Two Sequencing Problems: Equivalence, Optimal Solution, and State-of-the-Art Results

Creemers, Stefan

doi:10.2139/ssrn.3082785

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…The author subsequently applied the results to finding the optimal sequence of stages in multistage sequential projects with stochastic stage durations, also obtaining exact, closed-form expressions for the moments of the NPV and using a three-parameter lognormal distribution to approximate the NPV distributions accurately [24]. He showed that the problem is equivalent to the least cost fault detection problem (LCFDP; this was also proven by [25]). Hermans and Leus [26] offered a new efficient algorithm and showed that in Markovian PERT networks, where activities are exponentially distributed and there are no resource constraints, the optimal preemptive solution solves the nonpreemptive case as well.…”

Section: A the Max-npv Problemmentioning

confidence: 97%

“…Constraints (24) bound the flow variables with the maximum resource consumption modes. Finally, constraints (25), which we introduced in [50], determine the value attributes according to the selected modes.…”

Section: The Proposed Mixed Integer Program (Mip) Formulationmentioning

confidence: 99%

“…We now present the value functions for each attribute, 25 , where [T P ] is transmitter power, [RS] is receiver sensitivity and [AG] is antenna gain, extracted from the activities of "transmitter design" (TD), "receiver design" (RD), and "antenna design" (AD), respectively, in ), allowing us to compute the value of function F 1 , the value for the (R) attribute.…”

Section: Examplementioning

confidence: 99%

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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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Section: A the Max-npv Problemmentioning

confidence: 97%

Section: The Proposed Mixed Integer Program (Mip) Formulationmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

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

Szwarcfiter,

Herer

2024

IEEE Access

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“…In doing so, exact expressions in closed form were derived for the moments of NPV, with the utilization of a three-parameter lognormal distribution to accurately approximate the distributions of NPV [69]. Additionally, it was demonstrated that this problem is equivalent to the least-cost fault detection problem, which was established by [69] and [70]. Hermans and Leus [71] contributed to the field by presenting a novel and efficient algorithm.…”

Section: Max-npv Problemmentioning

confidence: 99%

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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Two Sequencing Problems: Equivalence, Optimal Solution, and State-of-the-Art Results

Cited by 2 publications

References 14 publications

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

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

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

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