Lot Splitting Under Learning Effects with Minimal Revenue Requirements and Multiple Lot Types

Abstract

The lot splitting problem in the presence of learning is addressed. This work is an extension of an approach proposed for splitting in the case of a single item. We address the issue of a minimal revenue requirement from partial deliveries until a predetermined time. This is achieved by imposing a constraint on what is originally an unconstrained optimization problem. When sublots of different items are involved, the optimal splitting decisions have to be combined with the sequencing of the deliveries. Numerical examples are presented to demonstrate the proposed approach.