In order to survive in a competitive environment, industries are required to adopt strategies that ensure their abilities to provide their customers with a product featured by good quality, low cost and short delivery time. Short term scheduling plays a pivotal role in this context by ensuring the operations to be executed and monitored in an optimal or sub-optimal manner which guarantees the product shipping within the customers’ due dates at lower cost and/or higher utilization of resources. However, the dynamic nature of the shop floor environment causes the predictive schedules to be no longer optimal or even feasible. Frequent disruptions occurring during the execution of the predictive schedule require the operations managers to be reactive to make appropriate decision considering the new situation. Adequate research works based on integer programming are available in literature to cope with static scheduling problems, but there is a dearth in integer programming based approaches for dynamic or reactive situations. The aim of this work is to formulate a model that solves the reactive flow-shop scheduling problem subject to arrival of new orders. Objective function for makespan minimization and the comprehensive equations for predictive and reactive schedules are presented with the necessary elaboration.
A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study
