The large-scale integration of electric vehicles (EVs) into power systems is expected to lead to challenges in the operation of the charging infrastructure. In this paper, we deal with the problem of an aggregator coordinating charging schedules of EVs with the objective of minimizing the total charging cost. In particular, unlike most previous studies, which assumed constant maximum charging power, we assume that the maximum charging power can vary according to the current state of charge (SOC). Under this assumption, we propose two charging schemes, namely non-preemptive and preemptive charging. The difference between these two is whether interruptions during the charging process are allowed or not. We formulate the EV charging-scheduling problem for each scheme and propose a formulation that can prevent frequent interruptions. Our numerical simulations compare different charging schemes and demonstrate that preemptive charging with limited interruptions is an attractive alternative in terms of both cost and practicality. We also show that the proposed formulations can be applied in practice to solve large-scale charging-scheduling problems.

The hydrogen economy refers to an economic and industrial structure that uses hydrogen as its main energy source, replacing traditional fossil-fuel-based energy systems. In particular, the widespread adoption of hydrogen fuel cell vehicles (HFCVs) is one of the key factors enabling a hydrogen economy, and aggressive investment in hydrogen refuelling infrastructure is essential to make large-scale adoption of HFCVs possible. In this study, we address the problem of effectively designing a hydrogen supply network for refuelling HFCVs in urban areas relatively far from a large hydrogen production site, such as a petrochemical complex. In these urban areas where mass supply of hydrogen is not possible, hydrogen can be supplied by reforming city gas. In this case, building distributed hydrogen production bases that extract large amounts of hydrogen from liquefied petroleum gas (LPG) or compressed natural gas (CNG) and then supply hydrogen to nearby hydrogen stations may be a cost-effective option for establishing a hydrogen refuelling infrastructure in the early stage of the hydrogen economy. Therefore, an optimization model is proposed for effectively deciding when and where to build hydrogen production bases and hydrogen refuelling stations in an urban area. Then, a case study of the southeastern area of Seoul, known as a commercial and residential center, is discussed. A variety of scenarios for the design parameters of the hydrogen supply network are analyzed based on the target of the adoption of HFCVs in Seoul by 2030. The proposed optimization model can be effectively used for determining the time and sites for building hydrogen production bases and hydrogen refuelling stations.

To build a sustainable social system in the future, the promotion of animal welfare, as well as human welfare, is essential. In this study, we address the issues related to animal euthanasia that are commonly encountered in animal shelters. Many animals entering an animal shelter are euthanized in shelters due to the lack of physical facilities, and new strategies are needed to reduce such euthanasia. For this purpose, we show that it is possible to reduce euthanasia numbers by utilizing the space resources of the animal shelters efficiently and by sharing these space resources appropriately among shelters. To achieve this, we propose mathematical optimization models that determine operational schedules of given shelters with the objective of minimizing euthanasia rates. To demonstrate the superiority of our approaches, we conducted a case study at the animal shelters in the Seoul capital area. Through the case study, we show that operating schedules obtained from our model can reduce euthanasia significantly, 30% on average, due to the efficient utilization of facility space in the shelters. We demonstrate that our approaches can have a high impact on improving animal welfare in shelters if they are properly combined with strategies encouraging adoptions.

This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance.