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Transportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function. WOS:000662077300020 Q1

Quantitative analyses of empirical data requirements for hydrological simulations are rare. This study aims to analyze how a multi-objective optimization framework and information content computations aid in quantifying field-scale data worth in drainage studies. The results showed how a 1D numerical model and a differential evolution algorithm performed in describing the field water balance. The choice of the optimization target (subsurface drain discharge and surface runoff) impacted the simulation results more than parameter deviations. While the information content of surface runoff data was higher than that of drain discharge, drain discharge data contained more information on most of the soil parameters. Uncertainties related to groundwater outflow data, which were not used in the optimization, were higher than those of drain discharge and surface runoff. A central weighing optimization scheme with two data types produced the best but still incomplete description of the field hydrology. Despite the modest model performance, the results demonstrated how the choice of empirical data and optimization strategy can lead to uncertainties in drainage simulations and how the uncertainties can be assessed. Practically, a low amount of information and a parameter sensitivity analysis can lead to a biased description of uncertainty related to such hydrological variables which are not used in the optimization. Benefits of the modeling framework were shown when assessing (1) model structure adequacy with the Pareto front analysis, (2) information content of different data types regarding different parameters, and (3) uncertainties related to simulating hydrological variables based on optimization against a given data type. © 2020 Elsevier B.V.

The in situ identification of fuel-cell material parameters is crucial both for guiding the research for advanced functionalized materials and for fitting multiphysics models, which can be used in fuel cell performance evaluation and optimization. However, this identification still remains challenging when dealing with direct measurements. This paper presents a method for achieving this aim by stochastic optimization. Such techniques have been applied to the analysis of fuel cells for ten years, but typically to specific problems and by means of semi-empirical models, with an increased number of articles published in the last years. We present an original formulation that makes use of an accurate zero-dimensional multi-physical model of a polymer electrolyte membrane fuel cell and of two cooperating stochastic algorithms, particle swarm optimization and differential evolution, to extract multiple material parameters (exchange current density, mass transfer coefficient, diffusivity, conductivity, activation barriers …) from the experimental data of polarization curves (i.e. in situ measurements) under some controlled temperature, gas back pressure and humidification. The method is suitable for application in other fields where fitting of multiphysics nonlinear models is involved.

金沢大学理工研究域機械工学系, In this paper, the basic characteristics of the differential evolution (DE) are examined. Thus, one is the meta-heuristics, and the other is the global optimization technique. It is said that DE is the global optimization technique, and also belongs to the meta-heuristics. Indeed, DE can find the global minimum through numerical experiments. However, there are no proofs and useful investigations with regard to such comments. In this paper, the DE is compared with the generalized random tunneling algorithm (GRTA) and the particle swarm optimization (PSO) that are the global optimization techniques for continuous design variables. Through the examinations, some common characteristics as the global optimization technique are clarified in this paper. Through benchmark test problems including structural optimization problems, the search ability of DE as the global optimization technique is examined. © 2011 Elsevier B.V. All rights reserved.