On Track Procession Assignment Problem at the Railway Network Sections

Gainanov, Damir; Игнатов, А. Н.; Наумов, А. В.; Rasskazova, V. A.

doi:10.1134/s0005117920060028

Cited by 8 publications

(1 citation statement)

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“…Integer linear programming is widely used in various fields, including transportation and industrial planning. In [1,2], integer linear programming models were developed for solving applied problems concerning the planning of railway transportation processes. In [3,4], scheduling problems were effectively reduced to the associated problems of integer linear programming.…”

Section: Introductionmentioning

confidence: 99%

On Propeties of the LIP Model in the Class of RCPSPs

Кибзун

Rasskazova

2023

Mathematics

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The Resource-Constrained Project Scheduling Problem (RCPSP) is a significant and important issue in the field of project management. It arises during project planning when resources must be allocated among tasks with specific time constraints. Solving this problem enables the optimization of project execution time, minimization of resource costs, and increased efficiency of the entire team’s work. Due to the increasing complexity of projects, the development of new methods and algorithms to solve RCPSP is relevant nowadays. The existing methods for obtaining approximate solutions with guaranteed accuracy are characterized by high computational complexity and are often ineffective in considering the specific constraints of the problem. Fast heuristic approaches also have several drawbacks related to fine-tuning algorithm parameters and strong dependence on the quality of the initial solution. This paper investigates the features of the linear integer programming (LIP) model to solve RCPSP. The proposed LIP model is universal and scalable, enabling it to fully consider all specific aspects of the problem. The paper provides a construction algorithm of a functional space of the model and discusses the estimation of complexity. From the estimation of the mentioned algorithm’s complexity, it is observed that the general complexity of the proposed approach is proportional to a controlled parameter of the LIP. Increasing this controlled parameter can significantly reduce the dimensionality of the initial problem, thus leading to the effectiveness of the LIP model-based approach in terms of computational resources. An upper bound for the value of this parameter is obtained for a special case of the RCPSP. Using the obtained balanced value, a numerical experiment was carried out on real-world samples.

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