Attention-Based Neural Network for Solving the Green Vehicle Routing Problem in Waste Management

23.08.23: Trekkes tilbake fra visning som løsning på at oppgaven ble ferdigstilt fra studieadministrasjonen litt for fort/IHTIThe transport sector is a major contributor to the emission of greenhouse gases and air pollution. As urbanization and population growth continue to increase, the demand for transportation services grows, emphasizing the need for sustainable practices. Therefore, incorporating sustainability into the transport sector can effectively reduce its negative impacts on the environment and optimize the utilization of resources. This thesis aims to address this issue by proposing a novel method that integrates neural networks into the development of a green vehicle routing model. By incorporating environmental considerations, particularly fuel consumption, into the optimization process, the model seeks to generate more sustainable route solutions. The integration of machine learning techniques, specifically an attention-based neural network, demonstrates the potential of combining machine learning with operations research for effective route optimization. While the effectiveness of the green vehicle routing problem (GVRP) has been demonstrated in providing sustainable routes, its practical applications in real-world scenarios are still limited. Therefore, this thesis proposes the implementation of the GVRP model in a real-world waste collection routing problem. The study utilizes data obtained from Remiks, a waste management company responsible for waste collection and handling in Tromsø and Karlsøy. The findings of this study highlight the promising synergy between machine learning and operations research for further advancements and real-world applications. Specifically, the application of the GVRP approach to waste management issues has been shown to reduce emissions during the waste collection process compared to routes optimized solely for distance minimization. The attention-based neural network approach successfully generates routes that minimize fuel consumption, outperforming distance-optimized routes. These results underscore the importance of leveraging the GVRP to address environmental challenges while enhancing decision-making efficiency and effectiveness. Overall, this thesis provides insights for developing sustainable and optimized routes for real-world problems

GIS Oriented Service Optimization Tool For Fecal Sludge Collection

In developing countries most of the urban dwellers don’t have access to sewer system. People are mostly using “onsite” systems such as septic tanks or pit latrines that need to be emptied periodically, as the densely built urban environment won’t allow new pits to be dug every time they fill up. In the conventional fecal sludge collection systems, authorities are collecting the sludge from house to house and dump on the plant. Fecal sludge collection system is different from traditional vehicle routing and even from solid waste collection system in terms of dynamic collection points, urgency of getting the service and diversity of demand. Due to those vibrant factors authorities are facing proper networking and management problems. This research describes algorithms that can accommodate constraints and prioritized customers who need immediate service. The GPS log data of the fecal sludge collection truck that maintained by Nonthaburi Municipality, Thailand has been considered as the base data during the development of this application. Spatial analysis has been done using Geographic Information Systems (GIS).Tabu Search has been implemented in order to optimize. Basically two algorithms were produced for assisting fecal sludge collection systems. First algorithm was able to produce multiple trip for each vehicle if required considering all the customers having equal priority, time window. The second one was able to perform optimization that can accommodate priority along with the first one. Input for the algorithms were very simple; distance matrix having distance between each customers and plant, customer order with latitude, longitude, order unit, time window, priority and vehicles with capacity. Algorithms were able to produce better result than the actual operation or even from shortest path algorithm in term of distance. After optimization, efficiency of the algorithms were tested with the actual travelling distance. Travelling distance were reduced to half compare to actual cost and it ensured maximum utilization of vehicle capacity by allocating maximum number of customers in each route

Application of “Travelling Salesman Problem” in Dynamic Programming for Efficient and Cost Effective Route Design for Distribution: Case of Chifles Distribution of ORFI Company, Ecuador

oai:ojs.pkp.sfu.ca:article/66Purpose: The present study is designed to optimize a route for the distribution of chifles of the ORFI Company in school bars within the Río Verde parish of the Santo Domingo City, Republic of Ecuador. Methodology: Time and distance data were collected regarding the route of the vendors, then distance matrices were developed between distribution points, and a graph was designed to find the best route using the Bellman-Hell-Karp algorithm. Results: The sector graph had 46 nodes and the optimal route was found by applying dynamic programming with the Held-Karp mathematical algorithm, the optimal route for the ORFI company chifles distribution is: 1-2-3-4-5-8-7-6-10-9-12-11-13-14-15-16-18-17-19-20-21-22-23-24-25-26-31-27-28-29-30-32-33-34-38-35-36-37-39-40-41-42-43-44-45-46-1, with 23089 meters of distance, optimizing in travel time 15% and travel distance 11%. Implications: The application of the findings is expected to reduce the cost and time of distribution expended by the OFRI Company. Similar approaches also can be applied by other companies operating in the city

Application of “Travelling Salesman Problem” in Dynamic Programming for Efficient and Cost Effective Route Design for Distribution: Case of Chifles Distribution of ORFI Company, Ecuador

Purpose: The present study is designed to optimize a route for the distribution of chifles of the ORFI Company in school bars within the Río Verde parish of the Santo Domingo City, Republic of Ecuador. Methodology: Time and distance data were collected regarding the route of the vendors, then distance matrices were developed between distribution points, and a graph was designed to find the best route using the Bellman-Hell-Karp algorithm. Results: The sector graph had 46 nodes and the optimal route was found by applying dynamic programming with the Held-Karp mathematical algorithm, the optimal route for the ORFI company chifles distribution is: 1-2-3-4-5-8-7-6-10-9-12-11-13-14-15-16-18-17-19-20-21-22-23-24-25-26-31-27-28-29-30-32-33-34-38-35-36-37-39-40-41-42-43-44-45-46-1, with 23089 meters of distance, optimizing in travel time 15% and travel distance 11%. Implications: The application of the findings is expected to reduce the cost and time of distribution expended by the OFRI Company. Similar approaches also can be applied by other companies operating in the city

Application of “Travelling Salesman Problem” in Dynamic Programming for Efficient and Cost Effective Route Design for Distribution

oai:ojs2.riiopenjournals.com:article/66Purpose: The present study is designed to optimize a route for the distribution of chifles of the ORFI Company in school bars within the Río Verde parish of the Santo Domingo City, Republic of Ecuador. Methodology: Time and distance data were collected regarding the route of the vendors, then distance matrices were developed between distribution points, and a graph was designed to find the best route using the Bellman-Hell-Karp algorithm. Results: The sector graph had 46 nodes and the optimal route was found by applying dynamic programming with the Held-Karp mathematical algorithm, the optimal route for the ORFI company chifles distribution is: 1-2-3-4-5-8-7-6-10-9-12-11-13-14-15-16-18-17-19-20-21-22-23-24-25-26-31-27-28-29-30-32-33-34-38-35-36-37-39-40-41-42-43-44-45-46-1, with 23089 meters of distance, optimizing in travel time 15% and travel distance 11%. Implications: The application of the findings is expected to reduce the cost and time of distribution expended by the OFRI Company. Similar approaches also can be applied by other companies operating in the city

Efficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithm

Efficient routing optimization yields benefits that extend beyond mere financial gains. In this thesis, we present a methodology that utilizes a graph convolutional neural network to facilitate the development of energy-efficient waste collection routes. Our approach focuses on a Waste company in Tromsø, Remiks, and uses real-life datasets, ensuring practicability and ease of implementation. In particular, we extend the dpdp algorithm introduced by Kool et al. (2021) [1] to minimize fuel consumption and devise routes that account for the impact of elevation and real road distance traveled. Our findings shed light on the potential advantages and enhancements these optimized routes can offer Remiks, including improved effectiveness and cost savings. Additionally, we identify key areas for future research and development

OPTIMIZING THE PROCESS OF PICK-UP AND DELIVERY WITH TIME WINDOWS USING ANT COLONY AND TABU SEARCH ALGORITHMS

The provision of goods shuttle services sometimes faces several constraints, such as the limitation on the number of vehicles, vehicle capacity, and service time, or the vehicle used has single transport access. To avoid losses, a strategy is needed in determining the optimal route and policy for arranging goods in the vehicle especially if there are two types of goods involved. Traveling Salesman Problem and Pick-up and Delivery with Handling Costs and Time Windows (TSPPDHTW) is a model of an optimization problem that aims to minimize the total travel and goods handling costs in the goods pick-up and delivery with the constraints previously mentioned. Solving that model using the exact method requires a very long computation time so it’s not effective to be implemented in real-life. This study aims to develop a (meta)heuristic based on Ant Colony Optimization (ACO) and Tabu Search (TS) to be ACOTS to solve TSPPDHTW with reasonable computation time. The development is carried out by adding functions of clustering, evaluating constraints, cutting tours, arranging of goods, and evaluating moves on the TS, as well as modifying transition rules. The result has a deviation of about 22% and 99.99% less computational time than the exact method

Sustainable urban delivery: the learning process of path costs enhanced by information and communication technologies

Today, local administrations are faced with the presence of greater constraints in terms of the use of space and time. At the same time, large amount of data is available to fleet managers that can be used for controlling their fleets. This work is set in the context defined by sustainable city logistics, and information and communication technologies (ICTs), to formalize the three themes of the smart city (transport, ICTs and energy savings) in a single problem. Following this, the main purpose of the study is to propose a unified formulation of the basic problem of fleets, i.e., the traveling salesman problem (TSP), which explicitly includes the use of emerging information and communication technologies (e-ICTs) pointing out the learning process of path costs in urban delivery. This research explores the opportunity to extend the path cost formation with a within-day and day-to-day learning process, including the specification of the attributes provided by e-ICTs. As shown through a real test case, the research answers to queries coming from operators and collectivities to improve city liveability and sustainability. It includes both economic sustainability for companies/enterprises and environmental sustainability for local administrations (and collectivities). Besides contributing to reduce the times and kms travelled by commercial vehicles, as well as the interference of freight vehicles with other traffic components, it also contributes to road accident reduction (social sustainability). Therefore, after the re-exanimation of TSP, this paper presents the proposed unitary formulation and its benefits through the discussion of results obtained in a real case study. Finally, the possible innovation guided by e-ICT is pointed out