Penyelesaian Vehicle Routing Problem with TIME Windows (VRPTW) Dengan Modified Differential Evolution Algorithm

This studydiscusses modification ofthe DifferentialEvolutionalgorithmto solve theVehicleRoutingProblem withTimeWindows(VRPTW). Algorithmdevelopmentis done byadding theinitialsolutiongeneratingtechnique. First initials solution generationtechniqueis use arandomfunction, then based onnearestneighbordistance (minimum distance). The next initials solution generationtechniqueis use solomon insertion. These resultsconfirmthat thedevelopment of algorithmscapable findingsolutionsthatdothe samewith thebestknownsolutions fromthe data usedasdata test, eitherthe number ofvehicles usedorthe resultingdistance. Modifieddifferentialevolutionalgorithmis able to workcompetitivelyin the solomon data test C105, C106, C107, C108 andC109with gapvalueof 0%

Penyelesaian Vehicle Routing Problem with Time Windows (VRPTW) dengan Modified Differential Evolution Algorithm

ABSTRAK   Penelitian ini membahas modifikasi algoritma  Differential Evolution  untuk menyelesaikan permasalahan Vehicle Routing Problem with Time Windows (VRPTW). Pengembangan algoritma dilakukan dengan jalan menambahkan teknik pembangkitan inisial solusi. Teknik pembangkitan insial solusi yang pertama adalah dengan menggunakan fungsi  random,kemudian menggunakan neighbor berdasarkan nearest distance (jarak terminimum). Sedangkan teknik pembangkitan solusi selanjutnya adalah dengan insersi solomon. Hasil penelitian ini mengkonfirmasikan bahwa pengembangan algoritma yang dilakukan mampu menemukan solusi yang sama dengan best known solusi dari data yang digunakan sebagai data uji, baik dari jumlah kendaraan yang digunakan ataupun jarak yang dihasilkan. Algoritma modified differential evolution mampu bekerja kompetitif pada data test solomon C105, C106, C107, C108 dan C109 dengan nilai gap sebesar 0%. Kata kunci: algoritma modified differential evolution, vrptw, random, nearest neighbor, insersi solomon.   ABSTRACT This studydiscusses modification ofthe DifferentialEvolutionalgorithmto solve theVehicleRoutingProblem withTimeWindows(VRPTW). Algorithmdevelopmentis done byadding theinitialsolutiongeneratingtechnique. First initials solution generationtechniqueis  use arandomfunction, then based onnearestneighbordistance (minimum distance).  The next initials solution generationtechniqueis use solomon insertion. These resultsconfirmthat thedevelopment of algorithmscapable findingsolutionsthatdothe samewith thebestknownsolutions fromthe data usedasdata test, eitherthe number ofvehicles usedorthe resultingdistance. Modifieddifferentialevolutionalgorithmis able to workcompetitivelyin the solomon data test C105, C106, C107, C108 andC109with gapvalueof 0%. Keyword: modified differentialevolution algorithm, vrptw,  random, nearestneighbor, solomon insertio

An Improved Whale Optimization Algorithm for Vehicle Routing Problem with Time Windows

The vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics operation management which attempts to establish routes for vehicles to deliver goods to customers. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within time window constraints. As the VRPTW is known to be NP-hard combinatorial problem, it is hard to be solved in reasonable computational time. Therefore, this paper proposes the modification of the whale optimization algorithm with local search to solve the VRPTW. The local search comprised 2-Operator and single insertion for solution improvement. Furthermore, the 2-Operator is used after the exploration phase and single insertion in the exploitation phase. The computational experiments were applied to Solomon’s instance that included small to large size problems. The experiment results show that the average gap of the total distance between the Best Known Solution (BKS) and the proposed solutions is within 5.82%. In addition, the best solution was found 29 out of 56 instances that is better than the PSO at 1.09%. This shows that this proposed provides a minimum value and outperforms other metaheuristics approaches.Keywords: Whale Optimization Algorithm; Vehicle Routing Problem; Time Constraint

Vehicle Coordinated Strategy for Vehicle Routing Problem with Fuzzy Demands

The vehicle routing problem with fuzzy demands (VRPFD) is considered. A fuzzy reasoning constrained program model is formulated for VRPFD, and a hybrid ant colony algorithm is proposed to minimize total travel distance. Specifically, the two-vehicle-paired loop coordinated strategy is presented to reduce the additional distance, unloading times, and waste capacity caused by the service failure due to the uncertain demands. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed approaches

Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data of Fangtianchong tunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction

Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem

Vehicle Routing Problem (VRP) like total routing distance, number of serve provisioning vehicles, and vehicles' waiting time are determined as the multi-objective constraints. Investigators pretend to handle these multi-constraint issues with the time window and fail to attain a prominent solution. Thus, there is a need for a global multi-objective vehicle routing solution. Here, a novel Particle Positioning Particle Swarm Optimization ( ) approach is designed to predict the robust route with the elimination of non-linearity measures. The linearity measure includes the movement of the vehicles, service time, and status of the move towards a particular direction. The lack of exploration and exploitation conditions during optimization is addressed with the inclusion of Grey Wolf Optimization (GWO). Therefore, the models attain a global solution with the least error rate. Simulation is done in MATLAB 2016b environment, and the experimental outcomes are compared with various approaches in large-scale and small-scale instances. The model intends to attain robustness and stability towards the measure in a linear manner. The model's time consumption and computational complexity are reduced with the adoption of a global routing-based optimization approach

Маршрутизація транспортних засобів з часовими вікнами

Магістерська дисертація: 108 с., 27 рис., 14 табл., 67 джерел. Актуальність. Згідно з 30м щорічним звітом з логістики від The Council of Supply Chain Management Professionals (CSCMP) [1] лише в США за рік було витрачено понад 1.64 трильйонів доларів на логістичні та транспортні операції, а кінцева вартість продукту, що потрапляє до споживача, може складатись до 70% з транспортних витрат. Тож перед галуззю транспортної логістики однією з важливіших задач постає економія ресурсів та мінімізація екологічного сліду при транспортуванні вантажів. Для вирішення цієї проблеми стоїть питання розробки алгоритмів та програмних продуктів, що будуть скорочувати маршрути транспортних засобів. В останні роки значно збільшилась частка персональних доставок, які прив’язанні до зайнятості клієнтів, актуальним є питання врахування часових вподобань одержувачів вантажу. Математичне формулювання цієї задачі відоме як задача маршрутизації транспортних засобів (далі VRP) з урахуванням часових вікон (далі VRPTW), яка накладає певні часові обмеження на обслуговування клієнтів транспортної мережі. Робота присвячена дослідженню та удосконаленню розв’язання задачі VRPTW. Зв'язок роботи з науковими програмами, планами, темами. Робота виконувалась на кафедрі автоматизованих систем обробки інформації та управління Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського» в рамках теми «Ефективні методи розв'язання задач теорії розкладів» (№ ДР 0117U000919). Мета роботи і завдання дослідження. Метою є підвищення ефективності методів розв’язання задачі маршрутизації транспортних засобів з часовими вікнами. Для досягнення поставленої мети необхідно вирішити такі завдання: -проаналізувати відомі результати розв’язання задачі маршрутизації транспортних засобів; -удосконалити існуючі алгоритми розв’язання задачі маршрутизації транспортних засобів з урахуванням часових вікон за рахунок модифікації та поєднання метаевристик; -розробити програмну реалізацію розроблених алгоритмів; -провести дослідження ефективності розроблених алгоритмів. Об’єкт дослідження – процес організації транспортних перевезень. Предмет дослідження – задача маршрутизації транспортних засобів з часовими вінками. Методи дослідження, застосовані в роботі, базуються на методах дослідження операцій, зокрема на метаевристичних алгоритмах. Наукова новизна отриманих результатів. Розроблені модифікований та гібридний алгоритми розв’язання задачі VRPTW. Публікації. Основні теоретичні та практичні положення викладено в матеріалах VI всеукраїнської науково-практичної конференції молодих вчених та студентів «Інформаційні системи та технології управління» (ІСТУ-2021).Master dissertation: 108 p., 27 fig., 14 tab, 67 sources. The relevance. According to the 30th Annual State of Logistics Report by the Council of Supply Chain Management Professionals (CSCMP) there was spent over 1.64 trillion on logistics and transportation operations in USA [1]. The final costs of the product that reaches the consumer can consist up to 70% of transportation costs. Therefore, one of the most important tasks for the transportation logistics industry is to save resources and minimize the environmental footprint during transportation of goods. To solve this problem, it is necessary to develop algorithms and software products that will decrease the routes of vehicles. In recent years significantly increased the proportion of personal delivery that employment associated with a client topical issue taking into account time preferences consignees. The mathematical formulation of this problem is known as the vehicle routing problem (VRP) with time windows (VRPTW), which imposes certain time constraints on the service of customers. The work is devoted to research and improvement of the VRPTW problem. The work is devoted to the study and improvement of solving VRPTW problem. Relationship of work with scientific programs, plans, themes. The work was done at the department of computer-aided management and data processing systems of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» within the theme «Effective methods for solving problems of scheduling theory» (№ DR 0117U000919). Purpose and objectives of the study. The goal of the research is to is to minimize the total cost of transportation of products to customers in a certain time period. To achieve this goal it is necessary to solve the following tasks: -to analyze known results of solving the Vehicle Routing Problem; -to improve the existing algorithms for solving the problem of vehicle routing considering time windows by modifying and combining metaheuristics; -to develop a software implementation of the developed algorithms; -to conduct research on the effectiveness of the developed algorithms. The object of study – the process organisation of transportation. Purpose of the study – Vehicle Routing Rroblem with Time Windows. Methods used in the paper are based on the methods of operations research, such as metaheuristics algorithms. Scientific novelty. New modified and hybrid algorithms developed for solving VRPTW. Publications. The results of the research were published in the materials of VI Ukrainian scientific and practical conference of young scientists and students "Information Systems and Management Technologies" (ISTU-2021)

Understanding Taxi Drivers’ Multi-day Cruising Patterns

This study investigates taxi drivers’ multi-day cruising behaviours with GPS data collected in Shenzhen, China. By calculating the inter-daily variability of taxi drivers’ cruising behaviours, the multi-day cruising patterns are investigated. The impacts of learning feature and habitual feature on multi-day cruising behaviours are determined. The results prove that there is variability among taxis’ day-to-day cruising behaviours, and the day-of-week pattern is that taxi drivers tend to cruise a larger area on Friday, and a rather focused area on Monday. The findings also indicate that the impacts of learning feature and habitual feature are more obvious between weekend days than among weekdays. Moreover, learning feature between two sequent weeks is found to be greater than that within one week, while the habitual feature shows recession over time. By revealing taxis\u27 day-to-day cruising pattern and the factors influencing it, the study results provide us with crucial information in predicting taxis\u27 multi-day cruising locations, which can be applied to simulate taxis\u27 multi-day cruising behaviour as well as to determine the traffic volume derived from taxis\u27 cruising behaviour. This can help us in planning of transportation facilities, such as stop stations or parking lots for taxis. Moreover, the findings can be also employed in predicting taxis\u27 adjustments of multi-day cruising locations under the impact of traffic management strategies

Vehicle Coordinated Strategy for Vehicle Routing Problem with Fuzzy Demands

The vehicle routing problem with fuzzy demands (VRPFD) is considered. A fuzzy reasoning constrained program model is formulated for VRPFD, and a hybrid ant colony algorithm is proposed to minimize total travel distance. Specifically, the twovehicle-paired loop coordinated strategy is presented to reduce the additional distance, unloading times, and waste capacity caused by the service failure due to the uncertain demands. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed approaches

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs