High variety and low volume manufacturing environment always been a challenge for organisations to maintain their overall performance especially because of the high level of variability induced by ever changing customer demand, high product variety, cycle times, routings and machine failures. All these factors consequences poor flow and degrade the overall organisational performance. For most of the organisations, therefore, process improvement has evidently become the core component for long term survival. The aim of this research here is to develop a methodology for automating operations in process improvement as a part of lean creative problem solving process. To achieve the stated aim, research here has investigated the job sequence and buffer management problem in high variety/low volume manufacturing environment, where lead time and total inventory holding cost are used as operational performance measures. The research here has introduced a novel approach through integration of genetic algorithms based multi-objective combinatorial optimisation and discrete event simulation modelling tool to investigate the effect of variability in high variety/low volume manufacturing by considering the effect of improvement of selected performance measures on each other. Also, proposed methodology works in an iterative manner and allows incorporating changes in different levels of variability. The proposed framework improves over exiting buffer management methodologies, for instance, overcoming the failure modes of drum-buffer-rope system and bringing in the aspect of automation. Also, integration of multi-objective combinatorial optimisation with discrete event simulation allows problem solvers and decision makers to select the solution according to the trade-off between selected performance measures.

[pt] O problema de sequenciamento de tarefas e troca de ferramentas (job sequencing and tool switching problem - SSP) tem sido extensivamente estudado na área de pesquisa operacional, devido à sua relevância prática e interesse metodológico. Dada uma máquina que pode carregar uma quantidade limitada de ferramentas simultaneamente e um número de tarefas que requerem um subconjunto das ferramentas disponíveis, o SSP procura uma sequência de tarefas que minimize o número total de trocas de ferramentas na máquina. Para resolver este problema, é proposta uma busca genética híbrida simples e efetiva baseada em uma representação de solução genérica, um operador de decodificação sob medida, buscas locais eficientes e técnicas de gerenciamento de diversidade. Para orientar a busca, um objetivo secundário desenvolvido para tratar empates é introduzido. Essas técnicas permitem explorar soluções estruturalmente distintas e escapar de ótimos locais. Conforme apresentado nos experimentos computacionais em instâncias clássicas, o algoritmo proposto supera significativamente todas as abordagens anteriores, mesmo sendo de fácil entendimento e implementação. Por fim, resultados obtidos em um novo conjunto de instâncias maiores são reportados para estimular futuras pesquisas e análises comparativas. / [en] The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.