G-VRP-IPD-TW

This is a data repository of the generated instances for a Green Vehicle Routing Problem with Integrated Pickup and Delivery and Time Windows (G-VRP-IPD-TW) model. The geographic information data is collected in real time through Amap based on a county-level city in China.

-- If you use this data in your scientific article, please cite our SEPS 2023 paper:
Tiannuo Yang, Zhongzhu Chu, Bailin Wang. Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model. DOI: https://doi.org/10.1016/j.seps.2023.101665

Background

Multimodal transport synergistically integrates passenger and freight demand, easing the economic pressure on public transport operators by reducing transportation cost. However, the occurrence of on-demand service changes the nature of scheduling approach during the transportation. In our work, we propose a demand-driven passenger-and-freight-integration service mode (DDPFIS mode) according to the characteristics in rural areas and construct a Green Vehicle Routing Problem with Integrated Pickup and Delivery and Time Windows (G-VRP-IPD-TW) model to assist public transport operators in routing decisions.

This repository records the problem instances generated dedicated to the above problem, classified with 6 experiments in our work. The geographic data is collected in real-time in Longkou, China. We mainly generate 4 sets of instances (sizes of 8, 12, 16 and 20). Other settings could be seen in Section 5.1 in our paper.

All numerical results are organized in file results.xlsx.

Usage

Naming Rule

Instances in our repository are named as:

Num_Type_TW_Mode_C2_Demand_Index.json

For example, an instance with Twenty service stations; Station ratio of 5:5:10; Time windows tightness of 0.1; Integration transportation mode; Initial unit passenger service cost; Initial demand amount; Index 1 for current setup; can be represented as:

20_5510_1_integration_1_1_1.json 

Attributes

Each instance mainly includes the following attributes:

• "SER_NUM": number of the service stations
• "VEH_NUM": maximum number of the vehicles availbale
• "c_1": unit fuel cost
• "c_2": unit passenger service cost
• "c_3": unit vehicle fixed cost
• "c_4": driver’s wage per minute
• "phi_0": fuel consumption rate with no load
• "yita": fuel consumption coefficient per unit load weight
• "omega": average weight of passengers
• "Q": maximum load of the vehicle, including the weight of passengers and freight
• "Q_h": maximum passenger capacity of the vehicle
• "M": an arbitrary large constant
• "N_s": set of all service stations
• "N": set of all staions (include service center)
• "K": set of all vehi
• "ser_node": service nodes chosen from all data
• "ser_class": corresponding class of service stations
• "d_h_i": number of boarding passengers at i
• "p_h_i": number of alighting passengers at i
• "d_g_i": weight of deliveredfreight at i
• "p_g_i": weight of picked up freight at i
• "D_h": total number of boarding passengers
• "P_h": total number of alighting passengers
• "D_g": total weight of deliveredfreight
• "P_g": total weight of picked up freight
• "s_i": service duration at i
• "a_i": early limit of time window at i
• "b_i": late limit of time window at i
• "delta_ij": distance from i to j
• "t_ij": travel time from i to j

Contributors

This work is contributed by these scholars:
*Tiannuo Yang, tiannuo_yang@126.com
Zhongzhu Chu, chuzhongzhu@126.com
Bailin Wang, wangbl@ustb.edu.cn

Feel free to discuss article-related issues with us.

Acknowledgments

We sincerely thank our friend, Dongxu Wang, who unfortunately passed away during the COVID-19 pandemic, for his valuable assistance in reviewing and polishing the paper.