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Abstract:This article collectively addresses the issues of optimizing the sailing speed of containerships and fleet deployment in intercontinental container liner shipping, taking cargo time values into consideration. A mixed-integer nonlinear programming model is developed to maximize the gross profits of a container shipping line. The sailing speed is divided into 0.1 knot intervals and a reciprocal-discretization method is proposed to transform the model into an integer-based linear programming model, which can be solved using optimization solvers such as CPLEX. A case study is conducted based on the Far East-West America liner route of a global container shipping line to assess the effectiveness of the proposed model and the reciprocal- discretization method. The results show that when the cargo time value is taken into consideration, the increased sailing speed of containerships on long legs and the decreased number of deployed containerships will generate greater operating profits for the container shipping line. The rise in bunker price will lead to a stepwise decline in sailing speed of containerships and a stepwise growth in the number of deployed containerships. Moreover, the downward trend of sailing speed due to the increase of bunker price will be somewhat delayed as the unit container cargo value increases. Several useful insights are drawn from analysis of the results.

This article collectively addresses the issues of optimizing the sailing speed of containerships and fleet deployment in intercontinental container liner shipping, taking cargo time values into consideration. A mixed-integer nonlinear programming model is developed to maximize the gross profits of a container shipping line. The sailing speed is divided into 0.1 knot intervals and a reciprocal-discretization method is proposed to transform the model into an integer-based linear programming model, which can be solved using optimization solvers such as CPLEX. A case study is conducted based on the Far East-West America liner route of a global container shipping line to assess the effectiveness of the proposed model and the reciprocal-discretization method. The results show that when the cargo time value is taken into consideration, the increased sailing speed of containerships on long legs and the decreased number of deployed containerships will generate greater operating profits for the container shipping line. The rise in bunker price will lead to a stepwise decline in sailing speed of containerships and a stepwise growth in the number of deployed containerships. Moreover, the downward trend of sailing speed due to the increase of bunker price will be somewhat delayed as the unit container cargo value increases. Several useful insights are drawn from analysis of the results.

AbstractStatistical monitoring of multivariate processes is becoming increasingly important in modern manufacturing environments. Typical equipment may have multiple key variables to be measured continuously. Hotelling’s chart was originally applied for monitoring the mean vector of multivariate quality measurements. In practical problems, estimated parameters are needed and their use will modify the properties of control charts. The Average Run Length (ARL), an indicator of the performance of the control charts, will be larger when the estimated parameters are used. As one contribution of the paper, we provide a rigorous proof of this phenomenon which has been reported in several empirical studies. Furthermore, in order to design an efficient chart with estimated parameters, it is necessary to have a method to calculate or approximate the ARL function. An existing approach in the literature is based on extensive Monte-Carlo simulations. In this paper, we propose a novel approach by providing an analytic approximation of the ARL function in the however limited case of univariate observations.

The chief aim of the present work was to achieve drag reduction in stator blades with liquid using boundary layer control. A stator blade of hydraulic torque converter with bionic grooves in suction side and hydrophobic surface in pressure side was designed. The hydrophobic surface was created using anodic oxidation method and irregular Al2O3thin films were found on the surface. They formed hierarchical structure consisting of mini porous structures and microscopic pore spaces, resulting in the hydrophobicity. The bionic groove was designed by Computational Fluids Dynamics (CFD) method. Multi-Island Genetic Algorithm (MIGA) was adopted for groove multi-objective optimization. Through optimization, the maximum drag reduction was close to 12% in oil. In addition, the drag reduction calculation was verified by closed channel experiment and “Tire Vortex” was proposed to explain the drag reduction mechanism. The bionic Janus blade could maintain its initial profile without any additional device, which had lower risk and less cost. The results are encouraging and show great potential to apply in other flow machineries.

The container sea–rail multimodal transport system faces complex challenges with demand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on revenue management (RM) as actual slots sale of multi-node container sea–rail multimodal transport usually includes contract sale to large shippers and free sale to scattered shippers. First stage in the model utilizes an origin-destination control approach, formulated as a stochastic integer programming equation, to settle long-term slot allocation in the contract market and empty container allocation. Second stage in the model is formulated as a stochastic nonlinear programming equation to solve a multiproduct joint dynamic pricing and inventory control problem for price settling and slot allocation in each period of free market. Considering the random nature of demand, the methods of chance constrained programming and robust optimization are utilized to transform stochastic models into deterministic models. A numerical experiment is presented to verify the availability of models and solving methods. Results of considering uncertain/certain demand are compared, which show that the two-stage optimal strategy integrating slot allocation with dynamic pricing considering random demand is revealed to increase the revenue for multimodal transport operators (MTO) while concurrently satisfying shippers' demand. Research resulting from this paper will contribute to the theory and practice of container sea–rail multimodal transport revenue management and provide a scientific decision-making tool for MTO.

As e-business grows rapidly in recent years, express delivery industry ushered in the unprecedented opportunities for development. This also resulted in a fierce competition within the express delivery industry. How to reduce logistics cost and serve customer better has become the focus of many express companies. The importance of express distribution center and the design of its location have aroused extensive attention. Therefore, the location optimization problem of express distribution center has been paid much more attention both in industry and academic world. This paper established an optimization model of regional express distribution center location based on the reality of the road network. The Fuzzy C Means (FCM) clustering algorithm was improved to solve the model. An empirical study was implemented via a case, and it is validated that the established model and its algorithm are feasible.

In order to improve the Cs ​+ ​immobilization performance of geopolymer, in-situ reduced graphene oxide (rGO) reinforced geopolymer composites (rGO/GP) were prepared and they were further high-temperature treated to get rGO/pollucite composites. Effect of rGO addition on the thermal & mechanical properties and Cs immobilization were explored. When rGO content is 0.0125%, compressive strength of the rGO/GP composite reaches the maximum value. With the rGO content further increasing, the strength of rGO/GP shows a downward trend. DSC results show that the addition of rGO can increase the pollucite formation temperature. The presence of rGO also shows an important influence on the Cs ​+ ​immobilization of geopolymer. Compared with pure geopolymer, Cs immobilization rate of composite with 0.0125% rGO content increases by 11.5%. For the composites treated at 1000 ​°C, all the samples show high immobilization rate for Cs, which are higher than 99.5%, due to pollucite formation in all the composites.