and carbon dioxide emission (TCQC) factors in generalized construction projects. The proposed algorithm modifies the operation mechanism to balance the exploration and exploitation abilities of the optimization process. The TCQC tradeoff problem considers all types of logical relationships between project activities. Two practical case studies demonstrate the ability of MOSGO-generated, non-dominated solutions. In addition, evidential reasoning is applied to select a compromise solution for project implementation. Comparisons between the MOSGO and four well-known algorithms (MODE, MOABC, MOPSO, and NSGA-II) to verify the efficiency and effectiveness of the developed algorithm. According to the statistical analysis, the proposed algorithm generated the highest values of diversification measurement (DM) of 26.113 and 40.27; the highest values of hyper-volume (HV) of 0.875 and 0.881 in case 1 and case 2, respectively. The proposed algorithm also found solutions with lowest mean ideal distance (MID) and spread (SP) values of 0.872 and 0.462 in the first case and of 0.754 and 0.689 in the second case. MOSGO showed had better diversify and convergence, gained wider spread, and yielded higher uniformity of solutions than the compared multiple objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

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Abstract Background Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system’s critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. Results To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. Conclusions Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon’s secondary role as a regulating factor, R. palustris’ metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.

• A novel geothermal system using a genetic algorithm based optimization is designed. • Using PEM and regenerative ORC with IHE, most of wasted energy is used. • Optimal points: exergy efficiency of 29.8 % and total product cost rate of 6 M$/year. • The combustion chamber and gasifier exhibit the highest exergy destruction. This study introduces an innovative approach by formulating and evaluating a synergistic biomass-geothermal structure, emphasizing optimized inter-component connections. The research stands out for its thorough analysis of parameter impacts on the system and variables, addressing an unexplored aspect in integrated energy systems. The multi-generation systems are the integration of a combined gasification gas turbine cycle, double flash geothermal cycle, and proton exchange membrane cycle for the generating power and hydrogen. The overall system and its subsystems are studied to explore how the performance of thermodynamics and the total cost rate are influenced by operating parameters. The best operational conditions for both subsystems and the overall system have been determined by analyzing the impact of operating parameters on the thermodynamic behavior and environmental impact through parametric studies. The findings indicate while Sabalan's current efficiency is 16.26 %, the system energy efficiency reached 24.89 % when coupled with other renewable source. To enhance the system's efficiency, a genetics algorithm was utilized to simultaneously optimize the total cost of exergy destruction and investment cost. The outcome of the multi-objective optimization revealed that the exergy efficiency of optimal point for the system is 29.8 % and a total investment cost is 6 (M$/year). [ABSTRACT FROM AUTHOR]

This paper aims at studying the Bi-Objective Insular Traveling Salesman Problem (BO-InTSP), which searches for a set of efficient, single visit sequences to collect (or distribute) freight from a set of islands. In this problem, the selection of ports (nodes) to be visited at each island, along with the associated port visit sequence, are optimized simultaneously, while the maritime transportation costs and the ground transportation costs inside the islands are minimized with a bi-objective perspective. This approach is employed since these costs are of a conflictive nature. A previous Approximated Formulation of the BO-InTSP relies on aggregating the actual demand locations within each island in a certain number of centroids for computing the ground transportation costs. Conversely, this paper proposes and develops a novel Exact Formulation for the problem based on the actual demand locations, instead of aggregating the demand inside the islands. Additionally, a systematic evaluation approach is developed to compare the two alternative formulations with different levels of demand aggregation inside the islands, considering the bi-objective nature of the problem. The results reveal that the novel Exact Formulation significantly outperforms the previous aggregated approach in terms of the solutions quality and computational resources.

Cellular manufacturing is an important tool for manufacturing firms which leads to better productivity, focused and specialized manufacturing process. To utilize this important tool, the machines have to be grouped into cells. This work is related to using cellular manufacturing in a pharmaceutical factory with alternative routing. This adds more choices in the decision making process and presses for a better tool to make optimal selection. Several objectives may be considered to improve the productivity objectives such as the total number of exits and planning and scheduling robustness related objectives like bottleneck utilization and load balance between and within the alternative routes. Analytical hierarchical process (AHP) is used as a multi-objective decision making process to evaluate the best scenario amongst generated using simulation as a tool for modeling and evaluating the output for each scenario. Three customer case studies were considered with different preferences and the AHP evaluated the best scenario to fit these preferences. The best scenario can vary from one customer preferences to another but for the current system it turned out to be the same choice.

There are two related characteristics of technological progress. It is very important to propose a multi-contribution model for offshore oil and gas operations. First, technology can be improved in the long run. Second, every time a new technology emerges to meet the needs of the operation. Lanford pointed out three characteristics of technological progress. First, the S-shaped Curve is suitable for deep water development because many practices over the years have proven this. Second, the modified logistics curve can be used to simulate the entire technological advancement, including major technological breakthroughs in deep water development for more than a decade. Finally, one technology replaces the other and also conforms to the characteristics of the S-curve. The initial replacement rate is very low, and then the new technology is received in large quantities. This substitution behavior is gradually reduced at the maturity stage. [ABSTRACT FROM AUTHOR]

Background: Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system's critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. Results: To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. Conclusions: Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon's secondary role as a regulating factor, R. palustris' metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates. [ABSTRACT FROM AUTHOR]

Highlights • A novel polygeneration system for homes and vehicles power supplies. • Investigate the best configuration considering grid connections and vehicle types. • Comprehensive analysis based on energy, economic and environmental. • A multi-criteria reliability considering more than the electricity performance. • Introduce a scenario of selling the excess hydrogen to cope a high energy cost. Abstract This study proposes a design of polygeneration system based on solid oxide fuel cell to supply electricity, hot water, cooling, and hydrogen. This system also integrates the stationary supply for electric and hydrogen cars. The polygeneration system is developed based on energy, economic and environment simulation models by taking into account its application for the residential building. Four system configurations were designed based on the grid connection and the vehicle type and subsequently evaluated to determine the performance of the system in regard to the criteria such as efficiency, reliability, primary energy saving, cost saving as well as carbon dioxide reduction. Moreover, a strategy of selling the available hydrogen was also considered to analyze the competitiveness of the proposed system with the conventional separated system. Depending on these criteria, analysis of fuel cell size with respect to the coverage of demands was also conducted. The proposed system achieved primary energy savings, cost saving and emission reduction of about 73%, 50% and 70% respectively. The hydrogen selling strategy has a significant effect in reducing energy cost close to 51% for the configuration with electric vehicle station. [ABSTRACT FROM AUTHOR]

Rapid and high-density urban development has led to limited Urban Green Space (UGS), which is crucial for human wellbeing and sustainable urban development. To avoid compromising human wellbeing while promoting environmental sustainability, we propose a novel research framework that quantifies the importance of each UGS on-site for human wellbeing, to support multi-objective UGS design. This paper presents a framework for spatial parameterised evaluation of human wellbeing based on accessibility and usability. The evaluation considers connectivity and walking distance for accessibility, and space enclosure and availability for usability. The proposed thresholds and weights can be adjusted to fit different study contexts and reflect various needs. The use case demonstrated that the framework allows for ranking of UGS sites facilitating integration of human needs and other sustainable environmental needs into a multi-objective UGS design workflow. This approach provides designers with evidence to accurately allocate UGS resources according to human needs on each UGS and space conditions of UGS. It also presents an opportunity to integrate with other quantified analyses for sustainable urban development. The framework provides a useful approach to promoting human wellbeing by identifying the most important UGS for safeguarding human needs. • Defined spatial parameters for UGS accessibility and usability criteria. • Designed a framework to integrate accessibility and usability for human wellbeing. • Case study performed to verify the framework for quantifying UGS quality. • Opportunity to integrate human and stormwater analysis in a spatial model. [ABSTRACT FROM AUTHOR]

In test-based problems, solutions produced by search algorithms are typically assessed using average outcomes of interactions with multiple tests. This aggregation leads to information loss, which can render different solutions apparently indifferent and hinder comparison of search algorithms. In this paper we introduce the performance profile, a generic, domain-independent, multi-criteria performance evaluation method that mitigates this problem by characterizing the performance of a solution by a vector of outcomes of interactions with tests of various difficulty. To demonstrate the usefulness of this gauge, we employ it to analyze the behavior of Othello and Iterated Prisoner’s Dilemma players produced by five (co)evolutionary algorithms as well as players known from previous publications. Performance profiles reveal interesting differences between the players, which escape the attention of the scalar performance measure of the expected utility. In particular, they allow us to observe that evolution with random sampling produces players coping well against the mediocre opponents, while the coevolutionary and temporal difference learning strategies play better against the high-grade opponents. We postulate that performance profiles improve our understanding of characteristics of search algorithms applied to arbitrary test-based problems, and can prospectively help design better methods for interactive domains.

Water availability and water demand are not evenly distributed in time and space. Many mega water diversion projects have been launched to alleviate water shortages in China. This paper analyzes the temporal and spatial features of 59 mega water diversion projects in China using statistical analysis. The relationship between nine major basins is measured using a network analysis method, and the associated economic, environmental and social impacts are explored using an impact analysis method. The study finds the development of water diversion has experienced four stages in China, from a starting period through to a period of high-speed development. Both the length of water diversion channels and the amount of transferred water have increased significantly in the past 50 years. As of 2015, over 100 billion m 3 of water was transferred in China through 16,000 km in channels. These projects reached over half of China's provinces. The Yangtze River Basin is now the largest source of transferred water. Through inter-basin water diversion, China gains the opportunity to increase Gross Domestic Product by 4%. However, the construction costs exceed 150 billion US dollars, larger than in any other country. The average cost per unit of transferred water has increased with time and scale but decreased from western to eastern China. Furthermore, annual total energy consumption for pumping exceeded 50 billion kilowatt-hours and the related greenhouse gas emissions are estimated to be 48 million tons. It is worth noting that ecological problems caused by water diversion affect the Han River and Yellow River Basins. Over 500 thousand people have been relocated away from their homes due to water diversion. To improve the sustainability of water diversion, four kinds of innovative measures have been provided for decision makers: national diversion guidelines, integrated water basin management, economic incentives and ex-post evaluation. [ABSTRACT FROM AUTHOR]

Despite the rapid growth of the sector, academic research on microcredit programs is still limited. In the economic development literature, the prevailing thesis is that the inadequate regulatory context is the main factor that explains the untapped potential of microfinance industry in developed economies. In this context, this paper proposes the use of microcredit not only in order to achieve social inclusion, but also above all to promote the socioeconomic development of a territory, according to sustainable development principles. Thus, the study meets the need to rebalance a territory through a stable programming approach. This research uses a multi-objective programming model, as it allows better representation of decision problems, describing the impacts on the pursued objectives, and finding a solution for reaching the best possible compromise among them. The paper sets out to examine the economic benefits of a microcredit proposal in relation to a specific project for the development of the female entrepreneurship in an Italian region. The findings reveal that multi-objective methods allow evaluating the impacts on the objectives associated to the chosen solution, to compare them with those associable to different solutions, and, finally, to reach the best compromise possible among the pursued objectives, according to external and internal constraints established by policy makers. [ABSTRACT FROM AUTHOR]

In developing countries like Pakistan, the capacity shortage (CS) of electricity is a critical problem. The frequent natural gas (NG) outages compel consumers to use electricity to fulfill the thermal loads, which ends up as an increase in electrical load. In this scenario, the authors have proposed the concept of a combined heat & power (CHP) plant to be a better option for supplying both electrical and thermal loads simultaneously. A CHP plant-based microgrid comprising a PV array, diesel generators and batteries (operating in grid-connected as well as islanded modes) has been simulated using the HOMER Pro software. Different configurations of distributed generators (DGs) with/without batteries have been evaluated considering multiple objectives. The multiple objectives include the minimization of the total net present cost (TNPC), cost of generated energy (COE) and the annual greenhouse gas (GHG) emissions, as well as the maximization of annual waste heat recovery (WHR) of thermal units and annual grid sales (GS). These objectives are subject to the constraints of power balance, battery operation within state of charge (SOC) limits, generator operation within capacity limits and zero capacity shortage. The simulations have been performed on six cities including Islamabad, Lahore, Karachi, Peshawar, Quetta and Gilgit. The simulation results have been analyzed to find the most optimal city for the CHP plant integrated microgrid. [ABSTRACT FROM AUTHOR]

To meet higher customer satisfaction and shorter production lead time, assembly lines are shifting to mixed-model assembly lines. Accordingly, sequencing is becoming an increasingly important operation scheduling that directly affects on efficiency of the entire process. In this study, such sequencing problem at the mixed-model assembly line has been formulated as a bi-objective integer programming problem so that decision making through trade-off analysis can bring about significant production improvements. Then we have developed a multi-objective analysis method by hybridizing conventional and recent meta-heuristic methods. After showing its generic idea, the car mixed-model assembly line sequencing problem is concerned as a case study. Certain measures are also introduced to quantitatively evaluate the performances of the method through comparison.

The building sector is responsible for a large part of the overall energy demand in Europe. Energy consumption may be reduced at the design stage by selecting the proper building elements. This study develops a multi-objective analysis for a highly efficient slab-on-ground floor, whose design is optimized for a warm climate. Possible floor configurations have been obtained using the software tools modeFRONTIER, for the multi-objective analysis, and MATLAB, for the computational code. To proceed with the optimization of the different floor layers, a dataset has been developed for several materials in relation to a number of parameters: thermo-physical properties, eco-sustainability score according to the ITACA Protocol, costs, source, and structural features. Results highlight how a high surface mass is preferable when guaranteed by concrete in the innermost and outermost layers. Furthermore, insulating materials are better placed in the middle layers, with the insulating and synthetic materials adjacent to the ground and insulating and natural materials adjacent to the floor. Results emphasize the importance of thermal transmittance close to the Italian regulation limit (0.38 W/m2 K) in the climatic zone C, to allow an adequate exchange with the ground in summer, avoiding overheating. The outcomes show that the obtained slab-on-ground floor configurations favor the use of local, recyclable, sustainable, and eco-friendly materials, which is in line with energy policies and sustainability protocols. The paper supports the decision making process that takes many variables into account at the building design stage.

The objective of this paper is to investigate different methods to generate future potential climatic scenarios at monthly scale considering meteorological droughts. We assume that more reliable scenarios would be generated by using regional climatic models (RCMs) and statistical correction techniques that produce better approximations to the historical basic and drought statistics. A multi-objective analysis is proposed to identify the inferior approaches. Different ensembles (equifeasible and non-equifeasible) solutions are analysed, identifying their pros and cons. A sensitivity analysis of the method to spatial scale is also performed. The proposed methodology is applied in an alpine basin, the Alto Genil (southern Spain). The method requires historical climatic information and simulations provided by multiple RCMs (9 RCMs are considered in the proposed application) for a future period, assuming a potential emission scenario. We generate future series by applying two conceptual approaches, bias correction and delta change, using five statistical transformation techniques for each. The application shows that the method allows improvement of the definition of local climate scenarios from the RCM simulation considering drought statistics. The sensitivity of the results to the applied approach is analysed.

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called “Multiple Objective Symbiotic Organisms Search” (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project. [ABSTRACT FROM AUTHOR]

The optimal supply chain configuration for biomass production on a given territory is identified by a twotier approach, which considers both the environmental and the economic points of view. The first tier performs a quick evaluation of the supply chain, based on simplified assumptions and on average values of the parameters characterizing the geographical territory. The second tier allows for the inclusion of spatially explicit parameters of the territory and realizes a more detailed optimization of the supply chain using a multi-objective Mixed Integer Linear Programming framework. A demonstrative case study is presented for the bio-fuel supply to a centralized electric power plant. The considered supply chain is based on miscanthus, cultivated in marginal terrains and converted to pyro-oil in a number of delocalized plants for long distance shipment. The results obtained from the two tiers of the model provide quantitative information, to support quick and effective decision making on the optimal configuration of the supply chain in terms of plant size, location, transport logistics and cultivation. [ABSTRACT FROM AUTHOR]

A multi-objective analysis for unidirectional S-2 glass and T700S carbon fibre reinforced epoxy hybrid composites under flexural loading has been presented in this paper. A classical lamination theory (CLT) based model was developed to predict the flexural properties of composite laminates under three-point bending. Four objective functions, namely, maximizing the flexural strength and robustness and minimizing the weight and cost were chosen. The weighted sum method (WSM) was applied to find the optimal solution with the weighting factors being calculated from the analytical hierarchy process (AHP). As an illustration of the method, five different scenarios for the relative objective preferences were examined with the corresponding optimal solutions being determined. [ABSTRACT FROM AUTHOR]

In developing countries like Pakistan, the capacity shortage (CS) of electricity is a critical problem. The frequent natural gas (NG) outages compel consumers to use electricity to fulfill the thermal loads, which ends up as an increase in electrical load. In this scenario, the authors have proposed the concept of a combined heat & power (CHP) plant to be a better option for supplying both electrical and thermal loads simultaneously. A CHP plant-based microgrid comprising a PV array, diesel generators and batteries (operating in grid-connected as well as islanded modes) has been simulated using the HOMER Pro software. Different configurations of distributed generators (DGs) with/without batteries have been evaluated considering multiple objectives. The multiple objectives include the minimization of the total net present cost (TNPC), cost of generated energy (COE) and the annual greenhouse gas (GHG) emissions, as well as the maximization of annual waste heat recovery (WHR) of thermal units and annual grid sales (GS). These objectives are subject to the constraints of power balance, battery operation within state of charge (SOC) limits, generator operation within capacity limits and zero capacity shortage. The simulations have been performed on six cities including Islamabad, Lahore, Karachi, Peshawar, Quetta and Gilgit. The simulation results have been analyzed to find the most optimal city for the CHP plant integrated microgrid.

Time, cost, and quality are three important but often conflicting factors that must be optimally balanced during the planning and management of construction projects. Tradeoff optimization among these three factors within the project scope is necessary to maximize overall project success. In this paper, the MOABCDE-TCQT, a new hybrid multiple objective evolutionary algorithm that is based on hybridization of artificial bee colony and differential evolution, is proposed to solve time–cost–quality tradeoff problems. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. A numerical construction project case study demonstrates the ability of MOABCDE-generated, non-dominated solutions to assist project managers to select an appropriate plan to optimize TCQT, which is an operation that is typically difficult and time-consuming. Comparisons between the MOABCDE and four currently used algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC), verify the efficiency and effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

• Building stock modelling is implemented through a bottom-up method. • Dynamic simulations are applied through calibrated and parameterized stock models. • Optimal retrofit solutions under a 2050 future weather scenario are proposed. • Results are provided for four different climatic regions in southern Spain. • Results can be interactively accessed by stakeholders and the general public. Retrofitting the existing building stock is widely accepted as a crucial factor to reaching 2030 and 2050 climate and energy targets, given that the building sector is among the top three most dominant energy consumers. This paper presents a bottom-up study which uses calibrated and parameterized energy stock models (building archetypes), while also incorporating building stock information from a large database. The thermal performance of the existing social housing stock of southern Spain is assessed through dynamic simulation under present and future climate change scenarios. Subsequently, several passive and low-cost operation-related strategies are numerically optimized through genetic algorithms to determine the best retrofit solutions, taking into consideration global warming scenarios. A multi-objective decision analysis is carried out by optimizing annual overheating hours (%), annual undercooling hours (%), and investment costs (€/m2). Among the conclusions reported, it is important to note the feasibility of implementing low-cost retrofit strategies considering investment costs of up to around 200 €/m2, which would lead to average annual overheating and undercooling hours below 55 % and 45 %, respectively. However, retrofit solutions exclusively based on passive and low-cost operation measures were proven to be significantly limited to improve thermal comfort results in the social stock. [ABSTRACT FROM AUTHOR]