Junliang He

Mechanical Property Improvement of Carbon Fiber-Reinforced PTFE Composites by PA6 Filler Dispersion

Mechanical properties of carbon fiber reinforced composites and polyamide6 (PA6) particles dispersed carbon fiber hybrid-reinforced composites were investigated. Mechanical properties were improved by incorporating PA6 particles into the PTFE matrix. Hybridization effects on the mechanical properties of carbon fiber reinforced-PTFE composites with PA6 particles are discussed. In the present experiment, an attempt has been taken to disperse PA6 particles into carbon fiber-reinforced composites (CFRP) matrix to fabricate carbon fiber-reinforced hybrid composites (CFHRP). The main purpose of hybridization is to optimize the properties of the matrix for high performance fiber-reinforced composites.

An investigation into yard allocation for outbound containers

Purpose – The purpose of this paper is to propose a yard allocation model via objective programming. This is initially postulated based on a rolling‐horizon strategy, which aims at allotting outbound containers into yard.Design/methodology/approach – To resolve the NP‐hard problem regarding the yard allocation model, a hybrid algorithm, which applies heuristic rules and distributed genetic algorithm (DGA), is then employed.Findings – It could be observed from the case study that this proposed approach is proven effective for resolving the container yard allocation problem. The total loading time onto vessels, the total horizontal transportation distance and the imbalance among blocks are improved.Research limitations/implications – This paper does not deal with equipment scheduling.Practical implications – This approach helps to minimize turnaround time; handling cost of vessels; the workloads among blocks are balanced for each vessel; and the total distance of container transportation.Originality/value –...

A Strategy for Yard Crane Scheduling Based on Hybrid Parallel Genetic Algorithm

Container terminals play a crucial role in container transportation, including shipping and land transportation. In particular, container yard management, which involves diverse operational services, significantly affects the operational efficiency of the entire container terminal. However, there is a major omission in existing work, viz., it is imperative to attain an effective workload scheduling to support the dynamic scheduling of yard cranes. Based on these understandings, the study aims at postulating a novel strategy in terms of yard crane scheduling. In this manner, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-hard problem regarding the yard crane scheduling, a hybrid algorithm which employs heuristic rule and parallel genetic algorithm, is then employed. Finally, a case simulation study has been used for system illustration, and then verifies the validity and usefulness of the model and the algorithm.

An Investigation into Berth Allocation and Quay Crane Assignment Based on Hybrid Parallel Genetic Algorithm

A rolling-horizon approach was proposed, which aims at the problem of berth allocation and quay crane assignment. Then a dynamic allocation model using objective programming was initially developed for berths allocation and quay crane assignment, which more closes realistic as result of basing continuum quayside. The model objective function was subject to the minimization of the total berthing location deviation, the total penalty and the energy consumption of quay cranes. Then, a hybrid parallel genetic algorithm (HPGA) was employed for solving the model, which combines parallel genetic algorithm (PGA) and heuristic Algorithm. Furthermore, a simulation model integrating HPGA was developed for evaluating this HPGA and executing gene repair techniques to repair the unfeasible individuals generated by HPGA. Finally, case study on a specific container terminal was used for system illustration, and then verified the validity and usefulness of this model and algorithm.