Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly

Abstract

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this study we undertake the optimization of chip shooter component placement machines which became popular in assembling printed circuit boards (PCB) in recent years. A PCB is usually a rectangular plastic board on which the electrical circuit to be used in a particular electronic equipment is printed. The overall optimization of the chip shooter placement machines leads to a very complicated optimization problem which we formulate here for the first time (without any simplifying assumptions). However, it is possible to decompose the problem into placement sequencing problem and feeder configuration problem which turn out to be sequence dependent traveling salesman problem (SDTSP) and Quadratic Assignment Problem (QAP), respectively. We use simulated annealing metaheuristic approach and the heuristics developed for the SDTSP in an earlier study to solve these two problems in an iterative manner. We also attempt to solve the combined overall optimization problem by simulated annealing and artificial bee colony metaheuristics and compare their performances with the iterative approach. The results are in favor of iterative approach.TÜBİTAK ; Marmara University Scientific Research Committe

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

“…Formula (15) represents that the ith sequence in X k is the encoding of the point which has a minimum value of σ k j,i . The pick-and-place sequence initial pseudo code is given below.…”

“…Because of the complexity of the problem, heuristic approaches, such as meta-heuristic approaches and multi-stage heuristics strategy are widely used, while several exact methods can be found in previous research. Meta-heuristic approaches such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO) and ant colony optimization (ACO), which play an important role in solving complex problems [8][9][10][11][12], are altered to adapt to solving the problem efficiently [13][14][15][16]. Zhu and Zhang [17] added self-variation behavior to the frog leaping algorithm, which has good accuracy.…”

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

“…Chyu and Chang (2008) applied a GA-based algorithm with 2-opt local search to solve the two sub-problems on a turret style chip shooter machine. Alkaya and Duman (2015) combined simulated annealing and the heuristics for TSP to solve the two problems for a chip shooter component placement machines. As for the mathematical model, only few research results can be found.…”

“…As for the mathematical model, only few research results can be found. Altinkemer et al (2000) provided an integrated mathematical model for the two sub-problems, Ho and Ji developed integer programming (IP) models for a kind of PAP machine (Ho and Ji, 2009) and a mathematical model for a turret machine (Ho and Ji, 2010), Luo and Liu (2014) proposed a MILP model to a SMM for LED assembly and Alkaya and Duman (2015) formulated the integration of the two problems to a nonlinear integer programming model.…”

“…Different from previous models (Ho and Ji, 2010;Kim and Park, 2004;Jeevan et al, 2002;Luo and Liu, 2014;Alkaya and Duman, 2015), more sub-problems are integrated and more real constraints are considered in our model: 1) Component pick-up sequence which is often omitted is considered here; 2)…”