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An improved adaptive variable neighborhood search algorithm for stochastic order allocation problem.

Zhenzhong Zhang1, Ling Zhang2, Weichun Li1

  • 1CAAC Academy, Civil Aviation Flight University of China, Chengdu, 618307, China.

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Summary
This summary is machine-generated.

This study introduces a new framework for intelligent stochastic optimization to improve order allocation in supply chains. The approach effectively handles uncertainties and high-dimensional problems, enhancing efficiency and profit.

Keywords:
Order allocationOrder uncertaintyScenario generationStochastic optimization algorithm

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Artificial Intelligence

Background:

  • Practical supply chain operations face significant complexity due to uncertainties like unpredictable customer orders.
  • Efficient order allocation is crucial for enhancing overall supply chain efficiency.
  • Real-world production environments are characterized by high-dimensional and stochastic conditions.

Purpose of the Study:

  • To develop a mathematical model for high-dimensional stochastic optimization in order allocation.
  • To address the uncertainty of customer order cancellations in production.
  • To maximize expected profit in order processing.

Main Methods:

  • Developed a mathematical model for a high-dimensional stochastic optimization problem.
  • Integrated an intelligent optimization algorithm with a scenario generation approach.
  • Employed an algorithm suitable for the generalized assignment problem and scenario generation for evaluation.

Main Results:

  • The proposed framework effectively addresses high-dimensional stochastic order allocation.
  • The approach demonstrated superior efficiency and capability compared to existing methods.
  • Experimental results validated the effectiveness of the intelligent stochastic optimization framework.

Conclusions:

  • The novel framework provides an effective solution for complex order allocation problems.
  • Intelligent optimization and scenario generation are key to managing supply chain uncertainties.
  • The study enhances the efficiency and profit maximization in stochastic supply chain environments.