Problem definition: The meaning of life must surely be more about well-being than wealth, but what does that have to do with operations? Well-being encompasses a lot: Are we happy as individuals? Are groups treated fairly? Is society sustainable? Operations management has many impacts on well-being at each of these levels, some more obvious than others. Methodology/results: This MSOM Fellow forum article offers a wide-ranging exploration of linkages between operations and well-being. It organizes “operations” into five broad areas: pace and productivity, predictability and probability, process and prevention, performance and payment, and pollution and protection. For each of those, it explores what makes individuals (un)happy, what is fair, and what is sustainable. Managerial implications: It concludes with 7 recurrent themes for research in Operations Management.
{"title":"OM Forum—The Operations of Well-Being: An Operational Take on Happiness, Equity, and Sustainability","authors":"Charles J. Corbett","doi":"10.1287/msom.2022.0521","DOIUrl":"https://doi.org/10.1287/msom.2022.0521","url":null,"abstract":"Problem definition: The meaning of life must surely be more about well-being than wealth, but what does that have to do with operations? Well-being encompasses a lot: Are we happy as individuals? Are groups treated fairly? Is society sustainable? Operations management has many impacts on well-being at each of these levels, some more obvious than others. Methodology/results: This MSOM Fellow forum article offers a wide-ranging exploration of linkages between operations and well-being. It organizes “operations” into five broad areas: pace and productivity, predictability and probability, process and prevention, performance and payment, and pollution and protection. For each of those, it explores what makes individuals (un)happy, what is fair, and what is sustainable. Managerial implications: It concludes with 7 recurrent themes for research in Operations Management.","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138563753","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: Uncrewed aerial vehicles (UAVs) are transforming emergency service logistics applications across sectors, offering easy deployment and rapid response. In the context of emergency medical services (EMS), UAVs have the potential to augment ambulances by leveraging bystander assistance, thereby reducing response times for delivering urgent medical interventions and improving EMS outcomes. Notably, the use of UAVs for opioid overdose cases is particularly promising as it addresses the challenges faced by ambulances in delivering timely medication. This study aims to optimize the integration of UAVs and bystanders into EMS in order to minimize average response times for overdose interventions. Methodology/results: We formulate the joint operation of UAVs with ambulances through a Markov decision process that captures random emergency vehicle travel times and bystander availability. We apply an approximate dynamic programming approach to mitigate the solution challenges from high-dimensional state variables and complex decisions through a neural network-based approximation of the value functions (NN-API). To design the approximation, we construct a set of basis functions based on queueing and geographic properties of the UAV-augmented EMS system. Managerial implications: The simulation results suggest that our NN-API policy tends to outperform several noteworthy rule- and optimization-based benchmark policies in terms of accumulated rewards, particularly for situations that are primarily characterized by high request arrival rates and a limited number of available ambulances and UAVs. The results also demonstrate the benefits of incorporating UAVs into the EMS system and the effectiveness of an intelligent real-time operations strategy in addressing capacity shortages, which are often a problem in rural areas of the United States. Additionally, the results provide insights into specific contributions of each dispatching or redeployment strategy to overall performance improvement.Funding: This work was supported by the National Science [Grant 1761022].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0166
{"title":"Shortening Emergency Medical Response Time with Joint Operations of Uncrewed Aerial Vehicles with Ambulances","authors":"Xiaoquan Gao, Nan Kong, Paul Griffin","doi":"10.1287/msom.2022.0166","DOIUrl":"https://doi.org/10.1287/msom.2022.0166","url":null,"abstract":"Problem definition: Uncrewed aerial vehicles (UAVs) are transforming emergency service logistics applications across sectors, offering easy deployment and rapid response. In the context of emergency medical services (EMS), UAVs have the potential to augment ambulances by leveraging bystander assistance, thereby reducing response times for delivering urgent medical interventions and improving EMS outcomes. Notably, the use of UAVs for opioid overdose cases is particularly promising as it addresses the challenges faced by ambulances in delivering timely medication. This study aims to optimize the integration of UAVs and bystanders into EMS in order to minimize average response times for overdose interventions. Methodology/results: We formulate the joint operation of UAVs with ambulances through a Markov decision process that captures random emergency vehicle travel times and bystander availability. We apply an approximate dynamic programming approach to mitigate the solution challenges from high-dimensional state variables and complex decisions through a neural network-based approximation of the value functions (NN-API). To design the approximation, we construct a set of basis functions based on queueing and geographic properties of the UAV-augmented EMS system. Managerial implications: The simulation results suggest that our NN-API policy tends to outperform several noteworthy rule- and optimization-based benchmark policies in terms of accumulated rewards, particularly for situations that are primarily characterized by high request arrival rates and a limited number of available ambulances and UAVs. The results also demonstrate the benefits of incorporating UAVs into the EMS system and the effectiveness of an intelligent real-time operations strategy in addressing capacity shortages, which are often a problem in rural areas of the United States. Additionally, the results provide insights into specific contributions of each dispatching or redeployment strategy to overall performance improvement.Funding: This work was supported by the National Science [Grant 1761022].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0166","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138563762","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: Large cities around the globe are facing an alarming growth in traffic congestion and greenhouse gas emissions, to which a significant contributor in recent years are on-demand cabs operated by ride-hailing platforms. Newly emerged pooled transportation options like shuttle services are cheaper and greener alternatives. However, those alternatives are still new to many customers and policy makers. The design of their promotion policies demands careful investigation. This paper studies how we can reduce the number of on-demand cabs on the road and, therefore, their GHG emissions by promoting pooled transportation such as shuttle services. Methodology/Results: In this work, we use detailed usage data and build a structural model to study customer preferences of price and service features when choosing between private cabs and a scheduled shuttle service. Using the estimated model, we identify and evaluate the efficacy of improving service features like reducing the walking distance to shuttle stops on customers’ choices of transport and, therefore, the number of ride-hailing vehicles on the road. We find that a 20% decrease in walking distance can achieve 40% of the benefits of commonly adopted congestion surcharge policies. It can also reduce up to 4.8 thousand tonnes of GHG emissions, which is worth over a million dollars per year. In addition, we demonstrate the implementability of walking distance reduction policies by adding stops on existing shuttle routes. Managerial implications: Reducing the number of ride-hailing vehicles on the road has become an important goal in many cities’ green transport policy design. For example, cities like New York have implemented congestion surcharge policies targeting ride-hailing vehicles in recent years. Our findings suggest that, by changing operations levers such as service features of pooled transport, cities can achieve a substantial amount of benefits from reducing congestion compared with congestion surcharge policies with essentially zero cost, leading to much more efficient green transport policies.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0569 .
{"title":"Private vs. Pooled Transportation: Customer Preference and Design of Green Transport Policy","authors":"Kashish Arora, Fanyin Zheng, Karan Girotra","doi":"10.1287/msom.2022.0569","DOIUrl":"https://doi.org/10.1287/msom.2022.0569","url":null,"abstract":"Problem definition: Large cities around the globe are facing an alarming growth in traffic congestion and greenhouse gas emissions, to which a significant contributor in recent years are on-demand cabs operated by ride-hailing platforms. Newly emerged pooled transportation options like shuttle services are cheaper and greener alternatives. However, those alternatives are still new to many customers and policy makers. The design of their promotion policies demands careful investigation. This paper studies how we can reduce the number of on-demand cabs on the road and, therefore, their GHG emissions by promoting pooled transportation such as shuttle services. Methodology/Results: In this work, we use detailed usage data and build a structural model to study customer preferences of price and service features when choosing between private cabs and a scheduled shuttle service. Using the estimated model, we identify and evaluate the efficacy of improving service features like reducing the walking distance to shuttle stops on customers’ choices of transport and, therefore, the number of ride-hailing vehicles on the road. We find that a 20% decrease in walking distance can achieve 40% of the benefits of commonly adopted congestion surcharge policies. It can also reduce up to 4.8 thousand tonnes of GHG emissions, which is worth over a million dollars per year. In addition, we demonstrate the implementability of walking distance reduction policies by adding stops on existing shuttle routes. Managerial implications: Reducing the number of ride-hailing vehicles on the road has become an important goal in many cities’ green transport policy design. For example, cities like New York have implemented congestion surcharge policies targeting ride-hailing vehicles in recent years. Our findings suggest that, by changing operations levers such as service features of pooled transport, cities can achieve a substantial amount of benefits from reducing congestion compared with congestion surcharge policies with essentially zero cost, leading to much more efficient green transport policies.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0569 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138546045","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}
Kevin Mayo, Eric Webb, George Ball, Kurt Bretthauer
Research problem: High turnover rates in long-term nursing facilities exacerbate the existing shortage of caregivers, a trend that will only worsen as the population of the United States ages. Part-time certified nursing assistants (CNAs) provide a significant amount of patient care in these facilities. CNAs also have high annual rates of turnover, which can harm health outcomes and increase the cost of care. In this study, we empirically analyze the effect of scheduling decisions on part-time CNA turnover. We explore three research questions that examine both how much and with whom a CNA is scheduled to work. We seek to empirically answer how (1) hours worked and (2) coworker variability influence turnover for part-time CNAs and if (3) coworker variability moderates the relationship between hours worked and CNA turnover. Methodology/results: Using novel data from one of the nation’s largest nursing home organizations, which includes data for 6,221 part-time CNAs at 157 facilities in the United States over a 26-month period, we identify two scheduling levers managers may be able to use to influence turnover. As hours worked increase, turnover first decreases and then increases, demonstrating a nonlinear U-shaped relationship between hours worked and turnover. We also find that high coworker variability increases turnover while also moderating the effects of hours worked on turnover. In post hoc analyses, we demonstrate that high CNA turnover has negative impacts on patient health. Managerial implications: These findings suggest that managers may be able to leverage part-time CNA scheduling to reduce turnover, improving both the quality and cost of care. Specifically, we demonstrate that managers can reduce CNA turnover by increasing hours worked, scheduling coworkers together consistently and doing both simultaneously.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0533 .
{"title":"Scheduling Smarter: Scheduling Decision Impact on Nurse-Aide Turnover","authors":"Kevin Mayo, Eric Webb, George Ball, Kurt Bretthauer","doi":"10.1287/msom.2021.0533","DOIUrl":"https://doi.org/10.1287/msom.2021.0533","url":null,"abstract":"Research problem: High turnover rates in long-term nursing facilities exacerbate the existing shortage of caregivers, a trend that will only worsen as the population of the United States ages. Part-time certified nursing assistants (CNAs) provide a significant amount of patient care in these facilities. CNAs also have high annual rates of turnover, which can harm health outcomes and increase the cost of care. In this study, we empirically analyze the effect of scheduling decisions on part-time CNA turnover. We explore three research questions that examine both how much and with whom a CNA is scheduled to work. We seek to empirically answer how (1) hours worked and (2) coworker variability influence turnover for part-time CNAs and if (3) coworker variability moderates the relationship between hours worked and CNA turnover. Methodology/results: Using novel data from one of the nation’s largest nursing home organizations, which includes data for 6,221 part-time CNAs at 157 facilities in the United States over a 26-month period, we identify two scheduling levers managers may be able to use to influence turnover. As hours worked increase, turnover first decreases and then increases, demonstrating a nonlinear U-shaped relationship between hours worked and turnover. We also find that high coworker variability increases turnover while also moderating the effects of hours worked on turnover. In post hoc analyses, we demonstrate that high CNA turnover has negative impacts on patient health. Managerial implications: These findings suggest that managers may be able to leverage part-time CNA scheduling to reduce turnover, improving both the quality and cost of care. Specifically, we demonstrate that managers can reduce CNA turnover by increasing hours worked, scheduling coworkers together consistently and doing both simultaneously.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0533 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521675","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: Designing effective operational strategies requires a good understanding of customer behavior. The classic economic theory of customer choice has long been the paradigm in the operations literature. However, the rise of online marketplaces such as e-commerce has triggered considerable efforts in academia and industry to develop alternative models that not only provide a good approximation of customer behavior but also are easily scalable for large-scale implementations. In this paper, we consider a multiproduct dynamic pricing problem with limited inventories under the so-called cascade click model, which is one of the most popular click models used in practice and has been intensively studied in the computer science literature. Methodology/results: We present some fundamental results. First, we derive a sufficiently general characterization of the optimal pricing policy and show that it has a different structure than the optimal policy under the standard pricing model. Second, we show that the optimal expected total revenue under the cascade click model can be upper bounded by the objective value of an approximate deterministic pricing problem. Third, we show that two policies that are known to have strong performance guarantees in the standard revenue management setting can be properly adapted (in a nontrivial way) to the setting with cascade click model while retaining their strong performance. Finally, we also briefly discuss the joint ranking and pricing problem and provide an iterative heuristic to calculate an approximate ranking. Managerial implications: Taking into account customers’ click-and-search behavior leads to different structures of the optimal pricing policy, and some common insights under the standard pricing models may no longer hold. Moreover, our simulation studies show that pricing under a (misspecified) classic choice model that is oblivious to customers click-and-search behavior can severely impact profitability.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0504 .
{"title":"Multiproduct Dynamic Pricing with Limited Inventories Under a Cascade Click Model","authors":"Sajjad Najafi, Izak Duenyas, Stefanus Jasin, Joline Uichanco","doi":"10.1287/msom.2021.0504","DOIUrl":"https://doi.org/10.1287/msom.2021.0504","url":null,"abstract":"Problem definition: Designing effective operational strategies requires a good understanding of customer behavior. The classic economic theory of customer choice has long been the paradigm in the operations literature. However, the rise of online marketplaces such as e-commerce has triggered considerable efforts in academia and industry to develop alternative models that not only provide a good approximation of customer behavior but also are easily scalable for large-scale implementations. In this paper, we consider a multiproduct dynamic pricing problem with limited inventories under the so-called cascade click model, which is one of the most popular click models used in practice and has been intensively studied in the computer science literature. Methodology/results: We present some fundamental results. First, we derive a sufficiently general characterization of the optimal pricing policy and show that it has a different structure than the optimal policy under the standard pricing model. Second, we show that the optimal expected total revenue under the cascade click model can be upper bounded by the objective value of an approximate deterministic pricing problem. Third, we show that two policies that are known to have strong performance guarantees in the standard revenue management setting can be properly adapted (in a nontrivial way) to the setting with cascade click model while retaining their strong performance. Finally, we also briefly discuss the joint ranking and pricing problem and provide an iterative heuristic to calculate an approximate ranking. Managerial implications: Taking into account customers’ click-and-search behavior leads to different structures of the optimal pricing policy, and some common insights under the standard pricing models may no longer hold. Moreover, our simulation studies show that pricing under a (misspecified) classic choice model that is oblivious to customers click-and-search behavior can severely impact profitability.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0504 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521674","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}
Timothy C. Y. Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K. Wong, Ian Yihang Zhu
Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.History: This paper has been accepted as part of the 2022 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0455 .
{"title":"Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization","authors":"Timothy C. Y. Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K. Wong, Ian Yihang Zhu","doi":"10.1287/msom.2022.0455","DOIUrl":"https://doi.org/10.1287/msom.2022.0455","url":null,"abstract":"Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.History: This paper has been accepted as part of the 2022 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0455 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521607","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: Disputes on online labor platforms have traditionally been mediated by the platform itself, which is often viewed as unhelpful or biased. However, there are emerging platforms that promise to resolve disputes with a novel tribunal system and relegate dispute resolution to individual platform users through a voting mechanism. We aim to examine the dispute resolution systems used by traditional platforms (i.e., the centralized dispute system) and emerging platforms (i.e., the decentralized dispute system) in order to assess whether the latter has an advantage over the former. Methodology/results: We use game theory to analyze both the centralized and decentralized dispute systems, and we model the tribunal’s voting game using the global games framework. Our findings indicate that in order to achieve a fair voting outcome, it is crucial to have sufficient heterogeneity in the assessments of tribunal members. Moreover, the decentralized dispute system outperforms the centralized dispute system only when the freelancer’s skill level is sufficiently high. Lastly, the decentralized dispute system has the potential to induce a more socially optimal quality level from the freelancer. Managerial implications: Our findings provide insights on the optimal adoption and implementation of the decentralized dispute system. The decentralized dispute system is more effective for tasks that involve subjective evaluations, and platforms should avoid strategies that homogenize the assessments of tribunal members. Moreover, platforms should consider switching to the decentralized dispute system only if they are able to verify the skill level of freelancers through certification or other means. Lastly, the decentralized dispute system may be more appealing to policy makers because of its potential to induce a more socially optimal outcome.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0398 .
{"title":"Should Gig Platforms Decentralize Dispute Resolution?","authors":"Wee Kiat Lee, Yao Cui","doi":"10.1287/msom.2022.0398","DOIUrl":"https://doi.org/10.1287/msom.2022.0398","url":null,"abstract":"Problem definition: Disputes on online labor platforms have traditionally been mediated by the platform itself, which is often viewed as unhelpful or biased. However, there are emerging platforms that promise to resolve disputes with a novel tribunal system and relegate dispute resolution to individual platform users through a voting mechanism. We aim to examine the dispute resolution systems used by traditional platforms (i.e., the centralized dispute system) and emerging platforms (i.e., the decentralized dispute system) in order to assess whether the latter has an advantage over the former. Methodology/results: We use game theory to analyze both the centralized and decentralized dispute systems, and we model the tribunal’s voting game using the global games framework. Our findings indicate that in order to achieve a fair voting outcome, it is crucial to have sufficient heterogeneity in the assessments of tribunal members. Moreover, the decentralized dispute system outperforms the centralized dispute system only when the freelancer’s skill level is sufficiently high. Lastly, the decentralized dispute system has the potential to induce a more socially optimal quality level from the freelancer. Managerial implications: Our findings provide insights on the optimal adoption and implementation of the decentralized dispute system. The decentralized dispute system is more effective for tasks that involve subjective evaluations, and platforms should avoid strategies that homogenize the assessments of tribunal members. Moreover, platforms should consider switching to the decentralized dispute system only if they are able to verify the skill level of freelancers through certification or other means. Lastly, the decentralized dispute system may be more appealing to policy makers because of its potential to induce a more socially optimal outcome.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0398 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521609","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: Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the offered assortment. Although this is valid in some cases, it contradicts practical observations in many shopping experiences, both in online and brick-and-mortar retail, where customers may buy a basket of products instead of a single item. In this paper, we incorporate customers’ multi-item purchase behavior into the assortment optimization problem. We consider both the uncapacitated and capacitated assortment problems under the so-called Multivariate MNL (MVMNL) model, which is one of the most popular multivariate choice models used in the marketing and empirical literature. Methodology/results: We first show that the traditional revenue-ordered assortment may not be optimal. Nonetheless, we show that under some mild conditions, a certain variant of this property holds (in the uncapacitated assortment problem) under the MVMNL model; that is, the optimal assortment consists of revenue-ordered local assortments in each product category. Finding the optimal assortment even when there is no interaction among product categories is still computationally expensive because the revenue thresholds for different categories cannot be computed separately. To tackle the computational complexity, we develop FPTAS for several variants of (capacitated and uncapacitated) assortment problems under MVMNL. Managerial implications: Our analysis reveals that disregarding customers’ multi-item purchase behavior in assortment decisions can indeed have a significant negative impact on profitability, demonstrating its practical importance in retail. We numerically show that our proposed algorithm can improve a retailer’s expected total revenues (compared with a benchmark policy that does not properly take into account the impact of customers’ multi-item choice behavior in assortment decision) by up to 14%.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0526 .
{"title":"Assortment Optimization with Multi-Item Basket Purchase Under Multivariate MNL Model","authors":"Stefanus Jasin, Chengyi Lyu, Sajjad Najafi, Huanan Zhang","doi":"10.1287/msom.2021.0526","DOIUrl":"https://doi.org/10.1287/msom.2021.0526","url":null,"abstract":"Problem definition: Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the offered assortment. Although this is valid in some cases, it contradicts practical observations in many shopping experiences, both in online and brick-and-mortar retail, where customers may buy a basket of products instead of a single item. In this paper, we incorporate customers’ multi-item purchase behavior into the assortment optimization problem. We consider both the uncapacitated and capacitated assortment problems under the so-called Multivariate MNL (MVMNL) model, which is one of the most popular multivariate choice models used in the marketing and empirical literature. Methodology/results: We first show that the traditional revenue-ordered assortment may not be optimal. Nonetheless, we show that under some mild conditions, a certain variant of this property holds (in the uncapacitated assortment problem) under the MVMNL model; that is, the optimal assortment consists of revenue-ordered local assortments in each product category. Finding the optimal assortment even when there is no interaction among product categories is still computationally expensive because the revenue thresholds for different categories cannot be computed separately. To tackle the computational complexity, we develop FPTAS for several variants of (capacitated and uncapacitated) assortment problems under MVMNL. Managerial implications: Our analysis reveals that disregarding customers’ multi-item purchase behavior in assortment decisions can indeed have a significant negative impact on profitability, demonstrating its practical importance in retail. We numerically show that our proposed algorithm can improve a retailer’s expected total revenues (compared with a benchmark policy that does not properly take into account the impact of customers’ multi-item choice behavior in assortment decision) by up to 14%.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0526 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521608","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}
{"title":"MSOM Society Student Paper Competition: Abstracts of 2019 Winners","authors":"","doi":"10.1287/msom.2020.0870","DOIUrl":"https://doi.org/10.1287/msom.2020.0870","url":null,"abstract":"","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141208045","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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