Bidding-based process planning and scheduling in a multi-agent system
Computers and Industrial Engineering
Computers and Industrial Engineering
Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Scheduling Algorithms
Engineering Applications of Artificial Intelligence
The distributed permutation flowshop scheduling problem
Computers and Operations Research
A hybrid genetic algorithm with the Baldwin effect
Information Sciences: an International Journal
An adaptive genetic algorithm with dominated genes for distributed scheduling problems
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
Minimum perimeter coverage of query regions in a heterogeneous wireless sensor network
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The effect of load on agent-based algorithms for distributed task allocation
Information Sciences: an International Journal
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Traditionally centralized manufacturing planning, scheduling, and control mechanisms are being found to be insufficiently flexible to respond to highly dynamic variations in the market requirements. In order to be competitive in today's rapidly changing business world, organizations have shifted from a centralized to a decentralized structure in many areas of decision making. Distributed scheduling is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. In this paper, we assumed that production takes place in several factories, which may be geographically distributed in different locations, in order to take advantage from the trend of globalization. In this approach, the factories that are available to process the jobs have different speeds in which each factory has parallel identical machine. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. After proposing mixed integer linear programming model for the problem, we developed a heuristic and genetic algorithm. For the proposed genetic algorithm, at first, to represent the solutions, we suggested a new encoding scheme, and then proposed a local search based on the theorem developed in the paper. Finally, we compare the obtained solutions using the lower bound developed in this paper. The results show the proposed algorithms to be very efficient for different structures of instances.