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Deep-lane shuttle-based storage systems with forklifts are widely used in bulk storage and finished-goods warehouses that require dense storage paired with high operational efficiency. In such a system, automated shuttles take care of the movements of loads within the storage lanes, and forklifts take care of the horizontal and vertical movements of loads. Since shuttles are expensive, the number of shuttles in the system is typically smaller than the number of storage lanes, so shuttle transfers are required with the help of the forklift. This paper focuses on optimizing the schedule: Given a set of retrieval requests and shuttles, how can the shuttle transfer sequence and the retrieval request sequence be arranged to minimize the makespan? A mathematical model is formulated. Since the problem is NP-hard, an efficient two-stage heuristic is proposed to compute near-optimal solutions. This study’s numerical results show that the two-stage heuristic can provide high-quality solutions in reasonable time. Compared to straightforward heuristics used in practice and in the literature, the makespan can be reduced considerably. The proposed two-stage heuristic can also be used to decide the optimal shuttle fleet size.

In the future, vehicle sharing platforms for passenger transport will be unmanned, autonomous, and electric. These platforms must decide which vehicle should pick up which type of customer based on the vehicle’s battery level and customer’s travel distance. We design dynamic vehicle allocation policies for matching appropriate vehicles to customers using a Markov decision process model. To obtain the model parameters, we first model the system as a semi-open queuing network (SOQN) with multiple synchronization stations. At these stations, customers with varied battery demands are matched with semi-shared vehicles that hold sufficient remaining battery levels. If a vehicle’s battery level drops below a threshold, it is routed probabilistically to a nearby charging station for charging. We solve the analytical model of the SOQN and obtain approximate system performance measures, which are validated using simulation. With inputs from the SOQN model, the Markov decision process minimizes both customer waiting cost and lost demand and finds a good heuristic vehicle allocation policy. The experiments show that the heuristic policy is near optimal in small-scale networks and outperforms benchmark policies in large-scale realistic scenarios. An interesting finding is that reserving idle vehicles to wait for future short-distance customer arrivals can be beneficial even when long-distance customers are waiting.

Credit purchasing has seen dramatic growth recently, requiring retailers to adapt their operations in response to an online platform's strategic credit offering. This article presents a model for exploring the platform's strategy for offering credit and the retailer's response, considering consumer spillover, that is, how much the benefit of credit outweighs the drawbacks. The consumers differ in their disposable personal incomes and valuations for the product, and they may perceive a spillover from the credit. We study an integrated system, where the platform acts as a retailer and determines the credit-offering strategy and the price simultaneously. We find that the platform will offer credit only when the spillover is above a given negative threshold, and will charge an interest rate for credit only when the spillover is positive. In addition, the price exhibits a nonmonotonic change with the credit spillover, but the demand remains unchanged at a high level. We then extend the analysis to an independent system, where the retailer sells through the platform. We find that the revenue-sharing percentage the retailer pays is critical for the platform to optimize its credit strategy. Specifically, the platform will never offer interest-free credit when the percentage is low, but in other cases may do so and the credit offering does not harm the retailer's profit. Interestingly, when the spillover is moderate, the demand may decrease instead of increase, indicating a deviation from the presumed intention of offering credit.

Platform-hardware business models (e.g., integrating a software technology platform into a hardware device such as a smartphone) have been widely applied in various industries. This study investigates the response of competing original equipment manufacturers (OEMs) when the developer strategically updates the platform quality. Consumers have two-dimensional valuations of the product: one valuation for the device provided by the OEM and another valuation for the platform provided by the developer. The developer offers two types of agreements: acceptance and rejection of preinstallation of core services that profit the developer. OEMs can preinstall the core services and pay nothing to use the platform, or they can pay a wholesale price for the platform and reject the installation of the core services. We propose a game-theoretic model to examine the strategic agreement choices of dominant and weak OEMs when the developer strategically offers an updated platform quality to enhance a weak OEM's competitiveness. We find that the developer benefits from the OEMs’ acceptance only when the exogenous wholesale price for the dominant OEM is low and the cost difference is moderate. Also, when the research and development (R&D) efficiency is high, an increase in the cost difference induces a conversion from acceptance to rejection for the dominant OEM, and an opposite outcome for the weak OEM. One OEM's installation acceptance may enhance or weaken the advantage of the other OEM's acceptance for the consumer surplus, depending on the value of the R&D efficiency. Several extensions are examined to complement the basic model, and implications behind each strategic choice are revealed.

Official prices are commonly publicized by firms as reference prices to influence consumers’ valuation of products. By characterizing the reference effect as a general nondecreasing concave function of the official price, we study the timing of pricing decisions in dual distribution channels where a manufacturer distributes the product by a direct channel and an indirect retail channel. We investigate two fundamental pricing policies wherein the manufacturer and the retailer put forward their selling prices simultaneously (ST) or sequentially (SQ). Counter intuitively, our analytical results demonstrate that despite facing a more severe downstream price competition, the manufacturer should charge a higher (rather than a lower) official price in the ST setting than in the SQ setting. In addition, with the reference effect of official price, the ideal case for the manufacturers that operate dual distribution channels is to let the direct channel focus on promoting products and let the retail channel function be the major selling channel, an idea which is supported by the practices of Nike, Apple, and many other leading brands around the world. Furthermore, our numerical examples disclose that the dual-channel efficiency under both decentralized cases (i.e., ST and SQ) increases in the magnitude and variability of the reference effect and the retail channel cost, but decreases in the unit direct channel cost. Finally, we extend our model to incorporate an online direct channel and an offline retailer. We find that, under all of CE, ST and SQ scenarios, the shipping fee charged in the online direct channel will reduce the manufacturer’s total profits by lowering her optimal official prices and direct-sales quantities simultaneously. Managerial implications of this study are discussed.

PurposeThe concept of open innovation has captured the attention of both academics and practitioners alike. However, there is a dearth of research on how innovations can be diffused within open innovation ecosystems, a critical condition for the sustainability of such ecosystems. In this regard, the study advances a research agenda for guiding future inquiries into innovation diffusion within open innovation ecosystems.Design/methodology/approachBased on a systematic review of the extant literature on open innovation, this article identifies knowledge gaps in innovation diffusion, along with recommendations for bridging these gaps in the future. The study advocates that future research should consider not only innovation generation processes, but also innovation diffusion processes, especially in light of the growing application of open innovation in the context of digital goods and services.FindingsSubscribing to an evolutionary view of innovation diffusion, the article draws on a five-phase framework – knowledge, persuasion, decision, implementation, and confirmation – to illustrate the roles played by three distinct yet interconnected parties (i.e. platforms, complementors, and individuals) within open innovation ecosystems as well as the research opportunities it brings.Originality/valueThe article examines the critical, yet underexplored role of innovation diffusion in sustaining open innovation ecosystems and outlines potential research avenues that can contribute to growing the understanding of the innovation diffusion process.

This paper studies the impact of bundled payment policy on healthcare cost, efficiency, quality, and shift of care. Using insurance claim data, we empirically offer a more nuanced understanding of the impact of bundled payment policy on hospital operations and provide new evidence from China. Our evidence suggests that transitioning from fee-for-service to bundled payment reimbursement resulted in declines in treatment costs and length of stay. Along with that decline, there was an increase in planned revisits to outpatient clinics, which indicates a shift of care from the inpatient to the outpatient setting, as well as a rise in unplanned revisits, indicating a decline in service quality. The increase in readmission rate to inpatient wards is very small and not statistically significant. In addition, we discuss the design and implementation of bundled payment. Our results imply that careful bundle design is vital to encouraging providers to implement the new program without sacrificing quality. Funding: This research is supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71921001], the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant HKU 17500217], and the Fundamental Research Funds for the Central Universities [Grant 2040000018].

Recently, firms in supply chains have begun to deploy popular mobile apps (e.g., WeChat) into their supply chain practices to improve demand visibility. These efforts rely on consumers to scan the products they purchase using these apps, which we refer to as consumer scanning technology (CST). CST can be an alternative to the conventional interorganizational information technology (IOIT) that relies on collaboration between supply chain firms. This paper develops a theoretical model to examine the value of CST to learn supply chain (demand) information and the impact of CST on IOIT. Using an extensive simulation analysis based on real-world data from a manufacturer that has implemented a CST program, we show that the value of CST to a manufacturer can be substantial and provide insights into how market conditions affect the value.

This paper explores a manufacturer’s production decisions and a government’s choice between cap-and-trade and carbon tax regulation. The manufacturer sells its product through offline and online channels, and we consider the online-offline spillover effect (OOSE) to reflect the influence of online sales on offline sales. We further compare carbon emission, the manufacturer’s profit, and social welfare for reselling and marketplace modes under the two types of regulations. First, we find that under both policies, when the government allocates a tight cap (high tax rate), the manufacturer should decrease online sales if the OOSE increases, and vice versa. Interestingly, we also discover that the manufacturer’s profit may decrease with the cap regardless of the modes. Second, under a low (high) environmental coefficient, the social welfare using reselling mode is lower (higher) than using marketplace mode, and the two modes can generate the same social welfare under a moderate environmental coefficient. Third, using either mode, when the environmental damage coefficient is low (high), the government should implement cap-and-trade regulation (carbon tax regulation). Finally, based on the data of a real-world company, when the environmental damage coefficient is low (high), the company should use an online platform with marketplace (reselling) mode to achieve optimal profit.

We provide a set of data on cross-border e-commerce sales from a fast-fashion e-retailer, Babyonlinedress, which stocks wedding gowns and full dresses from China but sells to markets overseas through multiple cross-border platforms, including Amazon, AliExpress, DHgate, etc. The data set contains normalized weekly orders on Amazon Marketplace over a three-year period, from January 1, 2015 to December 31, 2017. In total, we have collected M = 13,719 weekly orders for N = 1,166 different stock keeping units (SKUs) in the two fulfillment channels facilitated by Amazon: (i) Fulfillment by Amazon (FBA) and (ii) Fulfillment by Seller (FBS, also known as Fulfillment by Merchant). The orders for some SKUs were fulfilled entirely by seller during their life cycles on Amazon, whereas the rest were first fulfilled by seller for several months (testing period) before being transferred to FBA. This data set serves to illuminate the impact of cross-border fulfillment options on sales and bottom line of e-retailers.

We study an online platform’s demand information sharing strategy in a distribution channel where a manufacturer distributes products through both platform and seller channels.

In this paper, we investigate the temperature sensitivity of gain and breakdown voltage of detectors based on cycling excitation process (CEP), an internal signal amplification mechanism found in amorphous silicon (a-Si). Changes in gain and breakdown voltage with temperature can result in pixel-to-pixel signal variation in a focal plane array and variations in photon detection efficiency for single photon detectors. We have demonstrated athermalized CEP detectors with their gain and breakdown voltage being nearly temperature independent from 200 K to 350 K, covering the temperature range for practical applications. The device appears to be more thermally stable than avalanche photodetectors (APDs) with different gain media such as Si, InP, InAlAs, etc. The excellent thermal stability of CEP detectors is attributed to the field-enhanced tunneling process for excitation of localized carriers into the mobile bands, which dominates over the phonon excitation process.

Recently, some giant retail platforms have begun to invest large amounts of money in building warehouses. With abundant warehousing space, these platforms are renting out space to third-party retailers (TPRs) with insufficient storage space. However, such rental programs generate a new operational problem for these platforms, namely, how to divide their space between space for self-use and space for rent. To address the optimal space allocation decisions for this operational problem, we study a finite horizon and periodic review warehouse model where some items are sold by the platform, and other items are sold by TPRs. More specifically, we start with a base model with two items and prove the optimality of a base stock policy for the base model and the monotonicity of the optimal space allocation decisions regarding several key system parameters (such as inventory and capacity parameters). We then study several extensions of the base model by considering demand forecasts, demand correlation, and multiple items (more than two). Particularly, when addressing the extension with multiple items, we propose a heuristic based on the idea of approximate dynamic programming to overcome the curse of dimensionality. To examine the effectiveness of our heuristic, we present a computational study that is based on real-life datasets provided by the largest furniture retail platform in China (Red-Star Macalline). The numerical results demonstrate that our heuristic has the potential to solve medium- and large-scale problems effectively and efficiently.

This paper studies a creator's quality commitment strategies with uncertain demand when the creator lacks setup funding and chooses crowdfunding to finance. Facing uncertain demand, the creator may...

PurposeThe purpose of this paper is to analyze the operational decisions of a manufacturer who produces multiple products and the government's selection of cap-and-trade and carbon tax regulations.Design/methodology/approachThis paper explores the production decisions of a multi-product manufacturer under cap-and-trade and carbon tax regulations in a cap-dependent carbon trading price setting and compares carbon emission, the manufacturer's profits and social welfare under the two regulations. Game theory and extreme value theory are used to analyze our models.FindingsFirst, the authors find that the optimal profit of the manufacturer (the optimal cap) increases and then decreases with the cap (the unit carbon emission of product). Second, if the environmental damage coefficient is moderate, the optimal cap of unit environmental damage coefficient is independent of the product carbon emission or other related product parameters. Ultimately, cap-and-trade regulation always generates more carbon emission than carbon tax regulation. And cap-and-trade regulation (carbon tax regulation) can generate more social welfare if the environmental damage coefficient is low (high), and the social welfare under the two regulations is equal to each other, or otherwise.Originality/valueThis paper contributes the prior literature by considering the inverse relationship of the allocated cap and the carbon trading price and discusses the social welfare under cap-and-trade and carbon tax regulations. Some important and new results are found, which can guide the government's implementation of the two regulations.

PurposeNew information technologies such as IoT and big data analytics have reshaped the development of smart green products. These products exhibit two important features that are not seen in traditional products: environmental friendliness and data network effect. Based on these unique features, the authors investigate a firm's optimal selling strategy of smart green products from both the profitability and environmental perspectives.Design/methodology/approachThe authors establish stylized models to consider the optimality of three selling strategies: (1) traditional strategy – only offering traditional products, (2) green strategy – only offering smart green products, and (3) hybrid strategy – offering both traditional and smart green products.FindingsThe authors’ analysis shows that in the absence of data network effect, there will always be a conflict between profit maximization and environmental protection. However, a strategy that benefits both the firm and the environment exists when data network effect is present. Interestingly, hybrid and traditional strategies can be win-win strategies, but the green strategy cannot. Also surprisingly, the green strategy may harm the environment more as smart products become greener.Originality/valueThis study examines the economic and environmental implications of selling smart green products, and contributes to existing literature on sustainable operations and green product design by incorporating the impact of both consumer environmental awareness and data network effect. The authors’ findings shed light on how to coordinate the profitability and environmental impact of selling smart green products in the era of big data and IoT.

Sustainability, which inspires the inclusive development of economy, environment and society, has received extensive attention from practical and academic communities. Existing review literature on...

This paper studies a cooperating mode selection problem of a manufacturer who sells its products through an offline channel and an online platform under cap-and-trade regulation. The platform can operate with marketplace or reselling mode. We investigate the manufacturer's optimal operational decisions and selection of the platform's modes considering demand disruptions. First, when the carbon cap decreases in the application process, this regulation is firstly easy and then hard to be implemented if the cross-channel effect is small. Even though the platform increases its commission rate, greater cross-channel effect can still bring more production. Second, the increase of demand disruptions brings more profits for the manufacturer and the platform. Specifically, considering demand disruptions, the total profit with reselling mode in decentralised case is larger than that in centralised case under some situations. Third, without demand disruptions, the manufacturer prefers marketplace mode (reselling mode) if the commission rate is low (high). However, demand disruptions make the manufacturer's mode selection a little complicated. Lastly, without demand disruptions, the two firms can be coordinated with reselling mode but cannot be coordinated with marketplace mode. With demand disruptions, the two firms can be coordinated with the two modes in some situations.

This paper considers a supply chain consisting of a manufacturer and a retailer. The manufacturer sells its products through the retailer and an online platform and adopts green technology in the b...

Bundling strategies have been widely adopted by online retail platforms that use agency selling, where suppliers pay a commission fee to the platform to gain access and sell directly to consumers. We study the optimal bundling strategy for a retail platform through which two independent suppliers distribute their products. The suppliers first set their product prices, and, subsequently, the platform decides whether to offer the two products as a bundle and, if so, the price of the bundled product. By analyzing a two‐stage Stackelberg game, we find that the platform adopts the bundling strategy only when the commission rate and the product prices are sufficiently high. The existence of the bundling option does not affect the suppliers’ optimal pricing decisions if the product costs are either above a high threshold or below a low threshold. However, the suppliers may strategically raise the retail prices to induce the platform to offer bundled products when the marginal costs are in the medium range. The strategic interaction between the platform and suppliers leads to interesting insights into the bundling problem. For example, the retail platform may not prefer a high commission rate (i.e., a greater bargaining power) if the platform has the option to provide the bundled product. Additionally, the platform having even the possibility of bundling may hurt the platform's profit, but it always benefits the suppliers.

Statistical inference for precision matrix is of fundamental importance nowadays for learning conditional dependence structure in high-dimensional graphical models. Despite the fast growing literat...

In this paper, we study a firm's optimal joint product quality and quantity decisions when the consumers' utilities change with the market environment. We develop a model framework to study a monop...

This paper considers a two‐agent scheduling problem with linear resource‐dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is NP‐hard in the ordinary sense, and there does not exist a polynomial‐time approximation algorithm with performance ratio unless P=NP; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.

With consideration of the ambiguity and overfunding effects, this study develops generalized models to compare the sequential and simultaneous mechanisms in reward-based crowdfunding. Ambiguity effect is a negative psychological perception of customers since they lack the information to infer the product’s quality, however, overfunding effect is a positive psychological perception that enhances customers’ confidence in the product’s quality. Theoretically, we show that the two mechanisms may dominate each other in terms of entrepreneur and the two opposite effects can play significant roles in deciding the optimal crowdfunding mechanism. Furthermore, we propose a simple compensation mechanism to improve entrepreneur’s expected payoff and the crowdfunding’s success rate under both mechanisms. Finally, we analyse the informational cascade in crowdfunding and show how entrepreneur can use this phenomenon to maximize her expected payoff.

Organic–inorganic hybrid perovskites have electronic and optoelectronic properties that make them appealing in many device applications1–4. Although many approaches focus on polycrystalline materials5–7, single-crystal hybrid perovskites show improved carrier transport and enhanced stability over their polycrystalline counterparts, due to their orientation-dependent transport behaviour8–10 and lower defect concentrations11,12. However, the fabrication of single-crystal hybrid perovskites, and controlling their morphology and composition, are challenging12. Here we report a solution-based lithography-assisted epitaxial-growth-and-transfer method for fabricating single-crystal hybrid perovskites on arbitrary substrates, with precise control of their thickness (from about 600 nanometres to about 100 micrometres), area (continuous thin films up to about 5.5 centimetres by 5.5 centimetres), and composition gradient in the thickness direction (for example, from methylammonium lead iodide, MAPbI3, to MAPb0.5Sn0.5I3). The transferred single-crystal hybrid perovskites are of comparable quality to those directly grown on epitaxial substrates, and are mechanically flexible depending on the thickness. Lead–tin gradient alloying allows the formation of a graded electronic bandgap, which increases the carrier mobility and impedes carrier recombination. Devices based on these single-crystal hybrid perovskites show not only high stability against various degradation factors but also good performance (for example, solar cells based on lead–tin-gradient structures with an average efficiency of 18.77 per cent). A solution-based lithography-assisted epitaxial-growth-and-transfer method is used to fabricate single-crystal hybrid perovskites on any surface, with precise control of the thickness, area and chemical composition gradient.

Purpose Considering the cross-market network externality, this paper aims to explore the platform’s pricing decisions and its optimal profit under the given government investment, and then investigate the investment decision to improve social responsibility, which is measured by the social welfare. Design/methodology/approach When exploring the optimal pricing decisions under the given government investment, extreme value theory and sensitive analysis are used. When investigating the investment level, game theory and optimization method are used. Numerical examples are conducted to further illustrate the results. Findings First, after considering the government investment, whether the buyers and the sellers are charged depends on the investment level and the difference of the cross-market network externality (CNC) of the sellers and the buyers. Second, the optimal price on the sellers is decreasing (increasing) in the CNC of the buyers (sellers). The optimal price on the buyers is significantly affected by the investment level. Finally, the government investment is win-win for both the platform and the government, and Chinese Government should invest on the sellers heavily. Originality/value This study specifies the role of the government investment on the sellers in determining the platform’s pricing decisions and the improvement of the social responsibility, which is measured by social welfare.

This paper aims to investigate a tower-based automated parking system, with the appearance of a cylinder. The tower crane can simultaneously implement two types of movement: (1) ascending/descendin...