An Optimization Model for Aircraft Service Logistics

Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with genetic algorithm is formulated . It is found that the company can produce an effective and efficient schedules to deploy the manpower and equipment resources. Simulation is used to verify the method and a MATLAB program is used to code the genetic algorithm. This model i s further illustrated by a case study in Hong Kong and the benefit elaborated. F inally, a conclusion is made to summarize the experience of this project and pro vide further improvement.

Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method

Production logistics(PL)is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems.To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits,this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain(MTDC).First,the manufacturing task chain(MTC)is defined to characterize the discrete production process of a product.To handle manufacturing big data,the MTC data paradigm is designed,and the MTDC is established.Then,the logistics trajectory model is presented,where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC.Based on this,a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL.Finally,a case study is applied to verify the proposed method,and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment,which can assist managers in implementing the optimization decisions.

Compound Method of Solving Logistics Routes Allocation

The logistics routes allocation discusses optimal routing from origin to destination through distribution center （DC） on condition that both transport and distribution cost should be taken into account. The problem can be solved by the iterative non-linear programming （INLP）, in which the transport cost and distribution cost are assumed to be linear and non-linear, respectively. The method works well in most situations. However, when the distribution cost predominates in the total cost, the method falls, and the solution given by the method is not a global minimum but a local minimum. Further study reveals that the INLP method is still a kind of transport routing method like vehicle routing problem （VRP）, and the failure of the method must happen when the distribution cost is a major one. On such a condition, further computation on other extreme points, which physically means forcing all routes to pass through one DC one by one, should be carried out. By comparing values on these extreme points, the global optimal solution can be got. The method has both theoretical and practical meaning. In theoretical field, it might force us to seek new method; in practice, it reminds us to do such kind of check when the transport distance is short and warehousing work is major that often happens in local consolidation center or de-vanning center.