整合揀貨與配送車輛路徑問題近年來逐漸受到重視，過去研究僅考慮配送成本與服務水準，而未將配送過程中車輛所產生的碳排放一併納入考量，因此，本研究同時考慮揀貨成本、配送成本、配送偏誤時間及配送過程中所產生的碳排放，而為一個具多目標之整合批次揀貨及環境車輛路徑問題。由於傳統方法在求解三個以上目標之多目標問題時常無法兼顧各目標間之權衡，本研究以基於參考點之非支配排序遺傳演算法NSGA-III (Non-dominated Sorting Genetic Algorithm III)法來求解柏拉圖最佳前緣，並與NSGA-II法求解之結果進行比較。研究結果顯示，本研究採用之NSGA-III法所求得之柏拉圖最佳前緣在配送成本與碳排放目標中可得到更優的結果，在評估最佳前緣的分布程度方面表現亦較佳，顯示以NSGA-III法所求得之柏拉圖最佳前緣解是具有競爭力的，能求得在同時考量揀貨成本、配送成本、配送偏誤時間與碳排放多目標下之不同組合解，提供物流業者做為決策之參考。

In the past, the problem of integrating picking and delivery vehicle routing mainly considered distribution costs and time gaps. This study considers the carbon emissions of distribution vehicles, as well as the picking cost, time gap and distribution cost, which makes the optimization problem a multi-objective problem and faces certain challenges in solving efficiency. Therefore, this study explore the multi-objective integrated batch picking and environmental vehicle routing problem contains the carbon emissions of distribution vehicles. First, a mathematical programming model is established. In order to be able to deal with large-scale and multi-objective problems, this study is based on the non-dominated sorting genetic algorithm NSGA-III method to solve the Pareto optimal front, and compared with the single-objective minimization problem and the solution from NSGA-II method. The results of the study show that single-objective optimal solution cannot dominate the Pareto optimal front solution obtained by the NSGA-III method. And compared with NSGA-II method, NSGA-III can obtain better solution which means that the Pareto optimal front solution obtained in this study is competitive. The result of different combinations of picking costs, time gaps, distribution costs and carbon emissions can can serve as decision recommendations.

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