Integrated Process Planning and Scheduling Based on Genetic Algorithms

2011 ◽  
Vol 291-294 ◽  
pp. 331-334
Author(s):  
Jin Feng Wang ◽  
Shi Jie Li ◽  
Shun Cheng Fan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Process Planning ◽  
Manufacturing System ◽  
Simulation Studies ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Mutation Operators ◽  
Crossover And Mutation

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

scholarly journals Integrated process planning, WSPT scheduling and WSLK due-date assignment using genetic algorithms and evolutionary strategies

2017 ◽  
Vol 8 (1) ◽  
pp. 73-83
Author(s):  
Halil Ibrahim Demir ◽  
Onur Canpolat
Keyword(s):  
Genetic Algorithms ◽  
Process Planning ◽  
Random Search ◽  
Evolutionary Strategies ◽  
Integrated Process ◽  
Due Dates ◽  
Due Date ◽  
Planning And Scheduling ◽  
Due Date Assignment ◽  
Genetic Search

Process planning, scheduling and due-date assignment are three important manufacturing functions in our life. They all try to get local optima and there can be enormous loss in overall performance value if they are handled separately. That is why they should be handled concurrently. Although integrated process planning and scheduling with due date assignment problem is not addressed much in the literature, there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment. Most of the works in the literature assign common due date for the jobs waiting and due dates are determined without taking into account of the weights of the customers. Here process planning function is integrated with weighted shortest processing times (WSPT) scheduling and weighted slack (WSLK) due date assignment. In this study unique due dates are given to each customer and important customers gets closer due dates. Integration of these three functions is tested for different levels of integration with genetic algorithms, evolutionary strategies, hybrid genetic algorithms, hybrid evolutionary strategies and random search techniques. Best combinations are found as full integration with genetic search and hybrid genetic search. Integration of these three functions provided substantial improvements in global performance.

Transmission Network Expansion Planning under Improved Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 1458-1461
Author(s):  
Hong Fan ◽  
Yi Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Linear Optimization ◽  
Large Integer ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

scholarly journals Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Yangbing Xu ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Process Planning ◽  
Imperialist Competitive Algorithm ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Machine Breakdown ◽  
Competitive Algorithm ◽  
Effectiveness And Efficiency

Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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