{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:29:37Z","timestamp":1773368977648,"version":"3.50.1"},"reference-count":110,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["2550\/21"],"award-info":[{"award-number":["2550\/21"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project\u2019s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders\u2019 risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans.<\/jats:p>","DOI":"10.3390\/a16080395","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:46:22Z","timestamp":1692665182000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7544-5560","authenticated-orcid":false,"given":"Claudio","family":"Szwarcfiter","sequence":"first","affiliation":[{"name":"Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, 52 Golomb Street, Holon 5810201, Israel"}]},{"given":"Yale T.","family":"Herer","sequence":"additional","affiliation":[{"name":"Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology, Haifa 3200003, Israel"}]},{"given":"Avraham","family":"Shtub","sequence":"additional","affiliation":[{"name":"Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology, Haifa 3200003, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"ref_1","first-page":"60","article-title":"Managing Project Uncertainty: From Variation to Chaos","volume":"43","author":"Loch","year":"2002","journal-title":"MIT Sloan Manag. 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