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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.

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.

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 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.

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.

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.

This paper studies two-stage lot-sizing problems with uncertain demand, where lost sales, backlogging and no backlogging are all considered. To handle the ambiguity in the probability distribution of demand, distributionally robust models are established only based on mean-covariance information about the distribution. Based on shortest path reformulations of lot-sizing problems, we prove that robust solutions can be obtained by solving mixed 0-1 conic quadratic programs (CQPs) with mean-risk objective functions. An exact parametric optimization method is proposed by further reformulating the mixed 0-1 CQPs as single-parameter quadratic shortest path problems. Rather than enumerating all potential values of the parameter, which may be the super-polynomial in the number of decision variables, we propose a branch-and-bound-based interval search method to find the optimal parameter value. Polynomial time algorithms for parametric subproblems with both uncorrelated and partially correlated demand distributions are proposed. Computational results show that the proposed models greatly reduce the system cost variation at the cost of a relative smaller increase in expected system cost, and the proposed parametric optimization method is much more efficient than the CPLEX solver.

In recent years, I.T.C. Limited (hereafter ITC) developed the “e-Choupals” for the rural areas of India. In this new business model, ITC reaches implicit agreements with some farmers (inside the network) that they can sell the products directly to ITC at the market price in the local market, but allow the farmers, both inside and outside the network, to access valuable information through the e-Choupals. In this study, we investigate ITC's incentive of offering such opportunities, especially to those farmers outside the network, and analyze the farmers’ strategic quantity decisions. We show that the implicit agreement behaves as a formal contract, regardless of the price elasticity of the local market: Once reaching an agreement with ITC, the farmers always give priority to delivering directly to ITC. The e-Choupal network leads naturally to the complete separation of selling channels, provided that ITC's capacity constraint is not tight. Surprisingly, in a variety of scenarios, ITC finds it optimal to provide the best available training to the farmers outside the network. We further show that our results are not prone to potential cheating in the mandi system, the possible exploitation via postponed payments, and the stochastic effects on the market-clearing price.

In this paper, we consider data-driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.

We consider a continuous-review inventory problem for a retailer who faces random disruptions both internally and externally (from its supplier). We formulate the expected inventory cost at this retailer and analyze the properties of the cost function. In particular, we show that the cost function is quasi-convex and therefore can be efficiently optimized to numerically find the optimal order size from the retailer to the supplier. Computational experiments provide additional insight into the problem. In addition, we introduce an effective approximation of the cost function. Our approximation can be solved in closed form, which is useful when the model is embedded into more complicated supply chain design or management models.

We study auctions for a set of commonly-ranked items where each buyer has unit demand. This setting has promising applications in areas such as keyword auctions in the search engine advertising industry, the sale of quality-ranked raw materials, etc. An auction mechanism suitable for this setting is the simultaneous pooled auction (SPA), where each bidder simultaneously submits a single bid and is allocated an object based on the rank of his bid among all the bids. We study how to improve the seller's expected revenue by enforcing a reserve price in an SPA. We find that the use of an appropriate reserve price may significantly increase the seller's revenue, especially when the number of items for sale is relatively large compared to the number of participating bidders. One inherent problem in the SPA is that some bidders may incur ex post losses; that is, they pay more than what they value the received objects. We propose a tailored VCG mechanism that generates the same expected revenue as the SPA does, while bidders do not incur any ex post loss. We also discuss the potential applications of this research to keyword auctions.

Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several future research directions.

This study examines the peer-to-Peer interactions among farmers when both knowledge learning and sharing are available. We construct a stylized model in which heterogeneous farmers are endowed with their initial production capabilities and can post questions in the platform for help. A representative expert regularly monitors the forum and provides answers to the farmers’ questions, but may be non-responsive sometimes due to the limited capacity. A knowledgeable core user (farmer) can choose to be silent or responsive, and is allowed to strategically determine the informativeness of her answers. The farmers face the minimum quantity restriction for attracting the buyers, and must make production before the time of sales. We show that in equilibrium the core user never provides answers that are more informative than the expert's, irrespective of her ex ante knowledge level. Redesigning or restructuring the platform does not help eliminate this inefficient knowledge provision. We also find that hiring more staff to frequently monitor the forum turns out to be detrimental for the peer-to-peer interactions. Moreover, the competition on knowledge sharing between the platform expert and the core user features strategic complementarity sometimes but strategic substitution at other times. Third, charging for the platform usage may discourage uninformative answers, but it could also discourage the core user from sharing knowledge with other farmers

In the industry with radical technology push or rapidly changing customer preference, it is firms' common wisdom to introduce high-end product first, and follow by low-end product-line extensions. A key decision in this �down-market stretch� strategy is the introduction time. High inventory cost is pervasive in such industries, but its impact has long been ignored during the presale planning stage. This study takes a first step toward filling this gap. We propose an integrated inventory (supply) and diffusion (demand) framework and analyze how inventory cost influences the introduction timing of product-line extensions, considering substitution effect among successive generations. We show that under low inventory cost or frequent replenishment ordering policy, the optimal introduction time indeed follows the well-known �now or never� rule. However, sequential introduction becomes optimal as the inventory holding gets more substantial or the product life cycle gets shorter. The optimal introduction timing can increase or decrease with the inventory cost depending on the marketplace setting, requiring a careful analysis.