Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing

Alongside the rise of ‘last-mile’ delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones’ social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform’s transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.

Optimal Fixed Route for Multimodal Transportation of Vehicle Logistics in Context of Soft Time Windows

In order to improve customer satisfaction and reduce the cost of vehicle logistics transportation, this paper adopts the mixed-integer programming model to analyze the delivery routes of vehicle logistics and make simulation and analysis based on the real delivery case. The results show that, compared with a single transportation scheme, the vehicle logistics optimization scheme based on the mixed-integer programming model is able to produce the optimal multimodal transportation plan, which can reduce the transportation costs, improve the service of transportation enterprises, and enhance their core competitiveness.

Mixed-Integer Programming Model for Ranking Universities: Letting Universities Choose the Weights

Regardless of the shortcomings and criticisms of world university rankings, these metrics are still widely used by students and parents to select universities and by universities to attract talented students and researchers, as well as funding. This paper proposes a new mixed-integer programming model for ranking universities. The new approach alleviates one of the criticisms -- the issue of the ``arbitrariness'' of the weights used for aggregation of the individual criteria (or indicators) utilized in the contemporary rankings. Instead, the proposed model uses intervals of different sizes for the weights and lets the universities themselves ``choose'' the weights to optimize their position in the rankings. A numerical evaluation of the proposed ranking, based on the indicator values and weights from the Times Higher Education World University Ranking, is presented.

Vehicle Routing Problem Considering Reconnaissance and Transportation

Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for wartime troop movements. The VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements and reconnaissance, and transportation vehicle routes were determined simultaneously in the VRPCRT. For this paper, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed, and computational experiments were conducted to compare the ACO algorithm’s performance and that of the mixed-integer programming model. The performance of the ACO algorithm was shown to yield excellent results even for the real-size problem. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.