LAUSR

The inherent uncertainty in supply chain systems compels managers to be more perceptive to the stochastic nature of the systems’major parameters, such as suppliers’ reliability, retailers’ demands, and facility production capacities. To deal with the uncertaintyinherent to the parameters of the stochastic supply chain optimization problems and to determine optimal or close to optimal policies,many approximate deterministic equivalent models are proposed. In this paper, we consider the stochastic periodic inventory routingproblem modeled as chance-constrained optimization problem. We then propose a safety stock-based deterministic optimizationmodel to determine near-optimal solutions to this chance-constrained optimization problem. We investigate the issue of adequatelysetting safety stocks at the supplier’s warehouse and at the retailers so that the promised service levels to the retailers are guaranteed,while distribution costs as well as inventory throughout the system are optimized. The proposed deterministic models strive tooptimize the safety stock levels in line with the planned service levels at the retailers. Different safety stock models are investigatedand analyzed, and the results are illustrated on two comprehensively worked out cases. We conclude this analysis with some insightson how safety stocks are to be determined, allocated, and coordinated in stochastic periodic inventory routing problem. Copyright© 2017 John Wiley & Sons, Ltd.