Abstract In practice, many retailers employ price‐matching guarantees (PMGs), committing to meet the price of an identical product at a competitor's outlet. Despite the profound linkage between retailers and manufacturers, existing literature has predominantly explored retailers' PMGs without contemplating the influence of manufacturers' wholesale pricing strategies. Employing a supply chain model comprising one manufacturer and two retailers, we scrutinize the implications of wholesale pricing—uniform or discriminatory—on supply chain members and consumers when retailers have the option to extend PMGs. Our analysis uncovers that retailers refrain from offering PMGs when the manufacturer is granted the discretion to set discriminatory wholesale prices—even if such offers align with the manufacturer's preferences. Conversely, under uniform wholesale pricing, PMGs thrive at equilibrium—even if the manufacturer opposes the practice—as long as the degree of demand or cost asymmetry between retailers and average hassle costs remains relatively modest. Although firms' preferences regarding PMGs vary, a Pareto zone exists where all entities prefer that either the efficient retailer under demand asymmetry or the inefficient retailer under cost asymmetry extends the PMG. Despite the potential advantages of PMGs for the more efficient retailer, the enforcement of uniform wholesale pricing diminishes supply chain profit, consumer welfare, and overall social welfare. The detrimental impacts on welfare owing to the imposition of uniform wholesale pricing persist, even amid the presence of hassle costs associated with price matching. Our findings thus instigate a dialogue for policymakers concerning the validity of regulating wholesale pricing when PMGs are in effect.
{"title":"Interaction between manufacturer's wholesale pricing and retailers' price‐matching guarantees","authors":"Arcan Nalca, Gangshu (George) Cai","doi":"10.1111/poms.14060","DOIUrl":"https://doi.org/10.1111/poms.14060","url":null,"abstract":"Abstract In practice, many retailers employ price‐matching guarantees (PMGs), committing to meet the price of an identical product at a competitor's outlet. Despite the profound linkage between retailers and manufacturers, existing literature has predominantly explored retailers' PMGs without contemplating the influence of manufacturers' wholesale pricing strategies. Employing a supply chain model comprising one manufacturer and two retailers, we scrutinize the implications of wholesale pricing—uniform or discriminatory—on supply chain members and consumers when retailers have the option to extend PMGs. Our analysis uncovers that retailers refrain from offering PMGs when the manufacturer is granted the discretion to set discriminatory wholesale prices—even if such offers align with the manufacturer's preferences. Conversely, under uniform wholesale pricing, PMGs thrive at equilibrium—even if the manufacturer opposes the practice—as long as the degree of demand or cost asymmetry between retailers and average hassle costs remains relatively modest. Although firms' preferences regarding PMGs vary, a Pareto zone exists where all entities prefer that either the efficient retailer under demand asymmetry or the inefficient retailer under cost asymmetry extends the PMG. Despite the potential advantages of PMGs for the more efficient retailer, the enforcement of uniform wholesale pricing diminishes supply chain profit, consumer welfare, and overall social welfare. The detrimental impacts on welfare owing to the imposition of uniform wholesale pricing persist, even amid the presence of hassle costs associated with price matching. Our findings thus instigate a dialogue for policymakers concerning the validity of regulating wholesale pricing when PMGs are in effect.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134948212","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 paper proposes an optimization model for the allocation of vaccines to a heterogeneous population composed of several subpopulations with different sizes and epidemiological disease transmission parameters. As the objective, an aggregated function combining a standard utilitarian efficiency criterion with a Gini index–related penalty term is considered. Contrary to previous work, we adopt an outcome equity view: The inequity measure is not based on vaccination fractions or other input factors, but on the fractions of individuals escaping infection, as predicted by an susceptible‐infectious‐removed (SIR) model. An adjusted pro rata (APR) policy of vaccine allocation minimizing inequity in this outcome view is introduced, and a numerical procedure for its determination is presented. The concepts are developed both for the case of segregated subpopulations and for that of interactions between the subpopulations. Interestingly, in a large number of instances, the optimal solution under the aggregated objective function turns out to be identical to APR. Whether APR is locally or even globally optimal in a concrete case depends on the relation of an inequity aversion parameter to certain threshold values. While the local optimality threshold can be determined by linear programming, the determination of the global optimality threshold, as the vaccine allocation problem itself, is a problem of nonconvex optimization. We suggest an exact optimization approach for smaller instances, and propose algorithms building on particle swarm optimization for threshold determination and allocation optimization at larger instances. Extensions to alternative outcome measures such as the number of fatalities are presented as well. In addition to the investigation of randomly generated instances, two test cases from the literature are revisited in the context of the present work. Moreover, a new case study based on data from the COVID‐19 outbreak in Austria in 2020 is introduced and analyzed.
{"title":"Fair and efficient vaccine allocation: A generalized Gini index approach","authors":"Walter J. Gutjahr","doi":"10.1111/poms.14080","DOIUrl":"https://doi.org/10.1111/poms.14080","url":null,"abstract":"Abstract The paper proposes an optimization model for the allocation of vaccines to a heterogeneous population composed of several subpopulations with different sizes and epidemiological disease transmission parameters. As the objective, an aggregated function combining a standard utilitarian efficiency criterion with a Gini index–related penalty term is considered. Contrary to previous work, we adopt an outcome equity view: The inequity measure is not based on vaccination fractions or other input factors, but on the fractions of individuals escaping infection, as predicted by an susceptible‐infectious‐removed (SIR) model. An adjusted pro rata (APR) policy of vaccine allocation minimizing inequity in this outcome view is introduced, and a numerical procedure for its determination is presented. The concepts are developed both for the case of segregated subpopulations and for that of interactions between the subpopulations. Interestingly, in a large number of instances, the optimal solution under the aggregated objective function turns out to be identical to APR. Whether APR is locally or even globally optimal in a concrete case depends on the relation of an inequity aversion parameter to certain threshold values. While the local optimality threshold can be determined by linear programming, the determination of the global optimality threshold, as the vaccine allocation problem itself, is a problem of nonconvex optimization. We suggest an exact optimization approach for smaller instances, and propose algorithms building on particle swarm optimization for threshold determination and allocation optimization at larger instances. Extensions to alternative outcome measures such as the number of fatalities are presented as well. In addition to the investigation of randomly generated instances, two test cases from the literature are revisited in the context of the present work. Moreover, a new case study based on data from the COVID‐19 outbreak in Austria in 2020 is introduced and analyzed.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947442","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 We study mechanisms that encourage manufacturers of health products to build production and distribution capacity. This is important for low‐ and middle‐income country (LMIC) markets where ability to pay is lower and demand risks are greater. Development finance institutions and philanthropies are beginning to utilize new instruments to incentivize manufacturers to build production/distribution capacity for LMIC markets. The goal of this paper is to understand the effectiveness of such mechanisms in different settings. We examine four instruments: (1) subsidy proportional to unit sales (sales subsidy), (2) subsidy proportional to unit capacity (variable‐capacity subsidy), (3) subsidy proportional to total capacity investment (total‐capacity subsidy), (4) a minimum volume guarantee. We analyze incentivized capacity as a function of social‐investor budget for each instrument. We show how our framework can be used to identify a social investor's preferred instrument given relevant parameter estimates, and we provide insight into the type of settings where a particular instrument dominates. A sales subsidy dominates when ability to pay is very low; a total‐capacity subsidy dominates when ability to pay is low. Outside of these settings, instrument preference is nuanced, though a sales subsidy is dominated by at least one other instrument. When ability to pay is moderate, a variable‐capacity subsidy tends to be preferred under high variable‐capacity cost and high budget, a volume guarantee tends to be preferred under low variable‐capacity cost and high budget, and a total‐capacity subsidy tends to be preferred under low budget. This article is protected by copyright. All rights reserved
{"title":"Increasing the supply of health products in underserved regions","authors":"Burak Kazaz, Scott Webster, Prashant Yadav","doi":"10.1111/poms.14085","DOIUrl":"https://doi.org/10.1111/poms.14085","url":null,"abstract":"Abstract We study mechanisms that encourage manufacturers of health products to build production and distribution capacity. This is important for low‐ and middle‐income country (LMIC) markets where ability to pay is lower and demand risks are greater. Development finance institutions and philanthropies are beginning to utilize new instruments to incentivize manufacturers to build production/distribution capacity for LMIC markets. The goal of this paper is to understand the effectiveness of such mechanisms in different settings. We examine four instruments: (1) subsidy proportional to unit sales (sales subsidy), (2) subsidy proportional to unit capacity (variable‐capacity subsidy), (3) subsidy proportional to total capacity investment (total‐capacity subsidy), (4) a minimum volume guarantee. We analyze incentivized capacity as a function of social‐investor budget for each instrument. We show how our framework can be used to identify a social investor's preferred instrument given relevant parameter estimates, and we provide insight into the type of settings where a particular instrument dominates. A sales subsidy dominates when ability to pay is very low; a total‐capacity subsidy dominates when ability to pay is low. Outside of these settings, instrument preference is nuanced, though a sales subsidy is dominated by at least one other instrument. When ability to pay is moderate, a variable‐capacity subsidy tends to be preferred under high variable‐capacity cost and high budget, a volume guarantee tends to be preferred under low variable‐capacity cost and high budget, and a total‐capacity subsidy tends to be preferred under low budget. This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135646116","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 Near‐constant Internet access through desktop or mobile devices has turned self‐service support forums into the first port of call for users seeking to troubleshoot product or service issues. The firms providing these products and services also benefit from this trend since it reduces user support costs by diverting service requests away from costlier support channels, such as help desks. For the continued success of such a forum, however, the managing entity must ensure that users receive timely solutions to their inquiries quickly and regularly. We develop a mathematical model of a user forum's operations to obtain a “white box” view of a user forum and reveal the support system's dynamics. Then, using a large and comprehensive dataset of questions and answers from Apple's iPhone user forum, we empirically estimate the forum's performance to validate the predictions of the mathematical model. Our results demonstrate that the predictions closely match the forum's actual performance, with an error of less than 10%. We then propose and analyze an optimal threshold policy that boosts a thread to rekindle user interest and demonstrate the benefit of our intervention policy in managing the iPhone forum.
{"title":"A boosting policy to optimize user forum performance: Model and validation","authors":"Radha Mookerjee, Wael Jabr, Harpreet Singh","doi":"10.1111/poms.14066","DOIUrl":"https://doi.org/10.1111/poms.14066","url":null,"abstract":"Abstract Near‐constant Internet access through desktop or mobile devices has turned self‐service support forums into the first port of call for users seeking to troubleshoot product or service issues. The firms providing these products and services also benefit from this trend since it reduces user support costs by diverting service requests away from costlier support channels, such as help desks. For the continued success of such a forum, however, the managing entity must ensure that users receive timely solutions to their inquiries quickly and regularly. We develop a mathematical model of a user forum's operations to obtain a “white box” view of a user forum and reveal the support system's dynamics. Then, using a large and comprehensive dataset of questions and answers from Apple's iPhone user forum, we empirically estimate the forum's performance to validate the predictions of the mathematical model. Our results demonstrate that the predictions closely match the forum's actual performance, with an error of less than 10%. We then propose and analyze an optimal threshold policy that boosts a thread to rekindle user interest and demonstrate the benefit of our intervention policy in managing the iPhone forum.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131289","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}
Chen‐Nan Liao, Ying‐Ju Chen, Vincent (Pei‐Ming) Chen
Abstract Medical rumors have become a threat to modern society. To study the spread and control of rumors, nonlinear differential equations modeling with the well‐mixed assumption is commonly used. However, this approach ignores the underlying network structure which plays an important role in information spreading. We establish a generalized differential equations model to study the spread and control of medical rumors in a highly asymmetric social network. In our model, each node represents a group of people and a “weighted” and “directed” network describes the communications between these nodes. This network can be generated from real‐world data by community detection algorithms. We provide methods to numerically calculate the final size of a rumor in each node and its derivatives with respect to each parameter. With these methods, if the government has resources to influence the parameters subject to certain constraints or cost functions, one can obtain the optimal resources allocation easily through nonlinear programming algorithms. We show that the implications on the government's resources allocation from the well‐mixed special case in the literature or conventional wisdom may become inapplicable in the general situation. Therefore, the underlying network should not be ignored. Because the final size of a medical rumor is not always the best measure of its damage, we extend our results to a wide class of objectives and show that different objectives result in very different implications. While the lack of a rule of thumb may sound negative, our flexible framework provides a powerful workhorse for interested parties to work out the details in their specific situations. Finally, we provide a sufficient condition for no outbreak of rumors. This condition can serve as a heuristic that a government with abundant resources can use to prevent the outbreak of rumors.
{"title":"Spread and control of medical rumors in a social network: A generalized diffusion model with a highly asymmetric network structure","authors":"Chen‐Nan Liao, Ying‐Ju Chen, Vincent (Pei‐Ming) Chen","doi":"10.1111/poms.14057","DOIUrl":"https://doi.org/10.1111/poms.14057","url":null,"abstract":"Abstract Medical rumors have become a threat to modern society. To study the spread and control of rumors, nonlinear differential equations modeling with the well‐mixed assumption is commonly used. However, this approach ignores the underlying network structure which plays an important role in information spreading. We establish a generalized differential equations model to study the spread and control of medical rumors in a highly asymmetric social network. In our model, each node represents a group of people and a “weighted” and “directed” network describes the communications between these nodes. This network can be generated from real‐world data by community detection algorithms. We provide methods to numerically calculate the final size of a rumor in each node and its derivatives with respect to each parameter. With these methods, if the government has resources to influence the parameters subject to certain constraints or cost functions, one can obtain the optimal resources allocation easily through nonlinear programming algorithms. We show that the implications on the government's resources allocation from the well‐mixed special case in the literature or conventional wisdom may become inapplicable in the general situation. Therefore, the underlying network should not be ignored. Because the final size of a medical rumor is not always the best measure of its damage, we extend our results to a wide class of objectives and show that different objectives result in very different implications. While the lack of a rule of thumb may sound negative, our flexible framework provides a powerful workhorse for interested parties to work out the details in their specific situations. Finally, we provide a sufficient condition for no outbreak of rumors. This condition can serve as a heuristic that a government with abundant resources can use to prevent the outbreak of rumors.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131461","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}
Joren Gijsbrechts, Christina Imdahl, Robert N. Boute, Jan A. Van Mieghem
Abstract We study inventory control with volume flexibility: A firm can replenish using period‐dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period‐dependent base‐stock levels but determining their values is not trivial, especially for nonstationary and correlated demand. We propose the Lookahead Peak‐Shaving policy that anticipates and peak shaves orders from future peak‐demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is threefold. First, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak‐Shaving policy. Second, we provide explicit expressions of the period‐dependent base‐stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is nonstationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.
{"title":"Optimal robust inventory management with volume flexibility: Matching capacity and demand with the lookahead peak‐shaving policy<sup>a</sup>","authors":"Joren Gijsbrechts, Christina Imdahl, Robert N. Boute, Jan A. Van Mieghem","doi":"10.1111/poms.14069","DOIUrl":"https://doi.org/10.1111/poms.14069","url":null,"abstract":"Abstract We study inventory control with volume flexibility: A firm can replenish using period‐dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period‐dependent base‐stock levels but determining their values is not trivial, especially for nonstationary and correlated demand. We propose the Lookahead Peak‐Shaving policy that anticipates and peak shaves orders from future peak‐demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is threefold. First, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak‐Shaving policy. Second, we provide explicit expressions of the period‐dependent base‐stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is nonstationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131455","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 CVaR (Conditional value at risk) is a risk metric widely used in finance. However, dynamically optimizing CVaR is difficult, because it is not a standard Markov decision process (MDP) and the principle of dynamic programming fails. In this paper, we study the infinite‐horizon discrete‐time MDP with a long‐run CVaR criterion, from the view of sensitivity‐based optimization. By introducing a pseudo‐CVaR metric, we reformulate the problem as a bilevel MDP model and derive a CVaR difference formula that quantifies the difference of long‐run CVaR under any two policies. The optimality of deterministic policies is derived. We obtain a so‐called Bellman local optimality equation for CVaR, which is a necessary and sufficient condition for locally optimal policies and only necessary for globally optimal policies. A CVaR derivative formula is also derived for providing more sensitivity information. Then we develop a policy iteration type algorithm to efficiently optimize CVaR, which is shown to converge to a local optimum in mixed policy space. Furthermore, based on the sensitivity analysis of our bilevel MDP formulation and critical points, we develop a globally optimal algorithm. The piecewise linearity and segment convexity of the optimal pseudo‐CVaR function are also established. Our main results and algorithms are further extended to optimize the mean and CVaR simultaneously. Finally, we conduct numerical experiments relating to portfolio management to demonstrate the main results. Our work sheds light on dynamically optimizing CVaR from a sensitivity viewpoint.
CVaR (Conditional value at risk)是金融中广泛使用的风险度量。然而，由于CVaR不是标准的马尔可夫决策过程(MDP)，动态规划原理失效，动态优化CVaR是一个难点。本文从基于灵敏度优化的角度出发，研究了具有长期CVaR准则的无限视界离散时间MDP。通过引入伪CVaR度量，我们将该问题重新表述为双层MDP模型，并推导出CVaR差异公式，该公式量化了任意两种政策下的长期CVaR差异。导出了确定性策略的最优性。我们得到了CVaR的一个Bellman局部最优方程，它是全局最优策略的充要条件和局部最优策略的充要条件。为了提供更多的敏感性信息，还推导了CVaR的导数公式。然后，我们开发了一种策略迭代型算法来有效地优化CVaR，并证明该算法在混合策略空间中收敛到局部最优。此外，基于我们的双层MDP公式和临界点的敏感性分析，我们开发了一个全局最优算法。建立了最优伪CVaR函数的分段线性和段凸性。进一步扩展了我们的主要结果和算法，以同时优化均值和CVaR。最后，我们进行了与投资组合管理相关的数值实验来证明主要结果。我们的工作从敏感性的角度阐明了动态优化CVaR。
{"title":"Risk‐sensitive markov decision processes with long‐run CVaR criterion","authors":"Li Xia, Luyao Zhang, Peter W. Glynn","doi":"10.1111/poms.14077","DOIUrl":"https://doi.org/10.1111/poms.14077","url":null,"abstract":"Abstract CVaR (Conditional value at risk) is a risk metric widely used in finance. However, dynamically optimizing CVaR is difficult, because it is not a standard Markov decision process (MDP) and the principle of dynamic programming fails. In this paper, we study the infinite‐horizon discrete‐time MDP with a long‐run CVaR criterion, from the view of sensitivity‐based optimization. By introducing a pseudo‐CVaR metric, we reformulate the problem as a bilevel MDP model and derive a CVaR difference formula that quantifies the difference of long‐run CVaR under any two policies. The optimality of deterministic policies is derived. We obtain a so‐called Bellman local optimality equation for CVaR, which is a necessary and sufficient condition for locally optimal policies and only necessary for globally optimal policies. A CVaR derivative formula is also derived for providing more sensitivity information. Then we develop a policy iteration type algorithm to efficiently optimize CVaR, which is shown to converge to a local optimum in mixed policy space. Furthermore, based on the sensitivity analysis of our bilevel MDP formulation and critical points, we develop a globally optimal algorithm. The piecewise linearity and segment convexity of the optimal pseudo‐CVaR function are also established. Our main results and algorithms are further extended to optimize the mean and CVaR simultaneously. Finally, we conduct numerical experiments relating to portfolio management to demonstrate the main results. Our work sheds light on dynamically optimizing CVaR from a sensitivity viewpoint.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135477762","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 Disaster relief workers face unique factors in their operating environments that can inhibit internal integration. For example, disaster relief often involves exposure to traumatic events affecting relief workers’ commitment to cooperation and the organization. As such, disaster relief organizations dedicate substantial amounts of scarce resources to support workers exposed to trauma. Unfortunately, contradictory views exist in the literature on how trauma exposure affects commitment and integrative behaviors and how supervisor support influences these relationships. Based on the approach‐avoidance coping theory, we test whether trauma exposure has positive or negative effects. We test our hypotheses on data from 300 disaster relief workers collected using a 2 × 3 factorial scenario‐based experiment. We find that trauma exposure evokes avoidance coping behaviors, which decrease individuals’ cooperative disposition and approach coping behaviors, which motivate organizational commitment. Next, we show that both forms of commitment have a nonlinear convex relationship with internal integration and mediate the relationship between trauma exposure and internal integration. Finally, we find that supervisor support amplifies these relationships. When exposed to trauma, supervisor approach and avoidance orientations provide higher internal integration levels than subjects exposed to no supervisor support. These findings extend the literature on disaster relief management, integration, and support, guiding decision‐making regarding support investments in disaster relief organizations. This article is protected by copyright. All rights reserved
{"title":"The influence of trauma on internal integration: An approach‐avoidance analysis in disaster relief operations","authors":"Llord Brooks, Iana Shaheen, David Dobrzykowski","doi":"10.1111/poms.14081","DOIUrl":"https://doi.org/10.1111/poms.14081","url":null,"abstract":"Abstract Disaster relief workers face unique factors in their operating environments that can inhibit internal integration. For example, disaster relief often involves exposure to traumatic events affecting relief workers’ commitment to cooperation and the organization. As such, disaster relief organizations dedicate substantial amounts of scarce resources to support workers exposed to trauma. Unfortunately, contradictory views exist in the literature on how trauma exposure affects commitment and integrative behaviors and how supervisor support influences these relationships. Based on the approach‐avoidance coping theory, we test whether trauma exposure has positive or negative effects. We test our hypotheses on data from 300 disaster relief workers collected using a 2 × 3 factorial scenario‐based experiment. We find that trauma exposure evokes avoidance coping behaviors, which decrease individuals’ cooperative disposition and approach coping behaviors, which motivate organizational commitment. Next, we show that both forms of commitment have a nonlinear convex relationship with internal integration and mediate the relationship between trauma exposure and internal integration. Finally, we find that supervisor support amplifies these relationships. When exposed to trauma, supervisor approach and avoidance orientations provide higher internal integration levels than subjects exposed to no supervisor support. These findings extend the literature on disaster relief management, integration, and support, guiding decision‐making regarding support investments in disaster relief organizations. This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536286","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}
Kai Sun, Minghe Sun, Deepak Agrawal, Ronald Dravenstott, Frank Rosinia, Arkajyoti Roy
Abstract This work addresses the practical anesthesiologist scheduling (AS) problem motivated by the needs of an academic anesthesiology department. The AS problem requires the department to plan and deploy providers to adequately meet clinical demand and institutional protocols of various clinical units over a planning horizon of up to several weeks. A data‐driven two‐step AS framework is developed by exploiting the historical demand data of anesthesia cases. The first step is a shift design which obtains the optimal shifts considering clinical demand under uncertainty using conditional value‐at‐risk constraints, and the second step is provider assignments that generate the schedule considering optimal and equitable workload distribution and provider availability using multiobjective mixed‐integer programming models. Moreover, the AS framework incorporates the provider specialties, and clinical and lifestyle preferences and aligns with the existing scheduling practices. An ɛ‐constraint solution method is applied for multiobjective optimization, and an iterative solution method is developed to improve solution quality for workload equity in clinical applications. Computational experiments are performed to evaluate the performance of three alternative forms of the workload equity objective function, and the results show that the minimization of the sum of the absolute deviations of provider workloads best balances solution runtime and quality. In the concerned academic anesthesiology department, two clinical problems, the budget and hiring planning and the monthly scheduling, are addressed via the application of the proposed AS framework. For budget and hiring, decision‐makers can make trade‐offs based on their preference using the nondominated frontiers obtained via the ɛ‐constraint method. For monthly scheduling, the iterative solution method can accommodate preassigned shifts capturing institutional requirements while improving workload equity. The workload variance has been substantially reduced from 2.92 to 1.39 after the implementation based on the historical schedule data. The provider schedule satisfaction is improved from 3.13/5 to 3.44/5, and at least 82% of scheduling burden on department leaders is relieved. The developed AS framework is generic and can be extended to the scheduling of other types of care providers, including nurses and residents.
{"title":"Equitable anesthesiologist scheduling under demand uncertainty using multiobjective programming","authors":"Kai Sun, Minghe Sun, Deepak Agrawal, Ronald Dravenstott, Frank Rosinia, Arkajyoti Roy","doi":"10.1111/poms.14058","DOIUrl":"https://doi.org/10.1111/poms.14058","url":null,"abstract":"Abstract This work addresses the practical anesthesiologist scheduling (AS) problem motivated by the needs of an academic anesthesiology department. The AS problem requires the department to plan and deploy providers to adequately meet clinical demand and institutional protocols of various clinical units over a planning horizon of up to several weeks. A data‐driven two‐step AS framework is developed by exploiting the historical demand data of anesthesia cases. The first step is a shift design which obtains the optimal shifts considering clinical demand under uncertainty using conditional value‐at‐risk constraints, and the second step is provider assignments that generate the schedule considering optimal and equitable workload distribution and provider availability using multiobjective mixed‐integer programming models. Moreover, the AS framework incorporates the provider specialties, and clinical and lifestyle preferences and aligns with the existing scheduling practices. An ɛ‐constraint solution method is applied for multiobjective optimization, and an iterative solution method is developed to improve solution quality for workload equity in clinical applications. Computational experiments are performed to evaluate the performance of three alternative forms of the workload equity objective function, and the results show that the minimization of the sum of the absolute deviations of provider workloads best balances solution runtime and quality. In the concerned academic anesthesiology department, two clinical problems, the budget and hiring planning and the monthly scheduling, are addressed via the application of the proposed AS framework. For budget and hiring, decision‐makers can make trade‐offs based on their preference using the nondominated frontiers obtained via the ɛ‐constraint method. For monthly scheduling, the iterative solution method can accommodate preassigned shifts capturing institutional requirements while improving workload equity. The workload variance has been substantially reduced from 2.92 to 1.39 after the implementation based on the historical schedule data. The provider schedule satisfaction is improved from 3.13/5 to 3.44/5, and at least 82% of scheduling burden on department leaders is relieved. The developed AS framework is generic and can be extended to the scheduling of other types of care providers, including nurses and residents.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134904067","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 We consider a two‐stage project supply chain with a downstream project firm producing an engineer‐to‐order (ETO) complex product or a make‐to‐order (MTO), low‐volume, customized industrial product as a project, and an upstream contract supplier supplying a key material to the project. The project faces two uncertainties: project activity time uncertainty and material consumption uncertainty, which may be positively or negatively correlated. In anticipation of these uncertainties, the project firm has to carefully decide its promised project due date to its project customer, against which harsh penalties will be assessed, and his material order quantity to commit to the contract supplier in advance. In most practical settings, project firms order from contracted suppliers via a flexible wholesale price contract consisting of a discounted advance order price and a risk‐premium adjusted expedite order price. The discounted advance order price encourages the project firm to take more inventory risk in the supply chain, and the expedite order price incentivizes the supplier to bear more inventory risk by carrying safety stock in excess of the project firm's advance material order. We formulate an optimization model that solves the project firm's project due date and material order problem, which takes into account the supplier's strategic reaction to the project firm's material order under the flexible wholesale price contract. We show that for MTO projects, risk‐sharing with suppliers on project materials is less important to the project firm, with the project firm assuming ownership of all material inventory in the channel and setting a deliberate project due date being the key. On the other hand, for ETO projects, risk‐sharing with contracted suppliers assumes critical importance. Project firms managing ETO projects should fully exploit the flexibility in the material supply contract to optimally drive the supplier's safety stock level and set the project due date reflecting the shared risk in the supply chain.
{"title":"Managing material shortages in project supply chains: Inventories, time buffers, and supplier flexibility","authors":"Panos Kouvelis, Xingxing Chen, Yu Xia","doi":"10.1111/poms.14059","DOIUrl":"https://doi.org/10.1111/poms.14059","url":null,"abstract":"Abstract We consider a two‐stage project supply chain with a downstream project firm producing an engineer‐to‐order (ETO) complex product or a make‐to‐order (MTO), low‐volume, customized industrial product as a project, and an upstream contract supplier supplying a key material to the project. The project faces two uncertainties: project activity time uncertainty and material consumption uncertainty, which may be positively or negatively correlated. In anticipation of these uncertainties, the project firm has to carefully decide its promised project due date to its project customer, against which harsh penalties will be assessed, and his material order quantity to commit to the contract supplier in advance. In most practical settings, project firms order from contracted suppliers via a flexible wholesale price contract consisting of a discounted advance order price and a risk‐premium adjusted expedite order price. The discounted advance order price encourages the project firm to take more inventory risk in the supply chain, and the expedite order price incentivizes the supplier to bear more inventory risk by carrying safety stock in excess of the project firm's advance material order. We formulate an optimization model that solves the project firm's project due date and material order problem, which takes into account the supplier's strategic reaction to the project firm's material order under the flexible wholesale price contract. We show that for MTO projects, risk‐sharing with suppliers on project materials is less important to the project firm, with the project firm assuming ownership of all material inventory in the channel and setting a deliberate project due date being the key. On the other hand, for ETO projects, risk‐sharing with contracted suppliers assumes critical importance. Project firms managing ETO projects should fully exploit the flexibility in the material supply contract to optimally drive the supplier's safety stock level and set the project due date reflecting the shared risk in the supply chain.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129919","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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