以ESG觀點探討全供給快速配送問題

快速配送 (Express Delivery) 在近年來受到高度重視，它帶來不少便利性，卻也同時帶來某些環境與社會的議題，例如過多的碳排放量、消費者權益等問題。有鑑於此，本研究從ESG (Environmental, Social, and Governance；環境、社會與公司治理) 的觀點出發，運用新穎且具前瞻性的「全供給」(Omni Supply) 模式處理快速配送問題。這個模式整合一區域內的供給點，主張「一次下單即可一次購足所需商品，並獲得單一的配送」(One-stop Ordering and One-trip Delivery)。
「全供給」模式若要有效，必須妥善處理某些問題：要有適當的訂單、供給點及外送員指派，而這些指派要能回應環境與社會的某些期待。環境與社會層面涉及的範圍非常廣泛，任何研究都無法面面俱到，必須擇要探討。因此，在環境面，本研究希冀降低碳排放量，以「最短總距離」為目標來縮短運送距離；在社會面，由於「全供給」模式主要的利益關係人為消費者、供應商、外送員以及企業，本研究以「最低總價」為目標，希望能使消費者以最低的價格購得商品。此外，也設法讓外送員及供給點能平均獲取訂單。最後，從公司治理角度，企望能在環境和社會面權衡取捨，符合整體面的期待。
為驗證「全供給」模式是否有效，本研究首先根據實務情境建立基礎案例，再使用基因演算法 (Genetic Algorithm, GA) 進行求解，在某些需求狀況下，進行多項實驗。結果顯示，不管是以「最短總距離」或「最低總價」為目標，GA均能獲得不錯的可行解。然而，縮短總運送距離會造成總價格提高，反之，降低總價格會使得運送總距離增加。鑑於上述二個目標相互衝突，本研究也利用多目標規劃得到柏拉圖解 (Pareto Solution)。另外，隨著供給點數量的增加，總距離與總價均隨之降低；當外送員達到某一數量時，外送員數量對總距離與總價的影響均有限；隨著訂單數量的增加，在供給點與外送員也跟著增加的情況下，每張訂單的平均運送距離將會降低，有利於環境保護。

In recent years, "Express Delivery" has received significant attention, bringing much convenience, but also given rise to several environmental and social issues, such as excessive carbon emissions, consumer rights, and fairness concerns. Taking these issues into consideration, this study adopts an ESG (Environmental, Social, and Governance) perspective and proposes an innovative and promising "Omni Supply" model to address the challenges faced by express delivery services. This model integrates supply points within a region and advocates for "One-stop Ordering and One-trip Delivery," where all necessary goods are purchased in a single order and delivered in one trip.
For the "Omni Supply" model to be effective, several problems need to be addressed: appropriate assignment of orders, selection of supply points, and assignment of delivery drivers, all of which should consider environmental and social factors. The environmental and social aspects encompass a wide range of considerations, and no single study can cover them all. Consequently, in terms of the environment, this study aims to reduce carbon emissions by targeting the "shortest total distance" to minimize delivery distances. On the social front, since the primary stakeholders of the "Omni Supply" model are consumers, suppliers, delivery drivers, and companies, this study sets the objective of achieving the "lowest total price" to enable consumers to purchase goods at the lowest cost. Additionally, efforts are made to ensure even allocation of orders among delivery drivers and supply points. Lastly, from a corporate governance perspective, the study seeks to strike a balance between environmental and social considerations to achieve maximum overall benefits.
To validate the effectiveness of the "Omni Supply" model, this study establishes a base case and then utilizes a Genetic Algorithm (GA) for optimization. Multiple experiments are conducted under various demand scenarios. The results demonstrate that GA can obtain satisfactory feasible solutions, whether the objective is to achieve the "shortest total distance" or the "lowest total price." However, reducing the overall delivery distance leads to an increase in the total price, and similarly, reducing the total price results in an increase in the total delivery distance. Given the conflicting nature of these objectives, the study also employs multi-objective programming to obtain Pareto Solutions. Furthermore, as the number of supply points increases, both the total distance and total price decrease. When the number of delivery drivers reaches a certain quantity, their influence on the total distance and total price becomes limited. With an increasing number of orders and a corresponding increase in supply points and delivery drivers, the average delivery distance decreases, which is favorable for environmental protection.