物流轉運與不確定供需匹配問題之研究

A Study on Logistic Transshipment and Matching Uncertain Demand and Supply

康達育(KATAYUT KAMANO)

指導教授 ： 周雍強

國立臺灣大學/工學院/工業工程學研究所/博士(2020年)

https://doi.org/10.6342/NTU202000058

摘要

逐漸增加的動態性將是物流領域中一項新的挑戰。面對這項挑戰，必須要能夠去描述，界定與分析這些流程的改變。甚或是整體物流的流程與網路必須被重新設計成具有能力去面對快速改變的條件與環境。
首先，本研究將會將探討動態物流下的多個配送員配送服務並得出一個配送協作的方式來達到雙重目標，降低顧客等待時間與減少送貨員之工作量。然而，這樣的配送協做卻可能將某些配送往後推移至下一個時間區間以達成整體的最佳性 。為此，本研究進行了在動態的環境與兩種準則下，對於兩種種類的配送，一般與急件的服務策略分析。在本研究提出的策略中可以得出各路經規劃方案的優先順序，再根據這個結果設計新的路徑規劃方法。實際的數據結果也顯示本研究提出的路徑規劃方法能有效地建立一個最佳等待時間與工作量的模型。這也成功地建立了將協作轉運中心應用於動態配送服務中的橋樑。
最後，不論是生產或是存貨模型中的需求與供給不確定性在本研究中被探討。當兩種不確定性同時出現時，所謂的中介庫存通常扮演著緩衝機制，以便於將兩種不確定性分別處理。在本研究中，我們解決了一個在現有共享單車系統中的供需平衡問題。我們顯示出，在同時考慮單車使用率與顧客流失率的情況下，各站點的單車數量最好能不少於某個低標值或大於某個高標值。所以我們接著提出一個以高低界線為基礎的整數規劃模型來處理動態平衡的問題。透過數值範例，我們發現問題有多種最佳解，可能會造成很零散的單車運輸，但這樣的情況可以透過重新排序候選傳輸來解決。而我們也比較了該模型與平均值模型，評估其利用提早放置單車來達到消弭不確定性的可能性。本研究貢獻了一個方法來匹配不確定的需求與供給。

關鍵字

配送協做 ； 配送 ； 轉運中心 ； 需求與供給不確定性 ； 共享單車系統 ； 供需平衡 ； 不確定供需匹配

並列摘要

The growing dynamic confronts the area of logistics is completely new challenges. It must become possible to describe, identify and analyze the process changes. Moreover, logistic processes and networks must be redevised to be rapidly and flexibly adaptable to continuously changing conditions.
First, this work will focus on the duel objectives dynamics logistics for pickup and delivery service with multiple couriers, a collaboration between couriers through hub of transshipment has the potential of improving customer waiting time and courier workload. However, transshipment collaboration calls for postponing the delivery of some jobs to the next time period for the benefit of the greater whole. Therefore, new ideas for mitigating the penalty on those postponed jobs should be further investigated. To this end, this work presents an analysis of service policy for two categories of jobs, regular and urgent, under two performance measures for a dynamic environment. For the developed policy in this work, dominance relationships between routing solutions are derived. A routing method is then developed. Numerical results show that the proposed policy and method is practical in constructing an efficient front in the waiting-time and workload performance space. This work bridges a methodology gap in applying collaborative hub of transshipment to dynamic pickup and delivery services.
Second, the production and inventory models address either demand or supply uncertainty are investigated. When both uncertainties are involved, intermediary inventory stock usually serves as a buffering mechanism so that they are dealt with separately. In this work, we address a new problem of directly matching uncertain demand with uncertain supply that arises in the dynamic bike balancing problem of bike sharing systems. We show that it is not optimal for the bike stock in any station to be less than a certain floor threshold or more than a certain upper threshold when the dual performance measures of bike utilization and lost customers are considered. We next construct a threshold-based Integer Programming model for dynamic balancing. Through numerical examples, we find that the problem is characterized by many multiple optimal solutions, which lead to dispersed transfers of bikes, but this imperfection is resolved by ranking and re-sequencing candidate transfers in an enhancing step. By using random numerical cases, we compare the merits of the model with a mean-value model, and assess its capability of prepositioning bikes to hedge the uncertainty. This work contributes to the methodology of matching uncertain demand with uncertain supply.

並列關鍵字

Collaborative Logistic ； Hub Transshipment ； Dynamic Pickup and Delivery ； Efficient Front ； Bike Rebalancing ； Uncertain Demand and Uncertain Supply ； Matching uncertain demand and supply ； Bike Sharing Systems

參考文獻

[1] Akcan, S., & Kokangul, A. (2013). A new approximation for inventory control system with decision variable lead-time and stochastic demand. International Journal of Industrial Engineering: Theory, Applications and Practice, 20(3-4), 262-272.

[2] Angelelli, E., Bianchessi, N., Mansini, R. and Speranza, M. (2009). Short Term Strategies for a Dynamic Multi-Period Routing Problem. Transportation Research Part C: Emerging Technologies, 17( 2), 106-119.

[3] Arslan, A., Agatz, N., Kroon, L. and Zuidwijk, R. (2016). Crowdsourced Delivery: A Dynamic Pickup and Delivery Problem with Ad-hoc Drivers. ERIM Report Series Reference, Erasmus Research Institute of Management.

[4] Berbeglia, G., Cordeau, J. and Laporte, G. (2010). Dynamic pickup and delivery problems. European Journal of Operational Research, 202(1), 8-15.

[5] Berger, S. and Bierwirth, C. (2010). Solutions to the request reassignment problem in collaborative carrier networks. Transportation Research Part E: Logistics and Transportation Review, 46(5), 627-638.