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ABSTRACT
This research addresses the capacitated dynamic lot-sizing problem with returns and hybrid products (). The problem is to identify how many of each product type to produce during each period for a hybrid system with manufacturing capacity constraints. The objective of
is to maximise total profit of the production system that consists of new, remanufactured and hybrid products.
is a multi-period CLSP, which is modelled as a mixed-integer nonlinear programming problem. The traditional CLSP is NP-hard, and the nonlinearity of
makes the problem even harder to solve. Therefore, a Simulated Annealing (SA) algorithm with a neighbourhood list (SA_NL) is proposed. By using a list of several neighbourhoods, the SA algorithm is improved. SA_NL is compared to SA, three variants of Genetic Algorithm (GA) and a Variable Neighbourhood Search (VNS) algorithm. The variants of GA are GA with one-point crossover (
), GA with two-point crossover (
) and GA with one-point period-based crossover (
). Over all instances, the results show that the proposed SA_NL outperforms SA, VNS,
,
and
by 0.54%, 0.34%, 1.92%, 1.78% and 2.92%, respectively.
Disclosure statement
No potential conflict of interest was reported by the authors.
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