High-end equipment development task decomposition and scheme selection method

Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.

Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition

How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.

Research and Implementation of Task Management Model in Product Development Process Management

The implementation of product development process management (PDPM) is an effective means of developing products with higher quality in shorter lead time. It is argued in this paper that product, data, person and activity are basic factors in PDPM With detailed analysis of these basic factors and their relations in product developmed process, all product development activities are considered as tasks and the management of product development process is regarded as the management of task execution A task decomposition based product development model is proposed with methods of constructing task relation matrix from layer model and constraint model resulted from task decomposition. An algorithm for constructing directed task graph is given and is used in the management of tasks. Finally, the usage and limitation of the proposed PDPM model is given with further work proposed.

Net-Based Task Management System

In net-based collaborative design environment, design resources become more and more varied and complex. Besides common information management systems, design resources can be organized in connection with design activities. A set of activities and resources linked by logic relations can form a task. A task has at least one objective and can be broken down into smaller ones. So a design project can be separated into many subtasks forming a hierarchical structure. Task Management System (TMS) is designed to break down these tasks and assign certain resources to its related task nodes. As a result of decomposition, all design resources and activities could be managed via this system. Based on this idea, we realized a TMS which manages collaborative resources in web environment.

Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making

Decomposing complex real-world tasks into simpler subtasks and devising a subtask execution plan is critical for humans to achieve effective decision-making.However,replicating this process remains challenging for AI agents and naturally raises two questions:(1)How to extract discriminative knowledge representation from priors?(2)How to develop a rational plan to decompose complex problems?To address these issues,we introduce a groundbreaking framework that incorporates two main contributions.First,our multiple-encoder and individual-predictor regime goes beyond traditional architectures to extract nuanced task-specific dynamics from datasets,enriching the feature space for subtasks.Second,we innovate in planning by introducing a top-K subtask planning tree generated through an attention mechanism,which allows for dynamic adaptability and forward-looking decision-making.Our framework is empirically validated against challenging benchmarks BabyAI including multiple combinatorially rich synthetic tasks(e.g.,GoToSeq,SynthSeq,BossLevel),where it not only outperforms competitive baselines but also demonstrates superior adaptability and effectiveness incomplex task decomposition.