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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
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Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers
Zhaolei He1, Miaohan Zhang2, Qiyong Chen2
1Metrology Center, Yunnan Power Grid Co., Ltd, Kunming, 650041, People's Republic of China.
Scientific Reports
|August 29, 2023
Summary
This study introduces an Improved Life-cycle Swarm Optimization (ILSO) algorithm to solve the vehicle routing problem (VRP) for industrial product distribution. The ILSO algorithm effectively reduces transportation costs and improves delivery efficiency.
Area of Science:
- Operations Research
- Logistics Management
- Industrial Engineering
Background:
- Industrial enterprises face significant challenges in optimizing product delivery concerning timeliness and cost.
- The vehicle routing problem (VRP) with diverse vehicle models across multiple distribution centers is a complex logistical challenge.
Purpose of the Study:
- To develop an optimized solution for the vehicle routing problem (VRP) considering multi-dimensional constraints.
- To minimize distribution time and cost while maximizing vehicle loading rates in industrial logistics.
Main Methods:
- Formulation of a multi-objective optimization model incorporating vehicle load and time window constraints.
- Development of an Improved Life-cycle Swarm Optimization (ILSO) algorithm inspired by life cycle theory.
- Conducting dispatching experiments using real-world order data from Yunnan Power Grid Company.
Main Results:
- The ILSO algorithm demonstrated a reduction in transportation costs by 0.8% to 1.6% compared to five other algorithms.
- Simulation and application results validated the effectiveness of the ILSO algorithm in practical scenarios.
Conclusions:
- The ILSO algorithm offers a superior approach for optimizing industrial product distribution.
- The proposed method effectively reduces costs and enhances operational efficiency for enterprises.
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