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.

A stochastic multiitem replenishment and delivery problem with leadtime reduction initiatives and the solving methodologies Abstract   With the intensification of timebased competition, the importance of reducing leadtime by rapidly delivering multiorders has been underscored in the process of replenishmentstoragetransportation. This perception has prompted enterprises to increase expenditure on purchasing modern timetracing technologies (e.g. RFID), and equipping facilities for item fast movement (e.g. highrack automatic shelves) to retain customers. In this study, we explore a novel extension of the multiitem joint replenishment problem (JRP) with leadtime compressing initiatives. By assuming controllable leadtime, we construct a stochastic periodicreview joint replenishment and delivery (JRD) model to investigate impacts of capital investment in leadtime reduction to the decisions of multiitem joint replenishment and delivery. To solve the proposed JRD, two heuristics and a differential evolutionary algorithm are presented based on the model property analyses. The experimental results reveal the performance differences (e.g., searching speed, robustness and searching effectiveness) of three algorithms. Furthermore, our findings have managerial implications that proper investment in leadtime reduction not only helps shorten replenishment time and cut major ordering cost, but can also reduce the system cost.