Learning complementarity and setup time reduction

This article studies the effects of transmission of learning in setups for the multi-item capacitated lot-sizing problem. This learning complementarity affects the scheduling of the products and the resulting model considers simultaneous decisions about lot-sizing and sequencing in a nonlinear formulation. Learning complementarity favors frequent setups and lower inventories and reaches the zero-inventory schedules even at low levels of learning. Through the use of an example the following three scenarios are examined: (i) no transmission of learning, (ii) partial transmission of learning, and (iii) complete transmission of learning. The schedules of the three scenarios are compared and the computational complexity of the nonlinear integer model considering lot-sizing and sequencing issues is discussed.