總完工時間最小化之訂單指派-以D公司為例

訂單指派是許多企業和組織中日常運作的關鍵流程之一。透過有效的指派策略，可以最大限度地提高工作效率並減少生產或服務交付的時間。本研究旨在探討如何最小化「總完工時間」（Makespan）的訂單指派問題，以提供一個有效的解決方案。
在現實生活中，各種因素會影響訂單的指派，例如不同的工作需求、資源限制、時間窗口和成本考量等。這些因素使得訂單指派變得複雜且具有挑戰性。因此，我們需要適當的策略和優化方法來解決這個問題，以實現最佳的訂單指派結果。過去的研究在訂單指派領域取得了一些重要的成果。例如，某些研究著重於最小化訂單的完成時間，而其他研究則專注於資源利用率的最大化。然而，目前對於最小化「總完工時間」的訂單指派問題的研究還相對有限。
為協助案例公司改善訂單指派的作業，本研究首先以最小化「總完工時間」為目標，根據實務作業情境建立數學模式，將一個真實問題變成數學問題。接著，依據問題的特徵以及實務的需求，發展出一個求解架構，最後運用演化式計算來獲得可行的解決方案。研究結果顯示，本研究所出的方法，確實可在短時間內獲得實務可行的方案，並能產生優於以往使用「依序揀選」的結果。此外，隨著訂單數量的增加，每張訂單的平均「總完工時間」減少；揀貨人員的增加，可以降低「總完工時間」。

Order assignment is one of the key processes in the daily operations of many businesses and organizations. Through effective assignment strategies, work efficiency can be maximized and time to production or service delivery can be reduced. This study aims to explore how to minimize the makespan of order assignment.
In real life, various factors affect the assignment of orders, such as different job requirements, resource constraints, time windows, and cost considerations. These factors make order assignment complex and challenging. Therefore, we need appropriate strategies and optimization methods to solve this problem to obtain the best order assignment results.
Past studies have obtained some important results in the field of order assignment. For example, some studies focus on minimizing the fulfillment time of individual orders, while others focus on maximizing resource utilization. However, the current research on the order assignment problem of minimizing the makespan is relatively limited.
In this study, we optimize order assignment aimed at minimizing the makespan. We consider various constraints, including resource availability, job time windows, and dependencies between jobs. To solve the complex optimization problem, we set up a mathematical model and use the tool based on an evolutionary algorithm to find the optimized order assignment solutions.
The results show that the method proposed in this study can obtain feasible solution in a short period of time and can produce better results than the previous use of "sequential selection." In addition, as the number of orders increases, the average makespan of each order decreases; the increase of pickers can reduce the makespan.