A Rescheduling Strategy for Multipurpose Batch Processes with Processing Time Variation and Demand Uncertainty

In this paper, we address the problem of dynamic scheduling of a multipurpose batch process subject to two types of disturbances, namely, processing time variation and demand uncertainty. We propose a rescheduling strategy that combines several ideas. First, when we generate a new schedule, we simul...

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Bibliographic Details
Published inProcesses Vol. 13; no. 2; p. 312
Main Authors Zheng, Taicheng, Li, Dan, Li, Jie
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2025
Subjects
Batch processes
Batch processing
Breakdowns
Computational efficiency
Computer applications
Computing time
Decision trees
Efficiency
Fines & penalties
Linear programming
Objectives
Rescheduling
Schedules
Scheduling
Simulation
Uncertainty
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ISSN2227-9717
2227-9717
DOI10.3390/pr13020312

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Summary:In this paper, we address the problem of dynamic scheduling of a multipurpose batch process subject to two types of disturbances, namely, processing time variation and demand uncertainty. We propose a rescheduling strategy that combines several ideas. First, when we generate a new schedule, we simultaneously construct a Directed Acyclic Graph (DAG) to represent this new schedule. While each node in the DAG represents an operation, each arc represents the dependency of an operation on another. Based on this DAG, we then use a simple procedure to determine how long an operation is allowed to be delayed without affecting the current makespan. After that, when the new schedule is used for online execution, we trigger a rescheduling procedure only when (1) we infer from the predetermined delayable time information that the current makespan will be extended, or (2) we observe new demands, or (3) the current schedule is not guaranteed to be feasible. In the rescheduling procedure, only the affected operations are allowed to be revised, while those unaffected operations are fixed. By doing this, we can reduce system nervousness and improve computational efficiency. The computational results demonstrate that our method can achieve an order of magnitude of reduction in both the number of operation changes and the computational time with a slightly better long-term makespan, compared to the widely used periodically–completely rescheduling strategy.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13020312