Hand anthropometry data are largely based on measurements of the hand in an outstretched hand posture and are, therefore, difficult to apply to tool gripping hand postures. The purpose of this project was to develop a representative, scalable hand model to be used with 3-D software drawing packages to aid in the ergonomic design of hand tools. Landmarks (66) on the palmar surface of the right hand of 100 subjects were digitised in four functional hand postures and, from these, 3-D surface models of a mean, 25th and 75th% hand were developed. The root mean square differences in hand length between the hand model and the digitised data for the 25th, 50th and 75th percentile hand were 11.4, 3.2 and 8.9 mm, respectively. The corresponding values for hand breadth were 2.0, 0.4 and 1.4 mm. There was good agreement between distances on the digitised hand and the hand model. The application of this research includes improved ergonomic hand tool design through the use of hand anthropometry reference values developed from the general population using grasping hand postures.

The issue of effective Airline crew rostering problem has been an ongoing issue for airline operation and many studies have tried to investigate and find ways to improve the crew rostering application. Airline crew rostering is a process to generate a timetable for crew members which is aligned with Occupational Health and Safety Policies. A fair and sensible roster can help to improve service quality and crew's enthusiasm. The main objective of this paper is to propose a particle swarm optimization to solve the airline crew rostering problem by assigning appropriate balance workload for each crew member. The proposed method has been tested on real data from Thai Airways. Computational performance of the proposed method is presented and analyzed. The results are to show that the new method is effective.

Aircraft routing and maintenance scheduling is a large-scaled and complex optimization undertaking that assigns an aircraft of each fleet type to each flight whilst satisfying maintenance regulations, shifts time of workers and other requirements. This paper presents the aircraft routing and maintenance scheduling problem for both international flights and domestic flights of Thai Airways with the major focus on minimizing the total waiting time for maintenance checks in order to reduce expense. The various test cases are generated from Thai Airways data set and solved by using the commercial optimizer, IBM ILOG CPLEX.

The airline crew scheduling problem is a combinatorial optimization problem and belongs to the class of NPhard problems. An effective method for solving the airline crew scheduling problem can reduce the crew costs and improve crew satisfaction. Because of its complexity, the problem is divided into two subproblems: the crew pairing problem and the crew rostering problem. In this paper, the crew rostering problem is focused on and the objective is to generate a fairness timetable in which the workloads are distributed among each crew equally. We propose a hybrid particle swarm optimization (PSO) and an improvement heuristic (IH) to solve this problem. The IH is designed to improve the standard deviation of the workloads by picking a workload from the high workload crew and assigning it to the low workload crew. The IH improves the solution of the particle after the particle changes position each generation. The proposed algorithm is tested on actual pairing data from Thai Airways and is compared with PSO without IH and the multi-commodity network flow approach. With the combination of PSO and IH, the algorithm can improve the quality of the solution by more than 20% in most cases, and PSO with IH also outperforms the network approach in 6 out of 9 cases and especially in the large size cases for which the network approach cannot find a feasible solution.