Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply...

Area of Science:

• Operations Research
• Supply Chain Management
• Optimization

Background:

• Inventory and transportation decisions are critical in supply chains.
• Balancing service level and total cost is a key challenge in joint replenishment and delivery (JRD).
• Existing models often struggle to address multiple objectives simultaneously.

Purpose of the Study:

• To propose a novel multiobjective stochastic joint replenishment and delivery (MSJRD) model for one-warehouse, n-retailer systems.
• To simultaneously consider service level and total cost in JRD decisions.
• To determine optimal replenishment intervals, safety stock factors, and routing.

Main Methods:

• Developed two approaches: Linear Programming (LP) for single-objective conversion and Multi-Objective Evolutionary Algorithm (MOEA) for direct multi-objective solving.
• Employed Differential Evolution (DE), Hybrid DE (HDE), and Genetic Algorithm (GA) within LP and MOEA frameworks.
• Utilized a contrastive numerical example to compare the performance of different approaches.

Main Results:

• The hybrid differential evolution algorithm (HDE) consistently outperformed standard differential evolution (DE) and genetic algorithm (GA).
• HDE showed superior results when integrated with both LP-based and MOEA-based approaches.
• Analysis of nondominated solutions using a specific metric provided insights into solution distribution.

Conclusions:

• The proposed MSJRD model effectively balances service level and total cost.
• HDE is a highly effective optimization algorithm for complex JRD problems.
• The study provides a robust framework for optimizing inventory and delivery strategies in multi-retailer systems.