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This study optimizes multi-period inventory systems by maximizing profit and minimizing storage space using a novel optimization method. The approach enhances supply chain performance and offers insights for digital supply chain management.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Optimization Techniques

Background:

  • Stochastic demand in multi-period inventory systems presents challenges for optimizing profit and storage.
  • Traditional inventory models often struggle to balance competing objectives effectively.
  • Effective inventory management is crucial for overall supply chain performance and resilience.

Purpose of the Study:

  • To develop and validate a nonlinear programming model for simulating inventory operations under stochastic demand.
  • To implement a multi-objective grey wolf optimization (MOGWO) method for simultaneous profit maximization and storage space reduction.
  • To provide a novel decision-making strategy for enhanced digital supply chain management against market volatility.

Main Methods:

  • Development of a nonlinear programming model to simulate inventory dynamics with random demand.
  • Application of the multi-objective grey wolf optimization (MOGWO) algorithm to solve the complex inventory problem.
  • Conducting numerical analyses and sensitivity analyses across four practical scenarios to validate the model's efficacy.

Main Results:

  • The MOGWO method successfully identified optimal solutions that balance profit maximization and storage space minimization.
  • Numerical outcomes confirmed the effectiveness of the proposed approach in practical inventory management scenarios.
  • Sensitivity analysis verified the robustness and reliability of the obtained optimal solutions.

Conclusions:

  • The proposed inventory optimization strategy effectively balances profit and storage space, enhancing supply chain performance.
  • The MOGWO-based approach offers a novel decision-making framework for digital supply chains facing market volatility.
  • This research provides valuable insights for businesses seeking to improve inventory management practices and operational efficiency.