A critical chains based distributed multi-project scheduling approach

Research on Collaborative Planning and Symmetric Scheduling for Parallel Shipbuilding Projects in the Open Distributed Manufacturing Environment

In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.

A multi-agent system for distributed multi-project scheduling with two-stage decomposition

A two-stage decomposition approach based on a novel multi-agent system (MAS) is proposed for the distributed resource constrained multi-project scheduling problem (DRCMPSP). In stage one, from the point of view of each local project manager, a forward-backward hybrid genetic algorithm (FBHGA) is developed to generate an initial local schedule with the objective of minimizing individual project makespan. In stage two, from the global perspective of project management office, a sequential game-based negotiation mechanism is employed to eliminate global resource conflicts with the objective of minimizing total tardiness cost (TTC). The proposed approach is tested on 140 benchmark problem instances. According to the computational results, high-quality local project schedules can be obtained by FBHGA in stage one. Furthermore, it is observed that our method is capable of dealing with various complex multi-project instances under different degrees of resource conflicts in reasonable CPU running time. Compared to the existing decentralized methods for DRCMPSP, the proposed approach with sequential game-based negotiation mechanism shows the superiority in producing multi-project schedules with lower TTC, especially for large-size and strong conflicting instances.

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.