PUBLIC SUPPORT AND PROSPECTS OF DRONES OR UNMANNED AERIAL VEHICLES (UAVs) TECHNOLOGIES FOR EFFECTIVE TRANSPORT AND LOGISTICS DELIVERY IN NIGERIA

The study focuses on the issue that prompt transportation and delivery of logistics from one location to the other in Nigeria is at present costly, difficult, unrealistic, and cumbersome as occasioned by; road congestion in urban cities, increased, insecurity pollution, unnecessary delays, and declined efficiency, among others. The menace often poses threats to city planners to maintain the pace of the ever increasing urbanization process and population growth in all ramifications. The current situation has, however, warranted possibility for drones or unmanned aerial vehicles (UAVs) technology for logistics delivery and transportation in Nigeria and other sub-Saharan African nations, to alleviate the stress posed by the conventional mode of transportation. This paper is poised to: investigate the public support and prospects of using of drones or UAVs for effective transport or logistics delivery, and also find out its applications in other sectors in Nigeria; examine the potential threats or barriers to the application; appraise the technology’s cost-effectiveness, acceptability, local sustainability; and recommend the parameters around adoption, safety or security, reliability of drones technology, and effective monitoring for future implementation in Nigeria. The study generated its data from both primary and secondary sources while the research findings are comprehensively and descriptively analyzed. The study, however, recommends to transport sector, governments, end-users, and drone providers on logistics delivery to swiftly ensure that: safety of drone operations; insurance coverage availability be taken care of; ensure regulatory and procedural frameworks, better assessment and evaluation of drone pilot programmes; strengthening national and local capacity to test, learn, use and maintain drones; and ensure that evidences and prospects that could aid advancing the use of drones for logistics delivery or transport in the country be adequately shared.

Transformasi Manajemen Badan Usaha Milik Negara di Era Persaingan Global (Studi Pada PT. Pos Indonesia, Kota Metro Tahun 2019)

Management transformation is a change in form, nature, function, which focuses on the use of resources effectively and efficiently in achieving organizational goals. Tight competition globally and nationally, especially in the field of logistics delivery, makes PT. POS Indonesia creates transformation, so that it can compete competitively and improve public services as a SOEs. This study aims to describe and analyze the transformation of the POS Indonesia office, especially the Metro POS office, so that it can become a reference for the transformation and management strategies carried out by PT. POS Indonesia. Data collection techniques used in this study were interviews, documentation, and observation. The focus of this research is the transformation of the management of the Metro Post Office and the causes of the failure of the transformation of Metro POS Office Management. The latest findings in this study are reveals, strategies of transformation and bureaucratic ineffectiveness that are too long, centralized decisions, innovations that are not fast, change is not holistic. It is hoped that this research can become a reference for science and become a consideration in decision making, for the object of research, namely the Metro POS Office to be able to continue to compete.

Model-Based Rolling Matching Strategy for Crowdsourced Drivers and Delivery Tasks Considering Uncertain Transportation Duration

The matching of crowdsourced drivers and delivery tasks is an important decision problem for the crowdsourced delivery platform. Although the existence of uncertainty of transportation duration in logistics delivery has been verified, uncertain transportation duration has not been considered in previous studies on the matching of crowdsourced drivers and delivery tasks. This would lead to the limitation that the results of the existing methods cannot meet the time requirements of senders. In this case, the profit and customer satisfaction of the crowdsourced delivery platform would decrease. In this paper, a model-based rolling matching strategy to match crowdsourced drivers and delivery tasks considering uncertain transportation duration is proposed. In addition, it is assumed that the crowdsourced delivery platform also has some dedicated drivers to implement the delivery tasks that cannot be implemented by crowdsourced drivers. First, a simpler problem is described, which is to match crowdsourced drivers and delivery tasks considering uncertain transportation duration in a static data environment. Then, a model is proposed to solve the above problem. Based on the proposed model, this paper further proposes a rolling procedure to solve the problem in a data refreshing environment. Moreover, a heuristic algorithm is presented for combining multiple delivery tasks to solve the one-to-many matching. Finally, a case study and comparison are given to illustrate the validity and the contribution of the proposed matching strategy. The results show that the proposed matching strategy has a distinct advantage of cost savings.

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

Models of zoning of urban territory for rational delivery in the microconsolidation system

Urban logistics (or city logistics) is developing rapidly due to the strong growth of e-commerce. Accordingly, the last-mile urban logistics faces a significant number of orders that need to be fulfilled in a dense urban development, environmental constraints and permanent congestion. One of the possible systems of rational city delivery is the use of a network of consolidation centers at the micro level. Such a network provides for a two-tier system of urban delivery - 1) from the central warehouse or warehouses to the network of microconsolidation centers; 2) from microconsolidation centers to end consumers. This scheme is especially relevant in the presence of restrictions on the movement of trucks or heavy vehicles in certain areas of the city, as well as in significant congestion and the problem of parking trucks when unloading at the location of the client. Methods (research methodology). To create a rational delivery network through a microconsolidation system, the primary task is to determine the delivery zones (or geographical clusters) - their number, size, location. To solve this problem, optimization models are proposed based on several minimization criteria - delivery distance, time, cost and integrated distance-time criterion. Results. The result is the optimization models creation, based on those it is possible to divide urban consumers into several delivery zones. Delivery routes are planned within each zone of the respective centroid and minimize the cost of last-mile logistics. Delivery of goods to the centroids can be carried out by light or medium trucks, and within the zones should be dominated by delivery of environmentally friendly modes of transport (motorcycle or moped, bicycle, car, on foot delivery with the possibility of public transport usage). Conclusion. Thus, the article provides a mathematical apparatus for obtaining territorial zoning of existing customers of the city in order to minimize the cost (distance, time or their combination) for delivery within each zone. Perspectives. A perspective study may be an analysis of the costs of operating a network of urban consolidation centers and the delivery of goods from the central warehouse or warehouses to this network. Accordingly, the task of minimizing the total costs of the city freight delivery system should be solved, taking into account economic, environmental and social aspects.

Mixed applied survey methodology for planning/enforcement of urban logistics delivery bays– An application to the Moroccan context

Roads and parking areas represent a place of conflict between freight vehicles and other urban activities, especially on mixed residential and commercial streets. This conflict results in traffic congestion, illegal parking, pollution and road safety problems. The challenge is to allocate public space between the right operating activities, parking activities, public transport and so on. To address that, urban logistics delivery bays, also known as loading/unloading (L/U) zones, have become a real solution to facilitate the delivery and pick-up operations of urban freight vehicles, ensure accessibility for delivery drivers, reduce congestion and improve road safety. Therefore, this paper reports on planning and enforcement of urban delivery bays needs. It is part of the urban freight transport (UFT) surveys. This involves consolidating with new contribution the development, implementation and statistical analysis of a survey in order to quantify the need of delivery areas. Compared to the existing literature, this paper presents a mixed applied methodology which is divided into two parts : “Exploratory survey” and “Establishment-vehicle observation” survey. These two surveys techniques were conducted to offer an overview of the freight vehicle delivery and pick-up frequency according to the daytime and weekdays and the operations related to the loading/unloading activities. This makes it possible to estimate the delivery bays requirement in the study area. The findings from a methodological and practical angle are illustrated through a real case study in a commercial street in Morocco. The findings suggest that 60% of deliveries are made between 8:00 A.M and 12 A.M, and the movements generated by each establishment are 257 movements. For this, the study zone requires the development of three loading/unloading (L/U) bays. The main contribution is to propose an approach that urban authorities can use to estimate urban delivery areas efficiently and thus allow simple replication of the proposed framework in other cities.

A Decision Support System for Data-Driven Driver-Experience Augmented Vehicle Routing Problem

Logistics delivery companies typically deal with delivery problems that are strictly constrained by time while ensuring optimality of the solution to remain competitive. Often, the companies depend on intuition and experience of the planners and couriers in their daily operations. Therefore, despite the variability-characterizing daily deliveries, the number of vehicles used every day are relatively constant. This motivates us towards reducing the operational variable costs by proposing an efficient heuristic that improves on the clustering and routing phases. In this paper, a decision support system (DSS) and the corresponding clustering and routing methodology are presented, incorporating the driver’s experience, the company’s historical data and Google map’s data. The proposed heuristic performs as well as [Formula: see text]-means algorithm while having other notable advantages. The superiority of the proposed approach has been illustrated through numerical examples.