In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

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Last-mile delivery concepts: a survey from an operational research perspective

A parallel compact cuckoo search algorithm for three-dimensional path planning

Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization

Digital watermarking with improved SMS applied for QR code

Containing the COVID-19 Pandemic with Drones - Feasibility of a Drone Enabled Back-up Transport System

Drone logistics can play an important role in logistics at the end of the supply chain and special environmental logistics. At present, drone logistics is in the initial development stage, and the location of drone logistics hubs is an important issue in the optimization of logistics systems. This paper implements a compact cuckoo search algorithm with mixed uniform sampling technology, and, for the problem of weak search ability of the algorithm, this paper combines the method of recording the key positions of the search process and increasing the number of generated solutions to achieve further improvements, as well as implements the improved compact cuckoo search algorithm. Then, this paper uses 28 test functions to verify the algorithm. Aiming at the problem of the location of drone logistics hubs in remote areas or rural areas, this paper establishes a simple model that considers the traffic around the village, the size of the village, and other factors. It is suitable for selecting the location of the logistics hub in advance, reducing the cost of drone logistics, and accelerating the large-scale application of drone logistics. This paper uses the proposed algorithm for testing, and the test results indicate that the proposed algorithm has strong competitiveness in the proposed model.

https://www.mdpi.com/2227-7390/8/3/333/pdf

Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub

This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

/pdf/simplified-phasmatodea-population-evolution-algorithm-for-2exipguktk.pdf

Simplified Phasmatodea population evolution algorithm for optimization

Extreme learning machine (ELM) is an effective classification and prediction learning algorithm based on feedforward neural network (FNN). This paper presents the Phasmatodea (stick insect) population evolution algorithm (PPE), which is different from other algorithms, in which each solution represents a population and has two attributes: quantity and growth rate. Combining the concept of similar evolution and the model of population competition, it is a new local search method. The algorithm is compared with the other algorithms on benchmark functions and engineering problems. Then use it to enhance a variant of the ELM model. The results show that the proposed algorithm has a certain competitiveness.

Pei-Cheng Song

Papers

A parallel compact cuckoo search algorithm for three-dimensional path planning

Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub

Simplified Phasmatodea population evolution algorithm for optimization

Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine