This study focuses on examining the final part of the package delivery process or better known as last-mile delivery. The last-mile delivery process is very identical to the home delivery delivery system and has a weakness, namely the frequent occurrence of first-time delivery failures, causing both material and time losses for logistics companies. However, the CDP delivery system also has a weakness, namely the lack of flexibility in this delivery system, which requires the recipient to leave the house to pick up the order package. Therefore, this study suggests a new delivery system, namely a combined delivery system between home delivery and a CDP (Collection and Delivery Point) delivery system where the mini market is the location for pick up and drop packages.

This research will use Clustering Method, Geographic Information System (GIS) approach, and Traveling Salesman Problem (TSP). Where two clustering methods, namely K-Means Clustering and X-Means Clustering are used to create village clusters in the city of Jakarta, GIS to map the buffer zone between mini markets and residential areas, and TSP to create optimal route designs. In this study, there are four scenarios for making routes design, namely scenario 1 where the logistics company must serve all mini markets spread across West Jakarta and only serve the CDP delivery system, scenario 2 where mini markets spread across Jakarta are divided into several clusters and then create shipping routes, scenario 3 combines home delivery and CDP delivery systems, and scenario 4 where the delivery system is purely a home delivery service. This study also provides advice to logistics companies regarding when is the best time to send packages considering the level of congestion in the city of Jakarta using a simple simulation. The results of this study indicate that clustering is needed to improve the effectiveness and efficiency of shipping routes, this combined shipping system can be applied in Indonesia, especially in the city of Jakarta and generates quite good results, and a good package delivery time is 12:00 PM

Abstract i
Table of Contents ii
List of Tables iii
List of Figures v
Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objectives 4
1.3 Research Contribution 4
1.4 Research Gap 5
1.5 Research Scope 5
1.5.1 Geography Scope 5
1.5.2 Research Procedure 6
Chapter 2 Literature Review 9
2.1 Last Mile Delivery 9
2.1.1 Collection and Delivery Point (CDP) Method 10
2.2 Clustering Method 14
2.2.1 K-Means Clustering 14
2.2.2 X-Means Clustering 14
2.2.3 Approaches to Selecting the Right Number of Clusters 14
2.3 Geography Information System (GIS) 15
2.4 Traveling Salesman Problem (TSP) 17
Chapter 3 Methodology 19
3.1 Research Methodology 19
3.1.1 Geographic Information of Jakarta City 20
3.2 Model Assumption 24
3.2.1 Clustering Method 24
3.2.2 Buffer Area between Mini Market and Housing 25
3.2.3 Traveling Salesman Problem 26
3.2.4 Determining Best Delivery Time for Selected Mini Market and Housing 27
Chapter 4 Computational Result 29
4.1 Data Collection 29
4.2 Result 40
4.2.1 Clustering West Jakarta Village 40
4.2.2 Buffer Area between Mini Market and Housing Cluster 3 45
4.2.3 Traveling Salesman Problem 46
4.2.4 Determination of The Best Time for Package Delivery for Selected Mini Markets and Housing 70
Chapter 5 Conclusion and Suggestion 93
5.1 Conclusion 93
5.2 Contribution that Can be Implemented in Indonesia 93
5.3 Managerial Implication 94
5.4 Research Limitation 95
5.5 Suggestion for Future Research 95
References 97

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