Design of mechanisms for ensuring the execution of tasks in project planning
- URL: http://arxiv.org/abs/2501.01255v1
- Date: Thu, 02 Jan 2025 13:47:20 GMT
- Title: Design of mechanisms for ensuring the execution of tasks in project planning
- Authors: Oksana Mulesa, Petro Horvat, Tamara Radivilova, Volodymyr Sabadosh, Oleksii Baranovskyi, Sergii Duran,
- Abstract summary: The paper reports an analysis of aspects of the project planning stage.<n>It takes into account restrictions on financial costs and duration of project implementation.<n>Models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper reports an analysis of aspects of the project planning stage. The object of research is the decision-making processes that take place at this stage. This work considers the problem of building a hierarchy of tasks, their distribution among performers, taking into account restrictions on financial costs and duration of project implementation. Verbal and mathematical models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed. Such indicators of the project implementation process efficiency were introduced as the time, cost, and cost-time efficiency. In order to be able to apply these criteria, the tasks of estimating the minimum value of the duration of the project and its minimum required cost were considered. Appropriate methods have been developed to solve them. The developed iterative method for assessing the minimum duration of project implementation is based on taking into account the possibility of simultaneous execution of various tasks. The method of estimating the minimum cost of the project is to build and solve the problem of Boolean programming. The values obtained as a result of solving these problems form an {\guillemotleft}ideal point{\guillemotright}, approaching which is enabled by the developed iterative method of constructing a hierarchy of tasks based on the method of sequential concessions. This method makes it possible to devise options for management decisions to obtain valid solutions to the problem. According to them, the decision maker can introduce a concession on the value of one or both components of the {\guillemotleft}ideal point{\guillemotright} or change the input data to the task. The models and methods built can be used when planning projects in education, science, production, etc.
Related papers
- Haste Makes Waste: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions [56.88110850242265]
We present Recipe2Plan, a novel benchmark framework based on real-world cooking scenarios.
Unlike conventional benchmarks, Recipe2Plan challenges agents to optimize cooking time through parallel task execution.
arXiv Detail & Related papers (2025-03-04T03:27:02Z) - On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability [59.72892401927283]
We evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks.
Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints.
arXiv Detail & Related papers (2024-09-30T03:58:43Z) - On Learning Action Costs from Input Plans [8.68471096727195]
We introduce a new problem: that of learning the costs of a set of actions such that a set of input plans are optimal under the resulting planning model.
We present $LACFIPk$, an algorithm to learn action's costs from unlabeled input plans.
arXiv Detail & Related papers (2024-08-20T14:20:19Z) - MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP)
MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.
We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
arXiv Detail & Related papers (2024-06-11T17:55:25Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling [0.43512163406552007]
A planner in charge of project planning has to select a set of activities to perform, determine their precedence constraints, and schedule them according to temporal project constraints.
We suggest a data-driven project planning approach for classes of projects such as infrastructure building and information systems development projects.
arXiv Detail & Related papers (2023-11-20T05:13:17Z) - On efficient computation in active inference [1.1470070927586016]
We present a novel planning algorithm for finite temporal horizons with drastically lower computational complexity.
We also simplify the process of setting an appropriate target distribution for new and existing active inference planning schemes.
arXiv Detail & Related papers (2023-07-02T07:38:56Z) - Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments [54.39292848359306]
We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks.
The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task.
We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
arXiv Detail & Related papers (2023-03-27T01:50:45Z) - Reinforcement Learning with Success Induced Task Prioritization [68.8204255655161]
We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning.
The algorithm selects the order of tasks that provide the fastest learning for agents.
We demonstrate that SITP matches or surpasses the results of other curriculum design methods.
arXiv Detail & Related papers (2022-12-30T12:32:43Z) - Task Scoping: Generating Task-Specific Abstractions for Planning [19.411900372400183]
Planning to solve any specific task using an open-scope world model is computationally intractable.
We propose task scoping: a method that exploits knowledge of the initial condition, goal condition, and transition-dynamics structure of a task.
We prove that task scoping never deletes relevant factors or actions, characterize its computational complexity, and characterize the planning problems for which it is especially useful.
arXiv Detail & Related papers (2020-10-17T21:19:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.