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Optimizing multi-supplier multi-item joint replenishment problem for non-instantaneous deteriorating items with

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This study introduces a new inventory model for deteriorating items, optimizing joint replenishment and supplier selection. An improved Moth-Flame Optimization algorithm effectively solves this complex NP-hard problem.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Inventory Optimization

Background:

  • Managing deteriorating inventory with multiple suppliers presents significant challenges.
  • Joint replenishment and supplier selection decisions are critical for efficiency.
  • Non-instantaneous deterioration adds complexity to traditional inventory models.

Purpose of the Study:

  • To formulate a mathematical model for a joint replenishment problem with non-instantaneous deteriorating items and multiple suppliers.
  • To develop an efficient optimization algorithm for this complex problem.
  • To evaluate the algorithm's performance and the model's effectiveness.

Main Methods:

  • Mathematical modeling integrating supplier selection and joint replenishment.
  • Development of a novel swarm intelligence algorithm: Improved Moth-Flame Optimization (IMFO).
  • Numerical experiments and comparative analysis to validate the model and algorithm.

Main Results:

  • The proposed mathematical model effectively formulates the complex inventory problem.
  • The IMFO algorithm demonstrates superior performance in solution quality and stability.
  • Extensive experiments confirm the algorithm's effectiveness for the non-instantaneous deteriorating items problem.

Conclusions:

  • The integrated model and IMFO algorithm provide an effective solution for joint replenishment with supplier selection.
  • The approach addresses the challenges of non-instantaneous deterioration in inventory management.
  • This research offers valuable insights for optimizing inventory systems in complex supply chains.