A new MIP approach for balancing and scheduling of mixed model assembly lines with alternative precedence relations

In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach.

Automated summary

In this paper, a new mixed integer programming (MIP) formulation is proposed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. The alternative precedence relations are represented by AND/OR assembly graph. Unlike other MIP approaches, the new model introduces a new disjunctive precedence selection and task assignment variable to optimally choose one relation for each subset of alternative precedence relations. The computational examples demonstrate the superior performance of the new modelling approach.