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From practical perspectives and to improve the working efficiency, trolley transportation and payload hoisting/lowering should be simultaneously controlled. Moreover, in practical crane applications, the transportation time is an important criterion for improving transportation efficiency. Based on these requirements, this paper aims to solve positioning and antiswing control problems and shorten the transportation time for underactuated varying-rope-length overhead cranes.

By choosing trolley acceleration and varying-rope-length acceleration as system inputs, the crane system dynamic model is converted into an equivalent model without linearizing/approximating. Then, based on the converted model and system state constraints, a time-optimal problem is formulated. Further, the original problem is converted into an optimization problem with algebraic constraints which can be conveniently solved. Finally, by solving the optimization problem, the optimal trajectories of system states, including displacements, velocities and accelerations, are obtained.

This paper first provides a nonlinear time-optimal trajectory planner for varying-rope-length overhead cranes, which achieves accurate and fast trolley positioning and eliminates payload residual swings. Meanwhile, all system states satisfy the given constraints during the entire process. Hardware experimental results show that the proposed time-optimal planner is effective and has better performance compared with existing methods.

This paper proposes a time-optimal trajectory planner for overhead crane systems with hoisting/lowering motion. The proposed planner achieves fast trolley positioning and eliminates payload residual swing with all the system states being constrained within given scopes. The planner is presented based on the original nonlinear system dynamics without linearization/approximation.

This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency.

An assembly sequence planning system for workpieces (ASPW) based on deep reinforcement learning is proposed in this paper. However, there exist enormous challenges for using DRL to this problem due to the sparse reward and the lack of training environment. In this paper, a novel ASPW-DQN algorithm is proposed and a training platform is built to overcome these challenges.

The system can get a good decision-making result and a generalized model suitable for other assembly problems. The experiments conducted in Gazebo show good results and great potential of this approach.

The proposed ASPW-DQN unites the curriculum learning and parameter transfer, which can avoid the explosive growth of assembly relations and improve system efficiency. It is combined with realistic physics simulation engine Gazebo to provide required training environment. Additionally with the effect of deep neural networks, the result can be easily applied to other similar tasks.

The purpose of this paper is to investigate the impacts of China’s urban segregation caused by hukou restrictions on food consumption.

Based on the 2007–2009 Urban Household Survey data from six China provinces conducted by the National Bureau of Statistics of China, the authors adopt a propensity score matching (PSM) method to correct for potential selection bias. A Rosenbaum bounds test is applied to evaluate the sensitivity of the PSM results to unobserved variables.

The results show that holding rural hukou (RHs) reduces the consumption of livestock products and vegetables and fruit by 8.8 and 4.8 percent, respectively. The status of hukou does not affect the consumption of grain and edible oil. Hukou impacts on food consumption are heterogeneous across income levels, with low-income and middle-income households more vulnerable to urban segregation and hukou discriminations. A stronger motivation for precautionary saving and higher welfare expenditures that not compensated by social security lead to the lower food consumption by migrant households with RHs.

This paper advances the research frontier by investigating the impacts of hukou system on the structure of food consumption, which accurately reflects the household welfare.

Agenda for knowledge creation within inter-project alliances and inter-firm supply chain networks has been extensively debated. However, the existing knowledge networks within consultant-supplier interfaces in the architecture, engineering and construction (AEC) industry seem to be vague, loose, incidental and insignificant. This study examines factors affecting knowledge networking intention (KNI) within construction service supply chain (CSSC) networks.

Data analysis was conducted on a quantitative survey of 161 consulting professional service firms in Tanzania, employing stepwise regression modelling as the statistical technique.

The results indicate that three types of knowledge inertia (KI) exert varying effects on KNI. While both procedural (PI) and learning inertia (LI) negatively impact KNI, experience inertia (EI) has no impact on KNI. In addition, knowledge governance (KG) mechanisms are found to strongly strengthen and leverage the negative effects of PI and LI on KNI and the positive link between EI and KNI within outbound and heterogeneous CSSC actors, with formal KG having greater leverage than informal KG.

The study offers guidance on how managers of PBOs should strategically orchestrate knowledge governance mechanisms within CSSC networks to leverage KI behaviours.

Current literature on KNI, KI and KG within CSSC networks offers a limited understanding of how KI behaviours influence KNI of project-based organizations (PBOs) in tapping vibrant outbound peripheral knowledge. The research presents two major original contributions. First, the empirical evidence contributes to deepening the current understanding of how heterogeneous external knowledge within consultant-supplier interactions is negatively influenced by KI. Lastly, the study suggests formal and informal knowledge governance strategies for managers on how to counteract KI forces, thus extending the theoretical debate on KNI, KI and KG literature.