Cost-aware task sequencing with flexible time windows

Abstract

Classical scheduling methods are generally designed to optimize a single objective, such as minimizing completion time or the number of delayed jobs. However, practical production environments are frequently characterized by multiple and often conflicting objectives, including delivery performance and economic considerations. In this study, an extended scheduling framework is proposed in which, besides processing times and due dates, task-specific delay costs are incorporated into the decision-making process. A representative production scenarios consisting of 10 and 100 tasks is used to evaluate the proposed approach. Three optimization models are developed and implemented using Microsoft Excel Solver. The first model maximizes the number of tasks completed within their due dates, the second minimizes the total due time, while the third minimizes the total delay-related cost. The obtained schedules are further compared with the results of several classical dispatching rules. It is demonstrated that different optimization objectives lead to substantially different production sequences and scheduling performance. The proposed framework provides a simple and transparent decision-support approach that can be implemented without specialized optimization software and can be readily adapted to various production environments.