Assembly Line Balancing in a Mixed-Model Sequencing Environment

We consider the mixed-model assembly line balancing and sequencing problem in a synchronous line. We rst discuss the synchronous sequencing problem, and then present an integer programming formulation of the assembly line balancing problem for a xed sequence of jobs. We also discuss solution procedures for the problem, and highlight the impact of sequencing decisions on the performance of the line

Using support vector machines to learn the efficient set in multiple objective discrete optimization

We propose using support vector machines (SVMs) to learn the efficient set in multiple objective discrete optimization (MODO). We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As one way of testing this idea, we embed the SVM-approximated efficient set information into a Genetic Algorithm (GA). This is accomplished by using a SVM-based fitness function that guides the GA search. We implement our SVM-guided GA on the multiple objective knapsack and assignment problems. We observe that using SVM improves the performance of the GA compared to a benchmark distance based fitness function and may provide competitive results.Multiple objective optimization Efficient set Machine learning Support vector machines

Multi-criteria optimization in industry

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