Tourism Route Planning Route Based on Tourist Category using Orientee ring Problem with Time Windows

2019 ◽  
Vol 1 (1) ◽  
pp. 42-49
Author(s):  
Indri Hapsari ◽  
◽  
Hazrul Is wadi ◽  
Yosvaldo Ongko Cahyadi ◽  
◽  
...  
Keyword(s):  
Time Windows ◽  
Route Planning ◽  
Ring Problem

Recent advances on urban taxi sharing systems with the path arrival probability

2020 ◽  
Vol 13 ◽  
Author(s):  
Dui Hongyan ◽  
Zhang Chi
Keyword(s):  
Failure Probability ◽  
Traffic Congestion ◽  
Optimal Strategy ◽  
Time Windows ◽  
Optimal Path ◽  
Route Planning ◽  
Driving Time ◽  
Load Rate ◽  
Travel Efficiency ◽  
Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

scholarly journals Implementasi Honey Bee Mating Optimization Pada Vehicle Routing Problem With Time Windows Dalam Perencanaan Jalur Wisata Malang

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 495
Author(s):  
M Syawaluddin Putra Jaya ◽  
Yufiz Azhar ◽  
Nur Hayatin
Keyword(s):  
Honey Bee ◽  
Royal Jelly ◽  
Time Windows ◽  
Route Planning ◽  
Routing Problem ◽  
Capacity Limit ◽  
Shortest Route ◽  
Mutation Ratio ◽  
Bee Colonies ◽  
Honey Bee Mating Optimization

Abstrak Vahicle Routing Problem adalah suatu masalah pencaian jalur yang akan dilalui dengan tujuan mencari rute yang paling cepat atau pendek. Vahicle Routing Problem with Time Windows (VRPTW) yang merupakan sebutan bagi VRP dengan kendala tambahan berupa adanya time windows pada masing-masing pelanggan yang dalam hal ini berupa destinasi wisata. Dalam penelitian ini diterapkan Honey Bee Mating Optimization (HBMO) dalam menyelesaikan VRPTW. HBMO sendiri terinspirasi oleh perilaku koloni lebah ketika bereproduksi. Algoritma tersebut bertujuan untuk mengevaluasi pencarian individu atau solusi terbaik. Tujuan dari penelitian ini adalah bagaimana mengimplementasikan Honey Bee Mating Optimization dalam menyelesaikan VRPTW pada perencanaan jalur wisata di Malang. Sehingga dapat meminimumkan waktu dan jarak tempuh perjalanan. Berdasarkan hasil pengujian, parameter yang optimal untuk optimasi VRPTW menggunakan HBMO pada kasus perencannan jalur wisata Malang yaitu dengan menggunakan 800 generasi, populasi lebah jantan sebesar 300, batas kapasitas spermatheca sejumlah 100, nilai mutation ratio (Pm) dan royal jelly masing-masing bernilai 0.5.Abstract Vahicle Routing Problem is a problem of finding the best route that will be passed with the purpose to finding the fastest or shortest route. Vahicle Routing Problem with Time Windows (VRPTW) is a part of VRP with additional obstacles in the form of time windows in each customer. In this research, Honey Bee Mating Optimization (HBMO) was applied to completing VRPTW. HBMO itself was inspired by the behavior of bee colonies when reproducing. The purpose of this algorithm is to evaluate the best individual or the best solutions. The purpose of this research is how to implement Honey Bee Mating Optimization to completing VRPTW in Malang tourism route planning. So that it can minimize travel time and distance. Based on the results of the testing, the optimal parameters for VRPTW optimization using HBMO in Malang tourism route planning case are using 800 generations, the male bee population is 300, the capacity limit of spermatheca is 100, the mutation ratio (Pm) and royal jelly are respectively 0.5.

scholarly journals Modified ALNS Algorithm for a Processing Application of Family Tourist Route Planning: A Case Study of Buriram in Thailand

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 23
Author(s):  
Narisara Khamsing ◽  
Kantimarn Chindaprasert ◽  
Rapeepan Pitakaso ◽  
Worapot Sirirak ◽  
Chalermchat Theeraviriya
Keyword(s):  
Processing Time ◽  
Time Windows ◽  
Route Planning ◽  
Exact Method ◽  
Neighborhood Search ◽  
Large Problem ◽  
Processing Application ◽  
Tourist Attractions ◽  
Time Required

This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied here. The solution finding performance of the MALNS method is compared with an exact method running on the Lingo program. As shown by various solutions, the MALNS method can balance travel routing designs, including when many tourist attractions are present in each path. Furthermore, the results of the MALNS method are not significantly different from the results of the exact method for small problem sizes. For medium and large problem sizes, the MALNS method shows a higher performance and a smaller processing time for finding solutions. The values for the average total travel cost and average travel satisfaction rating derived by the MALNS method are approximately 0.18% for a medium problem and 0.05% for a large problem, 0.24% for a medium problem, and 0.21% for a large problem, respectively. The values derived from the exact method are slightly different. Moreover, the MALNS method calculation requires less processing time than the exact method, amounting to approximately 99.95% of the time required for the exact method. In this case study, the MALNS algorithm result shows a suitable balance of satisfaction and number of tourism places in relation to the differences between family members of different ages and genders in terms of satisfaction in tour route planning. The proposed solution methodology presents an effective high-quality solution, suggesting that the MALNS method has the potential to be a great competitive algorithm. According to the empirical results shown here, the MALNS method would be useful for creating route plans for tourism organizations that support travel route selection for family tours in Thailand.

scholarly journals Evolutionary Algorithm for the Electric Vehicle Routing Problem with Battery Degradation and Capacitated Charging Stations

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Juan Pablo Futalef ◽  
Diego Muñoz-Carpintero ◽  
Heraldo Rozas ◽  
Marcos Orchard
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Combustion Engine ◽  
Time Windows ◽  
Route Planning ◽  
Routing Problem ◽  
Charging Infrastructure ◽  
Conventional Vehicle ◽  
Battery Degradation

As CO2 emission regulations increase, fleet owners increasingly consider the adoption of Electric Vehicle (EV) fleets in their business. The conventional Vehicle Routing Problem (VRP) aims to find a set of routes to reduce operational costs. However, route planning of EVs poses different challenges than that of Internal Combustion Engine Vehicles (ICEV). The Electric Vehicle Routing Problem (E-VRP) must take into consideration EV limitations such as short driving range, high charging time, poor charging infrastructure, and battery degradation. In this work, the E-VRP is formulated as a Prognostic Decision-Making problem. It considers customer time windows, partial midtour recharging operations, non-linear charging functions, and limited Charge Station (CS) capacities. Besides, battery State of Health (SOH) policies are included in the E-VRP to prevent early degradation of EV batteries. An optimization problem is formulated with the above considerations, when each EV has a set of costumers assigned, which is solved by a Genetic Algorithm (GA) approach. This GA has been suitably designed to decide the order of customers to visit, when and how much to recharge, and when to begin the operation. A simulation study is conducted to test GA performance with fleets and networks of different sizes. Results show that E-VRP effectively enables operation of the fleet, satisfying all operational constraints.

Assessing Customer Service Reliability in Route Planning with Self-Imposed Time Windows and Stochastic Travel Times

2019 ◽  
Vol 53 (1) ◽  
pp. 256-281 ◽  
Author(s):  
Anastasios D. Vareias ◽  
Panagiotis P. Repoussis ◽  
Christos D. Tarantilis
Keyword(s):  
Customer Service ◽  
Time Windows ◽  
Route Planning ◽  
Service Reliability ◽  
Travel Times ◽  
Stochastic Travel Times

scholarly journals UAV Mission Planning with SAR Application

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1080 ◽  
Author(s):  
Wojciech Stecz ◽  
Krzysztof Gromada
Keyword(s):  
Time Window ◽  
Scheduling Algorithm ◽  
Time Windows ◽  
Route Planning ◽  
Mission Planning ◽  
Mixed Integer ◽  
Technical Parameters ◽  
Aerial Vehicle ◽  
Flight Route ◽  
Electronic Intelligence

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

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