A Robust Lot Sizing Problem with Ill-known Demands

TL;DR

This paper introduces a robust lot sizing approach under fuzzy demand uncertainty using possibility theory, providing algorithms for optimal planning and evaluation, supported by computational experiments.

Contribution

It presents new optimization criteria and algorithms for robust lot sizing with fuzzy demands, advancing decision-making under uncertainty.

Findings

Algorithms effectively determine robust production plans.

Proposed criteria improve planning under fuzzy demand uncertainty.

Computational experiments validate the approach.

Abstract

The paper deals with a lot sizing problem with ill-known demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Some algorithms for determining optimal robust production plans with respect to the proposed criteria, and for evaluating production plans are provided. Some computational experiments are presented.