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This paper studies the multiple period inventory replenishment problem for a capacitated warehouse when procurement prices and market demands are both uncertain, with the goal to minimise the procuring and holding costs. We analyse this problem under the framework of competitive analysis, where neither probabilistic distributions nor sets are available to characterise the unknown price and demand parameters. An efficient online real-time replenishment algorithm is developed, which is free of any distribution assumption, and the decisions are made based entirely on past and present information. We derive an instance-independent competitive ratio of the algorithm, which provides a worst-case theoretical performance guarantee, and shows that the proposed algorithm performs well for situations with high volatility uncertainty over time and is naturally risk-averse. Finally, a set of numerical experiments further verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This article examines a real-time parking reservation service that dynamically allocates a limited number of parking slots (which can be reused) to serve parking requests in a crowded area. The study takes into account two stochastic factors: (i) random arrivals of parking requests and (ii) driver parking unpunctuality, which are seldom simultaneously considered in the existing literature. This study formulates the dynamic parking resource allocation problem as a joint chance-constrained model to maximize the expected total revenue over a finite horizon. Due to the complex nature of the model considering the two stochastic factors, the study proposes a data-driven approach using joint chance constraint decomposition and sample average approximation to approximate the model to a deterministic mixed-integer programming model. Furthermore, a stratified ranked set sampling method is introduced to construct a high-quality sample set as the input to the deterministic model, and valid inequalities are designed to accelerate the solution process. Numerical experiments based on real-world data are conducted to validate the performance of the proposed approach under multiple parking scenarios and provide findings. [ABSTRACT FROM AUTHOR]

This paper explores the use of initial coin offerings (ICOs) as a means of fundraising for companies through the issuance of blockchain‐based tokens. Specifically, we investigate how a company can implement ICOs and issue tokens supported by its products while taking into account the presence of network effects. We examine several aspects of ICOs, including the impacts of network effect on the company's optimal token sales design, such as the optimal token issuance price, the ICO cap (which refers to the number of tokens to be issued), and the amount of funds raised. We also consider the cases of deterministic and uncertain network effects, the impacts of limited speculators purchasing tokens during the ICO period, the comparison between ICOs and traditional bank financing, and the impact of ICO cost. The research unfolds in five key parts. Firstly, leveraging a baseline model, we derive equilibrium outcomes and the optimal token sales design, discerning the impact of deterministic and uncertain network effects. Notably, network effect uncertainty consistently benefits companies, ensuring profit resilience despite potential challenges, although in the face of such uncertainty, the ICO company may decrease the raised fund. Secondly, we investigate the consequences of a limited number of speculators in the ICO landscape, finding that adjustments on the token sales design are not required under low expected network effect. Thirdly, a comparative study between ICOs and bank financing reveals distinctive advantages of ICOs. ICOs prove capable of decreasing profit volatility while maintaining an equivalent profit compared to traditional bank financing. This insight offers valuable guidance for companies seeking optimal financing methods aligned with their risk preferences. Lastly, the impact of ICO cost on outcomes and token sales design is scrutinized. Contrary to expectations, ICO cost does not uniformly lead to an increase in the ICO cap, underscoring the nuanced relationship between costs and fundraising strategies. [ABSTRACT FROM AUTHOR]

Fraudulent returns have become increasingly frequent in the development of online retail platforms, leading to huge losses for retailers. A blockchain‐based system can help eliminate this problem primarily because the data stored in a blockchain network cannot be modified. In this paper, we study the impact of blockchain technology that aims to eliminate fraudulent return behavior on retailers and their upstream suppliers. We develop a game‐theoretic model that comprises a supplier, a retailer, and customers and analyze the equilibrium outcomes. The results show that the retailer would (not) adopt blockchain technology if the efficiency of the supplier's investment in the innovation of its products is high (low). Moreover, if the retailer's loss from the return is low (high), the adoption of blockchain technology decreases (increases) the supplier's investment and the wholesale and retail prices. In terms of its effect on profitability, we reveal that if the fraudulent return losses are high, using blockchain technology always benefits the supplier; otherwise, it increases retailer profitability and supply chain performance but may make the supplier suffer losses in profits. Interestingly, the retailer would adopt blockchain technology to ensure profitability in situations where the number of customers without fraudulent returns and the intensity of losses are low; however, this weakens supplier profitability and meanwhile reduces supply chain performance. [ABSTRACT FROM AUTHOR]

Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices. [ABSTRACT FROM AUTHOR]