A two-phase resource-constrained project scheduling approach for design and development of complex product systems

Peykani, Pejman; Gheidar-Kheljani, Jafar; Shahabadi, Sheida; Ghodsypour, Seyed Hassan; Nouri, Mojtaba

doi:10.1007/s12351-023-00750-4

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…proposed a matrix that can help obtain the optimal design strategy for determining product design specifications [35]. Peykani et al (2023) developed a two-stage resource scheduling model to address the scheduling challenges of design and development projects for complex product systems [36]. However, these models are overly intricate and challenging to compute.…”

Section: Literature Review 21 Modeling Of the Product Development Pro...mentioning

confidence: 99%

Research and Application of the Simulation Method for Product Development Process Based on System Dynamics

Yin,

Gao,

Sun

2024

Systems

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Product development is a complex process involving intricate components, dynamics and constantly evolving internal and external environments, as well as numerous influencing factors. In order to accurately simulate and predict the effectiveness of the development process, this paper proposes a system dynamics simulation method based on information maturity. Different types of development processes are simulated, and the discussion includes the impact of activity information correlation, information evolution coefficient, start time, and other parameters on the dynamic behavior of the process. This study examines a specific mold development process as a case study to validate the method’s feasibility, accurately predicting the duration and cost of the process. It also investigates dynamic fluctuations resulting from uncertain events such as changes in customer demand and resource shortages. The method provides support for process optimization and resource scheduling.

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Section: Literature Review 21 Modeling Of the Product Development Pro...mentioning

confidence: 99%

Research and Application of the Simulation Method for Product Development Process Based on System Dynamics

Yin,

Gao,

Sun

2024

Systems

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“…We formulate the problem as a mixed integer program (MIP). To model resource allocations, we employ a flow-based formulation adopted in many recent project scheduling works, e.g., [51]- [58]. This formulation is especially suitable for stochastic models, where the activity start or finish times may vary according to the realized durations.…”

Section: The Proposed Mixed Integer Program (Mip) Formulationmentioning

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 dynamic DSM optimization serves to reduce iterations and rework by reordering activities to push feedback markers toward the lower left corner of the matrix (diagonal), or to block diagonalization. Objective functions include minimizing the amount of feedback in the DSM [28], minimizing the feedback length [29], and reducing the total project iteration time [30] to optimize the total project coordination cost. Optimization methods include the path searching method, powers of the adjacency matrix method, reachability matrix method, triangularization algorithm, and depth first search algorithm, all of which sort and group tasks to minimize iterations.…”

Section: Dsm Optimizationmentioning

confidence: 99%

Design Structure Matrix Approach Applied to Lunar Habitat Design

2023

Buildings

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Lunar habitat design is a complex endeavor characterized by complicated task-composition, numerous internal iterations, and intense task-coupling. The design process of assembled building in terrestrial construction and the system composition of lunar habitats have been constructed. However, there is insufficient experience to fully understand lunar habitat design missions and likewise there is insufficient coordination between architects and various disciplines. The task flow for sequencing optimization can be determined using a design structure matrix (DSM), which is widely used in engineering. The DSM can reveal necessary interfaces within the lunar habitat system as per relevant interface variables and processes. By decomposing the lunar habitat design process, an initial activity-based DSM is established in the present study. Informational interactions between each design task and its respective intensity are statistically investigated to clarify them across the four dimensions of energy, space, materials, and information. A sequencing algorithm is applied to optimize the design process. Finally, 20 design tasks of lunar habitat design are clarified among four phases: Pre-planning, spatial design, environmental design, and optimization. Related disciplines should coordinate in the design process according to the optimization results, and use the optimized task coupling relationship to build the requirement model and design model in an orderly manner to improve the design efficiency.

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A two-phase resource-constrained project scheduling approach for design and development of complex product systems

Cited by 5 publications

References 60 publications

Research and Application of the Simulation Method for Product Development Process Based on System Dynamics

Research and Application of the Simulation Method for Product Development Process Based on System Dynamics

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

Design Structure Matrix Approach Applied to Lunar Habitat Design

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