Small-World Optimization Algorithm and Its Application in a Sequencing Problem of Painted Body Storage in a Car Company

In the car company, the painted body storage (PBS) is set up between the paint shop and the assembly shop. It stores the vehicles in production and reorders the vehicles sequence. To improve production efficiency of assembly shop, a mathematical model is developed aiming at minimizing the consumption rate of options and the total overtime and idle time. As the PBS sequencing process contains upstream sequence inbound and downstream sequence outbound, this paper proposes an algorithm with two phases. In the first phase, the discrete small-world optimization algorithm (DSWOA) is applied to schedule the inbound sequence by employing the short-range nodes and the long-range nodes in order to realize the global searching. In the second phase, the heuristic algorithm is applied to schedule the outbound sequencing. The proposed model and algorithm are applied in an automobile enterprise. The results indicate that the two-phase algorithm is suitable for the PBS sequencing problem and the DSWOA has a better searching performance than GA in this problem. The sensitivity of model parameters is analyzed as well

Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives

International audienc

Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives

International audienc

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model

Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias TecnológicasThe automotive industry is under a deep competitive reorganization process that manifests itself both on the demand and on the supply side. The competitiveness of this reorganization is highly dependent on a flexible production system, able to produce on demand, different vehicles (models) on a single assembly line. Due to demand requirements, production has to adjust faster to new models, each one with a large number of individual feature variants, and complexity grows in production. In addition, lean manufacturing principles introduced Just-in-Sequence as a further key issue of modern automotive production. In order to explore the single line concept related with the Just-in- Sequence principle, Car Sequencing policies avoiding blockage and starvation caused by product variety are needed. The main goal of this project was the development of a mathematical model and a computational tool to define the car sequence in the final assembly line in a daily production run. The car sequence depends on the daily demand, called the production mix, and should avoid line stoppages and minimize the number of workers needed to complete the sequence in the minimum time. This goal was achieved and we have created a new exact approach for Car Sequencing that considers limited capacity, special markets priorities and clustering of colors. An Integer Programming model was developed, but when considering clustering colors it became more complex and hard to solve. To overcome this difficulty a heuristic procedure was presented. Results show that the use of this new heuristic integrated with the exact integer model is a good approach for the Car Sequencing problems. The models were run in the software IBM ILOG 12.2 framework in a Intel® Core™2 Duo CPU T9600 Toshiba laptop @ 2.80GHz with 6 GB of RAM and we obtained good results for the heuristic in less than half an hour, for three hundred cars and seventeen options. As a result of our work we show that the clustering of colors improves the performance of the global manufacturing system and that our tool can be used daily for Car Sequencing in automotive companies.Atualmente, a indústria automóvel encontra-se sobre profunda reorganização como resultado das alterações na procura dos automóveis. Estas reestruturações serão tanto mais competitivas quanto mais flexível for o sistema de produção, tendo capacidade de se adaptar a diferentes procuras, de diferentes variantes de carros com graus de complexidade diferentes e sendo capaz de produzir estes carros numa linha única. Os princípios Lean introduziram o conceito just-in-sequence como a chave para a modernização e para a capacidade de adaptação a esta nova realidade da indústria automóvel. O conceito de linha única associada ao conceito just-in-sequence levou à procura de políticas de sequenciação de carros que evitassem o bloqueio e a paragem das linhas. O principal objetivo desta tese de doutoramento foi desenvolver um modelo matemático e uma ferramenta computacional para definir a sequência dos carros na montagem final, num dia de produção. A sequência de carros depende da procura diária e deve evitar paragens de linha e minimizar o número de operários necessários para completar a sequência no mínimo tempo possível. Este objetivo foi alcançado e foi desenvolvida uma nova abordagem exata para sequenciar carros considerando capacidade limitada, prioridades para mercados especiais e o agrupamento de carros da mesma cor. Foi desenvolvido um modelo de programação inteira, mas quando se considerou o agrupamento de carros da mesma cor, o modelo tornou-se mais complexo e difícil de resolver. Por este motivo, foi criado um modelo heurístico integrado com o modelo inteiro exato, que resulta numa boa abordagem para os problemas de sequenciação. Os modelos foram testados no software IBM ILOG 12.2 num computador Intel® Core™2 Duo CPU T9600 Toshiba laptop @ 2.80GHz with 6 GB of RAM e obtiveram-se bons resultados em menos de meia hora, para trezentos carros e dezassete opções. Como resultado deste trabalho demonstramos que agrupar os carros por cores na montagem final melhora a performance global do sistema de produção e que a nossa ferramenta pode ser usada diariamente para sequenciar os carros a produzir na montagem final da indústria automóvel.The author, Cláudia Sofia Rodrigues Duarte, was supported by the Portuguese Foundation for Science and Technology (SFRH / BD / 43010 / 2008) and the MIT Portugal Program