An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly

Abstract

Abstract:This paper focuses on an operation optimization problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzle types as well as components to heads, assigning feeders to slots and determining component pick-up and placement sequences. According to the depth of making decisions, the sub-problems are first classified into two layers. Based on the classification, a two-stage mixed integer linear programming (MILP) is developed to describe it and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA) is proposed. In the first stage, a constructive heuristic is developed to determine the set of nozzle types assigned to each head and the total number of assembly cycles; in the second stage, constructive heuristics, an evolutionary algorithm with two evolutionary operators and a tabu search (TS) with multiple neighbourhoods are combined to solve all the sub-problems simultaneously, where the results obtained in the first stage are taken as constraints. Computational experiments show that the HEA can obtain good near-optimal solutions for small-size instances when compared with an optimal solver, Cplex, and can provide better results when compared with a TS and an EA for actual instances.

Printed circuit board assembly time minimisation using a novel Bees Algorithm

“…A variety of intelligent techniques were investigated to minimise PCB assembly time. These methods include Evolutionary Algorithms (Wong and Leu, 1993;EAs) (Nelson and Wille, 1995;Maimon and Brha, 1998;Ong and Khoo, 1999;Ho and Ji, 2007), Particle Swarm Optimisation (PSO) (Hsu, 2017), minimal spanning tree optimisation (Leipala and Nevalainen, 1989), integer programming (Seth et al 2016;Li and Yoon, 2017), Tabu Search (Luo et al 2016), and rulebased expert systems (Yeo et al 1996), as well as combinations of different optimisation algorithms (Alkaya and Duman, 2015;Luo et al 2017;Han and Seo, 2017). For an overview of intelligent techniques for the general problem of assembly line planning the reader is referred to Rashid's et al's (2012) review.…”

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

“…During each iteration, X k needs to be recorded if the value of the length is maximum, and this sequence needs to be reset first in next iteration, which is x 1 1 = iter x (see Section 3.1). iter x can be generated by formula (20).…”

“…This algorithm leads to better performance than genetic algorithm or 2-opt swap search. Luo and Liu [20] proposed a two-stage mixed-integer linear programming model and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA). The constructive two-stage heuristics is not only determine the set of nozzle types assigned to each head and the total number of assembly cycles, but solve all the sub-problem respectively.…”

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm

“…In reference, Gao et al [37] proposed a two-stage method for the combinatorial optimization of the component placement sequence and feeder allocation. With many types of mounting components, the frequent replacement of suction nozzles will also affect the mounting efficiency [38] . Luo et al [39] constructed a two-stage mixed-integer linear programming model by combining nozzle replacement, feeder allocation, and component placement sequence for optimization.…”