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Get Free AccessThis paper presents an innovative approach to compressing and decompressing activity durations within the context of multi-project scheduling under uncertainty and resource flexibility. In dynamic project environments, fixed activity durations often restrict optimal scheduling outcomes. To address this challenge, the proposed method systematically adjusts activity durations—through compression and decompression—while maintaining resource and precedence feasibility. By integrating Critical Path Method (CPM) analysis with heuristic rules, the approach identifies candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. By optimizing resource usage, reducing idle time and overuse, and improving responsiveness to real-world conditions, it contributes to minimizing waste, increasing operational resilience, and aligning project execution with strategic sustainability goals.
Marzieh Aghileh, Anabela Tereso, Filipe Alvelos, Odete Lopes (2025). Compressing and Decompressing Activities in Multi-Project Scheduling Under Uncertainty and Resource Flexibility. , DOI: https://doi.org/10.20944/preprints202507.2224.v1.
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Type
Preprint
Year
2025
Authors
4
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.20944/preprints202507.2224.v1
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