| dc.description.abstract | A crucial aspect of efficient project management was project scheduling, which
depends on carefully considering cost and time factors. Conventional approaches for
maintaining this delicate balance, including mathematical programming or heuristic
methods, frequently break down when faced with the complicated complexities of
Critical Path Method (CPM) analysis, especially when applied in large scale problem.
This paper presents a new approach to address these issues by identifying
critical path inside project schedules by combining the conventional Critical Path
Method (CPM) with Activity-on-Node (AON) methods. Additionally, the study
proposes the construction of a specialized Metaheuristic algorithm, specifically using
Genetic algorithms (GA), in recognition of the difficulties of existing methods in
tackling Time-Cost Trade-Off Problems (TCTP). With so many variables and aspects
interacting, finding the best solutions can be a challenging effort. This new approach is
made to navigate the complex world of project scheduling.
The investigation provides an ideal resolution designed for medium-sized project
scheduling problems, which includes identifying critical path and minimizing project
costs. This research intends to equip project managers with helpful information and tools
for making decisions by offering an exhaustive framework for analyzing project
scheduling processes via both perspectives of time and cost. A thorough analysis of a
real-world case study is conducted to confirm the effectiveness and practical application
of the suggested methodology. The paper demonstrates the algorithm's outstanding
ability to handle the complex aspects of project scheduling while effectively negotiating
the trade-offs between time and cost through this empirical proof. These results carry
important implications for improving operational efficiency and resource allocation in
a variety of industries and contexts, in addition to furthering the theoretical foundations
of project management. | en_US |
