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Multi-agent job shop negotiation scheduling optimization method

A job shop, optimization method technology, applied in the fields of genetic laws, data processing applications, instruments, etc., can solve the problems of small scale of applicable agents, poor optimization effect, and rough final plan selection.

Active Publication Date: 2020-12-18
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when this method solves the multi-agent job shop scheduling problem, it has the problems of poor optimization effect, rough final solution selection and small scale of applicable agents.

Method used

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  • Multi-agent job shop negotiation scheduling optimization method
  • Multi-agent job shop negotiation scheduling optimization method
  • Multi-agent job shop negotiation scheduling optimization method

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] Such as figure 1 As shown, the present invention provides a kind of multi-agent job shop negotiation scheduling optimization method, comprising the following steps:

[0070] Step 1: Initialize multi-agent job shop negotiation scheduling:

[0071] Step 1-1: The job shop server starts the coordinator program, referred to as the coordinator; the coordinator sets the agent set {A 1 ,...,A a_i ,...,A num_a}, where A a_i Represents the agent a_i, a_i∈[1, num_a]; the coordinator program listens to the port and prepares to establish a link with the user agent;

[0072] Step 1-2: The user computer starts its own agent program, referred to as agent; each agent establishes a TCP / IP connection with the coordinator, and transmits its own set of workpieces to be processed to the coordinator where J i represents the workpiece to be processed by agent a_...

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Abstract

The invention discloses a multi-agent job shop negotiation scheduling optimization method. The method is divided into two stages: in the first stage, a scheme set is guided to evolve to a Pareto frontier in an inter-agent iterative negotiation mode; and in the second stage, the coordinator selects the scheme with the best social benefit from the scheme set based on the agent score. According to the method, each agent selects a part of random search schemes to put forward and form an initial parent scheme set; the coordinator performs crossover variation on the parent scheme set to generate a child scheme set, then ranks the parent and child scheme sets based on agents, and selects non-dominated schemes from the parent and child scheme sets to enter a new parent scheme set; and the negotiation is iterated for multiple times, and after a negotiation round is reached, the coordinator selects the scheme with the best social benefit from the scheme set based on the agent score. The method is based on the genetic evolution thought, and the optimization effect is effectively improved; and a more accurate information batch exposure mode is adopted, so that the social benefit is effectivelyimproved.

Description

technical field [0001] The invention belongs to the field of production scheduling, and in particular relates to a negotiation scheduling optimization method. Background technique [0002] Job shop production scheduling is the core for enterprises to implement production management and ensure the orderly and efficient operation of workshops. As the production mode of personalized customization gradually draws attention, the goal of job shop scheduling changes to coordinate resources to meet the individual needs of multiple users, thus the traditional job shop scheduling problem is transformed into a multi-agent job shop scheduling problem. Since the multi-agent scheduling problem has the characteristics of scattered and asymmetric system information, the traditional centralized method based on complete information has poor application effect, and the distributed decision-making mechanism suitable for the environment of information asymmetry has become the focus of research. ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/12G06Q10/06G06Q50/04
CPCG06Q10/04G06Q10/06312G06N3/126G06Q50/04Y02P90/30
Inventor 孙树栋常昇博吴自高刘亚琼代进伦
Owner NORTHWESTERN POLYTECHNICAL UNIV
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