The development of a conceptual rural logistics system model to improve products distribution in Indonesia

Purpose: The role of speculators in distributing products across rural areas is increasing the poverty rate in Indonesia. Therefore, this study aims to develop a conceptual framework of the rural logistics system model to influence the welfare and sustainability of farmers. Design/methodology/approach: Conceptual framework was used to evaluate logistics and supply chain networks. The method consists of developing stages based on four components, namely network structure, management, resources, and business processes. Furthermore, it also proposed the management function of the rural logistics system models. Findings: The model of a rural logistics system obtained in this study consists of 1) a trade related to the network of business, 2) a freight, related to the flow of goods, and 3) management functions related to crucial activities in rural logistics management. Research limitations/implications: This model is conceptual, therefore, future studies must accommodate optimizing models to predict the performance of rural logistics systems when they are applied in Indonesia. Practical implications: This study promotes the actors in intermediaries of the rural logistics system to synergize the distribution of goods effectively and efficiently. It also reduces the role of speculators in product distribution in form of availability and price in rural areas. Social implications: This model is a strategy to achieve the Rural Sustainable development Goals (Rural-SDGs) agenda and complements the Blueprint of The National Logistics System. Originality/value: There are fewer studies in rural logistics compared to other fields such as agricultural logistics, food logistics, disaster logistics, etc. Therefore, this study organizes the actors in the rural logistics network and plans management functions for the efficient distribution of products across Indonesia. It also raises the awareness of logistics management to improve the welfare of rural communitiesPeer Reviewe

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products

Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-Mile Technologies and Strategies

Caltrans 65A0686 Task Order 066USDOT Grant 69A3551747114E-commerce can potentially make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities, particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include the use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection-points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic

Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141

A structured method for the optimization of the existing last mile logistic flows

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn a fast-moving world some business exists due to the interconnectivity between countries. This happens because transports are able to reach the other side of the globe within few days and without being too expensive compensating the lower costs of production and competitive advantages. This is true for well-organized and big supply chains but even them can benefit from integration with disconnected and more complex supply chain as it is the case of e-commerce chains. The transaction of small packages from online shopping required in a totally distinct country of the place of production have very specific characteristics as they are spot flows, hard to predict and to combine with other goods owing to the fact that the destination of flows are different every time and it is not always worth it to dedicate a transport for such a small goods value and in addition most times, logistics have to answer to some challenging marketing requirements meaning they have time windows to fulfil. Last mile is a big part of logistics transports and is one important part of it that can really help companies having better prices and revenues for their transports. Last mile solutions need to be easy to implement and really have to translate in quick gains to logistic companies that are largely reducing their margins to increase competitiveness. In this context, the study aims to investigate and define a method following design Research Methodology hopping to draw some innovative solutions for the problem of last mile. In this respect, the work developed intends to study the solutions already implemented and extract insights on how distribution is made and how to maximize last mile profit through the mature of an algorithm able to reduce inefficiencies in a simple way without having to wiggle too much the structure of businesses as resources of last mile service providers are understood to be scarce as many last mile companies are small sized and running under big logistic players. The solution aims to attain the different marketing requirements exactly as it was defined without having to compromise anything but still being able to make good profit margins and perhaps make room for new opportunities to arise that previously were not profitable

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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