Collaborative Two-Echelon Location Routing Problem under Demand Uncertainty: A Case Study of Yogyakarta, Indonesia

• Main Author: Ramadhani Nur
• Other Authors: Vincent F. Yu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/8t38eq

碩士 === 國立臺灣科技大學 === 工業管理系 === 107 === A significant increase in urban population causes demand for daily needs to rise. To fulfill all the order request, logistic companies are required to be more responsive and flexible. To maximize their profit, each company will try to use its resources efficiently in distributing goods to customers. A conventional way is to dispatch more vehicles operating around the city center. Consequently, more logistic activities are expected to happen, which might be accompanied by the occurrence of freight transport issues, such as traffic congestion, excess emission, noise, and crashes. Enabling collaboration between logistic companies and setting up intermediate facilities (satellites) in the city can be considered as alternatives to overcome the problems above. Previous researches have claimed that the implementation of both strategies might help to reduce the volume of vehicles traveling around the urban area. Given the positive impacts, this study applies a collaborative strategy along with the use of intermediate facilities to solve logistical problems in the Special Region of Yogyakarta, as one of the most densely populated cities in Indonesia. However, in solving real-world problems, there are daily uncertainties that need to be considered, such as variability in demand. To capture demand variability, two-stage stochastic programming with recourse model is developed. This research proposes a mathematical programming model for collaborative two-echelon location routing problem under demand uncertainty and uses CPLEX to solve small instances. A simulated annealing algorithm is employed to solve bigger problems. To evaluate the performance of the collaborative strategy, a comparative study between collaborative and non-collaborative scenario is conducted. The result shows that a collaborative approach can reduce the total distribution cost along with the decrement in total traveled distances and the number of utilized resources. Moreover, several cost allocation mechanisms based on cooperative game theory are presented to give a better overview of collaborative logistics as well as provide more options to allocate the post-collaboration cost among companies involved.