A Multiobjective Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Time Windows

Abstract

The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and metaheuristic algorithms have been developed to solve the various vehicle routing problems, including the VRPTW, only a few of them face these problems as multiobjective. In the present paper, a Multiobjective Large Neighborhood Search (MOLNS) algorithm is developed to solve the VRPTW. The algorithm is implemented using the Python programming language, and it is evaluated in Solomon’s 56 benchmark instances with 100 customers, as well as in Gehring and Homberger’s benchmark instances with 1000 customers. The results obtained from the algorithm are compared to the best-published, in order to validate the algorithm’s efficiency and performance. The algorithm is proven to be efficient both in the quality of results, as it offers three new optimal solutions in Solomon’s dataset and produces near optimal results in most instances, and in terms of computational time, as, even in cases with up to 1000 customers, good quality results are obtained in less than 15 min. Having the potential to effectively solve real life distribution problems, the present paper also discusses a practical real-life application of this algorithm.

Selection of the Transport Mode Using the AHP Method Within Distribution Logistics of Motor Fuels

“…The complexity of providing timely and cost-effective distribution logistics of finished goods from industrial facilities to customers makes effective operational coordination difficult; on the other side, effectiveness is crucial for maintaining customer service levels and sustaining a business [12]. Recently, there have been efforts to achieve more effective and less polluting freight transportation systems; an advanced system which uses advanced methods to solve vehicle routing and scheduling problems, considering a plethora of constraints has been developed within a research project co-funded by Greece and the European Union [13,14]. Big companies often plan distribution logistics operations based on their empirical experience, without the use of optimisation methods, because adequate methods or tools, for example for selecting a suitable mode of transport, supplier, etc., are not sufficient [15].…”

An Application of an Urban Freight Transportation System for Reduced Environmental Emissions

“…This system operates in a cloud environment and it is offered as a service to potentially interested companies in order to efficiently plan their deliveries and perform the routing of their vehicles based on various parameters, such as time-windows, distribution costs, and environmental emissions. The system uses advanced methods to solve vehicle routing and scheduling problems, taking into account a plethora of constraints [26,27]. The main goal, however, in the research project's context, is to minimize the environmental impact of urban freight transports.…”

A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series

“…Recent trends have showed a great need for the adoption of intelligent transport systems (ITS), especially in metropolises. This would generate various impacts on both passenger transport and freight logistics [1][2][3], which helps to reduce traffic emissions and energy consumption [4][5][6][7]. Moreover, train transport plays an essential role in a multimodal transport system for both inter-city and intra-city travelers.…”

“…In this paper, we propose a comprehensive architecture of deep learning methodology for long-term train delay prediction. The contributions of this paper are summarized as: (1) The causes of train delay, run-time delay and dwell delay are accurately investigated and distinguished. We aim to predict the running time and dwell time instead of performing train delay prediction directly.…”