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In service systems with delay-sensitive customers, customer decisions to join the queue or balk influence demand, impacting service provider profits. Studies show that customers can be risk-seeking for short waits and risk-averse when waits exceed expectations. Prior research on this mixed-risk attitude is limited, focussing on optimal pricing policies from a social welfare perspective. This study fills this gap by investigating the impact of this behaviour on customer joining strategies and the provider's pricing and capacity policies. We analyse a profit-maximising firm managing an unobservable queue where customers display mixed-risk behaviour towards delay parameterised by a risk-switching point and a degree of risk propensity. Customers join or balk based on expected utility, while the firm determines the service price and rate to maximise expected profit. We derive optimal strategies for both customers and the firm under scenarios with concave or convex capacity costs. Computational experiments show that optimal pricing, capacity, and profit peak at intermediate risk-switching points. For smaller points, optimal price and capacity rise with the customers' degree of risk propensity, while larger points have minimal impact. Numerical exploration emphasises the significance of capacity cost and the effectiveness of leveraging price over controlling capacity for maximising firm profitability. [ABSTRACT FROM AUTHOR]

Profit contributed by take-away service has become an increasingly essential element of the restaurant operating revenue. Since take-away service usually relies on the third-party platform and there are many differences between the cost of take-away service and that of sit-down service. We focus on the restaurant which provides both sit-down service and take-away service, the service system is modeled as a two-stage tandem queueing system. We study the restaurant's optimal capacity level for each stage. Besides, as there exist price/waiting time difference between the two services, inequity aversion is also investigated in our model. We study symmetrical and asymmetrical inequity aversion. We find that the optimal service capacity consists of two parts, base capacity and safety capacity. And the loss resulted from waiting time lag is equal to the waste of resources caused by fluctuations in arrival rate. Further, when customers really long for the restaurant, even if high price will lead to severe inequity aversion, restaurant can always earn more by raising the price in service channel with high revenue. While when customers are indifferent of the restaurant and the others, the price gap is meant to result in revenue decrease. In addition, reduction in customers' susceptibility can help to enhance operation profit in general. Market environment plays a decisive role in choosing the optimal service level. [ABSTRACT FROM AUTHOR]

Capacity management and supply chain disruptions are the most challenging problems faced in high tech industries. This paper focuses on establishing a capacity reservation contract between a single supplier and two heterogeneous retailers in a two-stage supply chain, wherein the retailers purchase a product from the supplier and sell it to the customers. In this study, a mathematical model is built for all parties involved, incorporating numerous essential elements of a real contract. The proposed model aims to assist in determining the best solutions for the retailers’ reserved capacities to the supplier and the supplier’s constructed capacity. Analytical results and sensitivity analyses are presented to demonstrate how various cost elements, price, and uncertainty affect the optimal capacity decisions. [ABSTRACT FROM AUTHOR]

Once public transport is fully automated, human operators will no longer be needed for tasks like manoeuvring, paying, and boarding. Interfaces must evolve to cover the entire interaction chain from booking to boarding. We present a user-centred design of a mobile-based booking application and an LED-based boarding interface for automated shuttles. Our approach included comprehensive requirements and feasibility analyses to ensure technical viability and user satisfaction. Laboratory study results highlight the advantages and challenges of the boarding interface, underscoring the importance of early user requirements and feasibility assessments in designing automated shuttle systems. [ABSTRACT FROM AUTHOR]

Demand Driven Material Requirements Planning (DDMRP) is a recent method mixing push and pull flow management. Although it claims to be the solution to traditional methods' limitations, the DDMRP method works at infinite capacity: manufacturing or supply orders are launched according to a logic of replenishment of stocks defined as buffers. This article proposes an evaluation of capacity management using visual charts developed by simulation. These charts correlate the bottleneck resource's loading rate to a service rate by considering one of the DDMRP method parameters, the Decoupled Lead Time (DLT). The charts are a decision support tool. They allow identifying to which loading rate the DLTs are representative of the flow times of manufacturing orders and which capacity level to use. We study different workshops, including a real industrial case. Our results show that it is better to control the flow times by adjusting capacity rather than adjust the DLT parameter. [ABSTRACT FROM AUTHOR]

Purpose: Although the authors of the Demand Driven Material Requirements Planning (DDMRP) argue that the method DDMRP is the solution to the limitations of traditional production management methods, its capacity management system remains unclear. Since DDMRP operates at infinite capacity, it is important to consider a capacity management approach to avoid under- or overloading production workshops. Design/methodology/approach: We propose a new dynamic capacity management approach for the DDMRP method. Our approach is based on the calculation of the anticipated workload, using DDMRP stock buffers and considering customer order spikes. Considering a real industrial case, we compare the proposed approach to a static one and a dynamic approach from the literature. Findings: The analysis of the results, supported by a two-way ANOVA, indicates that the proposed capacity management approach outperforms the performance of the other two approaches by maximizing the resource loading rate while ensuring a high customer service level. Originality/value: The originality of the article comes on the one hand from the capacity adjustment module by calculating the anticipated workload, and on the other hand from the comparison of this approach with two others, one of which comes from the literature. [ABSTRACT FROM AUTHOR]

In order to adjust the electric power network to today's trends—such as the charging of electric vehicles, design of the Internal Electricity Market (IEM), headway of renewable energy sources, etc.—the demand for flexible network solutions is increasingly significant. Dynamic line rating (DLR) technology offers a flexible solution for the uprating of overhead lines. Moreover, the DLR methodology can be used in a wider system approach in order to establish a line management system, including functions such as ice prevention subsystems or the real-time tracking of clearances. Therefore, the extension possibilities and operational features of DLR-based systems have been extensively researched in the last few years, which is also confirmed by the high number of pilot projects funded for research and development on the topic of DLR. The key question during the implementation of a DLR system is the obtainable operational safety of such a system. This mainly depends on the installation places of the field equipment and the accuracy of the line rating calculation models. The aim of this article is the comparison of the so far available international line rating calculation models and also to propose another way for the determination of the real-time line rating. Moreover, laboratory measurements and case studies are presented for the confirmation of the proposed model's reliability. [ABSTRACT FROM AUTHOR]

With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of couriers that continuously perform pickups and deliveries. Operating such a rapid delivery system is very challenging, primarily because of the high service expectations and the considerable uncertainty in both demand and delivery capacity. Delivery providers typically plan courier shifts for an operating period based on a demand forecast. However, because of the high demand volatility, it may at times during the operating period be necessary to adjust and dynamically add couriers. We study the problem of dynamically adding courier capacity in a rapid delivery system and propose a deep reinforcement-learning approach to obtain a policy that balances the cost of adding couriers and the cost-of-service quality degradation because of insufficient delivery capacity. Specifically, we seek to ensure that a high fraction of orders is delivered on time with a small number of courier hours. A computational study in the meal delivery space shows that a learned policy outperforms policies representing current practice and demonstrates the potential of deep learning for solving operational problems in highly stochastic logistic settings. History: This paper has been accepted for the Transportation Science Special Issue on Machine-Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo [72180404]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0042. [ABSTRACT FROM AUTHOR]

This paper studies how capacity collaboration can benefit two competing firms. We consider a two-stage model where capacity decisions are made in the first stage when there are significant uncertainties about market conditions, and then production decisions are made in the second stage after most of these uncertainties are resolved. We vary the degree of collaboration between the two firms in their capacity and production decisions, examining multiple models and comparing the outcomes. We find that a firm can benefit from collaboration even with its competitors. Interestingly, the firms do not have to make production decisions jointly to realize the benefits of collaboration. Additionally, while collaborative capacity investment proves beneficial, collaborating on production with existing capacity can often yield greater benefits. We find that the advantages of collaboration are most pronounced when competition intensifies, demand fluctuates significantly, and investment costs are high. [ABSTRACT FROM AUTHOR]

The production of photovoltaic utility varies within the day/night cycle. At night, photovoltaic cells do not produce anything. However, their day-light production, unconsumed on a current basis and exported to the grid, is compensated for with supply from the grid at night. This scheme of exploitation is called net-metering and is considered herein. Solar energy produced by a prosumer and fed into the grid has to be equal to the electricity supplied from the grid at night; otherwise, a shortage or waste of photovoltaic production occurs. The above finding leads us to the proposition for the optimal solution of photovoltaic capacity. We derived a closed-form capacity solution to the maximized non-linear profit function. It solves harmonic and 2-point production functions that vary symmetrically around the mean production. To verify the solution methodology, harmonic and 2-point models from empirical production data are estimated. Then, the solution is presented together with its return rate and internal return rate. The main finding is that the unit cost of the grid electricity, photovoltaic capacity unit cost and exploitation time all affect the total profit and return rate values while not impacting the optimal capacity of the photovoltaics. The optimal capacity depends on the prosumer’s energy consumption volume and on the natural conditions of production captured here by the technology efficiency coefficient estimated from the production time series.

This paper considers a supply chain with one supplier and two retailers. The supplier has limited normal capacity. When the orders of two retailers exceed the capacity, the supplier can satisfy the retailers with the limited normal capacity, or he can start an emergent production to increase his capacity in a short time with a high unit production cost. When the cost of quickly expanding capacity is lower than the market price, it is profitable for the whole system to start the emergent production. But the supplier has no incentives to do additional production if his cost is higher than the wholesale price. To resolve this issue, we propose a supply commitment contract that the supplier will fully fill a retailer's order if the retailer pays a premium to the supplier. We consider a practical setting that each retailer has private information on her average demand. Two contracting scenarios of the supplier are compared: contracting with the two retailers simultaneously (the simultaneous scenario) and sequentially (the sequential scenario). It is shown that the retailers will adopt the threshold strategy based on their private demand information to pay the premium. By numerically studies, we further illustrate the supplier's optimal contract choices and the consequent system performance. First, the suppliers always prefer the retailers to move simultaneous although the supplier's profit is only slightly higher than that of the sequential scenario; second, the supplier sets a lower wholesale price and a higher commitment fee in the sequential scenario than those of simultaneous scenario; finally, the sequential scenario is a better choice from the perspectives of both the whole supply chain system and the retailers. Our research recommends the sequential moving in the supply commitment contract that it benefits the whole supply chain system with only slightly less efficient for the supplier. [ABSTRACT FROM AUTHOR]

We study a classic problem in revenue management: quantity-based, single-resource revenue management with no-shows. In this problem, a firm observes a sequence of T customers requesting a service. Each arrival is drawn independently from a known distribution of k different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources B and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than B, the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, that is, its expected loss is independent of T. This improves upon prior works achieving Ω (T) guarantees. Funding: The authors gratefully acknowledge generous support by the Sloan School of Management at MIT. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2022.1293. [ABSTRACT FROM AUTHOR]

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

The gig economy open a new business model in which services have access to a large pool of workers that are compensated based on their actual production, which can be useful to operate at lower levels of utilization to improve response times to customers. However, having a large pool of workers with low utilization may lower their motivation and increase employee turnover, which can hurt productivity in the long run. "Balancing Agent Retention and Waiting Time in Service Platforms" looks at this tradeoff and provides a data-driven approach to manage worker capacity in on-demand service platforms, showing evidence through a real-world application of an outbound call center with freelance agents. In many service industries, the speed of service and support by experienced employees are two major drivers of service quality. When demand for a service is variable and the staffing requirements cannot be adjusted quickly, choosing capacity levels requires making a tradeoff between service speed and operating costs, both of which depend on worker utilization. However, recent business models have enabled service systems to access a large pool of employees with flexible working hours that are compensated through piece-rates. Although this business model can operate at low levels of utilization without increasing operating costs, a different tradeoff emerges: The service platform must control employee turnover, which may increase when employees are working at low levels of utilization. Hence, to make staffing decisions and manage worker utilization, it is necessary to understand both customer conversion and employee retention, measuring their sensitivity to service time and utilization, respectively. In our application, we study an outbound call center that operates with a pool of flexible agents working remotely to sell auto insurance. We develop an econometric approach to model customer behavior that captures two key features of outbound calls: time sensitivity and employee heterogeneity. We find a strong impact of contact time on customer behavior: Conversion rates drop by 31% when the time to make the first outbound call increases from 5 to 30 minutes. In addition, we use a survival model to measure how agent retention is affected by utilization (which determined by workload and total staffing capacity) and find that, for more experienced worker, a 10% increase in utilization translates into a 33% decrease in weekly agent attrition. These empirical models of customer and agent behavior are combined to illustrate how to balance customer conversion and employee retention, showing that both are relevant to plan staffing and allocate workload in the context of an on-demand service platform. History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo (ANID) [Grant PIA/APOYO AFB220003] and FONDECYT-Chile 1181201. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2418. [ABSTRACT FROM AUTHOR]

Our study analyzes capacity management for promising vaccine candidates before regulatory approval (i.e., at‐risk capacity building) in the presence of production outsourcing and different operational challenges: misaligned interests, possible ex post negotiations, asymmetric information between developers and manufacturers, and government involvement. We develop analytical models to compare two vaccine production modes: (1) the integrated mode (a single company determines the at‐risk capacity and produces in‐house) and (2) the outsourcing mode (a manufacturer determines the at‐risk capacity and a developer determines a funding level to share the capacity‐building cost). Our study reveals that outsourcing can achieve a higher at‐risk capacity only if it can achieve sufficient cost savings compared to the integrated mode. Our research also proves that both vaccine production modes tend to underinvest in the at‐risk capacity. Following this, we suggest measures to improve the at‐risk capacity building in both vaccine production modes. Our signaling game model reveals that a developer with high competence cannot always send credible signals of its true competence level to the manufacturer. Our incomplete contract model verifies that the relative performance of the two vaccine production modes is robust when ex post negotiation occurs under the outsourcing mode; however, the two parties may show incompatible preferences for the ex post negotiation. Our study also analyzes the optimal allocation of government financial support to development funding and capacity funding to incentivize at‐risk capacity building. We present comprehensive guidelines for the different stakeholders to collectively contribute to ramping up the at‐risk capacity of promising vaccines. [ABSTRACT FROM AUTHOR]

This contribution examines the responses of five health systems in the first wave of the COVID-19 pandemic: Denmark, Germany, Israel, Spain and Sweden. The aim is to understand to what extent this crisis response of these countries was resilient. The study focuses on hospital care structures, considering both existing capacity before the pandemic and the management and expansion of capacity during the crisis. Evaluation criteria include flexibility in the use of existing resources and response planning, as well as the ability to create surge capacity. Data were collected from country experts using a structured questionnaire. Main findings are that not only the total number but also the availability of hospital beds is critical to resilience, as is the ability to mobilise (highly) qualified personnel. Indispensable for rapid capacity adjustment is the availability of data. Countries with more centralised hospital care structures, more sophisticated concepts for providing specialised services and stronger integration of the inpatient and outpatient sectors have clear structural advantages. A solid digital infrastructure is also conducive. Finally, a centralised governance structure is crucial for flexibility and adaptability. In decentralised systems, robust mechanisms to coordinate across levels are important to strengthen health care system resilience in pandemic situations and beyond. [ABSTRACT FROM AUTHOR]

Abstract Background Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients. Method Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management. Results The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p

The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grants BI-1057/I-9 and 405008493]. M. Bichler gratefully acknowledges funding by the German National Science Foundation [DFG BI 1057/9-1]. [ABSTRACT FROM AUTHOR]

This study aimed to propose an optimization model for slot allocation and contractual pricing that considers spot and contractual shipments and empty container repositioning under a stochastic environment. In that respect, a two-stage stochastic non-linear programming modelwas proposed. The model considers contractual pricing that is overlooked by previous studies. Experimentation results revealed that decreasing market demand and spot market prices could cause serious profit loss while creating a high level of idle capacity. With the increasing market demand, capacity utilization reaches saturation at 90% requiring a capacity increase in the service. In the increasing market, slots allocated to empty containers get reduced while taking advantage of other options for empty container supply. Experimentation of symmetric uncertainty revealed that the range of uncertainty should be minimized since it creates a serious loss in profits and capacity utilization. Calculations also demonstrated that the applications of the stochastic modeling solutions would provide higher profit margins than the solutions of their deterministic equivalents. The model can easily be applied to the real-life situations of container liner services for managing and optimization of their service capacities as well as determining optimum contractual prices. [ABSTRACT FROM AUTHOR]

In the arena of airport terminal design, operators and architects are faced with how to capitalise on opportunities to create additional operational space where none exists. The assumption that capacity challenges are solved by expansion or building new exterior areas can be costly and disrupt passenger efficiencies. By identifying underutilised or functional areas that can be repurposed, existing terminal capacity can be expanded within the current footprint, thus delivering a higher level of passenger experience, and extending the life of existing facilities. This paper examines current capacity issues airports are encountering, provides examples of what they are doing to solve the problem and offers practical solutions to increase passenger levels within existing terminals, so they can properly prepare for the future. [ABSTRACT FROM AUTHOR]

Omnichannel catering firms need to pay attention to the consumer behavior of customers in both online and offline channels to make the corresponding capacity decisions. However, customers' consumption behavior is not only affected by base utility, but also by reference utility, which indicates that customers have reference dependence when making consumption decisions. In order to better help omnichannel catering firms to make capacity decisions more in line with customer consumption behavior in their actual operations, this paper adopts the queuing theory to construct an optimal capacity decision-making model for omnichannel catering firms. We also analyzed the impact of channel information availability on customer reference behavior and the subsequent impact on the firm's optimal decision-making. The findings were as follows: when the channel information is unavailable, customers in each channel take their expectation of waiting time as the reference point. With the improvement of the sensitivity of customers in different channels to the reference point, firms should improve their safety capacity to meet customers' needs. Moreover, at a higher reference point, the increase in customer sensitivity can make the firm obtain higher profits. When channel information is available, customers take the waiting time of customers in different channels as the reference point, and the increased sensitivity of online customers to the reference point will prompt firms to reduce safety capacity. Comparing the two scenarios, we also found that omnichannel catering firms could develop lower safety capacities and obtain higher profits more easily when channel information is available. This not only contributes to the development of omnichannel catering firms, but also expands the application scope of reference theory. [ABSTRACT FROM AUTHOR]

Many catering merchants are currently adopting omnichannel sales methods. At a restaurant, customers can place their orders, and while waiting for the food to be completed, they can enjoy the restaurant's service environment. Online customers, by placing orders on various apps, can skip the queue, increasing their utility by reducing the opportunity cost, but they cannot enjoy the restaurant's service environment. For merchants, it is important to adequately address how to choose the optimal capacity to provide services to customers in different channels and obtain optimal profits based on the service environment. In this paper, we establish a stylized theoretical model to study the impact of the service environment on customers' shopping behavior online and offline, and we study how merchants can formulate the optimal capacity. Our main results are as follows: First, improving the service environment was conducive to reducing the merchants' safety capacity and could help them obtain higher profits. In short-term omnichannel operations, merchants obtained higher profits than in traditional offline single-channel operations. Improving the service environment increased profits, but the profit difference between short-term omnichannel and offline single-channel operations gradually decreased. Finally, in long-term operations, the process of online customers transferring to offline channels based on a high-quality service environment might be detrimental to merchants' profits. [ABSTRACT FROM AUTHOR]

Purpose: Tactical capacity planning is crucial when hospitals must cope with substantial changes in patient requirements, as recently experienced during the Covid-19 pandemic. However, there is only little understanding of the nature of capacity limitations in a hospital, which is essential for effective tactical capacity planning., Design/methodology/approach: We report a detailed analysis of capacity limitations at a Norwegian tertiary public hospital and conducted 22 in-depth interviews. The informants participated in capacity planning and decision-making during the Covid-19 pandemic. Data are clustered into categories of capacity limitations and a correspondence analysis provides additional insights., Findings: Personnel and information were the most mentioned types of capacity limitations, and middle management and organizational functions providing specialized treatment felt most exposed to capacity limitations. Further analysis reveals that capacity limitations are dynamic and vary across hierarchical levels and organizational functions., Research Limitations/implications: Future research on tactical capacity planning should take interdisciplinary patient pathways better into account as capacity limitations are dynamic and systematically different for organizational functions and hierarchical levels., Practical Implications: We argue that our study possesses common characteristics of tertiary public hospitals, including professional silos and fragmentation of responsibilities along patient pathways. Therefore, we recommend operations managers in hospitals to focus more on intra-organizational information flows to increase the agility of their organization., Originality/value: Our detailed capacity limitation analysis at a tertiary public hospital in Norway during the Covid-19 pandemic provides novel insights into the nature of capacity limitations, which may enhance tactical capacity planning., (© Emerald Publishing Limited.)

Background: Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients.Method: Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management.Results: The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p < 0.05 and 0.2985, p < 0.01, respectively, for non-Covid-19 and Covid-19 patients; 0.0026, p < 0.05 and 0.0069, p < 0.01, respectively, for non-Covid-19 and Covid-19). In contrast, hospital networks are negatively related to efficiency in Covid-19 patients (-0.1037, p < 0.05), while the relationship is not significant in non-Covid-19 patients.Conclusions: Managing the needs of Covid-19 patients while also caring for other patients requires a response from the entire healthcare system. Our findings could have two important implications for effectively managing health-care demand during and after the Covid-19 pandemic. First, as a result of a naturally progressive learning process, the resource balance between Covid-19 and non-Covid-19 patients improves over time. Second, it appears that demand management to control the flow of patients necessitates targeted interventions that combine agile structures with decentralization. Finally, untested integration models risk slowing down the response, giving rise to significant costs without producing effective results. [ABSTRACT FROM AUTHOR]

Abstract Background Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg. Methods Based on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries. Results We identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators. Conclusions Our simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

Services of many public transportation systems are regulated by static timetables. Tactical decisions to improve operations are usually limited to supply-side measures that fail to quickly react to short-term changes in passenger demand and disturbances. Where the realized level of service (LoS) might be substantially different from what was expected, demand management measures, such as advising travelers to use specific routes in the interest of congestion and travel time of the entire population (social rerouting), can be implemented alongside supply-side measures to improve the LoS over different timescales. In this study, we introduce multi-modal social rerouting strategies, considering trains, buses, and trams running with predefined timetables, to improve the LoS of capacitated public transport networks by asking a portion of the passenger demand to change departure time, line, or service. In fact, we aim to balance network load by rerouting passengers using a centrally-coordinated information strategy. The strategy anticipates feedback effects due to failed boardings and discomfort because of overcrowding by incorporating behavioral responses to advice over different timescales. We explore theoretical and practical challenges by evaluating the effectiveness and performance of strategies using real-world data from the Zürich and Twente public transport networks. Numerical experiments reveal that (1) system efficiency can already be improved with the compliance of a small portion of passengers to the social rerouting advice; (2) with 20%–50% steerable passengers, the improvement gradient of system efficiency is at a maximum; (3) targeting a certain level of system efficiency, a smaller detour tolerance requires the compliance of more travelers, and a larger detour tolerance allows fewer travelers to comply with the detour. • We strategize multimodal social travel advice (STA) in capacitated public transport systems. • We introduce an optimization framework to determine the best possible advice. • The STA achieves a maintainable and attainable yet quasi-optimal demand distribution. • We evaluate real-world impacts of social rerouting strategy for efficiency and fairness. [ABSTRACT FROM AUTHOR]

Empty storage shelves and long customer lead times due to a sharp rise in market demand from industries on the one hand (e.g., pharmaceutical products). On the other hand, increasing short-term working or unemployment due to a rapid decline in demand (e.g., automotive). Current supply and demand gaps caused by the COVID-19 pandemic remind us that successful competition in volatile business environments requires rapid adjustments of production capacities. Capacity management (CM) addresses these adjustments by adapting production capacity to market demand. Operations managers of flexible manufacturing systems can adjust the capacity by using various levers (e.g., overtime, used machines, ...). To guide these managers, decision support systems (DSS) exist for short-term CM (e.g., shop floor scheduling). However, due to complexity and runtime problems, the decision-making process for medium-term CM is usually carried out with low technical support. Increases in computing power and advances in algorithm performance over the past decades have enabled Machine Learning to solve ever more complex problems such as the aforementioned issues. Reinforcement Learning (RL) in particular has shown good performance in solving short-term CM problems when compared to humans or other established heuristics. In this work we review the current literature for CM using RL in flexible manufacturing systems. We identify an existing lack of knowledge within the overlap of medium-term CM and RL. However, good performance of RL in short-term CM indicates that an application in medium-term CM should be evaluated. In addition, we propose a concept of a method for medium-term CM based on RL to support operations managers in the decision-making process. The resulting DSS could have a significant impact on production performance, especially in terms of capacity adjustment speed. [ABSTRACT FROM AUTHOR]

This research considers situations in which buyers pay to reserve their suppliers' capacity for future use. The study specifically explores whether suppliers should provide transfer rights, allowing buyers unable to use all of their reserved capacity to transfer the excess to another buyer, and whether they should charge a transfer fee. The study finds that, in most cases, the supplier maximizes financial outcomes when the buyer releasing the excess capacity keeps most of the retail‐level profit from the transfer and the supplier does not charge a transfer fee. [ABSTRACT FROM AUTHOR]

The combination of increasing demand and a shortage of nurses puts pressure on hospital care systems to use their current volume of resources more efficiently and effectively. This study focused on gaining insight into how nurses can be assigned to units in a perinatology care system to balance patient demand with the available nurses. Discrete event simulation was used to evaluate the what-if analysis of nurse flexibility strategies and care system configurations from a case study of the Perinatology Care System at Radboud University Medical Center in Nijmegen, the Netherlands. Decisions to exercise nurse flexibility strategies to solve supply–demand mismatches were made by considering the entire patient care trajectory perspective, as they necessitate a coherence perspective (i.e., taking the interdependency between departments into account). The study results showed that in the current care system configuration, where care is delivered in six independent units, implementing a nurse flexibility strategy based on skill requirements was the best solution, averaging two fewer under-/overstaffed nurses per shift in the care system. However, exercising flexibility below or above a certain limit did not substantially improve the performance of the system. To meet the actual demand in the studied setting (70 beds), the ideal range of flexibility was between 7% and 20% of scheduled nurses per shift. When the care system was configured differently (i.e., into two large departments or pooling units into one large department), supply–demand mismatches were also minimized without having to implement any of the three nurse flexibility strategies mentioned in this study. These results provide insights into the possible solutions that can be implemented to deal with nurse shortages, given that these shortages could potentially worsen in the coming years. [ABSTRACT FROM AUTHOR]

The present article uses game experiments to understand the dynamics of oligopolistic competition in liner shipping markets. We show how a limited number of carriers, interacting over time, acting independently or grouped into global shipping alliances, are able to effectively and jointly reduce excess capacity. A serious game (called TRALIN) has been designed to this end, mimicking the global liner shipping market where four to five global shipping alliances compete on a set of 12 routes, connecting four ports of call for a few sequential voyages. Carriers are initially subject to low profits due to over-capacity and have to anticipate competitor capacity decisions and vessel deployment simultaneously. Results from 18 experimental games with 4644 decisions were collected and statistically analysed to confirm the main tenets of oligopoly theory and to highlight the existence of a learning effect from successive interactions (rounds in games). Our results suggest that a 'coordinated' reduction in capacity is more likely to occur when the number of competitors is limited, but even more when excessive capacity is high, urging the need for cooperation; a learning effect amongst market participants is detected over time. Serious games are flexible tools for improving our understanding of competition, the organization of liner shipping networks, and the role played by global shipping alliances. This tool may help practitioners to understand how over-capacity is evolving within the competitive process, and what factors may influence it. Although voluntarily made simplistic for the purpose of experiments, our design allows one to focus on the main tenets of oligopoly theory as applied to shipping markets. [ABSTRACT FROM AUTHOR]

Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach—termed kernelized empirical risk minimization (kernelized ERM)—to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kernelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics. [ABSTRACT FROM AUTHOR]

Featured Application: Management model for air mobility and Service-oriented On-Demand Air Mobility. Modeling method for aircraft units between different vertiports within a given region considering mobility needs, capacity constraints, maintenance and charging needs. Exemplary application in a simulation model for a regional area of fifteen vertiports and their interconnection by means of electric aircraft units. Vertical mobility, as a commercial service, has been considered for scheduled volume and long-distance mobility services. To overcome its limits and increase its potential coverage, flexibility, and adaptability, centralized mobility hubs, similar to airports, will need to be constructed. Within this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules could provide a solution for regional mobility needs. The aim of this research was to provide a generic framework for various mobility schemes as well as to design a holistic air mobility management concept for electric vertical mobility. A system dynamics simulation case study applied the conceptual model for an on-demand air mobility network of electric aircraft in a regional area with capacity constraints including vertiports, aircraft, charging, and parking stations. Therefore, bottlenecks and delays were quantified using a digital twin tool for customized scenarios. Simulation results showed that optimized maintenance management and the redistribution of aircraft units improved service indicators such as the number of customers served, and customer wait times as well as a reduction in the amount of time an aircraft spent on the ground. As a result, a digital twin air mobility network model with simulation capabilities may be a key factor for future implementation. [ABSTRACT FROM AUTHOR]

Omnichannel catering firms need to pay attention to the consumer behavior of customers in both online and offline channels to make the corresponding capacity decisions. However, customers’ consumption behavior is not only affected by base utility, but also by reference utility, which indicates that customers have reference dependence when making consumption decisions. In order to better help omnichannel catering firms to make capacity decisions more in line with customer consumption behavior in their actual operations, this paper adopts the queuing theory to construct an optimal capacity decision-making model for omnichannel catering firms. We also analyzed the impact of channel information availability on customer reference behavior and the subsequent impact on the firm’s optimal decision-making. The findings were as follows: when the channel information is unavailable, customers in each channel take their expectation of waiting time as the reference point. With the improvement of the sensitivity of customers in different channels to the reference point, firms should improve their safety capacity to meet customers’ needs. Moreover, at a higher reference point, the increase in customer sensitivity can make the firm obtain higher profits. When channel information is available, customers take the waiting time of customers in different channels as the reference point, and the increased sensitivity of online customers to the reference point will prompt firms to reduce safety capacity. Comparing the two scenarios, we also found that omnichannel catering firms could develop lower safety capacities and obtain higher profits more easily when channel information is available. This not only contributes to the development of omnichannel catering firms, but also expands the application scope of reference theory.

Many catering merchants are currently adopting omnichannel sales methods. At a restaurant, customers can place their orders, and while waiting for the food to be completed, they can enjoy the restaurant’s service environment. Online customers, by placing orders on various apps, can skip the queue, increasing their utility by reducing the opportunity cost, but they cannot enjoy the restaurant’s service environment. For merchants, it is important to adequately address how to choose the optimal capacity to provide services to customers in different channels and obtain optimal profits based on the service environment. In this paper, we establish a stylized theoretical model to study the impact of the service environment on customers’ shopping behavior online and offline, and we study how merchants can formulate the optimal capacity. Our main results are as follows: First, improving the service environment was conducive to reducing the merchants’ safety capacity and could help them obtain higher profits. In short-term omnichannel operations, merchants obtained higher profits than in traditional offline single-channel operations. Improving the service environment increased profits, but the profit difference between short-term omnichannel and offline single-channel operations gradually decreased. Finally, in long-term operations, the process of online customers transferring to offline channels based on a high-quality service environment might be detrimental to merchants’ profits.