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In this study, an urban airspace assessment mechanism is proposed and validated using the actual urban building data, offering a systematic approach to airspace selection for unmanned aerial vehicle (UAV) operations. Two metrics are involved to assess the urban airspace accurately, which are the airspace availability and risk to ground population. The former is measured by analyzing the connectivity of the urban airspace which particularly emphasizes the impact of urban features like buildings and obstacles. The latter is quantized by using a previously proposed risk estimation model, with which an urban risk map can be generated. Quadrant analysis and Pareto ranking are then employed to evaluate the available airspace for UAVs. Quadrant analysis maps the urban airspace availability and risk to ground population onto a two-dimensional space. Additionally, Pareto ranking determines a set of Pareto-optimal solutions wherein no objective can be improved without compromising at least one other objective. The topology of urban airspace could be constructed by using the top 50% of grids ranked by Pareto ranking based on the actual building data. A case study is conducted in a densely populated urban area in Changqing District, Jinan, Shandong Province, China. The connectivity of the airspace topology is verified by employing the A-star algorithm to generate a feasible path for UAVs. [ABSTRACT FROM AUTHOR]

In this study, an urban airspace assessment mechanism is proposed and validated using the actual urban building data, offering a systematic approach to airspace selection for unmanned aerial vehicle (UAV) operations. Two metrics are involved to assess the urban airspace accurately, which are the airspace availability and risk to ground population. The former is measured by analyzing the connectivity of the urban airspace which particularly emphasizes the impact of urban features like buildings and obstacles. The latter is quantized by using a previously proposed risk estimation model, with which an urban risk map can be generated. Quadrant analysis and Pareto ranking are then employed to evaluate the available airspace for UAVs. Quadrant analysis maps the urban airspace availability and risk to ground population onto a two-dimensional space. Additionally, Pareto ranking determines a set of Pareto-optimal solutions wherein no objective can be improved without compromising at least one other objective. The topology of urban airspace could be constructed by using the top 50% of grids ranked by Pareto ranking based on the actual building data. A case study is conducted in a densely populated urban area in Changqing District, Jinan, Shandong Province, China. The connectivity of the airspace topology is verified by employing the A-star algorithm to generate a feasible path for UAVs.

Lysine production via the fermentation process is one of the most economical production routes that involves simultaneous conflicting objectives. Two multi-objective differential evolution algorithms MODE-III and MODE-III-IMS are used to obtain the optimal control parameters of the lysine bioreactor. Two conflicting objectives, namely yield and productivity, are studied. Singular and constant feeding policies using both algorithms are studied, and respective Pareto fronts are reported. Tournament selection and penalty constraint handling methods are used, and their performances are compared for both algorithms. The higher bound of feeding rate (2.0 L/s) is found to be the best feeding rate compared to other feeding rates. The MODE-III-IMS algorithm converged to the Pareto front faster than the MODE-III algorithm. The COPRAS method is used to carry out the Pareto ranking, and the best optimal solution is reported. The decision tree method is used to predict and report the best optimal solution with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Evolutionary single- and bi-objective optimization of industrial problems, namely, naphtha cracking and styrene reactor are considered. Bi-objective optimization is solved using the HMODE-DLS algorithm, whereas an improved black hole optimizer (BHO) is employed for the single-objective optimization (SOO) problem. For the naphtha cracking process, ethylene selectivity (SE) and severity index (SI) are selected as objectives. Similarly, styrene selectivity (SST) and styrene flow rates (FST) are considered objectives for styrene reactor. Pareto ranking is carried out by using the net flow method (NFM) and the best solution is compared with the single- and multi-objective optimization results. In a single objective optimization study, the optimum solution corresponds to the lowest value of the SI (1.571) at the cost of the worst value of SE (0.269). Similarly, the highest values of FST (16.642 kmol/h) and SST (96.158%) are resulted in the SOO study of styrene reactor. [ABSTRACT FROM AUTHOR]

Landscape patterns have a global impact on ecosystem services (ESs). Managing land to enhance the provision of multiple ESs is vital for ecological resilience and sustainable development. Identifying critical landscape patterns that simultaneously influence multiple ESs provision based on understanding ES trade-offs is needed. Our study aims to develop an approach to find the relationship between multiple ESs provision and landscape patterns, identify the critical landscape patterns that simultaneously promote multiple ESs provision, and give suggestions on land management based on an understanding of the relationships of ESs. Taking the central district of Wuhu City as an example, production possibility frontier (PPF) and Pareto ranks are used to identify relationships between ESs and extract the landscape pattern characteristics of samples that effectively enhance multiple ESs. Our analysis of trade-offs between ESs highlights the complexity of ES relationships and emphasizes the necessity for multi-dimensional investigative approaches. Landscape metrics of Mean Perimeter Area Ratio (PARA_MN), Coefficient of Variation of Perimeter (PERI_CV), Area-weighted Mean Fractal Dimension (FRAC_AM), and Relative Mutual Information (RELMUTINF) emerged as critical determinants of multiple ESs supply. Our findings suggest enhancing patch shape complexity when conducting land use planning to avoid homogenization and keeping differences between the diversity of land use categories and the diversity of adjacencies small. Also, we recommend caution in using high contagion as the only criterion for land use planning, as its effects may vary on different ES targets.

Natural gas processing is currently facing economic and environmental challenges due to abrupt changes in oil prices and the development of an alternate source of energy. Therefore it is essential to optimize the processing unit to make it profitable and environmentally friendly. Sustainable optimization of an industrial natural gas treatment plant is carried out using the NSGA-II algorithm for the methyl diethanol amine (MDEA) process to optimize CO 2 removal along with payback period and damage index. This multi-objective optimization study includes seven decision variables such as temperature and pressure of feed gas, feed flow rate, temperature and pressure of regenerator feed, lean amine temperature, and MDEA concentration. Three separate two-objective optimization study problems are developed and applied for retrofitted case and base case studies. Two different ProMax models are developed and validated the model by using actual plant data. All the retrofitted cases and base cases are solved and the Pareto optimal fronts are obtained. Trade-offs between different objectives are illustrated for all the problems. The lean vapor compression process can facilitate maximum H 2 S removal of 99.75% and maximum CO 2 removal of 98.52% simultaneously by maintaining a DI value of 476. TOPSIS method is used to rank and find the best optimal solution. Optimization study for uncertain CO 2 concentration (4%mole) in the feed gas is also analyzed and compared with normal feed conditions. The machine learning approach is used to obtain the predictions of selected objective functions for all the problem cases using the decision tree method. [Display omitted] [ABSTRACT FROM AUTHOR]

Rural areas are essential for achieving the Sustainable Development Goals (SDGs), necessitating the evaluation of rural sustainability transitions. A precondition for evaluating sustainable rural development is to adapt the SDGs to rural contexts while maintaining global comparability. Besides, tradeoff relationships among evaluation indicators should be captured to overcome the limitations of traditional weighting aggregation methods. In this study, we propose an integrated framework that incorporates the SDGs into a composite indicator system for assessing sustainable rural development. Our framework quantifies these indicators using the index thresholds outlined in the SDGs dashboard, along with customized rules that reflectrural contexts. Furthermore, we employ the Pareto ranking approach to evaluate the sustainable development potentials of rural areas regarding tradeoff relationships among different indicators. In a case study of Zhaoyuan City, China, our findings indicate that only 20 % of the sustainable development goals are achieved on average across all indicator dimensions. The economic and social dimensions are closer to their ideal targets and more compatible with each other, while the ecological dimension remains relatively unsustainable. Sustainable rural development depends primarily on natural resource endowment, ecological sustainability, and residential sustainability of villages. Understanding tradeoff relationships among different indicators enables us to better adapt rural policies to the specific contexts of villages. • Incorporating the SDGs for comprehensive assessment of sustainable rural development. • Constructing composite indicator system for global comparability and local rural contexts. • Redefining potentials for rural sustainability as Pareto ranks of villages across indicators. • Identification of targeted rural policies based on tradeoff relationships of indicators. [ABSTRACT FROM AUTHOR]

Recommender systems have been developed to assist decision making by recommending a list of items to the end users. The multi-criteria recommender system (MCRS) is a special type of recommender systems, where user preferences on multiple criteria can be taken into account in recommendation models. Traditional algorithms for MCRS usually predict user ratings on these criteria, and finally estimate the overall rating by different aggregation functions. In this paper, we propose a novel multi-criteria recommendation framework, Multi-Criteria Ranking, where we can directly infer a ranking score for an item candidate from the predicted ratings on multiple criteria. The proposed framework is general enough and most of the existing algorithms in MCRS can be easily integrated with our framework. Our experimental results can demonstrate the effectiveness of the proposed framework by evaluating top- $N$ recommendations over multiple real-world data sets. We believe that multi-criteria ranking opens the door to develop more effective and promising multi-criteria recommendation models.

Smallholder agriculture contributes substantially to global food production, yet often does not follow the most sustainable practices. Different agricultural inputs result in variegated and unpredictable economic and environmental outcomes. Here, we developed a generalizable approach to promote multi-objective coordination by optimizing smallholder agricultural inputs, and then applied it in winter wheat-summer maize rotation systems in the North China Plain. We found that smallholder farmers used a large range of agricultural inputs, with maximum and minimum values differing by a factor of 2.4–14. Using a structural equation model, we determined that pesticide, working hours, irrigating water, and chemical N fertilizer were the key inputs influencing food security, economic sustainability, resource sustainability, and environmental sustainability, respectively, with standardized path coefficients of −0.224, −0.436, −0.643, and −0.901. Lastly, we calculated that optimizing agricultural inputs can potentially improve food security, economic sustainability, resource sustainability, and environmental sustainability by 9–27%, 15–61%, 8–20%, and 5–14%, respectively. Our framework provides a generalizable approach for smallholder agriculture-dominated areas to achieve multi-objective sustainability. [Display omitted] • A generalizable approach for optimizing smallholder farmers' management is proposed. • Agricultural inputs vary greatly among smallholder farming systems even in same growing pattern. • There exists huge difference in sustainability performances among smallholder farming systems. • Pesticide, working hours, irrigating water, and N fertilizer are key inputs that influence sustainability most. • Optimizing agricultural inputs can potentially improve various sustainability dimensions by 14–61%. [ABSTRACT FROM AUTHOR]

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations. Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses. This paper proposes the Ensemble based temporal weighting and pareto ranking (ETP) model for Root-cause identification. Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model. The obtained aspects are validated and ranked using the proposed aspect weighing scheme. Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making. Experiments were performed with the standard five product benchmark dataset. Performances on all five product reviews indicate the effective performance of the proposed model. Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates. The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%-15% and detection rates at 4%-24% compared to the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Carbon dioxide emissions (CO 2) during the dehydration process of natural gas are of important concerns as this gas negatively affects the climate and environment in general. Dehydration process also encounters many heating, cooling and pumping units, which leads to high energy consumption. Reducing these emissions along with minimizing the utilized energy while keeping the high production is a complex problem that can be solved by multi objective optimization (MOO). This study focuses on minimizing CO 2 emissions, energy consumption (ENG) along with water content in the gas (WT). This means that the performance of the plant is improved from operational, environmental and energy point of view. The process is simulated with ProMax 4.0 and approved to be valid with the real plant data. Non-dominated sorting genetic algorithm (NSGA-II) was used for attaining the Pareto fronts for the decided MOO cases. The affecting decision variables and limitations are decided based on the capacity of the plant and industrial practice. Two bi-objective cases and a tri-objective case are considered, which are; minimizing CO 2 emissions and WT (case 1), minimizing ENG and WT (case 2) and minimizing WT, ENG and CO 2 emissions simultaneously (case 3). An attempt to retrofit the current process is also proposed and the cases are carried out with the modified process. Results showed noticeable improvements and enhancements in the given process. [ABSTRACT FROM AUTHOR]

Background: The Rohingya refugee crisis in Bangladesh continues to outstrip humanitarian resources and undermine the health and security of over 900,000 people. Spatial, sector-specific information is required to better understand the needs of vulnerable populations, such as women and girls, and to target interventions with improved efficiency and effectiveness. This study aimed to create a gender-based vulnerability index and explore the geospatial and thematic variations in gender-based vulnerability of Rohingya refugees residing in Bangladesh by utilizing pre-existing, open source data. Methods: Data sources included remotely-sensed REACH data on humanitarian infrastructure, United Nations Population Fund resource availability data, and the Needs and Population Monitoring Survey conducted by the International Organization for Migration in October 2017. Data gaps were addressed through probabilistic interpolation. A vulnerability index was designed through a process of literature review, variable selection and thematic grouping, normalization, and scorecard creation, and Pareto ranking was employed to rank sites based on vulnerability scoring. Spatial autocorrelation of vulnerability was analyzed with the Global and Anselin Local Moran's I applied to both combined vulnerability index rank and disaggregated thematic ranking. Results: Of the settlements, 24.1% were ranked as 'most vulnerable,' with 30 highly vulnerable clusters identified predominantly in the northwest region of metropolitan Cox's Bazar. Five settlements in Dhokkin, Somitapara, and Pahartoli were categorized as less vulnerable outliers amongst highly vulnerable neighboring sites. Security- and health-related variables appear to be the most significant drivers of gender-specific vulnerability in Cox's Bazar. Clusters of low security and education vulnerability measures are shown near Kutupalong. Conclusion: The humanitarian sector produces tremendous amounts of data that can be analyzed with spatial statistics to improve research targeting and programmatic intervention. The critical utilization of these data and the validation of vulnerability indexes are required to improve the international response to the global refugee crisis. This study presents a novel methodology that can be utilized to not only spatially characterize gender-based vulnerability in refugee populations, but can also be calibrated to identify and serve other vulnerable populations during crises. [ABSTRACT FROM AUTHOR]

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

To ensure reliability of making decisions concerning investments into urban planning projects, it is useful to have a model of the urban real property value, e.g. apartment value. This article proposes an algorithm for estimating the value of an urban real estate object using Pareto ranking and a method of mutual concessions. The article begins with a brief description of the Pareto ranking method. Then parameters to be taken into account in the apartment value model are defined. After that the task solution algorithm is described. The algorithm is implemented in the FORTRAN-90 language. The software developed is used for solving the task of estimating the value of Moscow apartments. The urban apartment valuation is made using information on similar objects. The software outputs an interval estimate of the apartment value.

Nondominated sorting is a key operation used in multiobjective evolutionary algorithms (MOEA). Worst case time complexity of this algorithm is ${O(MN^{2})}$ , where ${N}$ is the number of solutions and ${M}$ is the number of objectives. For stochastic algorithms like MOEAs, it is important to devise an algorithm which has better average case performance. In this paper, we propose a new algorithm that makes use of faster scalar sorting algorithm to perform nondominated sorting. It finds partial orders of each solution from all objectives and use these orders to skip unnecessary solution comparisons. We also propose a specific order of objectives that reduces objective comparisons. The proposed method introduces a weighted binary search over the fronts when the rank of a solution is determined. It further reduces total computational effort by a large factor when there is large number of fronts. We prove that the worst case complexity can be reduced to ${\Theta }({MNC}_{{max}}\mathrm {log}_{{2}} {(F+1)})$ , where the number of fronts is ${F}$ and the maximum number of solutions per front is ${C}_{\mathrm {max}}$ ; however, in general cases, our worst case complexity is still ${O(MN^{2})}$. Our best case time complexity is ${O}({MN}\mathrm {log} {N})$. We also achieve the best case complexity ${O}({MN}\mathrm {log} {N+N^{2}})$ , when all solutions are in a single front. This method is compared with other state-of-the-art algorithms—efficient nondomination level update, deductive sort, corner sort, efficient nondominated sort and divide-and-conquer sort—in four different datasets. Experimental results show that our method, namely, bounded best order sort, is computationally more efficient than all other competing algorithms. [ABSTRACT FROM AUTHOR]

• Synergies and trade-offs are found between several ES pairs based on their PPFs. • Relationships between landscape patterns and multiple ESs are revealed. • Patch shape complexity has a positive contribution to multiple ESs supply. • Landscape contagion does not necessarily promote multiple ESs provision. • Priority should be given to contrast between category diversity & adjacency diversity. Landscape patterns have a global impact on ecosystem services (ESs). Managing land to enhance the provision of multiple ESs is vital for ecological resilience and sustainable development. Identifying critical landscape patterns that simultaneously influence multiple ESs provision based on understanding ES trade-offs is needed. Our study aims to develop an approach to find the relationship between multiple ESs provision and landscape patterns, identify the critical landscape patterns that simultaneously promote multiple ESs provision, and give suggestions on land management based on an understanding of the relationships of ESs. Taking the central district of Wuhu City as an example, production possibility frontier (PPF) and Pareto ranks are used to identify relationships between ESs and extract the landscape pattern characteristics of samples that effectively enhance multiple ESs. Our analysis of trade-offs between ESs highlights the complexity of ES relationships and emphasizes the necessity for multi-dimensional investigative approaches. Landscape metrics of Mean Perimeter Area Ratio (PARA_MN), Coefficient of Variation of Perimeter (PERI_CV), Area-weighted Mean Fractal Dimension (FRAC_AM), and Relative Mutual Information (RELMUTINF) emerged as critical determinants of multiple ESs supply. Our findings suggest enhancing patch shape complexity when conducting land use planning to avoid homogenization and keeping differences between the diversity of land use categories and the diversity of adjacencies small. Also, we recommend caution in using high contagion as the only criterion for land use planning, as its effects may vary on different ES targets. [ABSTRACT FROM AUTHOR]

Most of the existing domain adaptation (DA) methods proposed in the context of remote sensing imagery assume the presence of the same land-cover classes in the source and target domains. Yet, this assumption is not always realistic in practice as the target domain may contain additional classes unknown to the source leading to the so-called open set DA. Under this challenging setting, the problem turns to reducing the distribution discrepancy between the shared classes in both domains besides the detection of the unknown class samples in the target domain. To deal with the openset problem, we propose an approach based on adversarial learning and pareto-based ranking. In particular, the method leverages the distribution discrepancy between the source and target domains using min-max entropy optimization. During the alignment process, it identifies candidate samples of the unknown class from the target domain through a pareto-based ranking scheme that uses ambiguity criteria based on entropy and the distance to source class prototype. Promising results using two cross-domain datasets that consist of very high resolution and extremely high resolution images, show the effectiveness of the proposed method.

Abstract: Multiobjective optimization (MO) allows for obtaining comprehensive information about possible design trade‐offs of a given antenna structure. Yet, executing MO using the most popular class of techniques, population‐based metaheuristics, may be computationally prohibitive when full‐wave EM analysis is utilized for antenna evaluation. In this work, a low‐cost and fully deterministic MO methodology is introduced. The proposed generalized Pareto ranking bisection algorithm permits identifying a set of Pareto optimal sets of parameters representing the best trade‐offs between considered objectives. The subsequent designs are found by iterative partitioning of the intervals connecting previously obtained designs and executing Pareto‐ranking‐based poll search. The initial approximation of the Pareto front found using the bisection procedure is subsequently refined to the level of the high‐fidelity EM model of the antenna at hand using local optimization. The proposed framework overcomes a serious limitation of the original, recently reported, bisection algorithm, which was only capable of considering two objectives. The generalized version proposed here allows for handling any number of design goals. An improved poll search procedure has also been developed and incorporated. Our algorithm has been demonstrated using two examples of UWB monopole antennas with four figures of interest taken into account: structure size, reflection response, total efficiency, and gain variability. [ABSTRACT FROM AUTHOR]

This study proposes a multi‐objective differential evolution algorithm (MO‐DEA) based on forced initialisation to solve the optimal power flow (OPF) problem. The OPF problem is formulated as a non‐linear MO optimisation problem. The considered objective functions are fuel cost minimisation, power losses minimisation, voltage profile improvement, and voltage stability enhancement. For solving the MO‐OPF, the proposed approach combines a new variant of DE (DE/best/1) with the ɛ‐constraint approach. This combination guarantees high convergence speed and good diversity of Pareto solutions without computational burden of Pareto ranking and updating or additional efforts to preserve the diversity of the non‐dominated solutions. The proposed approach has the ability to generate Pareto‐optimal solutions in a single simulation run through adaptive variation of the ɛ‐value. In addition, the best compromise solution is extracted based on fuzzy set theory. The effectiveness of the proposed MO‐DEA is tested on the IEEE 30‐bus and IEEE 57‐bus standard systems. The numerical results obtained by the proposed MO‐DEA are compared with other evolutionary methods reported in this literature to prove the potential and capability of the proposed MO‐DEA for solving the MO‐OPF at acceptable economical and technical levels. [ABSTRACT FROM AUTHOR]

Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks. [ABSTRACT FROM PUBLISHER]

Flexible job-shop problem has been widely addressed in literature. Due to its complexity, it is still under consideration for research. This paper addresses flexible job-shop scheduling problem (FJSP) with three objectives to be minimized simultaneously: makespan, maximal machine workload, and total workload. Due to the discrete nature of the FJSP problem, conventional particle swarm optimization (PSO) fails to address this problem and therefore, a variant of PSO for discrete problems is presented. A hybrid discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm is proposed to identify an approximation of the Pareto front for FJSP. In the proposed hybrid algorithm, DPSO is significant for global search and SA is used for local search. Furthermore, Pareto ranking and crowding distance method are incorporated to identify the fitness of particles in the proposed algorithm. The displacement of particles is redefined and a new strategy is presented to retain all non-dominated solutions during iterations. In the presented algorithm, pbest of particles are used to store the fixed number of non-dominated solutions instead of using an external archive. Experiments are performed to identify the performance of the proposed algorithm compared to some famous algorithms in literature. Two benchmark sets are presented to study the efficiency of the proposed algorithm. Computational results indicate that the proposed algorithm is significant in terms of the number and quality of non-dominated solutions compared to other algorithms in the literature. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and λ-interchange mechanism. The algorithm is applied to solve the benchmark Solomon''s 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature. [Copyright &y& Elsevier]

Urban green space (UGS) can simultaneously provide social and ecological benefits for humans. Although numerous studies have evaluated the multifunctional benefits of urban green space, few of them have determined the differentiated conservation priorities of UGS towards the tradeoff relationship of multiple UGS functions. Here, we proposed an integrated framework to explore the targeted conservation strategies of UGS patches. Specifically, the circuit theory model and gravity floating catchment area method were adopted to evaluate ecological connectivity and spatial accessibility of UGS under multiple scenarios in terms of different species dispersal distances and resident travelling modes, and Pareto ranking analysis was utilized to identify conservation priorities of UGS. Wuhan City in central China was taken as a case study. The results show that Wuhan exhibits low synergic relationship of ecological connectivity and spatial accessibility of UGS, and only approximately 7.51% of UGS patches on average rank high. Based on the frequency of UGS Pareto ranks under different scenarios, the differentiated conservation strategy was developed, which identified 10 key green areas that need to be protected and 11 green areas that need to be restored. This work is expected to provide an applicable framework to identify key UGS patches and assist in urban planning and layout optimization of multifunctional UGS in Wuhan, China. [ABSTRACT FROM AUTHOR]

G-Protein coupled receptors (GPCRs) are important targets for drug discovery, and combinatorial chemistry is an important tool for pharmaceutical development. The absence of detailed structural information, however, limits the kinds of combinatorial design techniques that can be applied to GPCR targets. This is particularly problematic given the current emphasis on focused combinatorial libraries. By linking an incremental construction method (OptDesign) to the very fast shape-matching capability of ChemSpace, we have created an efficient method for designing targeted sublibraries that are topomerically similar to known actives. Multi-objective scoring allows consideration of multiple queries (actives) simultaneously. This can lead to a distribution of products skewed towards one particular query structure, however, particularly when the ligands of interest are quite dissimilar to one another. A novel pivoting technique is described which makes it possible to generate promising designs even under those circumstances. The approach is illustrated by application to some serotonergic agonists and chemokine antagonists. [ABSTRACT FROM AUTHOR]

An important goal of vulnerability assessment is to create an index of overall vulnerability from a suite of indicators. Constructing a vulnerability index raises several problems in the aggregation of these indicators, including the decision of assigning weights to them. The purpose of this paper is to demonstrate a method of aggregating vulnerability indicators that results in a composite index of vulnerability, but that avoids the problems associated with assigning weights. The investigators apply a technique based on Pareto ranking to a complex, developed socioeconomic landscape exposed to storm surges associated with hurricanes. Indicators of social vulnerability to this hazard are developed and a principal components analysis is performed on proxies for these indicators. Overall social vulnerability is calculated by applying Pareto ranking to these principal components. The paper concludes that it is possible to construct an effective index of vulnerability without weighting the individual vulnerability indicators. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW. [ABSTRACT FROM AUTHOR]