Techniques of Generating Schedules for the Problem of Financial Optimization of Multi-Stage Project

Klimek, Marcin

doi:10.35784/acs-2019-02

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Although TVNPV is a new model and no previous method has been applied to solve it, TS has been extensively used in max-NPV problems, as seen in [49,59,72,107]. Our results indicate the great potential for the application of RL in this area, which has up until now been tackled by heuristics [60].…”

Section: Discussionmentioning

confidence: 77%

“…[57] devised a methodology that involved grouping activities together, and in a subsequent publication [59], they further expanded their work to encompass capital constraints and various cash outflow models. Klimek [60] explored projects characterized by payment milestones and investigated diverse scheduling techniques, including activity right-shift, backward scheduling, and left-right justification.…”

Section: Max-npv Problemmentioning

confidence: 99%

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Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach

Szwarcfiter,

Herer,

Shtub

2023

Algorithms

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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’s 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’ 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.

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Section: Discussionmentioning

confidence: 77%

Section: Max-npv Problemmentioning

confidence: 99%

Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach

Szwarcfiter,

Herer,

Shtub

2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…Later they extended their work to include capital constraints and different cash outflow models [13]. Klimek [14] examined projects with payment milestones and different scheduling techniques such as activity right-shift, backward scheduling and left-right justification are compared. In [15], the RCPSPDC is solved by combining a genetic algorithm and an immune algorithm.…”

Section: A the Max-npv Problemmentioning

confidence: 99%

Managing the Balance Between Project Value and Net Present Value Using Reinforcement Learning

Szwarcfiter,

Herer

2024

IEEE Access

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Project managers make decisions weighing financial returns (net present value, NPV) and value creation expected by stakeholders. Often, plans maximizing NPV neglect stakeholder benefits while those focused strictly on value creation may reduce financial viability. This paper puts forth a new stochastic optimization model handling this compromise using a mixed integer program solved with reinforcement learning. The model incorporates uncertain activity durations and considers positive and negative cash flows. Our Monte Carlo control method with ϵ-greedy policies and timed start actions for activities facilitates the simultaneous maximization of NPV and project value. The resulting efficient frontier delineates various project plans, demonstrating the trade-off between maximizing NPV and project value, providing decision makers with visual analysis to select plans that fit organizational needs. Computational experiments demonstrate superior performance over a mathematical solver limited by the problem's complexity and a metaheuristic lacking guided online learning. The results help senior management select satisfactory plans that balance financial returns with stakeholder preferences. The methodology contributes a novel tool for quantitatively incorporating value creation alongside financial objectives in project planning.

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The Algorithms for Robust Scheduling of Production Jobs Under Machine Failure and Variable Technological Operation Times

Sobaszek

Gola

Świć

2021

Innovations in Industrial Engineering

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Techniques of Generating Schedules for the Problem of Financial Optimization of Multi-Stage Project

Cited by 6 publications

References 29 publications

Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach

Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach

Managing the Balance Between Project Value and Net Present Value Using Reinforcement Learning

The Algorithms for Robust Scheduling of Production Jobs Under Machine Failure and Variable Technological Operation Times

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