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Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

In this study, we propose an integrated model of two‐sided stochastic matching platforms to understand the design and operations of free‐float shared‐mobility systems. In particular, we address the joint design of incentives (via “crowd‐sourcing”) and spatial capacity allocations (enabled by “geo‐fencing”). From the platform's perspective, we formulate stylized models based on strategic double‐ended queues. We optimize the design and operations of such systems in a case study using a data set from a leading free‐float bicycle‐sharing system, and solve it via mixed‐integer second‐order conic programs (SOCPs). Both stylized results and computational studies generate insights about fundamental trade‐offs and triangular relationships among operational costs, capacity utilization rates and service levels. Interestingly, we identify the role of spatial capacity (parking) management to fine‐tune the market thickness (transient service availability) in such a two‐sided marketplace. We show that a “capacity‐dependent approximation” can be very close to optimality, and outperforms policies ignoring capacity management. We also demonstrate that this framework can be operationalized in multiple directions, which generates insights concerning matching efficiency, performance comparison between crowd‐sourcing and repositioning, strategic server behaviors and network externalities. Our insights guide the platform and the policy‐maker to embrace “crowd‐sourcing” and “geo‐fencing” technologies for shared‐mobility systems.

Data center networks provide the physical infrastructure that hosts Internet‐related services and cloud computing. Designing data center networks properly is imperative for Internet‐related service and cloud computing providers to gain competitive edges through cost efficiency and service quality. In this study, we formulate a mathematical programming model to address the data center network design problem, in which the objective is to minimize total operating cost and the service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features, such as latency, power, multiple resources, configuration limits, and interdependent footprints. We employ a queueing model to approximate the service latency and provide tractable reformulations. To enhance computational efficiency for large‐scale problems, we further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem. Our numerical studies demonstrate that the proposed model, which jointly optimizes location, allocation, and resource provisioning, can achieve significant cost reductions and improvements in service quality compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state‐of‐the‐art commercial software in terms of computational efficiency. Based on real‐world datasets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.

Two effective strategies that mitigate a firm’s demand risk are resource flexibility investment and responsive pricing. In addition to demand uncertainties firms also face capacity uncertainties and capacity disruptions and the effectiveness of these strategies under these risks are less clear. We investigate the value of resource flexibility and responsive pricing under different risk settings for a firm that produces two substitutable products each with its own dedicated resource that can be optionally reconfigured to produce the other product. Reconfiguration or cross-production incurs efficiency loss which can be mitigated by choosing the degree of flexibility of these resources, at a cost, in the planning stage along with capacity levels. In the production stage, after capacities and market potentials are realized, the firm allocates resources and sets prices. We find that under only demand uncertainties the value of flexibility is very low and only a moderate degree of flexibility is sufficient under high demand risk. Responsive pricing is the dominant strategy as the firm avoids investment in costly flexibility. When facing both demand and capacity uncertainties the firm invests in higher levels of flexibility but the value of flexibility is lower than the value of responsive pricing. However, under demand and capacity disruptions flexibility arises as the dominant strategy due to the resource risk pooling effect and the value of flexibility eclipses the value of pricing as the firm invests in full flexibility. For a firm with responsive pricing investment in flexibility is economically justified under high capacity uncertainties and capacity disruptions.

Trade credit is widely used by companies to obtain external funds. The extant literature has demonstrated that trade credit benefits the buyer and the supplier in multiple ways, but might still ind...

Most pricing and revenue management models have at their core an optimization problem; one needs to determine the optimal price or quantity to maximize a profit or revenue function. To ensure tractability, conditions that assure the objective function has a unique solution are enormously helpful. So far, several technical assumptions have been proposed for the continuous case, but comparatively little attention has been given to the discrete counterpart despite its prevalence in practice. Thus, this study aims to develop new technical assumptions, built upon relevant economic concepts, to guarantee the tractability of revenue management models in discrete settings. In particular, we present two sufficient conditions for the revenue function to be concave, in terms of quantity or price and propose a condition for the revenue function to be unimodal, called discrete increasing generalized failure rate (IGFR). Our definition has an appropriate economic interpretation and offers comparable properties to those of the continuous version. Finally, we show the discrete IGFR property holds for several discrete distributions.

In this paper, a single-step framework for predicting quantiles of time series is presented. Subsequently, we propose that this technique can be adopted as a data-driven approach to determine stock levels in the environment of newsvendor problem and its multi-period extension. Theoretical and empirical findings suggest that our method is effective at modeling both weakly stationary and some nonstationary time series. On both simulated and real-world datasets, the proposed approach outperforms existing statistical methods and yields good newsvendor solutions.

When product recalls happen, firms not only have to incur additional logistics costs but also suffer from a damaged reputation. Besides, they may also supply disruption risk, under which a supplier could fail to deliver the quantity ordered at all. In this study, we discuss how to mitigate supply disruption and recall risks in the perspective of (outbound) product distribution strategy in joint with (inbound) sourcing decision. Specifically, we compare the dedicated and uniform distribution strategies as follows: in the dedicated strategy, the firm divides its market into several regions, and ships products from different suppliers based on the regions; whereas, in the uniform strategy, the firm serves the entire market by randomly shipping the products from several suppliers without distinguishing their sources. Through the comparison of both strategies, we show that the role of recall risk is different from that of supply disruption on the sourcing and distribution decision. In particular, single sourcing is optimal when both recall risk and disruption probability are low; dual sourcing with the dedicated strategy is optimal when the disruption probability is moderate and the recall risk is high; dual sourcing with the uniform strategy is optimal when the disruption probability is high. We also find that the positive correlation in recalls improves (undermines) the performance of the uniform (dedicated) strategy. In contrast, the positive correlation in supply disruptions only undermines the performance of the uniform strategy.

It is well‐known that adding a little flexibility to the right place is an effective strategy to improve the performance of operations in the face of demand uncertainties, to ensure high level of capacity utilization. However, given that system disruptions are ubiquitous, the legacy flexibility designs may perform poorly under disruptions to supply or capacity installations. In this study, we focus on the design of reliable and sparse flexibility structures that consistently meet a reasonable performance criterion under disruptions to both demand and supply. Specifically, we propose a class of structures termed as extended probabilistic expanders, based on the conjecture that the expansion property, rather than the global connectivity, is critical to good performance of the structures. We prove that for a system with n retailers, essentially only O(n) supply routes between suppliers and retailers are necessary to ensure good performance under disruption. In addition, we present an efficient randomized algorithm to construct extended probabilistic expanders, and demonstrate that the construction yields very good structure with the least number of edges asymptotically. We also investigate an extension to systems with structural constraints. Numerical results demonstrate that our design has not only a wide range of applications, but also better performance than a variety of known structures.

We herein investigate the single item, multi-period inventory management problem with fixed ordering cost, holding cost, and lost sales cost. Here, both the demand and vendor lead time (VLT) are uncertain; Additionally, the contextual information that may be useful for predicting the distribution of the demand and VLT is available. We assume that the retailer has access to rich history observations and wishes to decide: (1) when to place an order and (2) how much to order. We solve this problem via an end-to-end (E2E) learning approach, where we develop a deep learning framework that directly outputs the optimal order timing and quantity with the given contextual information as the input. Our numerical experiments, which use the real-world data obtained from an online retailer, demonstrate the advantages of our approach over benchmark methods, such as the (r, Q) policy, predict-then-optimize (PTO) framework, and the E2E method with given order timing. Furthermore, we analyze the convergence of our E2E approach and provide upper bounds for the expected daily average cost of our E2E approach under certain conditions. To compare our approach with the optimal policy, we also provide lower bounds for the cost of the optimal policy under certain assumptions.

The Triple‐A supply chain (Lee 2004) has become one of the most influential concepts for practitioners and researchers in supply chain management. It stipulates that supply chains should strive to improve along the dimensions of agility, adaptability, and alignment, as opposed to focusing exclusively on cost and efficiency improvements. While various strategies have since been adopted across different industries to foster the Triple‐A, the digitalization movement poses both new challenges and opportunities for developing Triple‐A supply chains. In this article, we shall discuss an emerging mode of supply chain innovation, known as the consumer‐to‐manufacturer (C2M) model, and how it enables new possibilities for achieving Triple‐A “digital” supply chains. C2M establishes digital links between end consumers and upstream manufacturers and product designers, and provides a variety of tactics to shorten the information flow process of the supply chain. Our discussion focuses on the implementation of C2M at JD.com, a leading online retailer in China.

Problem definition: Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Academic/practical relevance: Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply overpromising or underpromising is undesirable because of the negative impacts on short-/long-term sales. To the best of our knowledge, we are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. Methodology: We apply and extend tree-based models to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant operational predictors. To account for the cost-sensitive decision-making problem structure, we develop a new split rule for quantile regression forests that incorporates an asymmetric loss function in split point selection. We further propose a cost-sensitive decision rule to decide the promised delivery day from the predicted distribution. Results: Our decision rule is proven to be optimal given certain cost structures. Tested on a real-world data set shared from JD.com, our proposed machine learning–based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of sales, cost, and accuracy as compared with the conventional promised time set by JD.com. Specifically, our simulation results indicate that the proposed delivery time promise policy can improve the sales volume by 6.1% over the current policy. Managerial implications: Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting and integrating the decision-making problem structure with the forecasting model.

As digitization enables service platforms to access users' information, important questions arise about how digital service platforms should disseminate information to improve platform operations. We contribute to this understanding by examining how providing customer-related information at the beginning of a service encounter affects service capacity and enjoyment. We evaluate this strategy via a field experiment on a live-streaming platform that connects hundreds of millions of individual broadcasters and viewers. When viewers entered shows, we provided viewer-related information to broadcasters who were randomly assigned to the treatment condition (but not to control broadcasters). Our analysis, involving a subsample of 49,998 broadcasters, demonstrates that relative to control broadcasters, treatment broadcasters expanded service capacity by 12.62% by increasing both show frequency (3.31%) and show length (7.10%), thus earning 10.44% more based on our conservative estimate. Moreover, our intervention increased service enjoyment (measured by viewer watch time) by 4.51%. Two surveys and additional analyses provide evidence for two mechanisms: (1) information about viewers presented shortly after their entry enables personalized greetings and facilitates social interaction, which addresses both viewers' and broadcasters’ needs for affiliation and satisfies viewers' preference for personalized treatment; (2) broadcasters receiving viewer-related information have a more vivid depiction of viewers and thus feel that their service is more appreciated and meaningful, which addresses broadcasters’ needs for competence. We also rule out several alternative explanations. Our low-cost, information-based intervention has important implications for digital service platforms that have little control over service providers’ work schedules and service quality.

We study a novel practice in the urban logistics sector, the transshipment-based urban courier (TUC) system. In the TUC system, large-capacity vehicles are utilized as moving warehouses to flexibly consolidate packages from individual couriers. The goal is to achieve a better trade-off between transportation costs and delivery efficiency. Because the performance of the TUC system depends significantly on the coordination of the transportation services and the pricing system for consumers, we consider the joint transportation coverage and pricing problem. In particular, we propose a data-driven robust pricing model in coordination with a transportation coverage model. Theoretical performance guarantees and numerical results verify the robustness of the pricing decision. The coverage-and-pricing model can be solved by a decomposition-based heuristic. This heuristic is a valid approximation algorithm under sufficient regularity conditions. After decomposition, the coverage-and-pricing problem can be reformulated as a maximum weighted coverage problem and solved via column generation, where the column-generating subproblems can be solved efficiently by a dynamic programming algorithm. Numerical experiments and a case study based on operational data from DML-Express demonstrate the efficiency of our method and provide managerial implications for the pricing policy in a TUC system.

Problem definition: We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Academic/practical relevance: Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. Methodology: We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Results: Moreover, we investigate the structure of optimal pricing policies and prove the suboptimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. Managerial implications: In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effect offers a strong motive for promotions and price fluctuations.

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse operation management. In this study, we first develop analytic models to estimate the critical performance indicators of a flow picking system, including picking density and turnover time of an order. Second, we leverage the proposed models and real warehouse data to compare the performance of batch picking and flow picking systems through simulation. Our results show that a flow picking system requires fewer order pickers and shorter walking distances than a batch picking system in most scenarios, especially those with a higher order arrival rate to achieve the same service level. Our study can provide valuable guidelines to warehouse managers and decision-makers for choosing an order fulfillment solution by comparing a batch picking system and a flow picking system.

Cylinder shell parts are widely used in industrial and civil areas, and 304 stainless steel cylinders were attempted to be formed by multi-pass drawing spinning. But wrinkling failure occurs in the spinning process. In order to suppress it, two comparative experiments with 206 mm and 245 mm mandrel diameters are carried out to reveal the wrinkling mechanism of multi-pass drawing spinning of 304 stainless steel cylinders. It is found that a thicker mandrel diameter is conducive to the wrinkle suppressing due to the larger instantaneous contacting zone caused by the bigger mandrel. The restraining mechanism of the wrinkling failure is due to the fact that a larger contacting zone can accommodate more circumferentially squeezed metal resulting in smaller tangential compressive stress. This is further proved by the spinning experiment with roller path amendment. The roller path is amended by increasing the curvature of the roller path arc and removing the backward arcs to reduce the tangential compressive stress. Almost no wrinkling failure occurs in the new 304 stainless steel cylinder spinning process.

We are entering an era of great expectations towards our cities. The vision of “smart city” has been pursued worldwide to transform urban habitats into superior efficiency, quality, and sustainability. This phenomenon prompts us to ponder what role the scholars in operations management (OM) can assume. In this essay, we express our initial thoughts on expanding OM to the smart‐city scope. We review smart‐city initiatives of governments, industry, national laboratories and academia. We argue that the smart‐city movement will transition from the tech‐oriented stage to the decision‐oriented stage. Hence, a smart city can be perceived as a system scope within which planning and operational decisions are orchestrated at the urban scale, reflective of multi‐dimensional needs, and adaptive to massive data and innovation. The benefits of studying smart‐city OM are manifold and significant: contributing to deeper understanding of smart cities by providing advanced analytical frameworks, pushing OM knowledge boundaries (such as data‐driven decision making), and empowering the OM community to deliver much broader impacts than before. We discuss several research opportunities to embody these thoughts, in the interconnected contexts of smart buildings, smart grid, smart mobility and new retail. These opportunities arise from the increasing integration of systems and business models at the urban scale.

This study investigates the characteristics of asymmetric multi-pass spinning for angled-flange cylinders. The roller movement and the mandrel rotation are controlled by a CNC spinning lathe to form an asymmetric shape. A set of roller passes was designed to achieve the oblique cylindrical shape with a planar flange. The effects of key parameters on the thickness distribution such as equivalent feed ratio f, clearance between roller and mandrel δ and transitional distance Δβ are investigated. The product has a non-uniform thickness distribution. δ has a great influence on the uniformity of wall thickness distribution. A larger Δβ and a smaller f can improve the uniformity of wall thickness distribution. Two types of fracture are observed during experiments, fracture on the wall and fracture on the flange. Forming limit experiments with various parameters are carried out. The depth limit achieved in this study is low compared to conventional spinning. The limit of flange inclination angle θ achieved in this study is 21°.

In this study, we propose a stylized Cournot competition model under asymmetric market information and study the formation of producers’ informational coalitions. We offer guidance towards how the information service operations can improve the producers’ welfare. Our results shed interesting light on farmer producer organizations (FPOs) in their efforts to link farmers in developing economies by integrating market information. To be specific, by focusing on Nash equilibria wherein producers make coalition†joining decisions, we find that no information†sharing coalition will be formed when the public information provision is either too low or too high, or when the private information provision is high. When the private information provisions are endogenous, a coalition can be formed between two producers when the public information provision is high, while a fair allocation of payoff can be achieved by the dominant group architecture, but not efficiency in terms of aggregate payoff. The main theme is that the government or NGO should provide right amount of market information to the right target, and too much informational intervention leads to more inefficient production and unstable organization.

Most studies of process flexibility have considered a make-to-order setting, whereas in practice, production systems usually have a make-to-stock environment. In this article, we investigate the pr...

In previous studies, an asymmetric product is formed by shear spinning. The wall of the product couldn't be perpendicular to the bottom since the thickness of the wall would reduce to zero in shear spinning. In order to break through the limitation and form an oblique cylindrical shape, an asymmetric multipass spinning method combing conventional spinning with asymmetric spinning was developed in this research. The roller trajectory of asymmetric multipass spinning is deduced. The thickness variation is discussed by analyzing the movement of the roller. The thickness distribution and the surface quality with different experiment parameters are discussed. It indicates that a smaller roller feed rate f and a smaller incremental angle ∆α can improve the surface quality.

Motivated by the recent boom of the sharing economy, this paper presents a scheme of sharing demand-side energy resources (DERs) among multiple prosumers. Successful sharing must achieve enhanced utilization efficiency of DERs and, in the mean time, ensure voluntary participation of prosumers and a sharing-enabling aggregator. It is also desirable to incentivize the adoption of DERs. To fulfill these goals, we formulate a mathematical program with equilibrium constraints (MPEC) for DER valuation within a sharing community. The aggregator coordinates DER operations in real-time; then it solve this MPEC problem after each billing period. In doing so, the aggregator evaluates two operating costs for each prosumer: the actual cost under coordination and the counter-factual cost if the prosumer independently traded power with the aggregator. We define the difference in these two costs as the coordination surplus, which the aggregator and the prosumer split. Simulation results demonstrate that this sharing procedure effectively achieves the aforementioned goals.

Most studies of process flexibility design have focused on homogeneous networks, whereas production systems in practice usually differ in many aspects, such as plant efficiency and product profitab...

The non-axisymmetric cone has broad application prospects and is feasible to be manufactured via die-less shear spinning process which however lacks of research. With the roller path equation and other boundary conditions, the finite element model for non-axisymmetric die-less shear spinning process is established and simulated by adopting ABAQUS/explicit software in this study. The FEM results are experimentally verified by comparing the morphology and the HCAs. The distribution of stress and strain in different working conditions and the ellipticity and the elongation of the material in different spinning process during non-axisymmetric die-less shear spinning process are analyzed. It shows that the 0° area of the flange is the position where the wrinkle occurs easily, and the elongation of the material reaches the maximum value; the larger equivalent stress and strain distribute on the conical surface with the smaller HCA. With the movement of the roller, the ellipticity of the cross section increases in the bigger HCA working conditions, but decreases in the smaller HCA working conditions due to the combined effects of the deformation and the springback.

In order to shorten the preparation period, dieless shear spinning is widely used in various industrial fields with advantage of economy of materials and easy control. Controlling roller path is one of the main methods for changing thickness distribution. A new discretization method is proposed by theoretical derivation in dieless shear spinning. It will generate a virtual axis by inclining the flange of the workpiece. And, the roller path is derived for spinning machine combined with the sine law in shear spinning. The derived path should be applied after the range of forming reaches the certain height. The thickness distribution of formed workpiece is basically in agreement with the theoretical value. It also illustrates that roller nose radius and axial feed will influence the surface quality.

Although vendor‐managed inventory (VMI) programs have gained popularity in practice, some empirical studies report that their implementations have not been successful. We conduct experiments to investigate behaviors in a supply chain where a supplier replenishes inventories for a retailer according to a VMI program under a revenue‐sharing contract. The results show that subjects’ decisions deviate significantly from the standard theoretical predictions because the retailer presents equality preference with adjustment, and the supplier exhibits fairness concern. Since the supplier bears not only the production cost, but also the risk of leftover inventory in the VMI program, we propose an approach that provides the retailer with an opportunity to voluntarily compensate the supplier with an additional percentage of revenue after demand realization. Experimental results based on this new operational procedure show that the retailer still presents equality preference with adjustment, but the supplier has a decision bias of ex post inventory error regret. The supply chain can perform better under the proposed approach than under only a revenue‐sharing contract. Interestingly, the proposed approach does not cause the retailer to share a higher percentage of revenue with the supplier, but it induces the supplier to replenish a higher stock quantity, which leads to higher profits for both the retailer and the supplier.

Under time-varying electricity prices, an end-user may be stimulated to delay flexible demands that can be shifted over time. In this article, we study the problem where each end-user adopts an energy management system that helps time flexible demands fulfillments. Discomfort costs are incurred if demand is not satisfied immediately upon arrival. Energy storage and trading decisions are also considered. We model the problem as a finite horizon undiscounted Markov Decision Process, and outline a tractable approximate dynamic programming approach to overcome the curse of dimensionality. Specifically, we construct an approximation for the value-to-go function such that Bellman equations are converted into mixed-integer problems with structural properties. Finally, we numerically demonstrate that our approach achieves close performance to the exact approach, while dominating the myopic policy and no-control policy. Most importantly, the proposed approach can take advantage of the price differences and efficiently shift demands.