Operating time is closely related to the performance of an order‐picking system. Knowing the probability distribution of operating time will greatly facilitate system performance analysis and optimization. When using an automated storage and retrieval system (AS/RS), travels of the storage and retrieval machine can be modeled using the Chebyshev metric. However, because of the dependence among the travel legs, analyzing the variance of travel time can be quite challenging. In this paper, a graphical approach based on contour lines is proposed for deriving the probability density function (PDF) of travel time in an AS/RS. With the PDF, the expected value and variance of travel time can be obtained. To demonstrate the use of the proposed approach in evaluating the performance of an AS/RS under stochastic conditions, we derive the PDF of travel time and apply it in a queueing analysis. Based on the queueing performance measures and an overall arrival rate, the minimum number of operation requests waiting or being served occurs when the arrival rates of storage and retrieval requests are approximately equal. Also, when arrival rates of storage and retrieval requests are equal, the number of operation requests waiting or being served is minimized when the rack is square‐in‐time.
{"title":"Technical note: A graphical approach to the analysis of travel times in an automated storage and retrieval system","authors":"Jingming Liu, H. Liao, John A. White","doi":"10.1002/nav.22051","DOIUrl":"https://doi.org/10.1002/nav.22051","url":null,"abstract":"Operating time is closely related to the performance of an order‐picking system. Knowing the probability distribution of operating time will greatly facilitate system performance analysis and optimization. When using an automated storage and retrieval system (AS/RS), travels of the storage and retrieval machine can be modeled using the Chebyshev metric. However, because of the dependence among the travel legs, analyzing the variance of travel time can be quite challenging. In this paper, a graphical approach based on contour lines is proposed for deriving the probability density function (PDF) of travel time in an AS/RS. With the PDF, the expected value and variance of travel time can be obtained. To demonstrate the use of the proposed approach in evaluating the performance of an AS/RS under stochastic conditions, we derive the PDF of travel time and apply it in a queueing analysis. Based on the queueing performance measures and an overall arrival rate, the minimum number of operation requests waiting or being served occurs when the arrival rates of storage and retrieval requests are approximately equal. Also, when arrival rates of storage and retrieval requests are equal, the number of operation requests waiting or being served is minimized when the rack is square‐in‐time.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"76 1","pages":"914 - 923"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77400436","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}
Pay‐for‐priority system is believed to be an efficient service mechanism in congested systems since it introduces service differentiation that prioritizes those who are more delay‐sensitive. However, in practice, not all customers are aware of the provision of such auxiliary service (i.e., priority access). Does the lack of awareness or ignorance of priority service make the social welfare or customer surplus worsen off? To answer this question, we establish a queueing‐game‐theoretic model by capturing the strategic interactions between service provider and customers to examine the effect of customer awareness on the priority queues. Our main results are as follows. First, we confirm that increasing the level of customer awareness indeed improves the revenue of service provider, and it triggers a higher priority premium price. Second, perhaps surprisingly, we find that under the profit‐maximizing priority price, the social welfare as well as the customer surplus are both nonmonotone in the level of customer awareness, that is, full or no customer awareness can be suboptimal for the social welfare and customer surplus. Third, despite the common belief that priority is socially efficient in congested systems, we demonstrate that the optimal information levels for social welfare and customer surplus are decreasing in the congestion level, and the full customer awareness is optimal only when the system load is relatively low. Finally, to reach the maximal social welfare or customer surplus, some regulation policies are proposed, whereby the social planner can provide advertising subsidy to (levy tax on) the service provider (advertising agency) when the system load is low (high).
{"title":"The effect of customer awareness on priority queues","authors":"Zhongbin Wang, Lei Fang","doi":"10.1002/nav.22049","DOIUrl":"https://doi.org/10.1002/nav.22049","url":null,"abstract":"Pay‐for‐priority system is believed to be an efficient service mechanism in congested systems since it introduces service differentiation that prioritizes those who are more delay‐sensitive. However, in practice, not all customers are aware of the provision of such auxiliary service (i.e., priority access). Does the lack of awareness or ignorance of priority service make the social welfare or customer surplus worsen off? To answer this question, we establish a queueing‐game‐theoretic model by capturing the strategic interactions between service provider and customers to examine the effect of customer awareness on the priority queues. Our main results are as follows. First, we confirm that increasing the level of customer awareness indeed improves the revenue of service provider, and it triggers a higher priority premium price. Second, perhaps surprisingly, we find that under the profit‐maximizing priority price, the social welfare as well as the customer surplus are both nonmonotone in the level of customer awareness, that is, full or no customer awareness can be suboptimal for the social welfare and customer surplus. Third, despite the common belief that priority is socially efficient in congested systems, we demonstrate that the optimal information levels for social welfare and customer surplus are decreasing in the congestion level, and the full customer awareness is optimal only when the system load is relatively low. Finally, to reach the maximal social welfare or customer surplus, some regulation policies are proposed, whereby the social planner can provide advertising subsidy to (levy tax on) the service provider (advertising agency) when the system load is low (high).","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"17 1","pages":"801 - 815"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72658440","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}
Retailers use different mechanisms to enable sales and delivery. A relatively new offering by companies is curbside pickup where customers purchase goods online, schedule a pickup time, and come to a pickup facility to collect their orders. To model this service structure, we consider a service system where each arriving job has a preferred service completion time. Unlike most service systems that operate on a first‐come‐first‐serve basis, the service provider makes a strategic decision for when to serve each job considering their requested times and the associated costs. For most of our results, we assume that all jobs must be served before or on their requested time period, and the jobs are handled in overtime when capacity is insufficient. Costs are incurred both for overtime and early service. We model this problem as a Markov decision process. For small systems, we show that optimal capacity allocation policies are of threshold type and provide additional structural results for special cases. Building on these results, we devise two capacity allocation heuristics that use a threshold structure for general systems. The computational results show that our heuristics find near‐optimal solutions, and dependably outperform the benchmark heuristics even in larger systems. We conclude that there is a considerable benefit in using our heuristics as opposed to a very greedy or a very prudent benchmark heuristic, especially when the early service costs are not prohibitively high and the service capacity is scarce or there are high volumes of customer arrivals. Our results also demonstrate that as the length of the customer order horizon increases, performance of all heuristics deteriorate but the benefits of using our threshold heuristic remain considerable. Finally, we provide guidelines to select an appropriate solution method considering the trade‐off between solution quality and computation time.
{"title":"Capacity allocation in a service system with preferred service completion times","authors":"Bahar Çavdar, T. Işik","doi":"10.1002/nav.22046","DOIUrl":"https://doi.org/10.1002/nav.22046","url":null,"abstract":"Retailers use different mechanisms to enable sales and delivery. A relatively new offering by companies is curbside pickup where customers purchase goods online, schedule a pickup time, and come to a pickup facility to collect their orders. To model this service structure, we consider a service system where each arriving job has a preferred service completion time. Unlike most service systems that operate on a first‐come‐first‐serve basis, the service provider makes a strategic decision for when to serve each job considering their requested times and the associated costs. For most of our results, we assume that all jobs must be served before or on their requested time period, and the jobs are handled in overtime when capacity is insufficient. Costs are incurred both for overtime and early service. We model this problem as a Markov decision process. For small systems, we show that optimal capacity allocation policies are of threshold type and provide additional structural results for special cases. Building on these results, we devise two capacity allocation heuristics that use a threshold structure for general systems. The computational results show that our heuristics find near‐optimal solutions, and dependably outperform the benchmark heuristics even in larger systems. We conclude that there is a considerable benefit in using our heuristics as opposed to a very greedy or a very prudent benchmark heuristic, especially when the early service costs are not prohibitively high and the service capacity is scarce or there are high volumes of customer arrivals. Our results also demonstrate that as the length of the customer order horizon increases, performance of all heuristics deteriorate but the benefits of using our threshold heuristic remain considerable. Finally, we provide guidelines to select an appropriate solution method considering the trade‐off between solution quality and computation time.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"56 1","pages":"746 - 765"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81840034","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}
We study the decisions of a supplier and retailer in a cooperative advertising program to sell divisible products to consumers in a social network with positive externalities. The supplier decides its participation rate, the retailer with a limited budget decides discriminative advertising expenditure for consumers, and consumers decide their purchase levels. In this study, we solve the subgame perfect equilibrium of the game between the supplier, retailer, and consumers. The equilibrium leads to a new demand function of advertising expenditure, which is different from those demand functions typically assumed in existing studies. In the equilibrium, the higher a consumer's network influence quantified by Bonacich centrality, the higher is the retailer's advertising expenditure on the consumer. We characterize the supplier's participation rate by two network‐dependent thresholds. If the retailer's budget level is lower than one threshold or higher than the other, the participation rate will not depend on the network structure. Although both parties' profits and the retailer's advertising expenditure increase with the network density, the supplier's participation rate decreases with it. Finally, the retailer's profits can be improved significantly by exploiting the network information and advertising cooperatively.
{"title":"Cooperative advertising in social networks with positive externalities","authors":"Dong Liang, Jinxing Xie, Wanshan Zhu, Xiaobo Zhao","doi":"10.1002/nav.22043","DOIUrl":"https://doi.org/10.1002/nav.22043","url":null,"abstract":"We study the decisions of a supplier and retailer in a cooperative advertising program to sell divisible products to consumers in a social network with positive externalities. The supplier decides its participation rate, the retailer with a limited budget decides discriminative advertising expenditure for consumers, and consumers decide their purchase levels. In this study, we solve the subgame perfect equilibrium of the game between the supplier, retailer, and consumers. The equilibrium leads to a new demand function of advertising expenditure, which is different from those demand functions typically assumed in existing studies. In the equilibrium, the higher a consumer's network influence quantified by Bonacich centrality, the higher is the retailer's advertising expenditure on the consumer. We characterize the supplier's participation rate by two network‐dependent thresholds. If the retailer's budget level is lower than one threshold or higher than the other, the participation rate will not depend on the network structure. Although both parties' profits and the retailer's advertising expenditure increase with the network density, the supplier's participation rate decreases with it. Finally, the retailer's profits can be improved significantly by exploiting the network information and advertising cooperatively.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"51 1","pages":"702 - 714"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85931788","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}
This paper proves continuity of value functions in discounted periodic‐review single‐commodity total‐cost inventory control problems with continuous inventory levels, fixed ordering costs, possibly bounded inventory storage capacity, and possibly bounded order sizes for finite and infinite horizons. In each of these constrained models, the finite and infinite‐horizon value functions are continuous, there exist deterministic Markov optimal finite‐horizon policies, and there exist stationary deterministic Markov optimal infinite‐horizon policies. For models with bounded inventory storage and unbounded order sizes, this paper also characterizes the conditions under which st,St$$ left({s}_t,{S}_tright) $$ policies are optimal in the finite horizon and an (s,S)$$ left(s,Sright) $$ policy is optimal in the infinite horizon.
本文证明了在有限和无限视域下，具有连续库存水平、固定订货成本、可能有界库存存储量和可能有界订单量的折扣周期复核单商品总成本库存控制问题中价值函数的连续性。在每个约束模型中，有限和无限视界值函数都是连续的，存在确定性马尔可夫最优有限视界策略，存在平稳确定性马尔可夫最优无限视界策略。对于有界库存和无界订单模型，本文还刻画了st, st $$ left({s}_t,{S}_tright) $$策略在有限视界最优和(s, s) $$ left(s,Sright) $$策略在无限视界最优的条件。
{"title":"Continuity of discounted values and the structure of optimal policies for periodic‐review inventory systems with setup costs","authors":"E. Feinberg, David Kraemer","doi":"10.1002/nav.22108","DOIUrl":"https://doi.org/10.1002/nav.22108","url":null,"abstract":"This paper proves continuity of value functions in discounted periodic‐review single‐commodity total‐cost inventory control problems with continuous inventory levels, fixed ordering costs, possibly bounded inventory storage capacity, and possibly bounded order sizes for finite and infinite horizons. In each of these constrained models, the finite and infinite‐horizon value functions are continuous, there exist deterministic Markov optimal finite‐horizon policies, and there exist stationary deterministic Markov optimal infinite‐horizon policies. For models with bounded inventory storage and unbounded order sizes, this paper also characterizes the conditions under which st,St$$ left({s}_t,{S}_tright) $$ policies are optimal in the finite horizon and an (s,S)$$ left(s,Sright) $$ policy is optimal in the infinite horizon.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"28 1","pages":"480 - 492"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74374852","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 practice, managers often face a trade‐off of choosing between committed and contingent pricing under cost uncertainty because contingent pricing may increase the firm's profit but may decrease consumer surplus and market share. This article compares the two pricing strategies under cost uncertainty in terms of the firm's profit, consumer surplus, and market share. We find conditions under which committed pricing would lead to higher consumer surplus and market share than contingent pricing and conditions under vice versa. The results highlight the properties of consumers' valuation distribution in the trade‐offs of choosing between committed and contingent pricing, specifically the properties of the so‐called Mills' ratio which is defined as the reciprocal of hazard rate. According to the curvature of Mills' ratio, we define three types of distributions. We then categorize some commonly used continuous distributions into the three types which lead to different preferences in the trade‐offs. Finally, we fit two sets of household income data from China and the United States into income distributions as the proxies of consumers' valuation distributions in an economy to illustrate those trade‐offs embedded in the two pricing strategies.
{"title":"Committed versus contingent pricing under cost uncertainty","authors":"Jing Peng","doi":"10.1002/nav.22045","DOIUrl":"https://doi.org/10.1002/nav.22045","url":null,"abstract":"In practice, managers often face a trade‐off of choosing between committed and contingent pricing under cost uncertainty because contingent pricing may increase the firm's profit but may decrease consumer surplus and market share. This article compares the two pricing strategies under cost uncertainty in terms of the firm's profit, consumer surplus, and market share. We find conditions under which committed pricing would lead to higher consumer surplus and market share than contingent pricing and conditions under vice versa. The results highlight the properties of consumers' valuation distribution in the trade‐offs of choosing between committed and contingent pricing, specifically the properties of the so‐called Mills' ratio which is defined as the reciprocal of hazard rate. According to the curvature of Mills' ratio, we define three types of distributions. We then categorize some commonly used continuous distributions into the three types which lead to different preferences in the trade‐offs. Finally, we fit two sets of household income data from China and the United States into income distributions as the proxies of consumers' valuation distributions in an economy to illustrate those trade‐offs embedded in the two pricing strategies.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"6 1","pages":"734 - 745"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76434272","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}
Huaxin Qiu, Dujuan Wang, Yunqiang Yin, T. Cheng, Yanzhang Wang
We study the home health care scheduling problem that considers the synchronized services of multiskilled caregivers necessitated by the simultaneous service requirements of patients. A characteristic feature of the problem is that there is a threshold on the maximum difference between the start times of the pairwise synchronized services at a patient, which enables flexible imposition of various synchronization constraints. We first derive some structural properties of the problem, based on which we provide a set‐partitioning formulation of the problem and devise a branch‐and‐price‐and‐cut solution algorithm. We develop a column generation scheme to obtain lower bounds for the problem, in which we design a labeling algorithm together with some enhancement strategies to address the pricing subproblems, and use the 2‐path inequalities and limited‐node‐memory subset row inequalities to strengthen the lower bounds. To test the algorithm, we apply it to solve the instances generated according to the well‐known Solomon's benchmark data set and real‐world instances. The results demonstrate that the algorithm can find the optimal solutions in most situations within reasonable computational times, confirming its efficacy. Finally, we perform sensitivity analysis of the optimal solution to some key model parameters and discuss the management implications of the results.
{"title":"An exact solution method for home health care scheduling with synchronized services","authors":"Huaxin Qiu, Dujuan Wang, Yunqiang Yin, T. Cheng, Yanzhang Wang","doi":"10.1002/nav.22044","DOIUrl":"https://doi.org/10.1002/nav.22044","url":null,"abstract":"We study the home health care scheduling problem that considers the synchronized services of multiskilled caregivers necessitated by the simultaneous service requirements of patients. A characteristic feature of the problem is that there is a threshold on the maximum difference between the start times of the pairwise synchronized services at a patient, which enables flexible imposition of various synchronization constraints. We first derive some structural properties of the problem, based on which we provide a set‐partitioning formulation of the problem and devise a branch‐and‐price‐and‐cut solution algorithm. We develop a column generation scheme to obtain lower bounds for the problem, in which we design a labeling algorithm together with some enhancement strategies to address the pricing subproblems, and use the 2‐path inequalities and limited‐node‐memory subset row inequalities to strengthen the lower bounds. To test the algorithm, we apply it to solve the instances generated according to the well‐known Solomon's benchmark data set and real‐world instances. The results demonstrate that the algorithm can find the optimal solutions in most situations within reasonable computational times, confirming its efficacy. Finally, we perform sensitivity analysis of the optimal solution to some key model parameters and discuss the management implications of the results.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"9 1","pages":"715 - 733"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89370435","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}
Assume a multi‐server memoryless loss system. Each server is associated with a service rate and a value of service. Customers from a common Poisson arrival process are routed to the servers in an unobservable way, where the goal is to maximize the long‐run expected reward per customer (which is the service value times the probability that the customer is not blocked). We first solve this problem under two criteria: social optimization and Nash equilibrium. Our main result is that the price of anarchy, defined as the ratio between the expected gain under the two criteria, is bounded by 2 . We also show, via examples, that this bound is tight for any number of servers.
{"title":"The price of anarchy in loss systems","authors":"Shoshana Anily, M. Haviv","doi":"10.1002/nav.22041","DOIUrl":"https://doi.org/10.1002/nav.22041","url":null,"abstract":"Assume a multi‐server memoryless loss system. Each server is associated with a service rate and a value of service. Customers from a common Poisson arrival process are routed to the servers in an unobservable way, where the goal is to maximize the long‐run expected reward per customer (which is the service value times the probability that the customer is not blocked). We first solve this problem under two criteria: social optimization and Nash equilibrium. Our main result is that the price of anarchy, defined as the ratio between the expected gain under the two criteria, is bounded by 2 . We also show, via examples, that this bound is tight for any number of servers.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"70 1","pages":"689 - 701"},"PeriodicalIF":0.0,"publicationDate":"2021-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74017435","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}
D. Bertsimas, A. Borenstein, Antonin Dauvin, Agni Orfanoudaki
Due to its prevalence and association with cardiovascular diseases and premature death, hypertension is a major public health challenge. Proper prevention and management measures are needed to effectively reduce the pervasiveness of the condition. Current clinical guidelines for hypertension provide physicians with general suggestions for first‐line pharmacologic treatment, but do not consider patient‐specific characteristics. In this study, longitudinal electronic health record data are utilized to develop personalized predictions and prescription recommendations for hypertensive patients. We demonstrate that both binary classification and regression algorithms can be used to accurately predict a patient's future hypertensive status. We then present a prescriptive framework to determine the optimal antihypertensive treatment for a patient using their individual characteristics and clinical condition. Given the observational nature of the data, we address potential confounding through generalized propensity score evaluation and optimal matching. For patients for whom the algorithm recommendation differs from the standard of care, we demonstrate an approximate 15.87% decrease in next blood pressure score based on the predicted outcome under the recommended treatment. An interactive dashboard has been developed to be used by physicians as a clinical support tool.
{"title":"Ensemble machine learning for personalized antihypertensive treatment","authors":"D. Bertsimas, A. Borenstein, Antonin Dauvin, Agni Orfanoudaki","doi":"10.1002/nav.22040","DOIUrl":"https://doi.org/10.1002/nav.22040","url":null,"abstract":"Due to its prevalence and association with cardiovascular diseases and premature death, hypertension is a major public health challenge. Proper prevention and management measures are needed to effectively reduce the pervasiveness of the condition. Current clinical guidelines for hypertension provide physicians with general suggestions for first‐line pharmacologic treatment, but do not consider patient‐specific characteristics. In this study, longitudinal electronic health record data are utilized to develop personalized predictions and prescription recommendations for hypertensive patients. We demonstrate that both binary classification and regression algorithms can be used to accurately predict a patient's future hypertensive status. We then present a prescriptive framework to determine the optimal antihypertensive treatment for a patient using their individual characteristics and clinical condition. Given the observational nature of the data, we address potential confounding through generalized propensity score evaluation and optimal matching. For patients for whom the algorithm recommendation differs from the standard of care, we demonstrate an approximate 15.87% decrease in next blood pressure score based on the predicted outcome under the recommended treatment. An interactive dashboard has been developed to be used by physicians as a clinical support tool.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"5 1","pages":"669 - 688"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75740786","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}
Hub network design problems with profits (HNDPPs) extend the classical hub location problems (HLPs), with the profit maximization objective. HNDPPs aim to design a service network with hub nodes and hub links, select the commodities to be served, and determine the transportation routes for the selected commodities. In this article, we allow the transportation route of each commodity to contain more than one hub link, which generalizes existing HNDPPs and is known to be more profitable. A new path‐based formulation is proposed, which is then reformulated in a Benders decomposition fashion, and an exact branch‐and‐cut algorithm is developed to solve the Benders master problem. At the nodes of the enumeration tree, Benders decomposition algorithm is used to solve a linear relaxation of the Benders master problem. When implementing the Benders decomposition algorithm, the special structure of the subproblem is explored, based on which an efficient method is proposed to generate Benders cuts. In addition, refinements and a heuristic strategy are used to speed up the branch‐and‐cut algorithm. Extensive numerical experiments on benchmark instances have been carried out to verify the effectiveness and efficiency of the proposed solution method.
{"title":"A branch‐and‐cut algorithm for hub network design problems with profits","authors":"Yuan Gao, Jun Xia, Hua Ke","doi":"10.1002/nav.22035","DOIUrl":"https://doi.org/10.1002/nav.22035","url":null,"abstract":"Hub network design problems with profits (HNDPPs) extend the classical hub location problems (HLPs), with the profit maximization objective. HNDPPs aim to design a service network with hub nodes and hub links, select the commodities to be served, and determine the transportation routes for the selected commodities. In this article, we allow the transportation route of each commodity to contain more than one hub link, which generalizes existing HNDPPs and is known to be more profitable. A new path‐based formulation is proposed, which is then reformulated in a Benders decomposition fashion, and an exact branch‐and‐cut algorithm is developed to solve the Benders master problem. At the nodes of the enumeration tree, Benders decomposition algorithm is used to solve a linear relaxation of the Benders master problem. When implementing the Benders decomposition algorithm, the special structure of the subproblem is explored, based on which an efficient method is proposed to generate Benders cuts. In addition, refinements and a heuristic strategy are used to speed up the branch‐and‐cut algorithm. Extensive numerical experiments on benchmark instances have been carried out to verify the effectiveness and efficiency of the proposed solution method.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"115 1","pages":"622 - 639"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79309070","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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