Optimal allocation of safety strategies in order to reduce threats of dust explosions is very challenging,
particularly when all potential accident contributors and various safety measures are to be taken into
account. In this paper, we have proposed a risk-based optimal allocation of safety measures while
considering both available budget and acceptable residual risk. The methodology is based on a Bayesian
network (BN) to model the risk of dust explosions, which in turn helps identify key contributing factors,
assess performances of relative safety measures, and decide on those safety measures to most efficiently
control the risks of dust explosions within a limited budget. The Bayesian network also facilitates the
implementation of diagnostic analysis to determine vulnerable parts in the system to which special
attention should be paid in safety measure allocation. The Net Risk Reduction Gain (NRRG) for each
relevant safety measure is also used to simultaneously account for both the cost of a safety measure
and the respective risk reduction. Accordingly, the risk-based optimal allocation of safety measures will
be achieved by maximizing the sum of the NRRG of all relevant safety measures under limited budgets,
which is regarded as a knapsack problem. We applied the methodology to the aluminum dust explosion
that occurred at Hayes Lemmerz International, Huntington, Indiana, US in October 2003. The result shows
the efficacy and applicability of the proposed methodology for optimal risk reduction within a limited
budget.