A disruption recovery model in a production-inventory system with demand uncertainty and process reliability

This paper develops a risk management tool for a productioninventory system that involves an imperfect production process and faces production disruption and demand uncertainty. In this paper, the demand uncertainty is represented as fuzzy variable and the imperfectness is expressed as process reliability. To deal with the production scheduling in this environment, a non-linear constrained optimization model has been formulated with an objective of maximizing the graded mean integration value (GMIV) of the total expected profit. The model is applied to solve the production-inventory problem with single as well as multiple disruptions on a real time basis that basically revises the production quantity in each cycle in the recovery time window. We propose a genetic algorithm (GA) based heuristic to solve the model and obtain an optimal recovery plan. A numerical example is presented to explain usefulness of the developed model.