Comparison of a bat and genetic algorithm generated sequence against lead through programming when assembling a PCB using a 6 axis robot with multiple motions and speeds

An optimal component feeder arrangement and robotic placement sequence are both important for improving assembly efficiency. Both problems are combinatorial in nature and known to be NP-hard. This paper presents a novel discrete hybrid bat-inspired algorithm for solving the feeder slot assignment and placement sequence problem encountered when planning robotic assembly of electronic components. In our method, we use the concepts of swap operators and swap sequence to redefine position, and velocity operators from the basic bat algorithm. Furthermore, we propose an improved local search method based on genetic operators of crossover and mutation enhanced by the 2-opt search procedure. The algorithm is formulated with the objective of minimizing the total traveling distance of the pick and place device. Through numerical experiments, using a real PCB assembly scenario, we demonstrate the considerable effectiveness of the proposed discrete Bat Algorithm (BA) to improve selection of feeder arrangement and placement sequence in PCB assembly operations and achieve high throughput production. The results also highlighted that the even though the algorithms out performed traditional lead through programming techniques, the programmer must consider the influence of different robot motions

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

This paper focuses on an operation optimisation 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 pickup 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

Elektronik dizgi işlemlerinin eniyilenmesi ve değişken maliyetli seyyar satıcı problemi

Duman, Ekrem (Dogus Author)Bu projede baskılı devre kartları (BDK) dizgi işlemlerinde ortaya çıkan yeni bir gezgin satıcı problemi (GSP) üzerinde çalışmalar yapılmış, sıraya dayalı GSP adı verilen bu problemin önce matematiksel modeli geliştirilmiş daha sonra onu çözecek özgün yöntemler geliştirilmiştir. Bunun dışında, bu problemin görüldüğü iki tip dizgi makinesinin diğer ilgili optimizasyon problemleri (karesel atama problemi - KAP) de çözülerek toplam dizgi süreleri enazlanmıştır. KAP ve problemin bütününün çözümlerinde metasezgisel yöntemlerden yararlanılmıştır.Doğuş Üniversitesi; TÜBİTA

Well-solvable special cases of the TSP : a survey

The Traveling Salesman Problem belongs to the most important and most investigated problems in combinatorial optimization. Although it is an NP-hard problem, many of its special cases can be solved efficiently. We survey these special cases with emphasis on results obtained during the decade 1985-1995. This survey complements an earlier survey from 1985 compiled by Gilmore, Lawler and Shmoys. Keywords: Traveling Salesman Problem, Combinatorial optimization, Polynomial time algorithm, Computational complexity

Improving the bees algorithm for complex optimisation problems

An improved swarm-based optimisation algorithm from the Bees Algorithm family for solving complex optimisation problems is proposed. Like other Bees Algorithms, the algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimisation of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems, including two problems on printed circuit board assembly machine sequencing

Optimization of product assignment to assembly lines

Dissertação de mestrado em Industrial engineering and ManagementThe work presented in this dissertation was developed in an industrial context integrated in the production control and management department of the Bosch Car Multimedia Portugal S.A – Braga automatic insertion. The problem addressed in this dissertation was finding the best distribution of product families to assign in different lines according to the physical and technical constraints of the assembly lines. In the approach of the problem, it was used tools and techniques of the Operational Research discipline through mathematical modeling, in order to analyze complex situation and obtain more efficient solutions to help in the decision-making process. Based on production data, production needs forecasts and assembly line physical availability, models with different sets of constraints and objective functions were created to present solutions that best fit the question and the specific problem of the present production context. Through specific software that suited the problem, the previously created models were solved, and the solutions were analyzed and evaluated to suit the company’s current needs and for possible and feasible implementation of the solutions.O trabalho apresentado nesta dissertação foi desenvolvido em contexto industrial integrado no departamento de planeamento e controlo de produção da área de inserção automática da Bosch Car Multimédia Portugal S.A - Braga. O problema abordado nesta dissertação foi encontrar a melhor distribuição de famílias de produtos a alocar nas diferentes linhas de produção de acordo com as suas restrições físicas e técnicas. Na abordagem do problema recorreu-se a técnicas de Investigação Operacional através de modelação matemática, para analisar situações complexas e obter soluções mais eficientes. Tendo como base dados da produção, previsões de necessidades e disponibilidade física da produção, foram criados modelos com diferentes conjuntos de restrições e funções objetivo por forma a apresentar soluções que melhor se adequassem à pergunta e ao problema específico do contexto produtivo presente. Através da utilização de software, foram resolvidos os modelos criados anteriormente, sendo que as soluções foram analisadas e avaliadas para a adequação às necessidades atuais da empresa e para a sua possível e viável implementação

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering