scholarly journals Load Planning of Transport Aircraft Based on Hybrid Genetic Algorithm

2018 ◽  
Vol 179 ◽  
pp. 01007
Author(s):  
Yang Chenguang ◽  
Liu Hu ◽  
Gao Yuan
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Transport Costs ◽  
Gravity Center ◽  
Transport Aircraft ◽  
Load Planning ◽  
Volume Weight ◽  
Loading Problem

Loading of transport aircraft attracts much attention as the airlift is developing rapidly. It refers to the process that various cargoes are loaded, in an appropriate manner, into kinds of transport aircrafts with constraints of volume, weight and gravity center. Based on two-dimensional bin packing with genetic algorithm (GA), a new hybrid algorithm is proposed to solve the multi-constraint loading problem of transport aircraft for seeking the minimum of fuel consumption. Heuristic algorithm is applied to optimize single-aircraft loading in GA decoding, and the procedure of hybrid GA is summarized for the multi-aircraft loading issues. In the case study, eight kinds of cargos are distributed in three different aircrafts. The optimal result indicates that this algorithm can rapidly generate the best plan for the loading problem regarding lower transport costs.

The Improvement of a Hybrid Genetic Algorithm for Three Dimensional Bin-Packing Problems

2012 ◽  
Vol 200 ◽  
pp. 470-473
Author(s):  
Zhen Zhai ◽  
Li Chen ◽  
Xiao Min Han
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Flow Process ◽  
Packing Problems ◽  
Loading Problem ◽  
The Cost ◽  
Process Diagrams

The multi-constrained bi-objective bin packing problem has many extensive applications. In the loading section of logistics it has mainly been transported by truck. The cost of transportation is not only determined by the bin space utilization, but also by the number of vehicles in transporta¬tion utilization. The type of items and bins is introduced in the mathematical model, as well as the volume of the items. In this paper, the hybrid genetic algorithm which tabu and simulated annealed rules are added for complex container-loading problem is studied. The effective coding and decod-ing method together with flow process diagrams are given.

Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study

2018 ◽  
Vol 38 (4) ◽  
pp. 511-523 ◽  
Author(s):  
Dongwook Kim ◽  
Dug Hee Moon ◽  
Ilkyeong Moon
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Temporary Workers ◽  
Labor Costs ◽  
Content Type ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

PurposeThe purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.Design/methodology/approachThis paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.FindingsComputational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.Originality/valueThe developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

Optimization for Mining Material Loading Based on Genetic Algorithm

2012 ◽  
Vol 457-458 ◽  
pp. 1342-1346
Author(s):  
Tong Bin Zhao ◽  
Shan Shan Liu ◽  
Fan Wei Bu
Keyword(s):  
Genetic Algorithm ◽  
Carrying Capacity ◽  
Optimization Method ◽  
Search Performance ◽  
Management Level ◽  
Optimization Calculation ◽  
Loading Problem ◽  
Satisfactory Answer ◽  
Loading Scheme

In order to improve management level of mining materials, optimum loading scheme is important. Based on the analysis of bulk cargo loading problem, taking carrying capacity and effective volume as constraint conditions, maximizing transport benefits as target, mathematical model on the base of optimization method is established. And genetic algorithm is introduced to case study. The result shows that genetic algorithm in solving the optimum loading scheme of mining materials has quick convergence, short term, and higher precision. The better satisfactory answer can be obtained after 100 generations. Before 600 generations optimum loading scheme can be educed. Genetic algorithm, with good adaptability and powerful search performance, is very suitable for optimization calculation of multiple constraints problem. Genetic algorithm can make full use of carrying capacity and volume in the process of bulk cargo loading transport, that promot mining enterprise’s operation efficiency. The study is useful for management work of mining material warehousing, scheduling, transportation etc.

A Hybrid Genetic Algorithm for Shipment Loading Problem with Consideration of Gas Emission

2014 ◽  
Vol 687-691 ◽  
pp. 5069-5074
Author(s):  
Can Tao Shi ◽  
Lu Xin Liu ◽  
Zhi Wei Luan ◽  
Zhen Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Entire Solution ◽  
Experimental Result ◽  
Gas Emission ◽  
Proposed Model ◽  
Loading Problem ◽  
Searching Ability ◽  
Operation Cost

For shipment loading problem, a mathematical model is established with objective of minimizing operation cost mainly led from gas emission. The genetic algorithm is applied to solve it with modifications: a segmented chromosome coding is adopted to represent the entire solution space; crossover operator and mutation operator are re-defined to make genetic algorithm suitable for the problem; a repair algorithm for infeasible solution is designed to improve the searching ability and increase the converging speed. The experimental result indicates that the proposed model and algorithm are feasible and effective.

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