scholarly journals Routing algorithms as tools for integrating social distancing with emergency evacuation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-Lin Tsai ◽  
Chetanya Rastogi ◽  
Peter K. Kitanidis ◽  
Christopher B. Field
Keyword(s):  
Reinforcement Learning ◽  
Emergency Evacuation ◽  
Decision Makers ◽  
Emergency Vehicle ◽  
Hurricane Evacuation ◽  
Routing Problem ◽  
Social Distancing ◽  
Sweep Algorithm ◽  
Time Required ◽  
Vehicle Capacity

AbstractOne of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

scholarly journals Routing Algorithms as Tools for Integrating Social Distancing with Emergency Evacuation

2021 ◽  
Author(s):  
Yi-Lin Tsai ◽  
Chetanya Rastogi ◽  
Peter K. Kitanidis ◽  
Christopher B. Field
Keyword(s):  
Vehicle Routing Problem ◽  
Emergency Evacuation ◽  
Routing Algorithms ◽  
Decision Makers ◽  
Emergency Vehicle ◽  
Routing Problem ◽  
Social Distancing ◽  
Sweep Algorithm ◽  
Time Required ◽  
Vehicle Capacity

Abstract We explore the implications of integrating social distancing with emergency evacuation, as would be expected when a hurricane approaches a city during the COVID-19 pandemic. Specifically, we compare DNN (Deep Neural Network)-based and non-DNN methods for generating evacuation strategies that minimize evacuation time while allowing for social distancing in emergency vehicles. A central question is whether a DNN-based method provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We describe the problem as a Capacitated Vehicle Routing Problem and solve it using a non-DNN solution (Sweep Algorithm) and a DNN-based solution (Deep Reinforcement Learning). The DNN-based solution can provide decision-makers with more efficient routing than the typical non-DNN routing solution. However, it does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

USULAN RUTE DISTRIBUSI MENGGUNAKAN ALGORITMA SWEEP DAN LOCAL SEARCH (STUDI KASUS DI PERUSAHAAN X)

2020 ◽  
Vol 6 (1) ◽  
pp. 40
Author(s):  
Muhamad Ruben ◽  
Arif Imran
Keyword(s):  
Local Search ◽  
Vehicle Routing Problem ◽  
Transportation Cost ◽  
Working Hours ◽  
Routing Problem ◽  
Local Searches ◽  
Sweep Algorithm ◽  
Vehicle Capacity ◽  
A Company ◽  
Better Than

A company has to deliver its product to thirty-five points/customers. In delivering process, there are limitations such as the number of vehicles, vehicle capacity, and driver's working hours. The company wants to find a better/shorter route in distributing its product to customer in order to minimize the transportation cost. The problem can considered as the Capacited Vehicle Routing Problem. In this paper, the Sweep algorithm are first used to find the initial solution. The local search procedures are used to the solution obtained by the Sweep algorithm. Four local searches are applied, 1-insertion intra-route, 1- insertion inter-route, 1-1 intra –route and 1-1 inter-route. The solutions produced by each local search are then compared, and the best one is selected. The best solution is given by the 1-Insertion intra-route procedure. It is 27,923 km shorter (15% better) than the current routes.

scholarly journals Emergency logistics vehicle routing optimization based on insufficient supply

2019 ◽  
Vol 136 ◽  
pp. 04068
Author(s):  
Yina Yuan ◽  
Xiaoguang Zhou ◽  
Mengke Yang
Keyword(s):  
Vehicle Routing ◽  
Optimization Method ◽  
Decision Makers ◽  
Emergency Vehicle ◽  
Routing Problem ◽  
Routing Optimization ◽  
Theoretical Support ◽  
Emergency Logistics ◽  
The Face ◽  
Vehicle Routing Optimization

In the face of various emergencies, emergency logistics vehicles are required to meet the needs of the affected areas in a short enough time. However, due to the suddenness of the incident and the shortage of relief supplies, it is necessary to further consider how to optimize the route of emergency vehicles in case of insufficient supply. In this paper, when the supply point is insufficient, the emergency vehicle routing can be optimized in the shortest possible time and at the same time to meet the requirements of the disaster site. By establishing the corresponding mathematical model and using the genetic algorithm to solve the relevant examples, the new solution is provided for the emergency logistics vehicle routing problem when the relief materials are insufficient. According to the analysis results of the example, the effectiveness of the optimization method is further demonstrated, and theoretical support is provided for relevant decision makers.

scholarly journals An estimation of the effects of social distancing measures on transit vehicle capacity and operations

2021 ◽  
Vol 10 ◽  
pp. 100398
Author(s):  
Camille Kamga ◽  
Rodrigue Tchamna ◽  
Patricio Vicuna ◽  
Sandeep Mudigonda ◽  
Bahman Moghimi
Keyword(s):  
Social Distancing ◽  
Vehicle Capacity

scholarly journals ANEGMA: an automated negotiation model for e-markets

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Pallavi Bagga ◽  
Nicola Paoletti ◽  
Bedour Alrayes ◽  
Kostas Stathis
Keyword(s):  
Reinforcement Learning ◽  
Deep Neural Network ◽  
Automated Negotiation ◽  
Learning To Learn ◽  
Negotiation Model ◽  
Negotiation Strategies ◽  
Exploration Time ◽  
Model Free ◽  
Bilateral Negotiations ◽  
Time Required

AbstractWe present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

scholarly journals A Deep Reinforcement Learning Approach for Global Routing

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Haiguang Liao ◽  
Wentai Zhang ◽  
Xuliang Dong ◽  
Barnabas Poczos ◽  
Kenji Shimada ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Integrated Circuits ◽  
Printed Circuit Boards ◽  
Greedy Algorithms ◽  
Global Routing ◽  
Hydraulic Systems ◽  
Routing Problem ◽  
Future Data ◽  
Circuit Components ◽  
Optimization Mechanism

Abstract Global routing has been a historically challenging problem in the electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems, and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and usually fail to solve sub-problems conjointly. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce a policy for routing based on the variety of problems, and it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in detail; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.

scholarly journals Clustering Control of Multi-Objective Problems with Application to E-commerce

2018 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Liang Chen ◽  
Xingwei Wang ◽  
Jinwen Shi
Keyword(s):  
Ant Colony Algorithm ◽  
Time Window ◽  
Ant Colony ◽  
Distribution Area ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Multi Objective ◽  
The Cost ◽  
Vehicle Capacity ◽  
Cost Of Transportation

In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.

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