{"title":"Equitable distribution of perishable items in a food bank supply chain","authors":"Irem Sengul Orgut, Emmett J. Lodree","doi":"10.1111/poms.14019","DOIUrl":"https://doi.org/10.1111/poms.14019","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48461881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Range and charge anxiety remain essential barriers to a faster electric vehicle (EV) market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an EV uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real‐time availability data but struggle with data inaccuracy, for example, due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, for example, sharing observations of charging stations' availability or visit intentions between drivers. Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multiagent stochastic charging station search problem as a finite‐horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single‐agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting. Even in long planning horizons, our algorithm reduces the system cost by 25% while increasing each driver's search reliability.
{"title":"Coordinated charging station search in stochastic environments: A multiagent approach","authors":"Marianne Guillet, Maximilian Schiffer","doi":"10.1111/poms.13997","DOIUrl":"https://doi.org/10.1111/poms.13997","url":null,"abstract":"Abstract Range and charge anxiety remain essential barriers to a faster electric vehicle (EV) market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an EV uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real‐time availability data but struggle with data inaccuracy, for example, due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, for example, sharing observations of charging stations' availability or visit intentions between drivers. Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multiagent stochastic charging station search problem as a finite‐horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single‐agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting. Even in long planning horizons, our algorithm reduces the system cost by 25% while increasing each driver's search reliability.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136319428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The promise of consumer data along with advances in information technology has spurred innovation not only in the way firms conduct their business operations but also in the manner in which data are collected. A prominent institutional structure that has recently emerged is a data cooperative —an organization that collects data from its members, and processes and monetizes the pooled data. A characteristic of consumer data is the externality it generates: Data shared by an individual reveal information about other similar individuals; thus, the marginal value of pooled data increases in both the quantity and quality of the data. A key challenge faced by a data cooperative is the design of a revenue‐allocation scheme for sharing revenue with its members. An effective scheme generates a beneficial cycle: It incentivizes members to share high‐quality data, which in turn results in high‐quality pooled data—this increases the attractiveness of the data for buyers and hence the cooperative's revenue, ultimately resulting in improved compensation for the members. While the cooperative naturally wishes to maximize its total surplus, two other important desirable properties of an allocation scheme are individual rationality and coalitional stability. We first examine a natural proportional allocation scheme —which pays members based on their individual contribution—and show that it simultaneously achieves individual rationality, the first‐best outcome, and coalitional stability, when members' privacy costs are homogeneous. Under heterogeneity in privacy costs, we analyze a novel hybrid allocation scheme and show that it achieves both individual rationality and the first‐best outcome, but may not satisfy coalitional stability. Finally, our RobinHood allocation scheme —which uses a fraction of the revenue to ensure coalitional stability and allocates the remaining based on the hybrid scheme—achieves all the desirable properties.
{"title":"Robin Hood to the Rescue: Sustainable Revenue‐Allocation Schemes for Data Cooperatives","authors":"Milind Dawande, Sameer Mehta, Liying Mu","doi":"10.1111/poms.13995","DOIUrl":"https://doi.org/10.1111/poms.13995","url":null,"abstract":"Abstract The promise of consumer data along with advances in information technology has spurred innovation not only in the way firms conduct their business operations but also in the manner in which data are collected. A prominent institutional structure that has recently emerged is a data cooperative —an organization that collects data from its members, and processes and monetizes the pooled data. A characteristic of consumer data is the externality it generates: Data shared by an individual reveal information about other similar individuals; thus, the marginal value of pooled data increases in both the quantity and quality of the data. A key challenge faced by a data cooperative is the design of a revenue‐allocation scheme for sharing revenue with its members. An effective scheme generates a beneficial cycle: It incentivizes members to share high‐quality data, which in turn results in high‐quality pooled data—this increases the attractiveness of the data for buyers and hence the cooperative's revenue, ultimately resulting in improved compensation for the members. While the cooperative naturally wishes to maximize its total surplus, two other important desirable properties of an allocation scheme are individual rationality and coalitional stability. We first examine a natural proportional allocation scheme —which pays members based on their individual contribution—and show that it simultaneously achieves individual rationality, the first‐best outcome, and coalitional stability, when members' privacy costs are homogeneous. Under heterogeneity in privacy costs, we analyze a novel hybrid allocation scheme and show that it achieves both individual rationality and the first‐best outcome, but may not satisfy coalitional stability. Finally, our RobinHood allocation scheme —which uses a fraction of the revenue to ensure coalitional stability and allocates the remaining based on the hybrid scheme—achieves all the desirable properties.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134986238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. One of the major challenges for humanitarian organizations when planning relief efforts is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in post-disaster settings may involve missing element sand inconsistencies, which can severely hamper effective humanitarian decision making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision making. We illustrate the proposed approach on a case study that focuses on locating shelters to serve internally displaced people in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates into the decision making for determining shelter locations in the district. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively.
{"title":"A machine learning approach to deal with ambiguity in the humanitarian decision making","authors":"E. Grass, Jan Ortmann, B. Balcik, W. Rei","doi":"10.1111/poms.14018","DOIUrl":"https://doi.org/10.1111/poms.14018","url":null,"abstract":". One of the major challenges for humanitarian organizations when planning relief efforts is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in post-disaster settings may involve missing element sand inconsistencies, which can severely hamper effective humanitarian decision making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision making. We illustrate the proposed approach on a case study that focuses on locating shelters to serve internally displaced people in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates into the decision making for determining shelter locations in the district. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45783814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengcheng Zhai, Kurt M. Bretthauer, Jorge Mejia, Alfonso J. Pedraza-Martinez
{"title":"Improving drinking water access and equity in rural sub‐saharan africa","authors":"Chengcheng Zhai, Kurt M. Bretthauer, Jorge Mejia, Alfonso J. Pedraza-Martinez","doi":"10.1111/poms.14016","DOIUrl":"https://doi.org/10.1111/poms.14016","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48884199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nils Roemer, Gilvan C. Souza, Christian Tröster, G. Voigt
{"title":"Offset or reduce: How should firms implement carbon footprint reduction initiatives?","authors":"Nils Roemer, Gilvan C. Souza, Christian Tröster, G. Voigt","doi":"10.1111/poms.14017","DOIUrl":"https://doi.org/10.1111/poms.14017","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46195187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Pandemics, trade frictions, and military conflicts may disrupt manufacturers' production capacity. This paper develops a two‐period supply chain model with supply disruption risk in the second period to investigate inventory decisions and the manufacturer's commitment strategies. We consider three strategies: no commitment, price commitment, and inventory commitment. Inventory holding within the supply chain is investigated under each strategy, and the dominant strategy is determined by comparing them. Distinct results arise due to supply disruption risk. First, the retailer may have two opposite motives for inventory holding: inventory‐building motive and inventory‐shifting motive (i.e., shifting inventory burden to the manufacturer by decreasing order quantity), where the latter is exclusive to situations with supply disruption risk. Price commitment suppresses both motives, and inventory commitment suppresses only inventory shifting. Second, the retailer never holds inventory under price commitment. Under no commitment and inventory commitment, for high (low) holding cost and disruption risk, only the manufacturer (retailer) holds inventory. Furthermore, the manufacturer's inventory may decrease as disruption risk increases. Third, regarding strategy choice, while each strategy can be the dominant choice for the retailer and the supply chain, the manufacturer (weakly) prefers inventory commitment to the other two strategies. However, the implementation of inventory commitment demands high supply chain transparency, as the manufacturer always has an incentive to secretly deviate by holding less inventory. When inventory commitment is infeasible, the price commitment strategy's performance varies compared to no commitment, contrasting with the disruption risk‐free literature where wholesale price commitment never outperforms no commitment.
{"title":"Commitment strategies and inventory decisions under supply disruption risk","authors":"Lezhen Wu, Xiaole Wu, Yu Zhou","doi":"10.1111/poms.13998","DOIUrl":"https://doi.org/10.1111/poms.13998","url":null,"abstract":"Abstract Pandemics, trade frictions, and military conflicts may disrupt manufacturers' production capacity. This paper develops a two‐period supply chain model with supply disruption risk in the second period to investigate inventory decisions and the manufacturer's commitment strategies. We consider three strategies: no commitment, price commitment, and inventory commitment. Inventory holding within the supply chain is investigated under each strategy, and the dominant strategy is determined by comparing them. Distinct results arise due to supply disruption risk. First, the retailer may have two opposite motives for inventory holding: inventory‐building motive and inventory‐shifting motive (i.e., shifting inventory burden to the manufacturer by decreasing order quantity), where the latter is exclusive to situations with supply disruption risk. Price commitment suppresses both motives, and inventory commitment suppresses only inventory shifting. Second, the retailer never holds inventory under price commitment. Under no commitment and inventory commitment, for high (low) holding cost and disruption risk, only the manufacturer (retailer) holds inventory. Furthermore, the manufacturer's inventory may decrease as disruption risk increases. Third, regarding strategy choice, while each strategy can be the dominant choice for the retailer and the supply chain, the manufacturer (weakly) prefers inventory commitment to the other two strategies. However, the implementation of inventory commitment demands high supply chain transparency, as the manufacturer always has an incentive to secretly deviate by holding less inventory. When inventory commitment is infeasible, the price commitment strategy's performance varies compared to no commitment, contrasting with the disruption risk‐free literature where wholesale price commitment never outperforms no commitment.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135708491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Managing the security of information systems with partially observable vulnerability","authors":"Radha V. Mookerjee, J. Samuel","doi":"10.1111/poms.14015","DOIUrl":"https://doi.org/10.1111/poms.14015","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46068268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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