Order Acceptance and Scheduling Considering LotSpitting in seru Production System For the manufacturing companies that take maketoorder production, due to limited production capacity, accept and process all orders may result in delayed delivery of some orders, so the companies not only have to pay a large amount of delay penalty, but also may lose a large number of customers. In this case, the order acceptance and scheduling problem in the seru production system with limited processing capability and the promised delivery time are considered in this paper. At the same time, lotspitting is also concerned, and an integrated decisionmaking model for order acceptance and scheduling with the goal of maximizing net profit is established. An improved genetic algorithm is designed for the proposed model. In the end, a numerical example is applied to illustrate the validity and feasibility of the proposed model and algorithm.

Hybrid stochastic and robust optimization model for lotsizing and scheduling problems under uncertainties Abstract   Uncertainty is among the significant concerns in production scheduling. It has become increasingly important to take uncertainties into consideration for lotsizing and scheduling. In this paper, we adopt the Hybrid Stochastic and Robust Optimization (HSRO) approach in lotsizing and scheduling problems in which suppliers have the flexibility of satisfying a fraction of demand based on the market and their policies. Two types of uncertainties have been considered simultaneously: demand and overtime processing cost. Robust optimization is adopted for uncertain demand and Sample Average Approximation (SAA) technique is applied to solve the stochastic program for uncertain overtime processing cost. Numerical results based on a manufacturing company has been conducted to not only validate the proposed hybrid model but also quantitatively demonstrate the merit of our approach. Sample size stability test and sensitivity analyses on various parameters have also been conducted.