A classic result of Korte and Hausmann [1978] and Jenkyns [1976] bounds the quality of the greedy solution to the problem of finding a maximum value basis of an independence system in terms of the rank‐quotient. We extend this result in two ways. First, we apply the greedy algorithm to an inner independence system contained in . Additionally, following an idea of Milgrom [2017], we incorporate exogenously given prior information about the set of likely candidates for an optimal basis in terms of a set . We provide a generalization of the rank‐quotient that yields a tight bound on the worst‐case performance of the greedy algorithm applied to the inner independence system relative to the optimal solution in . Furthermore, we show that for a worst‐case objective, the inner independence system approximation may outperform not only the standard greedy algorithm but also the inner matroid approximation proposed by Milgrom [2017]. Second, we generalize the inner approximation framework of independence systems to inner approximations of packing instances in by inner polymatroids and inner packing instances. We consider the problem of maximizing a separable discrete concave function and show that our inner approximation can be better than the greedy algorithm applied to the original packing instance. Our result provides a lower bound to the generalized rank‐quotient of a greedy algorithm to the optimal solution in this more general setting and subsumes Malinov and Kovalyov [1980]. We apply the inner approximation approach to packing instances induced by the FCC incentive auction and by two knapsack constraints.
{"title":"On inner independence systems","authors":"Sven de Vries, Stephen Raach, Rakesh V. Vohra","doi":"10.1002/nav.22210","DOIUrl":"https://doi.org/10.1002/nav.22210","url":null,"abstract":"A classic result of Korte and Hausmann [1978] and Jenkyns [1976] bounds the quality of the greedy solution to the problem of finding a maximum value basis of an independence system in terms of the rank‐quotient. We extend this result in two ways. First, we apply the greedy algorithm to an <jats:italic>inner independence system</jats:italic> contained in . Additionally, following an idea of Milgrom [2017], we incorporate exogenously given prior information about the set of likely candidates for an optimal basis in terms of a set . We provide a generalization of the rank‐quotient that yields a tight bound on the worst‐case performance of the greedy algorithm applied to the inner independence system relative to the optimal solution in . Furthermore, we show that for a worst‐case objective, the inner independence system approximation may outperform not only the standard greedy algorithm but also the inner matroid approximation proposed by Milgrom [2017]. Second, we generalize the inner approximation framework of independence systems to inner approximations of packing instances in by inner polymatroids and inner packing instances. We consider the problem of maximizing a separable discrete concave function and show that our inner approximation can be better than the greedy algorithm applied to the original packing instance. Our result provides a lower bound to the generalized rank‐quotient of a greedy algorithm to the optimal solution in this more general setting and subsumes Malinov and Kovalyov [1980]. We apply the inner approximation approach to packing instances induced by the FCC incentive auction and by two knapsack constraints.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.
{"title":"On a fair and risk‐averse urban air mobility resource allocation problem under demand and capacity uncertainties","authors":"Luying Sun, Haoyun Deng, Peng Wei, Weijun Xie","doi":"10.1002/nav.22217","DOIUrl":"https://doi.org/10.1002/nav.22217","url":null,"abstract":"Urban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongbin Wang, Yongjian Li, Song Yao, Jinting Wang
Price promotion is an effective way to capture market share, as consumer sensitivity to price is universal. Yet, consumer satisfaction with services‐such as logistics‐plays a critical role. Deep price discounts can indeed spike demand, but they can also congest systems, thus prolonging delivery times and diminishing consumer satisfaction. Despite observed significant effects of logistics capacity on consumer and firm payoffs in logistics systems in recent years, economic and operational analysis of these effects remains under explored. This study theoretically examines the influence of logistics capacity on competition between two firms using a refined Hotelling model that incorporates system congestion via a BPR‐type congestion function. Our primary findings include: First, the possibility of multiple equilibria emerges with intermediate product values, fueled by either unilateral price reductions to seize greater market share or price increases to enhance marginal benefits. Moreover, a firm with superior logistics capacity may not always set higher prices at equilibrium. Second, we show that equilibrium pricing exhibits a non‐monotonic relationship with logistics capacity and market size. Lastly, we scrutinize firms' long‐term strategic reactions to changes in logistics capacity and the implications of marginal capacity costs, symmetric or otherwise. Our findings provide the following insights. We caution that the equilibrium pricing strategy is ambiguous when the product value is not extreme because both firms are more likely to engage in random price wars. Intriguingly, while augmenting logistics capacity might elevate service satisfaction, it could paradoxically reduce firm revenue or consumer surplus. Our analysis also indicates that higher marginal capacity costs could, counter intuitively, benefit firms or consumers.
{"title":"Impact of logistics capacity on duopoly competition: Implications for firms and consumers","authors":"Zhongbin Wang, Yongjian Li, Song Yao, Jinting Wang","doi":"10.1002/nav.22209","DOIUrl":"https://doi.org/10.1002/nav.22209","url":null,"abstract":"Price promotion is an effective way to capture market share, as consumer sensitivity to price is universal. Yet, consumer satisfaction with services‐such as logistics‐plays a critical role. Deep price discounts can indeed spike demand, but they can also congest systems, thus prolonging delivery times and diminishing consumer satisfaction. Despite observed significant effects of logistics capacity on consumer and firm payoffs in logistics systems in recent years, economic and operational analysis of these effects remains under explored. This study theoretically examines the influence of logistics capacity on competition between two firms using a refined Hotelling model that incorporates system congestion via a BPR‐type congestion function. Our primary findings include: First, the possibility of multiple equilibria emerges with intermediate product values, fueled by either unilateral price reductions to seize greater market share or price increases to enhance marginal benefits. Moreover, a firm with superior logistics capacity may not always set higher prices at equilibrium. Second, we show that equilibrium pricing exhibits a non‐monotonic relationship with logistics capacity and market size. Lastly, we scrutinize firms' long‐term strategic reactions to changes in logistics capacity and the implications of marginal capacity costs, symmetric or otherwise. Our findings provide the following insights. We caution that the equilibrium pricing strategy is ambiguous when the product value is not extreme because both firms are more likely to engage in random price wars. Intriguingly, while augmenting logistics capacity might elevate service satisfaction, it could paradoxically reduce firm revenue or consumer surplus. Our analysis also indicates that higher marginal capacity costs could, counter intuitively, benefit firms or consumers.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the problem of screening a large population for an infectious disease (i.e., classifying subjects as positive or negative) using group testing while considering important test and population‐level characteristics. Group testing, in which multiple samples are pooled together into a master sample and tested simultaneously, has the potential to significantly expand screening efforts, and, owing to the COVID‐19 pandemic, the topic has seen a surge of interest recently. In this paper, we construct optimal group testing designs that consider a heterogeneous population (i.e., with subject‐specific risk), imperfect tests, and while also modeling the dilution effect of grouping (a phenomenon in which the test accuracy of the master sample is affected by the concentration of the virus in the pool), which is often ignored in the literature. We conduct an exhaustive analysis under both a general dilution function and a specific (yet still calibratable) form of the dilution function. Our analytical results of the resulting challenging optimization problems unveil key structural properties that hold in an optimal solution, which we utilize to construct efficient solution schemes. We complement the analysis with two case studies, one on the screening of blood for the Hepatitis B Virus and the other on the screening of subjects for COVID‐19. Our results reveal significant benefits over current practices, individual testing, as well as prior studies that ignore the dilution effect. Such results underscore the importance of incorporating both test and population‐level characteristics into the modeling framework.
{"title":"Capturing the dilution effect of risk‐based grouping with application to COVID‐19 screening","authors":"Sohom Chatterjee, Hrayer Aprahamian","doi":"10.1002/nav.22205","DOIUrl":"https://doi.org/10.1002/nav.22205","url":null,"abstract":"We investigate the problem of screening a large population for an infectious disease (i.e., classifying subjects as positive or negative) using group testing while considering important test and population‐level characteristics. Group testing, in which multiple samples are pooled together into a master sample and tested simultaneously, has the potential to significantly expand screening efforts, and, owing to the COVID‐19 pandemic, the topic has seen a surge of interest recently. In this paper, we construct optimal group testing designs that consider a heterogeneous population (i.e., with subject‐specific risk), imperfect tests, and while also modeling the dilution effect of grouping (a phenomenon in which the test accuracy of the master sample is affected by the concentration of the virus in the pool), which is often ignored in the literature. We conduct an exhaustive analysis under both a general dilution function and a specific (yet still calibratable) form of the dilution function. Our analytical results of the resulting challenging optimization problems unveil key structural properties that hold in an optimal solution, which we utilize to construct efficient solution schemes. We complement the analysis with two case studies, one on the screening of blood for the Hepatitis B Virus and the other on the screening of subjects for COVID‐19. Our results reveal significant benefits over current practices, individual testing, as well as prior studies that ignore the dilution effect. Such results underscore the importance of incorporating both test and population‐level characteristics into the modeling framework.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate a data‐driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long‐run average performances. When demand has a finite support, we can accomplish a regret bound in the order of which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between and depending on the nature of moment‐related bounds that help characterize the degree of ambiguity. These are improvable to when distributions are light‐tailed. Our simulation demonstrates the merits of various policy ideas.
{"title":"Data‐driven inventory control involving fixed setup costs and discrete censored demand","authors":"Michael N. Katehakis, Ehsan Teymourian, Jian Yang","doi":"10.1002/nav.22211","DOIUrl":"https://doi.org/10.1002/nav.22211","url":null,"abstract":"We investigate a data‐driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long‐run average performances. When demand has a finite support, we can accomplish a regret bound in the order of which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between and depending on the nature of moment‐related bounds that help characterize the degree of ambiguity. These are improvable to when distributions are light‐tailed. Our simulation demonstrates the merits of various policy ideas.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.
{"title":"Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms","authors":"Hannah Horner, Jennifer Pazour, John E. Mitchell","doi":"10.1002/nav.22212","DOIUrl":"https://doi.org/10.1002/nav.22212","url":null,"abstract":"This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many governments worldwide offer various types of consumer‐specific subsidy programs, such as a trade‐in subsidy (TS) program that targets existing consumers only, a consumption subsidy (CS) program that covers both new and existing consumers with undifferentiated subsidy levels, or a mixed subsidy (MS) program that targets the two consumer segments with differentiated subsidy levels. However, which program is more beneficial to social welfare and other stakeholders is largely unknown. In this paper, we establish a game‐theoretic model to explore the impacts of these subsidy programs on different stakeholders (i.e., the firm, consumers, the environment, and social welfare). Interestingly, we uncover that the TS and MS programs have equal effectiveness in stimulating demand (collecting old products) from existing consumers, whereas the CS and MS programs have relative advantages in expanding the total demand from both new and existing consumers. We further find that (i) when product durability is low, the flexible MS scheme can lead to a quadruple‐win for all stakeholders, (ii) when product durability is moderate, the MS scheme is better for social welfare and the environment, whereas the CS scheme benefits the firm and consumers more, and (iii) when product durability is high, the MS scheme can achieve a triple‐win for the firm, consumers, and social welfare, whereas the CS scheme is better for the environment. Moreover, we identify the differential impacts of each program on different stakeholders when considering that the earmarked subsidy is limited and a secondary market exists. Our findings not only shed light on why the TS, CS, and MS programs are all likely to be adopted in practice but also provide helpful guidelines for governments aiming to offer a more effective subsidy program.
世界上许多国家的政府都提供了各种类型的针对特定消费者的补贴计划，例如只针对现有消费者的以旧换新补贴（TS）计划、同时覆盖新老消费者且补贴水平无差别的消费补贴（CS）计划，或者针对这两个消费群体且补贴水平有差别的混合补贴（MS）计划。然而，究竟哪种方案对社会福利和其他利益相关者更有利，这在很大程度上还是个未知数。在本文中，我们建立了一个博弈论模型来探讨这些补贴计划对不同利益相关者（即企业、消费者、环境和社会福利）的影响。有趣的是，我们发现 TS 和 MS 计划在刺激现有消费者的需求（回收旧产品）方面具有同等效力，而 CS 和 MS 计划在扩大新老消费者的总需求方面具有相对优势。我们进一步发现：(i) 当产品耐用性较低时，灵活的 MS 方案可以为所有利益相关者带来四赢；(ii) 当产品耐用性适中时，MS 方案更有利于社会福利和环境，而 CS 方案则更有利于企业和消费者；(iii) 当产品耐用性较高时，MS 方案可以实现企业、消费者和社会福利的三赢，而 CS 方案则更有利于环境。此外，考虑到专项补贴有限且存在二级市场，我们还确定了每种方案对不同利益相关者的不同影响。我们的研究结果不仅揭示了为什么 TS、CS 和 MS 方案都有可能在实践中被采用，而且还为政府提供了有益的指导，以提供更有效的补贴方案。
{"title":"Stakeholder perspectives on government subsidy programs: Trade‐in subsidy, consumption subsidy, or mixed subsidy?","authors":"Fei Tang, Zu‐Jun Ma, Ying Dai, Tsan‐Ming Choi","doi":"10.1002/nav.22208","DOIUrl":"https://doi.org/10.1002/nav.22208","url":null,"abstract":"Many governments worldwide offer various types of consumer‐specific subsidy programs, such as a trade‐in subsidy (TS) program that targets existing consumers only, a consumption subsidy (CS) program that covers both new and existing consumers with undifferentiated subsidy levels, or a mixed subsidy (MS) program that targets the two consumer segments with differentiated subsidy levels. However, which program is more beneficial to social welfare and other stakeholders is largely unknown. In this paper, we establish a game‐theoretic model to explore the impacts of these subsidy programs on different stakeholders (i.e., the firm, consumers, the environment, and social welfare). Interestingly, we uncover that the TS and MS programs have equal effectiveness in stimulating demand (collecting old products) from existing consumers, whereas the CS and MS programs have relative advantages in expanding the total demand from both new and existing consumers. We further find that (i) when product durability is low, the flexible MS scheme can lead to a quadruple‐win for all stakeholders, (ii) when product durability is moderate, the MS scheme is better for social welfare and the environment, whereas the CS scheme benefits the firm and consumers more, and (iii) when product durability is high, the MS scheme can achieve a triple‐win for the firm, consumers, and social welfare, whereas the CS scheme is better for the environment. Moreover, we identify the differential impacts of each program on different stakeholders when considering that the earmarked subsidy is limited and a secondary market exists. Our findings not only shed light on why the TS, CS, and MS programs are all likely to be adopted in practice but also provide helpful guidelines for governments aiming to offer a more effective subsidy program.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Token sales design under network effect","authors":"Zhao Liu, Xiaoqiang Cai, Fasheng Xu, Lianmin Zhang","doi":"10.1002/nav.22206","DOIUrl":"https://doi.org/10.1002/nav.22206","url":null,"abstract":"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.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper optimizes condition‐based replacement policies for a mission‐oriented system. The key challenge in our problem is that the system does not work under a fixed mission type but is subject to an infinite sequence of random types of missions assigned in a Markovian manner, which is realistic in many practical situations. The mission process modulates the deterioration process. Taking advantage of the opportunities when missions are switched, condition monitoring is conducted to support replacement decision‐making. This paper considers two practical scenarios in which the type of the next mission is either available or unavailable at each decision epoch. The objective is to determine the optimal replacement decisions for both scenarios that minimize their long‐run expected average cost rates. The optimization problems are analyzed in the framework of the Markov decision process. The optimal decisions of both scenarios are proven to be of partially monotone control‐limit forms. Near‐optimal policies with multilevel thresholds are provided for more convenient decision‐making. The policy iteration algorithm is modified for efficient optimization. A numerical example is used to demonstrate the feasibility of the proposed approach.
{"title":"Structured replacement policies for a system subject to random mission types","authors":"Rui Zheng","doi":"10.1002/nav.22201","DOIUrl":"https://doi.org/10.1002/nav.22201","url":null,"abstract":"This paper optimizes condition‐based replacement policies for a mission‐oriented system. The key challenge in our problem is that the system does not work under a fixed mission type but is subject to an infinite sequence of random types of missions assigned in a Markovian manner, which is realistic in many practical situations. The mission process modulates the deterioration process. Taking advantage of the opportunities when missions are switched, condition monitoring is conducted to support replacement decision‐making. This paper considers two practical scenarios in which the type of the next mission is either available or unavailable at each decision epoch. The objective is to determine the optimal replacement decisions for both scenarios that minimize their long‐run expected average cost rates. The optimization problems are analyzed in the framework of the Markov decision process. The optimal decisions of both scenarios are proven to be of partially monotone control‐limit forms. Near‐optimal policies with multilevel thresholds are provided for more convenient decision‐making. The policy iteration algorithm is modified for efficient optimization. A numerical example is used to demonstrate the feasibility of the proposed approach.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141196584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The counting process has abundant applications in reality, and Poisson process monitoring actually has received extensive attention and research. However, conventional methods experience poor performance when shifts appears early and only small number of historical observations in Phase I can be used for estimation. To overcome it, we creatively propose a new online monitoring algorithm under the transfer learning framework, which utilizes the information from observations of additional data sources so that the target process can be described better. By making the utmost of the somewhat correlated data from other domains, which is measured by a bivariate Gamma distributed statistic presented by us, the explicit properties (e.g., posterior probability mass function, posterior expectation, and posterior variance) are also strictly proved. Furthermore, based on the above theoretical results, we design two computationally efficient control schemes in Phase II, that is a control chart based on the cumulative distribution function for large shifts and an exponentially weighted moving average control chart for small shifts. For a better understanding of the more practical applications and transferability matter, we provide some optimal values for parameter setting. Extensive numerical simulations and a case of skin cancer incidence in America verify the superiorities of our approach.
{"title":"Online detection of the incidence via transfer learning","authors":"Miaomiao Yu, Zhijun Wang, Chunjie Wu","doi":"10.1002/nav.22191","DOIUrl":"https://doi.org/10.1002/nav.22191","url":null,"abstract":"The counting process has abundant applications in reality, and Poisson process monitoring actually has received extensive attention and research. However, conventional methods experience poor performance when shifts appears early and only small number of historical observations in Phase I can be used for estimation. To overcome it, we creatively propose a new online monitoring algorithm under the transfer learning framework, which utilizes the information from observations of additional data sources so that the target process can be described better. By making the utmost of the somewhat correlated data from other domains, which is measured by a bivariate Gamma distributed statistic presented by us, the explicit properties (e.g., posterior probability mass function, posterior expectation, and posterior variance) are also strictly proved. Furthermore, based on the above theoretical results, we design two computationally efficient control schemes in Phase II, that is a control chart based on the cumulative distribution function for large shifts and an exponentially weighted moving average control chart for small shifts. For a better understanding of the more practical applications and transferability matter, we provide some optimal values for parameter setting. Extensive numerical simulations and a case of skin cancer incidence in America verify the superiorities of our approach.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：