Identifying the Optimal Packing and Routing to Improve Last-Mile Delivery Using Cargo Bicycles
TL;DR: Methods developed in order to solve a combination of problems for cargo bicycle logistics, including efficient packing, routing and load-dependent speed constraints, are described.
Abstract: Efficient vehicle routing is a major concern for any supply chain, especially when dealing with last-mile deliveries in highly urbanized areas. In this paper problems considering last-mile delivery in areas with the restrictions of motorized traffic are described and different types of cargo bikes are reviewed. The paper describes methods developed in order to solve a combination of problems for cargo bicycle logistics, including efficient packing, routing and load-dependent speed constraints. Proposed models apply mathematical descriptions of problems, including the Knapsack Problem, Traveling Salesman Problem and Traveling Thief Problem. Based on synthetically generated data, we study the efficiency of the proposed algorithms. Models described in this paper are implemented in Python programming language and will be further developed and used for solving the problems of electric cargo bikes’ routing under real-world conditions.
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TL;DR: In this paper , a multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets is presented to evaluate zero-emission last-mile delivery (LMD) solutions for sustainable city logistics.
Abstract: For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.
9 citations
TL;DR: In this article , a multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets is presented to evaluate zero-emission last-mile delivery (LMD) solutions for sustainable city logistics.
Abstract: For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.
8 citations
TL;DR: In this paper , a literature review selected a collection of articles mostly from 2011 to 2022 and reviewed them in terms of key technical challenges, such as routing, cargo distribution optimization, battery management, data communication, and environmental protection.
Abstract: Sustainable green products and services garner more attention from companies and enterprises that aim to succeed and grow in highly competitive markets by imposing less harms on the environment and ecosystems. Last-mile delivery from local distribution centers to customers plays an essential role in the retail business. Retail companies are leaning towards implementing green, efficient transportation methods, such as drones, in their last-mile delivery operations for reasons of ecological conservation. Accordingly, researchers have documented numerous research findings on last-mile drone delivery in recent years. This literature review selected a collection of articles mostly from 2011 to 2022 and reviewed them in terms of key technical challenges, such as routing, cargo distribution optimization, battery management, data communication, and environmental protection. These challenges are interrelated in a sense of achieving eco-friendly, efficient, lean, last-mile drone delivery. The selection of these technical challenges is based on the top challenges discussed in the literature.
6 citations
TL;DR: In this paper, the authors address the issue of incorporating rail transport into an urban delivery system and identify the possibilities of utilising rail transport in a zero-emission Urban Delivery System (ZUDS) by applying Light Freight Railway (LFR) electric trains.
Abstract: This paper addresses the issue of incorporating rail transport into an urban delivery system. Its main purpose was to identify the possibilities of utilising rail transport in a Zero-emission Urban Delivery System (ZUDS) by applying Light Freight Railway (LFR) electric trains. The study applied the following research methods: literature review, observation, case study, and mathematical computations. In order to estimate the volume of transport external costs reduction resulting from shifting urban deliveries from road to rail transport in the city of Szczecin, the EU methodology was applied to specify the amounts of external costs generated by individual modes and means of transport. The research study showed that application of LFR electric trains makes it possible to significantly reduce external costs generated by transport. Moreover, this solution may have an impact on developing Clean Transport Zones (CTZs) and may also contribute to expansion of the ZUDS. The research study results also provide grounds to conclude that application of the LFR system makes it possible to reduce negative effects generated by Urban Freight Transport (UFT) and to achieve a coherent zero-emission system for handling cargo and passenger flows in cities, which consequently contributes to achieving electromobility goals in transport.
6 citations
TL;DR: Transportation, like other spheres of human activity, is constantly changing due to economic development as mentioned in this paper , and it is a dynamic and dynamic sphere of human activities that is constantly evolving.
Abstract: Transportation, like other spheres of human activity, is constantly changing due to economic development [...]
2 citations
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TL;DR: A procedure based on a linear programming formulation is given for obtaining a near optimal solution to the optimum routing of a fleet of gasoline delivery trucks between a bulk terminal and a large number of service stations supplied by the terminal.
Abstract: The paper is concerned with the optimum routing of a fleet of gasoline delivery trucks between a bulk terminal and a large number of service stations supplied by the terminal. The shortest routes between any two points in the system are given and a demand for one or several products is specified for a number of stations within the distribution system. It is desired to find a way to assign stations to trucks in such a manner that station demands are satisfied and total mileage covered by the fleet is a minimum A procedure based on a linear programming formulation is given for obtaining a near optimal solution. The calculations may be readily performed by hand or by an automatic digital computing machine. No practical applications of the method have been made as yet. A number of trial problems have been calculated, however.
3,531 citations
01 May 2015
TL;DR: The performance of proposed hybrid method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.
Abstract: The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters α and β which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.
309 citations
TL;DR: Atabu search algorithm that iteratively invokes an inner tabu search procedure for the solution of the loading subproblem is proposed, which is experimentally evaluated both on instances adapted from vehicle routing instances from the literature and on new real-world instances.
Abstract: This article considers a combination of capacitated vehicle routing and three-dimensional loading, with additional constraints frequently encountered in freight transportation. It proposes a tabu search algorithm that iteratively invokes an inner tabu search procedure for the solution of the loading subproblem. The algorithm is experimentally evaluated both on instances adapted from vehicle routing instances from the literature and on new real-world instances.
298 citations
TL;DR: A review of the recent scientific literature contributions on innovative strategies for last-mile logistics, focusing on externalities cost reduction, is presented in this paper, which is consistent with a general formulation proposed to evaluate external costs in urban area.
Abstract: In this paper, a review of the recent scientific literature contributions on innovative strategies for last mile logistics, focusing on externalities cost reduction, is presented. Transport is causing problems in urban areas, in particular in freight transport: modern cities need solutions to reduce externalities costs such as congestion, pollution and others, which have increased in the last few years, especially due to the growth of goods delivery. Online sales and globalization lead to new trends in freight transport, and moreover, a larger quantity of goods is expected to be delivered in the next future. In this context, most of the delivered goods end up in the city centers. Last mile logistics is the least efficient stage of the supply chain and comprises up to 28% of the total delivery cost. Therefore, the improvement of last mile logistics and a significant externalities reduction are very important challenges for researchers. New technologies and transport means, innovative techniques and organizational strategies allow handling in a more effective way the last mile delivery in urban areas. Based on the Systematic Literature Review (SLR) method, recent papers that significantly contributed, with original proposals, to the reduction of externalities in urban logistics are identified and analyzed in this work. Furthermore, a classification of the papers dealing with the externality reduction problem is presented. It is consistent with a general formulation proposed to evaluate external costs in urban area. The innovative contributions are classified into five main categories: innovative vehicles, proximity stations or points, collaborative and cooperative urban logistics, optimization of transport management and routing, innovations in public policies and infrastructures. The new paradigm of smart logistics is based on the combination of these concepts and on the proposed innovations.
230 citations
TL;DR: The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimized algorithm, the multiple phase neighborhood search-greedy randomized adaptive search procedure (MPNS-GRASP), the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy.
180 citations