持続可能性の観点から時間依存のネットワークと同時ピックアップと配送を考慮した冷却チェーンロジスティックの流通路の最適化に関する研究
Yanqiu Liu1, Jiaqi Hou1, Chao Cai1
1School of Management, Shenyang University of Technology, Shenyang, Liaoning, China.
PloS one
|September 5, 2025
まとめ
この研究は,タイムウィンドウと変数の速度を考慮して,同時にピックアップと配送のためのコールドチェーンのロジスティックルートを最適化します. 開発されたヒューリスティック・クロス・ブレインストーミング・オプティマイゼーション・アルゴリズム (HCBSO) は,総コストを削減し,持続可能な配布を強化します.
科学分野:
- 運用研究
- ロジスティック管理
- 持続可能なサプライチェーン
背景:
- コールドチェーンのロジスティックは経済発展にとって不可欠であり,持続可能で人為的なアプローチが必要です.
- タイムウィンドウ,変速車,交通渋滞などの課題に直面しています.
- 経済,環境,社会,公平性) の総コストを最小限に抑えることは,持続可能な冷却チェーン事業に不可欠です.
研究 の 目的:
- コールドチェーンのロジスティクスのための車両ルーティングを最適化し,時間枠内で同時にピックアップと配送を行う.
- 経済的,環境的,社会的,公平な要因を組み込み,総コストを最小限に抑える数学的モデルを開発する.
- ダイナミックな交通条件を考慮して,冷却チェーン配送の効率と持続性を高める.
主な方法:
- コールドチェーンの物流における総コストを最小限に抑えるための数学的モデルの開発.
- パス最適化のためのヒューリスティック・クロス・ブレインストーム・最適化アルゴリズム (HCBSO) の適用.
- セグメントベースの初期ソリューション構築,ヒューリスティッククロスオーバー,出発時間の最適化を含む戦略の実装.
主要な成果:
- HCBSOアルゴリズムは,出発時間と車両路線を効率的に最適化し,交通渋滞を軽減します.
- モデルとアルゴリズムは,全体的な運用コストを大幅に削減することを示しています.
- コールドチェーン・ロジスティック・オペレーションの科学的な最適化に改善が確認された.
結論:
- 開発されたモデルとHCBSOアルゴリズムは,コールドチェーンの物流の最適化に科学的に検証されたアプローチを提供します.
- 戦略は,全体的なコストを効果的に削減し,冷却チェーン配送システムの持続可能性を高めます.
- 最適な出発時間と路線計画により,より効率的で環境に配慮した物流業務が進められます.
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