This article focuses on the appointment scheduling problem with stochastic service times in a two-phase healthcare service system. In this system, the two phases’ services refer to medical test and physician consulting, respectively. And two types of patients with different service process exist on a specific day. One type of patient is the new-coming patient who only needs to be served by the physician (if a test is needed, it will be booked for another day due to resource limitation). The other type of patient is the “revisiting” patient who enters the system to get complex test followed by previous suggestions of physician, and he/she will visit the physician with the test results on the same day. All patients get their services through appointments. And the current policy of managing those appointments applied in hospitals is to schedule the appointments in two phases separately. However, as this policy does not take the interaction of two phases’ service into consideration, lots of conflicts emerge in the operation process. To avoid the conflict of patients from different types and improve the system’s efficiency, we innovatively proposed a jointly scheduling policy, which aims to make a joint scheduling decision for all the patients in two phases. To achieve this, we first formulate this problem as a stochastic program and conduct the sample average approximation approach to reformulate it as a deterministic problem. To solve it efficiently, a hybrid VNS_LSHAPE algorithm which combines the advantage of VNS (Variable Neighborhood Search) and L shape algorithm is subtly developed based on the properties of the problem. Finally, numerical experiments show that our proposed jointly scheduling policy has an overwhelming performance on the system’s total cost compared with separately scheduling policy. Both types of patients’ satisfaction and the utility of physicians are improved with this policy.Note to Practitioners—In many departments of hospitals, medical test and physician diagnose are two necessary service items provided to the patients. Such a department can be regarded as a two-phase service system. Generally, two types of patients usually exist in this system simultaneously on a work day. One type refers to the new-coming patients who come for an initial consulting of the physician. And the other type refers to the patients who revisit this system to take medical test and physician’s further diagnosis sequentially. Since making appointments can relieve systems’ congestion, many hospitals begin to provide service through appointing. How to schedule the appointments reasonably for those two types of patients in a two-phase service system proves to be a crucial issue. In this article, a jointly scheduling policy which determines the appointment times for all the patients in two phases is proposed. To obtain a high-quality schedule in reasonable computational time according to this policy, a hybrid VNS_LSHAPE algorithm is also developed. Numerical experiments based on the cardiovascular department of Tongji Hospital in Shanghai is conducted in this article. It is proved that the proposed jointly scheduling policy can reduce the waiting for both types of patients and improve the system’s efficiency at the same time.

In this paper, an urban aerial delivery problem (UADP) is investigated, where the parcel transportation service is accomplished by drones in an urban setting. The aim of the problem is to minimize the total service completion time, by taking into account of the flow balance, the energy consumption, and the response time window. To fully explore the structure of the UADP, a mixed integer linear programming (MILP) model is constructed based on an arc-flow scheme. However, directly handling the UADP with commercial solvers is time consuming. In order to enhance the responsiveness of urban courier services and speed up the solving process, a set-covering model (UADP-SC) is proposed with a linear programming based relaxation. Then a branch-and-price algorithm is designed with pricing accelerating strategies based on heuristics. The computational experiments show that the proposed branch-and-price algorithm outperforms the off-the-shelf commercial solvers in terms of computation efficiency. In the mean time, the proposed algorithm can also serve to obtain optimal battery swapping and path planing decisions in face of the large-scale urban aerial delivery problem with energy constraints.Note to Practitioners—With the intensification of the aging population issue, the manual labor costs in logistics have sharply increased. Simultaneously, advancements in drone technology enable uncrewed aerial vehicles to participate in logistics distribution systems, addressing the last-mile delivery challenge. In the planning of drone delivery routes, the constraint of drone batteries cannot be ignored. This constraint not only affects delivery safety but also impacts delivery efficiency—both crucial considerations for decision-makers. Consequently, we propose a model considering the energy constraints. We introduce a branch-and-pricing algorithm to expedite the problem solving. The results show that the proposed algorithm performs well across various problem scales. Moreover, adopting a strategy of replacing batteries only when necessary can save approximately 17% to 23% of the total completion time. We also conducted performance comparisons under different ratios of orders to drones, providing decision-makers with a useful benchmark.

Many electric vehicle (EV) companies have begun providing energy replenishment services to EV owners. Valet-charging is one such service where valet-staff are dispatched to substitute for EV owners in charging their EVs. This paper considers a routing scheduling problem for the service. We explore the charging options available at both the service provider’s self-operating charging stations and third-party charging stations. We formulate the problem via a robust optimization framework to capture the uncertain waiting time at the third-party stations. A Partial-Branch-and-Price algorithm (PB&B) is designed to solve the problem to optimal efficiently for large scales. Specifically, we innovatively proposed acceleration methods to speed up our extended robust labelling algorithm within the PB&B. We conduct a case study to show the effect of uncertainty in detail and provide investment suggestions for the firm’s investment on whether to contract a third-party charging station. Note to Practitioners—The inconvenience of charging has received significant attention for EVs, and energy replenishment services offer an effective approach beyond infrastructure expansion. Valet-charging, first introduced by NIO Inc., followed by FAW-Volkswagen and NETA AUTO, represents a notable attempt in this direction. Valet-staff routing scheduling needs to consider station assignment decisions, which introduces another potential issue: the number of charging stations owned by valet-charging service providers is insufficient. Including third-party charging stations with uncertain waiting times complicates this issue further. We believe our work develops an effective tool for valet-charging service providers to handle routing scheduling with both types of stations and analyze the investment value on charging stations.

The inconvenience of charging is one of the major concern for potential electric vehicle (EV) users. In addition to building more charging facilities, electric vehicle charging assistance service has emerged for making EV charging more convenient to customers. In this paper, we consider an optimal EV charging station location problem with two types of customers. One is ordinary self-charging customers whereas the other is customers using a new service mode called valet-charging. We formulate the problem via bi-level location optimization model, where the lower level problem is a game model that characterizes customers’ station choice behaviors. To solve the hard nonlinear mixed-integer optimization problem, we design an adaptive large neighbourhood search (ALNS) algorithm for the upper level problem and a construct-improve heuristic for the lower level problem. We conduct numerical experiments to justify the efficiency of our solution method. We also conduct a need-inspired case study to derive practical insights which will help EV charging assistant service providers make strategic decisions.Note to Practitioners—The convenience of charging service is one major concern for EVs. In China, NIO Inc., NETA AUTO, and FAW-Volkswagen have started to provide valet-charging service. Charging station location problem becomes complicated while taking this service into account. We believe our work develops an effective tool for charging station planners to analyze station locations as well as the impact of valet charging services.

Healthcare capacity shortage contributes to poor access in many countries. Moreover, rapid urbanization often occurring in these countries has exacerbated the imbalance between healthcare capacity and need. One way to address the above challenge is expanding the total capacity and redistributing the capacity spatially. In this research, we studied the problem of locating new hospitals in a two-tier outpatient care system comprising multiple central and district hospitals, and upgrading existing district hospitals to central hospitals. We formulated the problem with a discrete location optimization model. To parameterize the optimization model, we used a multinomial logit model to characterize individual patients’ diverse hospital choice and to quantify the patient arrival rates at each hospital accordingly. To solve the hard nonlinear combinatorial optimization problem, we developed a queueing network model to approximate the impact of hospital locations on patient flows. We then proposed a multi-fidelity optimization approach, which involves both the aforementioned queuing network model as a surrogate and a self-developed stochastic simulation as the high-fidelity model. With a real-world case study of Shanghai, we demonstrated the changes in the care network and examined the impacts on the network design by population center emergence, governmental budget change and considering patients with different age groups or income levels. Note to Practitioners—Our work focuses on improving system-wide care access in a two-tier care network. We believe that our work can lead to effective development of a location analytics tool for city-wide healthcare system planners. We also think the importance of this study is further strengthened by the case studies based on real-world hospital choice experimental data from Shanghai, China, a region suffering from the imbalance between healthcare capacity and need. Our case studies are expected to make recommendations on care facility expansion and dispersion to better align with the spatial distribution of residential communities and patient hospital choice behavior.

Service coordination among hospitals is a crucial strategy to improving care outcomes and operational efficiency of healthcare delivery systems. This article proposes a referral cooperation system where patients can be referred from one upper-level hospital (ULH) to one lower-level hospital (LLH) for services provided in both types of hospitals with similar service qualities. For multitype patients with different disease conditions, a method for determining the types and quantities of patients that should be referred is explored based on ensuring the interests of the ULH and LLH simultaneously. This article proposes a threshold control method to address these issues along with the Pareto-based negotiation process to implement referral. The control decisions of both ULH and LLH can be achieved by the developed Pareto optimization based on particle swarm optimization-optimal computing budget allocation (POPSO-OCBA) simulation optimization method. To implement the threshold control easily, we further propose a simplified referral control method and conclude that when the flow variability is small, the method can approach similar effects as the previously proposed control while significantly reducing the administrative costs. The methodologies are studied using numerical investigation and a real case exploration in Shanghai. The results show that the methodologies are effective in helping to make the decisions in practice.Note to Practitioners—Under the tide of the sharing economy, healthcare providers are more ready to coordinate on delivering integrated care to patients, especially on care transition operations. With the government’s promotion of hospital alliances, hospitals should pay increasing attention to dynamic care transition control at the operational level to ensure better quality of service. This article investigates an easily implementable threshold control policy for patient referral coordination, which is expected to aid hospitals in dealing with imbalanced resource utilization to share service requests based on their respective resource availability. More importantly, we expect that the derived Pareto frontier of control thresholds can help the hospitals with the presentation of quantitative evidence for facilitating the negotiation on the control agreement of patient referral coordination. At the same time, the control-based methodology proposed can help hospitals to make decisions on their implementation strategy. A case study on negotiating the control agreement between a representative urban-area comprehensive hospital and its partner is provided in this article. We emphasize the importance of applying system engineering and mathematical modeling to study the referral cooperation system and real-time patient control decisions through our analyses.

Making rapid decisions in intervention resource planning is crucial for mitigating morbidity, mortality, and costs to the societies during epidemic outbreaks. This study presents a data-driven optimization approach for multiperiod resources planning, based on a sequential decision framework, considering up-to-date information and uncertainty in the spread of epidemics. In this method, a new$SEI^{3}H^{2}RD$spread model is constructed to generate the most potential scenarios of an epidemic, based on all the historical information, and risk-averse stochastic programming was proposed to arrive at an optimal resource planning solution. The data-based numerical experiments demonstrate that our approach could control the epidemic by reducing the infected cases and deaths with similar or fewer resources than in the reality. In addition, we also find that the risk-averse design of the objective was able to take a steadier approach to resource planning helped avoid large fluctuations in resource allocations compared to a risk-neutral design. The other insight obtained from these experiments was that a moderate decision interval along with a planning horizon, which is slightly larger than the decision interval, would be a good choice for the sequential planning problem.Note to Practitioners—In the event of an outbreak of a new infectious disease, the uncertainty of the epidemic parameters limits the ability of the model to provide accurate predictions. However, decision-makers cannot wait for more information to alleviate this uncertainty and must immediately make decisions to control the epidemic. This article proposes a sequential decision-making approach with an innovative$SEI^{3}H^{2}RD$spread model and a risk-averse scenario-based stochastic programming to help decision-makers arrive at real-time decisions. We illustrate the performance of this approach using the COVID-19 outbreak in Wuhan. The results show that our model predictions closely fit the real outbreak data and prove that our decision approach could reduce the cost of using the available resources and achieve the goal of controlling the epidemic. The proposed modeling framework can be adopted to study other infectious diseases and provide tangible policy recommendations for controlling outbreaks of such diseases.

During the outbreak of epidemics such as coronavirus disease (COVID-19), the local hospitals often withstand a sharp increase of patient influx, which renders the healthcare system on the verge of collapse. To alleviate the situation, the effective allocation of scarce medical resources during the pandemic plays a vital role. The essence of the healthcare system in time of emergency is to stay functional, and to be able to diagnose and hospitalize as many patients as possible. Fangcang shelter hospital, as a novel way to temporarily increase the capacity of the local healthcare system, is proven to be effective against the COVID-19 pandemic. To improve the performance of the healthcare system with Fangcang, many practical factors need to be taken into account, such as the patient deterioration during waiting to be admitted, the referral mechanism according to the severity of the patients, and the selective admission regulations. To address the high volatility and time-varying feature of the COVID-19, a multistage and multi-type medical service network model is established, and a dynamic allocation strategy of the medical resources at each stage is proposed based on a stochastic optimization problem, which is then solved via the fluid queueing approximation. Combined with the real data collected from Wuhan, it is revealed that the proposed algorithm could help with the allocation of medical resources during the outbreak of epidemics. Even with limited medical resources available, the method could still maintain a guaranteed service level while keeping the healthcare system operational. Furthermore, the simulation analysis validates that our method can effectively allocate medical resources at each stage, so as to stabilize the system performance and fulfill the medical demand for multiple types of patients.Note to Practitioners—To fend off the outbreak of epidemics, the lessons have to be learned from the past. The successful control of the spread of COVID-19 in Wuhan (China) is a classical example of applying modern medical practices and management tools. In the present article, the treating procedure of COVID-19 in Wuhan is modeled as a multistage decision problem, which includes the screening with nucleic acid testing, the further testing, the treatment of patients with mild/severe symptoms, or even critical patients. The introduction of Fangcang shelter hospital is crucial for winning the battle against COVID-19. The current study attempts to determine the timing of introducing the Fangcang shelter hospital during the outbreak of a major epidemic, and helps allocate the medical resources needed to contain the spread of the virus. It is discovered that the actual number of beds in the Fangcang shelter hospital is far more than what is necessary, and it would be better to have built the Fangcang some time in advance. In the meantime, the number of designated hospitals for COVID-19 is in line with the results obtained via the optimal staffing strategy proposed here, but it is also noticeable that these hospitals should be released of duty sooner to fight against not only COVID-19 but also other diseases in reality.

In recent years, imbalanced utilization of medical resources is widely concerned within the tiered Chinese hospital system. Reverse referral, as a measure of promoting patient flows from upper level hospitals (ULHs) to lower level hospitals (LLHs), has demonstrated its advantages on alleviating ULH workload and balancing resource utilization between ULHs and LLHs. Nevertheless, it remains unclear on how to control the reverse referral decision process at the operational level. In this paper, we consider an ULH-dominant setting at which the LLH must accept patient referrals from the ULH whenever it has available beds. We focus our attention on an easy-to-implement threshold policy for the ULH to make the reverse referral decision. To investigate, we first formulate an analytically tractable queueing model for a simplified reverse referral process. We then investigate a more general patient flow control model, for which we analyze the patient population dynamics with a Markov chain process, and apply the concept of state-dependent Markovian arrival process to generate an infinitesimal generator of the system. We use RG factorization to compute the system performance measures. We next formulate a threshold optimization problem with the objective of maximizing the ULH profit. Simulation experiments are performed, which conclude that the threshold control policy is insensitive to the service time distribution. Finally, we report real-world inspired numerical studies, from which we generate insights into effective adjustment of the control threshold in response to the system parameters and discuss potential hindrance from the LLH incorrectly informing its real-time resource availability to the ULH. Our work is the first that applies systems engineering to the real-time reverse referral decision problem in China. It provides the novel perspective of resource balancing to patient flow control studies in the care transition management literature.