Hyun-Soo Ahn, C. Ryan, J. Uichanco, Mengzhenyu Zhang
When underlying demand is uncertain and follows a complex stochastic process, pricing problems are difficult to solve. In such cases, certainty equivalent (CE) policies, based on solving the deterministic relaxation of a stochastic pricing problem, can be used as practical alternatives. CE policies have lighter computational and informational requirements compared to solving the optimal problem. This is particularly true when the firm does not have complete information about the underlying demand distribution.
While the effectiveness of CE pricing policies has been theoretically studied in some settings (e.g, independent demand, continuous price changes), the performance of CE policies are not known in general. This paper analyzes the performance of CE policies in a pricing problem (for a given inventory level) where future demand depends on sales and inventory and the firm has limited opportunities to change price. We show that CE policies are asymptotically optimal: as the problem scale (denoted by m) becomes large, the percentage regret decreases at the rate of O (m^(-1/2)). We also extend the result to the joint pricing and (initial) inventory problem. Our numerical results are even more promising. Even in non-asymptotic settings (small scaling factor and a few price changes), CE policies perform well and often result in revenues that are only a few percentage points lower than optimal.
In addition, we compare the benefit of a closed-loop CE policy over an open-loop CE policy and provide a theoretical comparison between the two policies.
{"title":"On the Performance of Certainty-equivalent Pricing","authors":"Hyun-Soo Ahn, C. Ryan, J. Uichanco, Mengzhenyu Zhang","doi":"10.2139/ssrn.3502478","DOIUrl":"https://doi.org/10.2139/ssrn.3502478","url":null,"abstract":"When underlying demand is uncertain and follows a complex stochastic process, pricing problems are difficult to solve. In such cases, certainty equivalent (CE) policies, based on solving the deterministic relaxation of a stochastic pricing problem, can be used as practical alternatives. CE policies have lighter computational and informational requirements compared to solving the optimal problem. This is particularly true when the firm does not have complete information about the underlying demand distribution. <br><br>While the effectiveness of CE pricing policies has been theoretically studied in some settings (e.g, independent demand, continuous price changes), the performance of CE policies are not known in general. This paper analyzes the performance of CE policies in a pricing problem (for a given inventory level) where future demand depends on sales and inventory and the firm has limited opportunities to change price. We show that CE policies are asymptotically optimal: as the problem scale (denoted by m) becomes large, the percentage regret decreases at the rate of O (m^(-1/2)). We also extend the result to the joint pricing and (initial) inventory problem. Our numerical results are even more promising. Even in non-asymptotic settings (small scaling factor and a few price changes), CE policies perform well and often result in revenues that are only a few percentage points lower than optimal.<br><br>In addition, we compare the benefit of a closed-loop CE policy over an open-loop CE policy and provide a theoretical comparison between the two policies.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123656284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study the optimal inventory ordering, expediting, and allocation decisions in a multiechelon supply chain over a finite horizon, in which customer orders are quoted with a fixed fulfillment time window, termed the service time target (STT). Academic/practical relevance: Service time target is commonly used as a marketing strategy to increase customer satisfaction and strengthen firms’ competitive edge. However, how to efficiently manage a multistage supply chain to meet the target has received relatively scant attention in the literature. Our study fills this gap. Methodology: We use dynamic programming to characterize the optimal policy. Results: We show that an echelon base stock policy and a rationing policy are optimal for inventory ordering and inventory allocation/expediting, respectively. We also develop a polynomial-time algorithm to compute the optimal policy. To derive these results, we uncover a new functional property named the decomposable of degree 2 property, which is a nontrivial generalization of the celebrated Clark-Scarf decomposition. This property further allows us to derive induced penalty and compensation to coordinate a decentralized serial system with STT and expediting. Managerial implications: Our result provides an efficient decision tool for managing centralized and decentralized serial supply chains with STT and expediting. Our model can be used to quantify the tradeoff between marketing and operational decisions, such as the impact of a marginal reduction in STT on system cost and expedition frequency, as explored in our numerical studies.
{"title":"Optimal Policies for a Multi-Echelon Inventory Problem with Service Time Target and Expediting","authors":"Xiaobei Shen, Yimin Yu, Jing-Sheng Song","doi":"10.2139/ssrn.3264204","DOIUrl":"https://doi.org/10.2139/ssrn.3264204","url":null,"abstract":"Problem definition: We study the optimal inventory ordering, expediting, and allocation decisions in a multiechelon supply chain over a finite horizon, in which customer orders are quoted with a fixed fulfillment time window, termed the service time target (STT). Academic/practical relevance: Service time target is commonly used as a marketing strategy to increase customer satisfaction and strengthen firms’ competitive edge. However, how to efficiently manage a multistage supply chain to meet the target has received relatively scant attention in the literature. Our study fills this gap. Methodology: We use dynamic programming to characterize the optimal policy. Results: We show that an echelon base stock policy and a rationing policy are optimal for inventory ordering and inventory allocation/expediting, respectively. We also develop a polynomial-time algorithm to compute the optimal policy. To derive these results, we uncover a new functional property named the decomposable of degree 2 property, which is a nontrivial generalization of the celebrated Clark-Scarf decomposition. This property further allows us to derive induced penalty and compensation to coordinate a decentralized serial system with STT and expediting. Managerial implications: Our result provides an efficient decision tool for managing centralized and decentralized serial supply chains with STT and expediting. Our model can be used to quantify the tradeoff between marketing and operational decisions, such as the impact of a marginal reduction in STT on system cost and expedition frequency, as explored in our numerical studies.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic has brought many countries to their knees, and the urgency to return to normalcy has never been greater. Epidemiological models, such as the SEIR compartmental model, are indispensable tools for, among other things, predicting how pandemic may spread over time and how vaccinations and different public health interventions could affect the outcome. However, deterministic epidemiological models do not reflect the stochastic nature of the actual infected populations for which the true distribution can never be determined precisely. When embedded in an optimization model, the impact of ambiguous risk can influence the desired outcomes of the mitigating strategy. To address these issues, we first propose a robust epidemiological model, which provides prediction intervals that is specified by the Aumann and Serrano (2008) riskiness index. With suitable approximations, the robust epidemiological optimization model that minimizes the riskiness index can be formulated as a mixed integer linear optimization problem. We illustrate how we can apply the robust epidemiological optimization model for strategic vaccine allocation by minimizing the model's riskiness indices for all the constraints on limiting infections across all time periods, and within a given budget for vaccinations. We conduct a simulation study using parameters estimated from open-source datasets on the COVID-19 pandemic. Simulation results illustrate that our robust vaccine allocation model yields solutions that outperform the benchmark models in controlling the spread of infections.
{"title":"Robust Epidemiological Prediction and Optimization","authors":"Chenyi Fu, Melvyn Sim, Minglong Zhou","doi":"10.2139/ssrn.3869521","DOIUrl":"https://doi.org/10.2139/ssrn.3869521","url":null,"abstract":"The COVID-19 pandemic has brought many countries to their knees, and the urgency to return to normalcy has never been greater. Epidemiological models, such as the SEIR compartmental model, are indispensable tools for, among other things, predicting how pandemic may spread over time and how vaccinations and different public health interventions could affect the outcome. However, deterministic epidemiological models do not reflect the stochastic nature of the actual infected populations for which the true distribution can never be determined precisely. When embedded in an optimization model, the impact of ambiguous risk can influence the desired outcomes of the mitigating strategy. To address these issues, we first propose a robust epidemiological model, which provides prediction intervals that is specified by the Aumann and Serrano (2008) riskiness index. With suitable approximations, the robust epidemiological optimization model that minimizes the riskiness index can be formulated as a mixed integer linear optimization problem. We illustrate how we can apply the robust epidemiological optimization model for strategic vaccine allocation by minimizing the model's riskiness indices for all the constraints on limiting infections across all time periods, and within a given budget for vaccinations. We conduct a simulation study using parameters estimated from open-source datasets on the COVID-19 pandemic. Simulation results illustrate that our robust vaccine allocation model yields solutions that outperform the benchmark models in controlling the spread of infections.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U. Khan, Yongzhao (Vincent) Lin, Zhiming Ma, D. Stice
We provide new insights on the value of strategic alliances from a debt financing perspective. We find that firms entering a strategic alliance receive a lower interest spread when they borrow from banks that have previously lent to their strategic partners, compared to loans from other banks. Importantly, this effect varies with the information and accounting environments of borrowing firms. The effect is stronger when a borrower’s transparency and accounting quality is low. We also find that strategic alliances lead borrowers to receive non-price loan term benefits in the form of larger loan amounts and less concentrated loan syndicates. Last, in addition to obtaining more favorable loan terms when dealing with alliance-related banks, we document that borrowers have a higher likelihood of obtaining debt financing from alliance-related banks than from other banks, evidence that strategic alliances are another channel through which lending relationships are formed.
{"title":"Strategic Alliances and Lending Relationships","authors":"U. Khan, Yongzhao (Vincent) Lin, Zhiming Ma, D. Stice","doi":"10.2139/ssrn.3861452","DOIUrl":"https://doi.org/10.2139/ssrn.3861452","url":null,"abstract":"We provide new insights on the value of strategic alliances from a debt financing perspective. We find that firms entering a strategic alliance receive a lower interest spread when they borrow from banks that have previously lent to their strategic partners, compared to loans from other banks. Importantly, this effect varies with the information and accounting environments of borrowing firms. The effect is stronger when a borrower’s transparency and accounting quality is low. We also find that strategic alliances lead borrowers to receive non-price loan term benefits in the form of larger loan amounts and less concentrated loan syndicates. Last, in addition to obtaining more favorable loan terms when dealing with alliance-related banks, we document that borrowers have a higher likelihood of obtaining debt financing from alliance-related banks than from other banks, evidence that strategic alliances are another channel through which lending relationships are formed.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122753790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The connection between inventory leanness and various dimensions of financial return has been explored in many previous research studies. However, there is still a void in the literature regarding the mechanism that is responsible for the consequences of inventory leanness on the firm’s financial outcome. Thus, the purpose of this work is to study how inventory leanness influences financial outcome, with the focus on the mediation effect of production efficiency. The empirical analysis is performed using a fixed-effect model on firm-level data of various US manufacturing industries from 2000-2018. The result on overall data indicates that the association between inventory leanness (IL) and financial outcome(FO) is partially mediated through enhancement in production efficiency. Further, empirical analysis on each industry-specific subsample reveals that the mediation effect of production efficiency varies across industries. Thus, this article contributes to the theory of inventory control in operations management literature by empirically verifying the mediation effect of production efficiency.
{"title":"Role of Production Efficiency: Inventory Leanness and Financial Outcome","authors":"Mamta Sahare, Saurabh Chandra","doi":"10.2139/ssrn.3795386","DOIUrl":"https://doi.org/10.2139/ssrn.3795386","url":null,"abstract":"The connection between inventory leanness and various dimensions of financial return has been explored in many previous research studies. However, there is still a void in the literature regarding the mechanism that is responsible for the consequences of inventory leanness on the firm’s financial outcome. Thus, the purpose of this work is to study how inventory leanness influences financial outcome, with the focus on the mediation effect of production efficiency. The empirical analysis is performed using a fixed-effect model on firm-level data of various US manufacturing industries from 2000-2018. The result on overall data indicates that the association between inventory leanness (IL) and financial outcome(FO) is partially mediated through enhancement in production efficiency. Further, empirical analysis on each industry-specific subsample reveals that the mediation effect of production efficiency varies across industries. Thus, this article contributes to the theory of inventory control in operations management literature by empirically verifying the mediation effect of production efficiency.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131172324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Can there be a bigger challenge for a dean facing an accreditation visit than deficiencies in faculty scholarship metrics? Given the publication lead-time (sometimes two years between manuscript completion and actually being in print), for many deans, this will be a crisis with no immediate, easy answer. If a school has not previously hired with scholarship metrics in mind and employed strategies to empower faculty to publish, this may be a problem without a comfortable short-term remedy. Internal, organic growth of publication metrics tends not to happen quickly, and not-at-all without communication of it being a priority “at the top.” This topic and case study has profound academic, financial and reputational implications for institutions of higher education everywhere. We live in an age that is obsessed with personal and institutional branding, competition and rankings. In our contemporary culture almost nothing escapes rating by some authority. Cities are rated by unemployment rate, cost of living and climate. Olympic athletes are ranked by appropriate measurements. Professional sports teams are ranked by conference in their quest to win the World Series or Super Bowl. While professional athletes are rated for batting average, runs batted-in, whatever measurement is relevant - almost everything in life that has a value is rated by some metric (s), provided by a credible rating authority. These ratings or rankings are then widely circulated in the regular and social media. They become an important part of the branding efforts and are readily available on the institutional webpage, social media communications and of course on the Internet. This paper contributes to the debate about business school curriculum, rankings, relevance, scholarly impact and value to current and future students. The paper draws lessons from the 2011 movie, Moneyball, based on Michael Lewis’ 2003 bestseller, Moneyball: The Art of Winning an Unfair Game. The book depicts the unlikely success of baseball’s Oakland A’s general manager Billy Beane [played by Brad Pitts] as he attempts to build a Major League championship-winning team in face of competition from teams with much larger salary budget for professional baseball players. Billy Beane out-thinks other team managers by hiring a recent Yale economics graduate (fictional character Peter Brand) and by employing computer-generated statistical analysis (in reality, sabermetrics) to identify and recruit undervalued young players and inexpensive discarded veterans. This paper is based on our experience, perspectives and success working in a College of Business in a mid-size, public university in Texas. However, the challenges we face - limited resources, and working within the bureaucratic framework of a public university - are applicable to many disciplines, programs, and universities. We believe that parallels may be drawn to the plight of less-endowed educational institutions as they seek to stand out in a crowded ma
{"title":"Improving University Rankings: Lessons Learned From the Movie 'Moneyball'","authors":"M. Quddus, L. Trautman","doi":"10.2139/ssrn.3677556","DOIUrl":"https://doi.org/10.2139/ssrn.3677556","url":null,"abstract":"Can there be a bigger challenge for a dean facing an accreditation visit than deficiencies in faculty scholarship metrics? Given the publication lead-time (sometimes two years between manuscript completion and actually being in print), for many deans, this will be a crisis with no immediate, easy answer. If a school has not previously hired with scholarship metrics in mind and employed strategies to empower faculty to publish, this may be a problem without a comfortable short-term remedy. Internal, organic growth of publication metrics tends not to happen quickly, and not-at-all without communication of it being a priority “at the top.” This topic and case study has profound academic, financial and reputational implications for institutions of higher education everywhere. \u0000 \u0000We live in an age that is obsessed with personal and institutional branding, competition and rankings. In our contemporary culture almost nothing escapes rating by some authority. Cities are rated by unemployment rate, cost of living and climate. Olympic athletes are ranked by appropriate measurements. Professional sports teams are ranked by conference in their quest to win the World Series or Super Bowl. While professional athletes are rated for batting average, runs batted-in, whatever measurement is relevant - almost everything in life that has a value is rated by some metric (s), provided by a credible rating authority. These ratings or rankings are then widely circulated in the regular and social media. They become an important part of the branding efforts and are readily available on the institutional webpage, social media communications and of course on the Internet. This paper contributes to the debate about business school curriculum, rankings, relevance, scholarly impact and value to current and future students. The paper draws lessons from the 2011 movie, Moneyball, based on Michael Lewis’ 2003 bestseller, Moneyball: The Art of Winning an Unfair Game. The book depicts the unlikely success of baseball’s Oakland A’s general manager Billy Beane [played by Brad Pitts] as he attempts to build a Major League championship-winning team in face of competition from teams with much larger salary budget for professional baseball players. Billy Beane out-thinks other team managers by hiring a recent Yale economics graduate (fictional character Peter Brand) and by employing computer-generated statistical analysis (in reality, sabermetrics) to identify and recruit undervalued young players and inexpensive discarded veterans. This paper is based on our experience, perspectives and success working in a College of Business in a mid-size, public university in Texas. However, the challenges we face - limited resources, and working within the bureaucratic framework of a public university - are applicable to many disciplines, programs, and universities. We believe that parallels may be drawn to the plight of less-endowed educational institutions as they seek to stand out in a crowded ma","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131758808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available, but also the status of each seat. Methodology/results: We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time and propose efficient approximation algorithms for general cases. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a “re-solving a dynamic primal” policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of our proposed policies on resource-allocation efficiency. Managerial implications: The results of this study reveal connections between our problem and traditional network revenue management problems. Particularly, we demonstrate that by adaptively using our proposed methods, the impact of the assign-to-seat restriction becomes limited both in theory and practice. Funding: S. Liu’s research is partly supported by the National Natural Science Foundation of China (NSFC) [Grant NSFC-72072117]. Z. Wang’s research is partly supported by the NSFC [Grant NSFC-72150002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1188 .
{"title":"Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets","authors":"Feng Zhu, Shaoxuan Liu, Rowan Wang, Zizhuo Wang","doi":"10.2139/ssrn.3477339","DOIUrl":"https://doi.org/10.2139/ssrn.3477339","url":null,"abstract":"Problem definition: We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available, but also the status of each seat. Methodology/results: We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time and propose efficient approximation algorithms for general cases. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a “re-solving a dynamic primal” policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of our proposed policies on resource-allocation efficiency. Managerial implications: The results of this study reveal connections between our problem and traditional network revenue management problems. Particularly, we demonstrate that by adaptively using our proposed methods, the impact of the assign-to-seat restriction becomes limited both in theory and practice. Funding: S. Liu’s research is partly supported by the National Natural Science Foundation of China (NSFC) [Grant NSFC-72072117]. Z. Wang’s research is partly supported by the NSFC [Grant NSFC-72150002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1188 .","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127289810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recycling and remanufacturing have emerged as a key instrument in pursuit of environment-friendly sustainability in the society. Using game-theoretic approaches, this article investigates the optimal pricing and remanufacturing strategies of an original equipment manufacturer (OEM) in recycling used products by either itself or a third-party (TP) under the name-your-own-price (NYOP) mechanism. Our analysis demonstrates that either the OEM recycling or the TP recycling can outperform the other in terms of firms’ profits, consumer utility, and environmental impact. However, there exists a conflicting zone, in which the TP recycling is more profitable for the firms, but the OEM recycling leads to more consumer utility and less environmental impact. Comparing the NYOP mechanism to the list-price mechanism reveals that the OEM conditionally prefers the NYOP mechanism to the list-price mechanism; however, the OEM and the TP may encounter another preference confliction, in which the OEM prefers the NYOP whereas the TP prefers the list-price. The firms’ preference for NYOP dwindles as the used-product seller’s belief of the NYOP reserve price grows. Enabling haggling in NYOP does not always enhance the firms’ profits, consumer utility, and environment-friendly level.
{"title":"Optimal Recycling Strategies with the Name-Your-Own-Price Mechanism","authors":"G. Cai, Jianbin Li, Qifei Wang, Stuart X. Zhu","doi":"10.2139/ssrn.3714610","DOIUrl":"https://doi.org/10.2139/ssrn.3714610","url":null,"abstract":"Recycling and remanufacturing have emerged as a key instrument in pursuit of environment-friendly sustainability in the society. Using game-theoretic approaches, this article investigates the optimal pricing and remanufacturing strategies of an original equipment manufacturer (OEM) in recycling used products by either itself or a third-party (TP) under the name-your-own-price (NYOP) mechanism. Our analysis demonstrates that either the OEM recycling or the TP recycling can outperform the other in terms of firms’ profits, consumer utility, and environmental impact. However, there exists a conflicting zone, in which the TP recycling is more profitable for the firms, but the OEM recycling leads to more consumer utility and less environmental impact. Comparing the NYOP mechanism to the list-price mechanism reveals that the OEM conditionally prefers the NYOP mechanism to the list-price mechanism; however, the OEM and the TP may encounter another preference confliction, in which the OEM prefers the NYOP whereas the TP prefers the list-price. The firms’ preference for NYOP dwindles as the used-product seller’s belief of the NYOP reserve price grows. Enabling haggling in NYOP does not always enhance the firms’ profits, consumer utility, and environment-friendly level.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123963880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the event of a virus outbreak such as COVID-19, testing is key. However, long waiting lines at testing facilities often discourage individuals from getting tested. This paper studies how testing facilities should set scheduling and pricing policies to incentivize individuals to test, with the goal to identify the most cases of infection. Our findings are as follows. First, under the FIFO discipline, the common practice of making testing free attracts the most testees, but may not catch the most cases. Charging a testing fee may surprisingly improve case detection. Second, even though people who show symptoms are more likely to carry the virus, prioritizing these individuals over asymptomatic ones (another common practice) may let more cases go undetected than FIFO testing does. Third, we characterize the optimal scheduling and pricing policy. To maximize case detection, there is no need to charge a testing fee; instead, it is optimal to give (partial) priority to asymptomatic testees when testing demand is moderately low, but (partially) prioritize individuals with symptoms when testing demand becomes high.
{"title":"Design of COVID-19 Testing Queues","authors":"Shiliang Cui, Zhongbin Wang, Luyi Yang","doi":"10.2139/ssrn.3722022","DOIUrl":"https://doi.org/10.2139/ssrn.3722022","url":null,"abstract":"In the event of a virus outbreak such as COVID-19, testing is key. However, long waiting lines at testing facilities often discourage individuals from getting tested. This paper studies how testing facilities should set scheduling and pricing policies to incentivize individuals to test, with the goal to identify the most cases of infection. Our findings are as follows. First, under the FIFO discipline, the common practice of making testing free attracts the most testees, but may not catch the most cases. Charging a testing fee may surprisingly improve case detection. Second, even though people who show symptoms are more likely to carry the virus, prioritizing these individuals over asymptomatic ones (another common practice) may let more cases go undetected than FIFO testing does. Third, we characterize the optimal scheduling and pricing policy. To maximize case detection, there is no need to charge a testing fee; instead, it is optimal to give (partial) priority to asymptomatic testees when testing demand is moderately low, but (partially) prioritize individuals with symptoms when testing demand becomes high.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121542747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regression based index tracking selects tracking portfolios based on the linear regression characteristics of the available assets. The approach is suited to Mixed Integer Linear Programs where the number of assets in the tracking portfolio is limited compared to the number of constituents in the benchmark portfolio. Current approaches do not account for the active risk of the tracking portfolio, resulting in concentrated portfolios with high tracking error. This paper considers two extensions, both a linear and quadratic formulation, to the current approaches. The resulting portfolios are more diversified, have lower tracking error and tracking difference, and have appropriate levels of beta.
{"title":"Active Risk Constraints for Regression Based Index Tracking","authors":"Simon du Plooy","doi":"10.2139/ssrn.3700083","DOIUrl":"https://doi.org/10.2139/ssrn.3700083","url":null,"abstract":"Regression based index tracking selects tracking portfolios based on the linear regression characteristics of the available assets. The approach is suited to Mixed Integer Linear Programs where the number of assets in the tracking portfolio is limited compared to the number of constituents in the benchmark portfolio. Current approaches do not account for the active risk of the tracking portfolio, resulting in concentrated portfolios with high tracking error. This paper considers two extensions, both a linear and quadratic formulation, to the current approaches. The resulting portfolios are more diversified, have lower tracking error and tracking difference, and have appropriate levels of beta.","PeriodicalId":275253,"journal":{"name":"Operations Research eJournal","volume":"133 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124254888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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