New model and heuristics for safety stock placement in general acyclic supply chain networks
We model the safety stock placement problem in general acyclic supply chain networks as a project scheduling problem, for which the constraint programming (CP) techniques are both effective and efficient in finding high quality solutions. We further integrate CP with a genetic algorithm (GA), which improves the CP solution quality significantly. The performance of our hybrid CPGA algorithm is evaluated on randomly generated test instances. CPGA is able to find optimal solutions to small problems in fractions of a second, and near optimal solutions of about 5% optimality gap to medium size problems in several minutes on average.
safety stock placement project scheduling constraint programming (cp) genetic algorithm (ga) inventory positioning general acyclic network