An Improved Genetic Algorithm of Vehicle Scheduling Problems for Military Logistic Distribution

2013 ◽  
Author(s):  
Gong Yancheng
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Scheduling ◽  
Logistic Distribution ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

Improved Genetic Algorithm of Vehicle Routing Problems with Time Window for Military Logistic Distribution

2011 ◽  
Vol 135-136 ◽  
pp. 585-591
Author(s):  
Zhi Gang Zhang ◽  
Yan Cheng Gong
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Vehicle Scheduling ◽  
Logistic Distribution ◽  
Minimum Length ◽  
Scheduling Problems ◽  
Distribution Centers ◽  
Improved Genetic Algorithm ◽  
Algorithm Efficiency

By changing the constrain conditions of delivery time windows and vehicle capacities to objective function, A vehicle scheduling model was built up based on minimum length of total transportation distance, which included penalty function terms of time window and vehicle capacity constrains, and the model characteristics and application prospects was analyzed. A improved Genetic Algorithm program was put forward to solve the model, in which a chromosome coding suitable to describe delivery routes was designed, a suitable-degree function was proposed, and a reproduction operator, a crossover operator and a mutation operator were constructed. An example was given to demonstrate feasibility of the algorithm. The study indicates that the Algorithm has higher algorithm efficiency and can effectively solve vehicle scheduling problems of military distribution centers.

Application of Improved Genetic Algorithm to Traffic Equipment’s Rush-Repair in the Wartime

2012 ◽  
Vol 605-607 ◽  
pp. 49-52
Author(s):  
Geng Sheng Wu ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Assignment Model ◽  
Practical Engineering ◽  
Poor Stability

Traffic equipment’s rush-repairs in the wartime optimal assignment model was established. Combining the features of Job-shop scheduling problems, described the complexity of this problem. In order to find global optimal results efficiently, traditional GAs were improved and used for study of this problem. Though genetic algorithm, as an effective global search method, had been used in many problems, it had the disadvantages of slow convergence and poor stability in practical engineering. In order to overcome these problems, an improved genetic algorithm was proposed in terms of creation of the initial population, genetic operators, etc. At the end, the steps to solve the optimal model were put forward. With this model we had obtained ideal results. This shows that the method can offer a scientific and effective support for a decision maker in command automation of the traffic equipment’s rush-repairs in battlefield.

scholarly journals Improved Genetic Algorithm Integrated with Scheduling Rules for Flexible Job Shop Scheduling Problems

2021 ◽  
Vol 243 ◽  
pp. 02010
Author(s):  
Muhammad Kamal Amjad ◽  
Shahid Ikramullah Butt ◽  
Naveed Anjum
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Improved Genetic Algorithm ◽  
Priority Rules ◽  
Data Set ◽  
Shop Scheduling

This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.

scholarly journals Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

2022 ◽  
Vol 14 (1) ◽  
pp. 491
Author(s):  
Chunxiao Zhao ◽  
Junhua Chen ◽  
Xingchen Zhang ◽  
Zanyang Cui
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
High Speed ◽  
Large Scale ◽  
Mathematical Formulation ◽  
Solution Space ◽  
Working Time ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan.

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