Capacity Decisions Research Articles

Multichannel Healthcare: Impact of Asynchronous Telemedicine Adoption on Patient Flow

Problem definition: This paper studies a multichannel healthcare system where physicians diagnose patients and prescribe treatment in-person or through asynchronous telemedicine (AT), a widely adopted yet relatively under-explored form of telemedicine. In collaboration with physicians at the Veterans Healthcare Administration (VHA), we examine the impact of introducing an AT channel on the existing in-person channel and on overall system performance. Methodology/results: VHA implemented AT at select clinics in the state of Georgia in 2012. Using a difference-in-differences design, we find that the introduction of the AT channel led to a sorting process whereby more complex patients were seen in the in-person channel. AT implementation led to a 20% increase in recommended visit time and an 8.5% increase in required clinical resources for in-person consultations. In addition, the adoption of AT resulted in higher throughput—more patients seen by the specialists per month across both channels. Using a fixed-effects model we find a reduction in average wait time for in-person referrals (37.5%), and for the most common medically necessary procedure (43%) despite an increase in the total number of consultations at the specialist clinic. We attribute the improved efficiency to early patient triage, better match between patient needs and treatment modality, and reduction of setup and switching costs in physicians’ workflow. Managerial implications: This paper contributes to our understanding of a rapidly expanding form of healthcare delivery: multichannel healthcare with in-person and AT channels. Our results suggest that healthcare managers and physicians can adopt AT to improve overall system efficiency. At the same time, they should take into account the additional impact of AT on the in-person channel when making capacity decisions and developing guidance on patient referrals. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0235 .

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Capacity management in supply chains under risk of supply disruptions: a mathematical model for establishing capacity reservation contracts

ABSTRACT 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.

Capacity management under uncertain demand and distribution policies: the case of Indian vaccine supply chain

The present study investigates the cause-and-effect relationships between production capacity decisions and the choice of distribution policy aspects of managing an extensive vaccination programme. The Indian COVID-19 vaccine supply chain is taken as a case study, and Indian demographic data is utilised. A Monte Carlo Markov Chain (MCMC) model is developed to estimate time period-wise demand corresponding to each distribution policy. Furthermore, eight different capacity management models involving different capacity addition and transfer strategies for four different distribution policies are formulated. The trade-off between effectiveness and efficiency objectives, namely Vaccine Supply Chain total cost and service level, is investigated by comparing the Pareto frontiers of the proposed models. Furthermore, the models are compared based on capacity transfer cost, capacity addition cost, and variation in region-wise service level. The results reveal that organisations can simultaneously save significant costs and achieve higher service levels if they cautiously align their capacity management approach and the distribution policy on the demand front. Capacity management interventions at the national level result in more service levels and lower variation and costs than at the regional level.

Retail category management under shelf-space dependent demand: The effectiveness of category captainship

Category captainship is a retail category management practice where the retailer delegates certain category decisions to a vendor, known as the category captain, to leverage the vendor’s market expertise and reduce the retailer’s workload in managing the category. We examine the effectiveness of category captainship compared to traditional retailer category management, focusing on scenarios where shelf space capacity decisions are delegated to the category captain, and demand is dependent on shelf space capacity. Our study considers two competing manufacturers, each selling a substitutable product through a common retailer. We find that when the retailer’s profit-sharing ratio is sufficiently but not excessively high, category captainship can enhance profits for both the retailer and the captain manufacturer. These profit improvements are more pronounced at intermediate values of shelf space elasticity of demand. Although the non-captain manufacturer may suffer from competitive exclusion under certain conditions, “win-win-win” outcomes in profits and sales occur when the products are highly differentiated by price or shelf-space elasticity. The retailer benefits from increasing demand elasticity of shelf space and prefers to designate the manufacturer with the higher elasticity as the category captain. When the profit-sharing ratio is determined through a retailer-led bargaining framework, the retailer benefits most by negotiating sequentially with the manufacturers, inducing the manufacturer with lower bargaining power to take on the category captain role. Our findings provide valuable insights into developing category management strategies under shelf-space elastic demand.

Optimal wind power capacity decision consider commitment contracts under uncertain power supply and electricity demand in China

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain.

Sustainable multi-biofuel production with stochastic lead time and optimum energy utilization under flexible manufacturing

Many nations obtain their energy from fossil fuels. The benefit of renewable energy is that it decreases the dependence on conventional fuel, and minimizes carbon emissions. However, the commercial feasibility of multi-biofuel production poses a major challenge. A resilient and cost-efficient biofuel supply chain network is essential for commercialization. Furthermore, disruption risks arise from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with the aforementioned requirements, a multi-biofuel supply chain network model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. The lead time crashing cost is utilized here to reduce the lead time, which increases because of transportation disruptions. To decrease energy expenses and carbon emissions, a variable production rate is applied for creating an economic way to advance the manufacturing industry. The model aims to show bioenergy’s effect on making a sustainable multi-biofuel supply chain network. Two numerical examples are used to illustrate the effectiveness of the proposed model. It is seen that by utilizing the presented method, the production cost of biofuels is significantly reduced through minimized energy consumption and carbon emissions. Moreover, the impact of critical parameters on the total system cost is explained through sensitivity analysis and graphical representations. The effort provides a systematic guideline for designing various environmental, economic, and social aspects of a sustainable supply chain network for bioenergy under minimized consumption. Multi-biofuel producers, distributors, investors, and the regulators surely be benefitted from the proposed model.

Pricing and Capacity Design for Profit-Driven and Welfare-Driven Healthcare Providers

In choosing healthcare services, price and waiting time are two important factors that matter to patients. Price is set by healthcare providers, while waiting time is usually endogenously determined by patient choice and the healthcare provider's capacity investment. We study the pricing and capacity design for profit-driven hospitals offering two types of healthcare services—regular and premium—serving heterogeneous patients who choose from either of the two types of services. Patients make their choices based on both price and endogenously determined equilibrium waiting time. We then benchmark profit-driven hospitals to welfare-driven hospitals to reveal how the behaviors of for-profit hospitals deviate from the socially optimal outcomes in patients waiting, service capacity, and price. We find that fewer patients are treated by premium services in profit-driven hospitals, and thus profit-driven hospitals invest less in premium service capacity and charge a higher premium for premium services than welfare-driven hospitals. These inefficiency distortions exist in profit-driven hospitals primarily because they are incentivized to differentiate between the waiting times of the two types of service to induce patients to pay a higher premium for premium services. Our results also show that if the inefficiency in patient partition is corrected, profit-driven hospitals would choose the welfare-maximizing level of premium service capacity and thus achieve socially optimal results. However, we also find that regulations such as price ceiling and capacity regulation cannot fully correct the inefficiency in patient partition and capacity decisions of profit-driven hospitals. Lastly, the model is extended in several ways to ensure robustness.

Optimal investment timing and size decision‐making with jump risk and time‐inconsistent preferences

AbstractThis paper analyzes a firm's investment timing and capacity decision by considering the manager's time‐inconsistent preferences and continuous economic depreciation of the capital stock cannot be completely offset. The demand shock of the underlying product evolves according to a double exponential jump‐diffusion process. Within a real options framework, we derive the explicit expressions for the optimal investment threshold and investment size when the manager is sophisticated. We find that time‐inconsistent preferences will lead the firm to delay investment when capital depreciation rate is low and accelerate investment when the rate of capital depreciation is high. Further, we show that capital depreciation rate has a monotonic positive effect on the investment size of a time‐consistent manager but a non‐monotonic effect on that of a sophisticated manager. A sophisticated manager tends to choose a smaller investment size than a time‐consistent manager. Finally, we demonstrate that compared with the geometric Brownian motion, the firm invests later with a larger capacity under the jump‐diffusion model.

Do shipping alliances affect freight rates? Evidence from global satellite ship data

Shipping alliances (SAs) can facilitate their members’ coordination on price and capacity decisions. Although this helps reduce the oversupply of capacity and cutthroat competition during market downturns, the possible freight rate increase due to potential collusion among member shipping lines is another concern. This study aims to empirically investigate the impacts of SAs on container freight rates on nine major shipping corridors for the period from 2015 to 2020. To measure market concentration, data from a satellite-based Automatic Information System (AIS) is used to identify liner shipping companies' services in each market. We propose to use the alliance-level Herfindahl-Hirschman Index (HHI) and Concentration Ratio Index (CR) to measure market concentration. Then we used the simulated market concentration measures as the instrumental variable to address the endogeneity issue between freight rates and alliances’ market share. The main finding is that both alliance-level HHI and CR are negatively associated with freight rates, implying that SAs may help reduce prices. This finding provides new insights for antitrust authorities when investigating alliance applications in the liner shipping sector.

Multi-period reverse logistics network design for water resource management in hydraulic fracturing

This paper addresses the effective management of water resources during the hydraulic fracturing process used for natural gas extraction. With increasing demand and scrutiny on shale gas production due to economic, environmental, and social factors, effective water management strategies are needed to address the challenges of flow-back water usage during hydraulic fracturing. This research contributes to the existing literature from a multi-period reverse logistics network design perspective with realistic modeling approaches. A mathematical formulation is developed for optimizing the design of a multi-period reverse logistics network for effective water resource management that incorporates production scheduling of a hydraulic fracturing process. This mixed integer programming model determines the optimal locations and capacities of impoundments, treatment facilities, and disposal sites while minimizing fixed and operational costs under constraints such as freshwater withdrawal limits, production schedules, inventory balance, and treatment and disposal options. A two-stage stochastic programming formulation is further developed as a means to address the uncertainty associated with water flows during the fracturing operation. The strategic location and capacity decisions are given in the first stage of this formulation, whereas operational decisions such as water flows are handled in the second stage under independently sampled scenarios. A sample average approximation algorithm is proposed to solve the stochastic formulation. The model is tested on a case study from the United States that explores potential impacts on water resource management due to cost variations, changes in operational parameters, and uncertainty in demands and supplies. The base case setup delivers a 12.8% reduction in freshwater usage. The benefit of multi-period reverse logistics network design is further demonstrated through variations in water demand and equipment degradation. The managerial insights derived through sensitivity analysis highlight the values of the proposed multi-period formulation, capacity expansions, and parameter estimations.

Benefits of Collaboration on Capacity Investment and Allocation

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.

Knowledge Management: High-Quality Production Decisions in Partial Volume Flexibility to Create Value for Customers

Uncertainty in the demand of individual customers increases demand volatility in the entire market. In this context, partial volume flexibility can help hedge against demand uncertainty and competition pressure, and subsequently help create value by means of high-quality production decisions and market responsiveness, within the assumption of transformation and applied knowledge incorporated into the production stages. The article aims to investigate this phenomenon. To achieve this, as part of the methodology, a unified measure, the flexibility degree, is taken to represent the level of partial volume flexibility. We find that under demand uncertainty, a firm goes through a process with four stages—capacity decision, stable production, flexible production, and pricing. In a monopoly model, we find that a slight change in the flexibility degree could significantly increase a firm's adjustable production range to respond to demand changes. Furthermore, we develop a general asymmetric duopoly model. We characterize the equilibrium and find that a firm with a higher flexibility degree is more sensitive to demand changes. Accordingly, in response to specific demand, a higher flexibility firm obtains more profit only when the demand is sufficiently high or low, otherwise, the lower flexibility firm wins. Finally, we observe that a firm's capacity increases in its flexibility degree and decreases in its rival's flexibility degree; this is because the firm balances the market share and product profitability.

Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework embedded with data envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. The proposed framework uses DEA to evaluate efficiency and effectiveness for reward estimation in RL, and also assesses conditional value-at-risk to characterize the risk-averse capacity decision. Instead of focusing on short-term fluctuations in demand, RL optimizes the expected future reward with sequential capacity decisions over time. An empirical study of U.S. power generation validates the proposed framework and provides the managerial implications to policy makers. The results show that the RL agent can successfully learn the optimal policy through observing the interactions between the agent and the environment, and suggest the capacity adjustment that can improve efficiency by 8.3% and effectiveness by 0.9%. We conclude that RL complements productivity analysis, and emphasizes ex-ante planning over ex-post evaluation.

Government GDP targets and corporate capacity expansion – Empirical evidence based on A-share listed companies

The phenomenon of Chinese-style capacity expansion is widespread and closely tied to the interventionist behavior of local governments. Using data from A-share listed companies between 2010 and 2021, we find a significant correlation between economic growth targets and corporate capacity expansion. The impact of growth targets on capacity expansion is more pronounced during the National Party Congress, when officials use stricter constraints and when the municipal party secretary is around 55 years old. Officials influence capacity decisions by allocating key resources such as land and finances. We provide valuable insights into Chinese-style capacity expansion, offering reference significance for promoting China's supply-side structural reform.

Capacity decisions and revenue sharing in a telemedicine healthcare system

Capacity decisions and revenue sharing in a telemedicine healthcare system

Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty

Given the steep rises in renewable energy's proportion in the global energy mix expected over several decades, a systematic way to plan the long-term deployment is needed. The main challenges are how to handle the significant uncertainties in technologies and market dynamics over a large time horizon. The problem is further complicated by the fast-timescale volatility of renewable energy sources, potentially causing grid instability and unfulfilled demands. As a remedy, energy storage and power-to-hydrogen systems are considered in conjunction with energy management system but doing so raises the complexity of the planning problem further. In this work, the long-term capacity planning for a hybrid microgrid (HM) system is formulated as a multi-period stochastic decision problem that considers uncertainties occurring at multiple timescales. Long-term capacity decisions are inherently linked with energy dispatch and storage decisions occurring at fast-timescale and it is best to solve for them simultaneously. However, the computational demand for solving it becomes quickly intractable with problem size. To this end, we propose to develop a Markov decision process (MDP) formulation of the problem and use simulation-based reinforcement learning for multi-period capacity investments of the planning horizon. The MDP includes the policies used for dispatch and storage operation, which are represented by linear programming as a part of the simulation model. The effectiveness of our proposed method is demonstrated with a case study, where decisions over multiple decades are considered along with various uncertainties of multi-timescales. Economic and environmental assessments are performed, providing valuable guidelines for government's energy policy.

Dynamic Pricing and Capacity Optimization in Railways

Problem definition: Revenue management in railways distinguishes itself from that in traditional sectors, such as airline, hotel, and fashion retail, in several important ways. (i) Capacity is substantially more flexible in the sense that changes to the capacity of a train can often be made throughout the sales horizon. Consequently, the joint optimization of prices and capacity assumes genuine importance. (ii) Capacity can only be added in discrete “chunks” (i.e., coaches). (iii) Passengers with unreserved tickets can travel in any of the multiple trains available during the day. Further, passengers in unreserved coaches are allowed to travel by standing, thus giving rise to the need to manage congestion. Motivated by our work with a major railway company in Japan, we analyze the problem of jointly optimizing pricing and capacity; this problem is more-general version of the canonical multiproduct dynamic-pricing problem. Methodology/results: Our analysis yields four asymptotically optimal policies. From the viewpoint of the pricing decisions, our policies can be classified into two types—static and dynamic. With respect to the timing of the capacity decisions, our policies are again of two types—fixed capacity and flexible capacity. We establish the convergence rates of these policies; when demand and supply are scaled by a factor [Formula: see text], the optimality gaps of the static policies scale proportional to [Formula: see text], and those of the dynamic policies scale proportional to [Formula: see text]. We illustrate the attractive performance of our policies on a test suite of instances based on real-world operations of the high-speed “Shinkansen” trains in Japan and develop associated insights. Managerial implications: Our work provides railway administrators with simple and effective policies for pricing, capacity, and congestion management. Our policies cater to different contingencies that decision makers may face in practice: the need for static or dynamic prices and for fixed or flexible capacity. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0246 .

Assessing carbon cap-and-trade policies on hybrid renewable energy investments: Implications for pricing and capacity decisions

Renewable energy sources such as solar and wind have received a lot of attention and research in recent years as the climate issue has become more serious. However, compared with traditional energy sources, renewable energy has unstable characteristics such as intermittency. Integrated systems consisting of two or more renewable energy systems, also known as hybrid renewable energy systems, are increasingly popular among environmentalists because their sources can complement each other and provide customers with higher quality and more reliable power than single-source systems. This paper develops an analytical framework to explore the impacts of carbon cap-and-trade policy on the pricing and capacity establishment decisions of a utility firm that invests in hybrid renewable energy (solar energy and wind energy) systems. Three carbon reduction mechanisms are considered: the No cap-and-trade mechanism, Grandfathering mechanism, and Benchmarking mechanism. We focus on the optimal investment decision of a monopolistic utility firm in a hybrid renewable energy system under such three mechanisms. We derive the equilibriums of the utility firm that invests in two renewable energy capacities and compare the performances of the three mechanisms. We find that, in terms of reducing carbon emissions, Grandfathering mechanism dominates the Benchmarking mechanism, but in terms of improving the firm's utilization efficiency of renewable energy power generation or the firm's investment in renewable energy, Benchmarking mechanism is preferred. Therefore, governments/regulators should fully consider how to use the cap-and-trade policies to guide firms to effectively reduce total carbon emissions, and provide different carbon policies for different purposes.

Port capacity-sharing decisions in the post-COVID-19 pandemic era

Considering the disruption risk and congestion deterioration caused by recurrent outbreaks of COVID-19 in several ports, this study developed a port capacity decision three-stage game model by adopting a sharing approach, which comprised a combined port management committee and a two-port system. The former determines the capacity-sharing rate, and the latter sequentially chooses its terminal handling charge (THC). Through backward induction, the capacity-sharing decisions among multiple extrema are derived, and then the “siphon effect” and “spillover effect” of capacity-sharing are analyzed. A case study of Shanghai and Ningbo Zhoushan Ports is presented to further illustrate the findings.