An Iterated Hybrid Local Search Algorithm for Pick-and-Place Sequence Optimization

This paper shows the results of our study on the pick-and-place optimization problem. To solve this problem efficiently, an iterated hybrid local search algorithm (IHLS) which combines local search with integer programming is proposed. In the section of local search, the greedy algorithm with distance weight strategy and the convex-hull strategy is developed to determine the pick-and-place sequence; in the section of integer programming, an integer programming model is built to complete the feeder assignment problem. The experimental results show that the IHLS algorithm we proposed has high computational efficiency. Furthermore, compared with the genetic algorithm and the memetic algorithm, the IHLS is less time-consuming and more suitable in solving a large-scale problem.

Optimizing the Cycle Time of a Multi-Spindle Pick-and-Place Machine

A typical Printed Circuit Board (PCB) assembly line comprises of three major process steps, namely solder paste printing, component placement, and soldering. A stencil printer is typically used to deposit adequate amount of solder paste at appropriate locations on the PCB. One or more component placement (pick-and-place type) machines are then used to populate the PCB. Finally, the entire assembly is passed through an oven for establishing the solder joint. It has been widely accepted that the component placement step is usually the bottleneck. Consequently, the cycle time of the placement machine has to be reduced in order to improve the throughput of the assembly line. Placement machines are expensive and hence their benefit-cost ratio can be improved by improving their cycle time. The objective of this research was to reduce the cycle time of a pick-and-place component placement machine with multiple spindles. The pick-and-place machine chosen for this study was the bottleneck in an assembly line at an electronics manufacturing facility. The primary functions of a placement machine are (1) to pick components from feeder slots, (2) check for any defects using the vision system, and (3) to place the components at appropriate locations. Two important decisions which affect the cycle time are (1) feeder assignment (component location along the feeder rack) and (2) the component placement sequence. A heuristic was proposed to determine the feeder assignments and the component placement sequence was determined by solving a multiple Traveling Salesman Problem (mTSP). The objective of the feeder assignment problem was to minimize the distance traveled by the head along the feeder rack (during the pick cycle). The objective of the component placement sequencing problem was to minimize the distance traveled by the head over the PCB (during the placement cycle). The individual placement tours are later sequenced such that the number of nozzle changes required is minimized. The time taken to populate a board (time to pick + place + nozzle changeover) using the proposed approach was compared to the software which was supplied by the vendor of the machine. Several ‘production boards’ were chosen for this experimental study. The proposed solution approaches outperformed the solutions suggested by the machine’s software for all the ‘production boards’ experimented with. The resulting improvements in cycle times demonstrate the effectiveness of the proposed approach.