A memetic algorithm-based heuristic for a scheduling problem in printed circuit board assembly

Neammanee, Patcharaporn; Reodecha, Manop

doi:10.1016/j.cie.2008.06.010

Cited by 19 publications

(3 citation statements)

References 23 publications

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“…Garcia-Naijera and Brizuela (11) proposed an efficient GA to solve PAPSP and FAP by setting the fitness function as the assembly time of the PCB. Neammanee et al (12) developed an MA to solve the placement sequence problem and FAP. MA gives a better solution; however, it requires a huge amount of calculation.…”

Section: Introductionmentioning

confidence: 99%

Component Placement Process Optimization for Multi-head Surface Mounting Machine Using a Hybrid Algorithm

Lin¹,

Lin²

2021

Sensors and Materials

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This paper addresses the optimization problem of printed circuit board (PCB) assembly scheduling for surface mount placement (SMP) machines with multiple heads. Most of the related studies have oversimplified the practical problems and limited their focus to three sub-problems: the feeder assignment problem (FAP), the nozzle setup problem, and the component pick-and-place sequence problem (PAPSP). To extend the range of problems that can be addressed, in this study, we propose a hybrid algorithm to consider the component height, the pick-and-place restrictions, and simultaneous pickup restrictions. A roulette wheel (RW) is designed to generate an automatic nozzle changer (ANC) sequence, with the nearest neighbor search (NNS) used to find an initial placement path. Furthermore, we apply the 2-optimization (2-opt) method to improve the placing sequence. Practical PCB datasets provided by an EVEST EM-780 multi-head SMP machine are adopted as experimental examples. Experimental results show that the proposed algorithm effectively enhances the productivity of the multi-head SMP machine and reduces the cycle time in PCB assembly.

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Section: Introductionmentioning

confidence: 99%

Component Placement Process Optimization for Multi-head Surface Mounting Machine Using a Hybrid Algorithm

Lin¹,

Lin²

2021

Sensors and Materials

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“…Jiang et al (2010aJiang et al ( , 2010bJiang et al ( , 2010c employed an improved ant colony algorithm to optimize place sequence and minimize nozzle changes. A memetic algorithm was proposed to solve a placement machine scheduling problem (Neammanee and Reodecha, 2009). For PCB assembly, Du et al, focused on feeder assignment and component pick-and-place sequence problems and presented a hybrid GA combining heuristic and genetic algorithms for evaluating the performance of feasible solutions (Du and Li, 2008).…”

Section: Introductionmentioning

confidence: 99%

Efficient hybrid group search optimizer for assembling printed circuit boards

Lin

Huang

2018

AIEDAM

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Assembly optimization of printed circuit boards (PCBs) has received considerable research attention because of efforts to improve productivity. Researchers have simplified complexities associated with PCB assembly; however, they have overlooked hardware constraints, such as pick-and-place restrictions and simultaneous pickup restrictions. In this study, a hybrid group search optimizer (HGSO) was proposed. Assembly optimization of PCBs for a multihead placement machine is segmented into three problems: the (1) auto nozzle changer (ANC) assembly problem, (2) nozzle setup problem, and (3) component pick-and-place sequence problem. The proposed HGSO proportionally applies a modified group search optimizer (MGSO), random-key integer programming, and assigned number of nozzles to an ANC to solve the component picking problem and minimize the number of nozzle changes, and the place order is treated as a traveling salesman problem. Nearest neighbor search is used to generate an initial place order, which is then improved using a 2-opt method, where chaos local search and a population manager improve efficiency and population diversity to minimize total assembly time. To evaluate the performance of the proposed HGSO, real-time PCB data from a plant were examined and compared with data obtained by an onsite engineer and from other related studies. The results revealed that the proposed HGSO has the lowest total assembly time, and it can be widely employed in general multihead placement machines.

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“…Comparison results showed that the meta-heuristics outperformed both the heuristics and a commercial solver. Studies in [9] and [10] revealed that memetic algorithms (MAs) implemented by integrating local search into GAs are very promising to efficiently find near optimal solutions for optimization problems. In this study, we propose a VNMA to minimize makespan (Cmax) on a BPM with non-identical job sizes.…”

Section: Introductionmentioning

confidence: 99%

A Variable Neighborhood Based Memetic Algorithm for Scheduling Single Batch Processing Machine with Non-Identical Job Sizes

Guo

2012

AMM

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A variable neighborhood based memetic algorithm (VNMA) is proposed to minimize makespan for a single batch processing machine in this paper. Random instances were generated to verify the effectiveness of VNMA. Comparisons are made through using a genetic algorithm (GA) addressed in the literature as a comparator method. Computational results demonstrate that VNMA outperformed the GA with respect to solutions quality and run times.

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A memetic algorithm-based heuristic for a scheduling problem in printed circuit board assembly

Cited by 19 publications

References 23 publications

Component Placement Process Optimization for Multi-head Surface Mounting Machine Using a Hybrid Algorithm

Component Placement Process Optimization for Multi-head Surface Mounting Machine Using a Hybrid Algorithm

Efficient hybrid group search optimizer for assembling printed circuit boards

A Variable Neighborhood Based Memetic Algorithm for Scheduling Single Batch Processing Machine with Non-Identical Job Sizes

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