Optimization of Route Planning for the Package Delivery Problem Using Fuzzy Clustering

2021 ◽  
pp. 239-252
Author(s):  
Paula Hernández–Hernández ◽  
Norberto Castillo–García
Keyword(s):  
Fuzzy Clustering ◽  
Route Planning ◽  
Delivery Problem ◽  
Package Delivery

scholarly journals Battery-Aware Electric Truck Delivery Route Exploration

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2096
Author(s):  
Donkyu Baek ◽  
Yukai Chen ◽  
Naehyuck Chang ◽  
Enrico Macii ◽  
Massimo Poncino
Keyword(s):  
Heuristic Algorithms ◽  
Combustion Engine ◽  
Optimal Solution ◽  
Optimal Routing ◽  
Total Energy Consumption ◽  
Delivery Problem ◽  
Package Delivery ◽  
Delivery Route ◽  
The Traveling Salesman Problem ◽  
Critical Metric

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which use a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study.

Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions

2017 ◽  
Vol 36 (2) ◽  
pp. 231-258 ◽  
Author(s):  
Shayegan Omidshafiei ◽  
Ali–Akbar Agha–Mohammadi ◽  
Christopher Amato ◽  
Shih–Yuan Liu ◽  
Jonathan P How ◽  
...  
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Decision Processes ◽  
Delivery Problem ◽  
Package Delivery ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
High Level ◽  
Partially Observable ◽  
Multi Robot

This work focuses on solving general multi-robot planning problems in continuous spaces with partial observability given a high-level domain description. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This work extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) to take advantage of the high-level representations that are natural for multi-robot problems and to facilitate scalable solutions to large discrete and continuous problems. The Dec-POSMDP formulation uses task macro-actions created from lower-level local actions that allow for asynchronous decision-making by the robots, which is crucial in multi-robot domains. This transformation from Dec-POMDPs to Dec-POSMDPs with a finite set of automatically-generated macro-actions allows use of efficient discrete-space search algorithms to solve them. The paper presents algorithms for solving Dec-POSMDPs, which are more scalable than previous methods since they can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed algorithms are then evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent realistic problems and provide high-quality solutions for large-scale problems.

scholarly journals Research on Coordination and Optimization of Order Allocation and Delivery Route Planning in Take-Out System

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Guofeng Sun ◽  
Zhiqiang Tian ◽  
Renhua Liu ◽  
Yun Jing ◽  
Yawen Ma
Keyword(s):  
Time Window ◽  
Route Planning ◽  
Route Optimization ◽  
Dynamic Allocation ◽  
Improved Genetic Algorithm ◽  
Time Dimension ◽  
Order Allocation ◽  
Routing Problem ◽  
Delivery Problem ◽  
Delivery Route

This paper studies the take-out route delivery problem (TRDP) with order allocation and unilateral soft time window constraints. The TRDP considers the order allocation and delivery route optimization in the delivery service process. The TRDP is a challenging version of vehicle routing problem. In order to solve this problem, this paper aims to minimize the total cost of delivery, builds an optimization model of this problem by using cumulative time, and adds time dimension in order allocation and path optimization dimensions. It can not only track the real-time location of delivery personnel but also record the delivery personnel to perform a certain task. The main algorithm is the dynamic allocation algorithm designed from the perspective of dispatch efficiency, and the subalgorithm is the improved genetic algorithm. Finally, some experiments are designed to verify the effectiveness of the established model and the designed algorithm, the order allocation and route optimization are calculated with/without the consideration of traffic jam, and the results show that the algorithm can generate better solution in each scene.

scholarly journals DeepETA: A Spatial-Temporal Sequential Neural Network Model for Estimating Time of Arrival in Package Delivery System

2019 ◽  
Vol 33 ◽  
pp. 774-781
Author(s):  
Fan Wu ◽  
Lixia Wu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Time Of Arrival ◽  
Delivery Time ◽  
Delivery Problem ◽  
Package Delivery ◽  
Fast Development ◽  
Delivery Routes ◽  
Fully Connected

Over 100 million packages are delivered every day in China due to the fast development of e-commerce. Precisely estimating the time of packages’ arrival (ETA) is significantly important to improving customers’ experience and raising the efficiency of package dispatching. Existing methods mainly focus on predicting the time from an origin to a destination. However, in package delivery problem, one trip contains multiple destinations and the delivery time of all destinations should be predicted at any time. Furthermore, the ETA is affected by many factors especially the sequence of the latest route, the regularity of the delivery pattern and the sequence of packages to be delivered, which are difficult to learn by traditional models. This paper proposed a novel spatial-temporal sequential neural network model (DeepETA) to take fully advantages of the above factors. DeepETA is an end-to-end network that mainly consists of three parts. First, the spatial encoding and the recurrent cells are proposed to capture the spatial-temporal and sequential features of the latest delivery route. Then, two attention-based layers are designed to indicate the most possible ETA from historical frequent and relative delivery routes based on the similarity of the latest route and the future destinations. Finally, a fully connected layer is utilized to jointly learn the delivery time. Experiments on real logistics dataset demonstrate that the proposed approach has outperforming results.

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State
Sign in / Sign up

Export Citation Format

Share Document