| dc.description.abstract | Capacitated production lot-sizing problems (CLSPs) are considered challenging problems to
solve due to their combinatorial nature. In many manufacturing problems, multi-objective
optimizations are representative models, because the objectives are considered a conflict with
one another. In real-life applications, optimizing a specific solution concerning one objective
may end up in unacceptable results concerning the other objectives.
In this thesis, a multi-objective mixed integer linear models is developed for multi-period lot
sizing problems involving multiple items and multiple suppliers. The model is constructed
with multiple objective functions (cost and service level) and a set of constraints.
Considering the complexity of these models on the one hand, and the ability of genetic
algorithms to obtain a set of Pareto optimal solutions, the multi-objective optimisation
problem in hand is targeted in two phases. In the first phase, non-dominated sorting genetic
algorithm II is applied to obtain the non-dominated solutions. In the subsequent stage, a
multiple attribute decision-making approach is employed to rank the pareto frontiers and one
best optimal solution is chosen for application. | en_US |
