A mobile and interactive multiobjective urban tourist route planning system

2017 ◽  
Vol 9 (1) ◽  
pp. 129-144 ◽  
Author(s):  
Inmaculada Ayala ◽  
Lawrence Mandow ◽  
Mercedes Amor ◽  
Lidia Fuentes
Keyword(s):  
Route Planning ◽  
Planning System

A Practical Delivery Route Planning System

2019 ◽  
Author(s):  
Asger Gitz-Johansen ◽  
Mikkel Elkjaer Holm ◽  
Laurids Vinther Kirkeby ◽  
Dan Kristiansen ◽  
Alexander Stoica Ostenfeld ◽  
...  
Keyword(s):  
Route Planning ◽  
Planning System ◽  
Delivery Route

PERMS: An efficient rescue route planning system in disasters

2021 ◽  
pp. 107667
Author(s):  
Xiaolong Xu ◽  
Lei Zhang ◽  
Marcello Trovati ◽  
Francesco Palmieri ◽  
Eleana Asimakopoulou ◽  
...  
Keyword(s):  
Route Planning ◽  
Planning System

scholarly journals Route planning module as a part of Supply Chain Management system

2012 ◽  
Vol 60 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Michal Košíček ◽  
Radek Tesař ◽  
František Dařena ◽  
Roman Malo ◽  
Arnošt Motyčka
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Route Planning ◽  
Planning System ◽  
Functional Requirements ◽  
Chain Management ◽  
Functional Specification ◽  
Supply Chain Management System ◽  
Basic Features ◽  
The Right

Today, the demand for creating a systematic approach for managing sales, ordering, and logistics has increased. Supply Chain Management (SCM) is one of the responses to problems that have arose with the need for managing complex supply chains. Nowadays, most of the activities of Supply Chain Management is realized or supported with computing technologies. Route planning is an important part of Supply Chain Management related to both procurement and distribution. Route planning systems specify the sequences in which the selected transport vehicles should supply the demand points by requested quantities of goods at the right time. The paper is focused on the analysis of a route planning system which could be used as a part of Supply Chain Management information system or as a standalone application. It describes basic techniques and frameworks of transportation problems as well as important functional requirements, considering recent trends in the field of distribution planning. As a result, functional specification of basic features and other components of system are provided. The paper is a result of a joint initiative of the authors and a vendor of business information systems.

scholarly journals Fuzzy polychromatic sets constraint model for route planning problem

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142095924
Author(s):  
Cai Chao ◽  
Gong Zhi Xing ◽  
Qin Xiao Wei ◽  
Zhou Qiu Shi ◽  
Sun Xi Xia
Keyword(s):  
Mutual Coupling ◽  
Optimization Problem ◽  
Route Planning ◽  
Planning System ◽  
Planning Problem ◽  
Multiobjective Optimization Problem ◽  
Constraint Condition ◽  
Operation Rules ◽  
Aerial Vehicle ◽  
Constraint Model

Unmanned aerial vehicle route planning is a complex multiconstrained multiobjective optimization problem. Due to the complexity of various constraints and the mutual coupling between them, the expression of constraint conditions is not universal and normative. The development, maintenance, and upgrading of an existing route planning system are very difficult. In this article, by establishing the polychromatic sets of aircraft, aircraft equipment, and flight actions, creating the fuzzy relational matrix between equipment and actions and between actions and actions, this article realizes the standardized and generalized expression of the constraint condition of the route planning problem. Then the analysis and inspection of the constraint conditions are realized by the polychromatic sets operation rules.

scholarly journals A Dynamic Route-Planning System Based on Industry 4.0 Technology

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Industry 4.0 ◽  
Big Data Analytics ◽  
Route Planning ◽  
Research Area ◽  
Automated Guided Vehicles ◽  
Planning System ◽  
Routing Problem ◽  
Dynamic Route

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

scholarly journals Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

2020 ◽  
Vol 13 (8) ◽  
pp. 1705-1726
Author(s):  
Theresia Perger ◽  
Hans Auer
Keyword(s):  
Energy Consumption ◽  
San Francisco ◽  
Electric Vehicles ◽  
Optimization Problem ◽  
Route Planning ◽  
Planning System ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Battery Lifetime ◽  
Multi Objective

Abstract In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

A quantitative research for determining the user requirements for an innovative route planning system targeted for the commercial vehicle fleets

2021 ◽  
Author(s):  
Florian Anghelache ◽  
Dan Alexandru Mitrea ◽  
Nicolae Goga ◽  
Andrei Vasilateanu ◽  
Vladut Radulescu ◽  
...  
Keyword(s):  
Quantitative Research ◽  
Commercial Vehicle ◽  
Route Planning ◽  
Planning System ◽  
User Requirements

Implementation and applications of harvest fleet route planning

2021 ◽  
Author(s):  
◽  
Andrés Villa Henriksen
Keyword(s):  
Internet Of Things ◽  
Soil Compaction ◽  
Production Efficiency ◽  
Route Planning ◽  
Theoretical Computer Science ◽  
Planning System ◽  
Practical Implementation ◽  
System Perspective ◽  
Selective Harvest ◽  
Arable Farming

In order to support the growing global population, it is necessary to increase food production efficiency and at the same time reduce its negative environmental impacts. This can be achieved by integrating diverse strategies from different scientific disciplines. As agriculture is becoming more data-driven by the use of technologies such as the Internet of Things, the efficiency in agricultural operations can be optimised in a sustainable manner. Some field operations, such as harvesting, are more complex and have higher potential for improvement than others, as they involve multiple and diverse vehicles with capacity constraints that require coordination. This can be achieved by optimised route planning, which is a combinatorial optimisation problem. Several studies have proposed different approaches to solve the problem. However, these studies have mainly a theoretical computer science perspective and lack the system perspective that covers the practical implementation and applications of optimised route planning in all field operations, being harvesting an important example to focus on. This requires an interdisciplinary approach, which is the aim of this Ph.D. project. The research of this Ph.D. study examined how Internet of Things technologies are applied in arable farming in general, and in particular in optimised route planning. The technology perspective of the reviewing process provided the necessary knowledge to address the physical implementation of a harvest fleet route planning tool that aims to minimise the total harvest time. From the environmental point of view, the risk of soil compaction resulting from vehicle traffic during harvest operations was assessed by comparing recorded vehicle data with the optimised solution of the harvest fleet route planning system. The results showed a reduction in traffic, which demonstrates that these optimisation tools can be part of the soil compaction mitigation strategy of a farm. And from the economic perspective, the optimised route planner of an autonomous field robot was employed to evaluate the economic consequences of altering the route in selective harvesting. The results presented different scenarios where selective harvest was not economically profitable. The results also identified some cases where selective harvest has the potential to become profitable depending on grain price differences and operational costs. In conclusion, these different perspectives to harvest fleet route planning showed the necessity of assessing future implementation and potential applications through interdisciplinarity.

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