Searching for an Optimized Single-objective Function Matching Multiple Objectives with Automatic Calibration of Hydrological Models

In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single-or multi-objective functions when utilizing automatic calibration approaches.In most previous studies, there is a general opinion that no single-objective function can represent all important characteristics of even one specific hydrological variable(e.g., streamflow).Thus hydrologists must turn to multi-objective calibration.In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes(i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds.The new objective function was applied to 196 model parameter estimation experiment(MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model.The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively.Results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration.The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed.

Disassembly Sequence Planning:A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled.They contain reusable resource that can be recycled and used to generate desired economic benefits.Therefore,performing their efficient disassembly is highly important in green manufacturing and sustainable economic development.Their typical examples are electronic appliances and electromechanical/mechanical products.This paper presents a survey on the state of the art of disassembly sequence planning.It can help new researchers or decision makers to search for the right solution for optimal disassembly planning.It reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded products.This paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty handling.This survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Project Scheduling Problem with Uncertain Variables

Project scheduling problem is mainly to determine the schedule of allocating resources in order to balance the total cost and the completion time. This paper chiefly uses chance theory to introduce project scheduling problem with uncertain variables. First, two types of single-objective programming models with uncertain variables as uncertain chance-constrained model and uncertain maximization chance-constrained model are established to meet different management requirements, then they are extended to multi-objective programming model with uncertain variables.

3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.