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AbstractGraphene enhances piezoelectric matrix properties due to its exceptional conductivity and physical characteristics. However, existing models encounter significant challenges in analyzing buckling behavior of graphene-reinforced composite piezoelectric (GRCP) actuators. Inaccuracy in describing transverse shear deformations impact plate stability, particularly due to the disparities in material properties at the interfaces. A new plate model is proposed, featuring a refined interlaminar shear stress field for accurate modeling. Validity is confirmed via 3D elasticity solutions and alternative theories. Compared to existing models, the proposed theory shows superior precision. Additionally, a parametric study explores key factors influencing buckling behaviors of piezoelectric composite plates. [ABSTRACT FROM AUTHOR]

Graphene is a highly conductive and exceptional material that has shown potential for significantly improving the piezoelectric and mechanical properties of piezoelectric matrix. This paper examines the stability of composite plates surface bonded with graphene‐reinforced composite piezoelectric (GRCP) actuators. However, the significant differences in material characteristics at the interfaces will pose some challenges in analyzing the buckling behavior of piezoelectric composite plates by means of existing higher‐order shear deformation theories. To address this issue, a refined plate theory is developed for the buckling analysis of piezoelectric composite plates. The developed theory includes a new interlaminar shear stress field that precisely describes the distribution of interlaminar shear stresses, which differs from earlier higher‐order theories. It should be emphasized that the finite element formulation can be simplified by removing the second‐order derivatives of in‐plane displacement parameters from interlaminar shear stresses. In terms of the developed theory, a four‐node C0 quadrilateral plate element is introduced to examine the buckling behavior of piezoelectric composite plates. Furthermore, the modified interlaminar shear stress field is absorbed into the strain energy, significantly improving the ability to predict the critical loads of composite plates with GRCP actuators. The refined plate model is evaluated through the utilization of three‐dimensional (3D) elasticity solutions and results obtained from other associated theories. Numerical results demonstrate that the refined plate model is capable of producing favorable results, and a comprehensive examination is conducted to analyze the effects of significant parameters on the buckling behaviors of piezoelectric composite plates. This study sets a solid foundation for future research and development on the application of graphene‐reinforced composite piezoelectric actuators in composite structures. Highlights: Buckling analysis of composite plates with GRCP actuators is conducted.A refined plate theory with modified interlaminar shear stresses is developed.The ability to predict critical loads can be significantly improved.The finite element formulation can be simplified based on the proposed model.Comprehensive parametric studies on buckling behavior are caried out. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

The increasing urban logistics transportation activities hugely impact the economy and environment. This paper investigates the combined impact of ambient temperature, path flexibility, and hybrid fleet on the economy and environment in urban logistics. The proposed problem is a variant of the location-routing problem (LRP) named dual-mode energy-conserving LRP (DMECLRP) that handles the combination of three types of decisions: the location of depots, the design of the distribution routes, and the location of charging stations (CSs) and battery swapping stations (BSSs). The objective is to minimize the total cost, including the daily fixed costs of operating depots, the cost of renting vehicles and depreciation, driver compensation, and the routing costs, where the latter can be defined concerning the cost of the consumed energy and CO 2 emissions. Due to the NP-hardness of the problem, this paper presents a Q-learning-based hyper-heuristic (QLHH) algorithm to address the DMECLRP. The QLHH employs a Q-learning approach to select appropriate heuristics through its search process and simulated annealing to determine the acceptance of solutions. Results show that the proposed algorithm is effective, providing competitive results for LRP benchmark instances within reasonable computing time, and the proposed model can effectively reduce logistics costs, energy consumption, and CO 2 emissions. Extensive analyses are carried out to empirically assess the effect of ambient temperature, path flexibility, and hybrid fleet on key performance indicators, including energy consumption, carbon emissions, and operational costs. Several managerial insights are provided. • Impacts of ambient temperature on electric vehicle in logistics network. • A strategy of path flexibility and a hybrid fleet of electric and fuel vehicles. • A model for the energy-conserving logistics network considering carbon emissions. • A Q-learning-based hyperheuristic combined with simulated annealing acceptance. [ABSTRACT FROM AUTHOR]

Economic, environmental, and social effects are the most dominating issues in cold chain logistics. The goal of this paper is to propose a cost-saving, energy-saving, and emission-reducing bi-objective model for the cold chain-based low-carbon location-routing problem. In the proposed model, the first objective (economic and environmental effects) is to minimize the total logistics costs consisting of costs of depots to open, renting vehicles, fuel consumption, and carbon emission, and the second one (social effect) is to reduce the damage of cargos, which could improve the client satisfaction. In the proposed model, a strategy is developed to meet the requirements of clients as to the demands on the types of cargos, that is, general cargos, refrigerated cargos, and frozen cargos. Since the proposed problem is NP-hard, we proposed a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms (MOEAs). Furthermore, in the experiments, we first examined the effectiveness of the proposed framework by assessing the performance of seven MOEAs, and also verified the efficiency of the proposed model. Extensive experiments were carried out to investigate the effects of the proposed strategy and variants on depot capacity, hard time windows, and fleet composition on the performance indicators of Pareto fronts and cold chain logistics networks, such as fuel consumption, carbon emission, travel distance, travel time, and the total waiting time of vehicles. [ABSTRACT FROM AUTHOR]

In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company. [ABSTRACT FROM AUTHOR]

Sustainable development is critical to cold chain logistics, including its economic, environmental, and social effects, especially in road transportation. To simultaneously address these issues, we propose a comprehensive cold-chain-based low-carbon location-routing-problem optimization model to minimize the total logistics costs and client and vehicle waiting time. The first objective comprises the fixed costs of depots to open and vehicles to rent, vehicle renting cost, driver salaries, fuel consumption cost, carbon emission costs, and damage costs of cargos that need to be refrigerated or frozen. The second objective consists of the waiting time of clients and vehicles to improve client satisfaction and the efficiency of the cold chain logistics network. In the proposed problem, we developed a strategy for improving the efficiency of the cold chain logistics network by mixing the types of cargos arranged in one vehicle. Aiming at efficiently solving the proposed model, six well-known multi-objective evolutionary algorithms (MOEAs) were used by combining an efficient framework, and first (FI) and best-improvement (BI) search mechanisms were considered. In the experiments, we examined the effectiveness of six MOEAs inserting the proposed framework and search mechanisms, and the result showed that NSGA-II/FI, SPEA2/FI, and NSGA-II/BI were the top three MOEAs. In the extensive experiments, the results showed that the delivery strategy, depot cost, depot capacity, crowding distance, and traveling speed have significant effects on the Pareto front, fuel consumption, carbon emission, vehicle and client waiting times, traveling distance, and traveling time. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

This paper proposes a low-carbon location routing problem (LCLRP) model with simultaneous delivery and pick up, time windows, and heterogeneous fleets to reduce the logistics cost and carbon emissions and improve customer satisfaction. The correctness of the model is tested by a simple example of CPLEX (optimization software for mathematical programming). To solve this problem, a hyper-heuristic algorithm is designed based on a secondary exponential smoothing strategy and adaptive receiving mechanism. The algorithm can achieve fast convergence and is highly robust. This case study analyzes the impact of depot distribution and cost, heterogeneous fleets (HF), and customer distribution and time windows on logistics costs, carbon emissions, and customer satisfaction. The experimental results show that the proposed model can reduce logistics costs by 1.72%, carbon emissions by 11.23%, and vehicle travel distance by 9.69%, and show that the proposed model has guiding significance for reducing logistics costs. [ABSTRACT FROM AUTHOR]