Yuerong Chen, Xianhao Xu, Bipan Zou, René De Koster, Yeming Gong
Autonomous mobile robots are increasingly used for order picking, order delivery, and parcel sorting. This article studies a robotic sorting system that uses robots to transport parcels from loading stations to drop‐off points. While this system provides more flexible throughput capacity than conventional sorting systems, its performance is significantly affected by the robot travel distance and robot congestion. We study the problem of assigning parcel destinations to drop‐off points to minimize the throughput time, trading off travel distance and congestion. First, an open queuing network (OQN) with finite capacity queues is constructed to estimate the congested throughput time. A decomposition method based on the analysis of the tandem queuing network of each aisle is developed to solve the OQN. Second, using the obtained throughput time as an objective and the destination assignments as decisions, we formulate an optimization model and solve the problem using an adaptive large neighborhood search (ALNS) algorithm. We validate the accuracy of the OQN by simulation and verify the efficiency of the ALNS algorithm by comparing it with Gurobi, a tabu search algorithm, several heuristic assignment rules, and the rule used by our case company, that assigns high demands close to loading stations. The results show that the ALNS solution provides a relatively low throughput time by dispersing destinations with high demands over drop‐off points. In addition, we investigate the effects of different system layouts and travel path topologies. We also show that the ALNS assignment rule produces substantially lower operational costs than the heuristic assignment rules for a given required throughput capacity.
{"title":"Assigning parcel destinations to drop‐off points in a congested robotic sorting system","authors":"Yuerong Chen, Xianhao Xu, Bipan Zou, René De Koster, Yeming Gong","doi":"10.1002/nav.22220","DOIUrl":"https://doi.org/10.1002/nav.22220","url":null,"abstract":"Autonomous mobile robots are increasingly used for order picking, order delivery, and parcel sorting. This article studies a robotic sorting system that uses robots to transport parcels from loading stations to drop‐off points. While this system provides more flexible throughput capacity than conventional sorting systems, its performance is significantly affected by the robot travel distance and robot congestion. We study the problem of assigning parcel destinations to drop‐off points to minimize the throughput time, trading off travel distance and congestion. First, an open queuing network (OQN) with finite capacity queues is constructed to estimate the congested throughput time. A decomposition method based on the analysis of the tandem queuing network of each aisle is developed to solve the OQN. Second, using the obtained throughput time as an objective and the destination assignments as decisions, we formulate an optimization model and solve the problem using an adaptive large neighborhood search (ALNS) algorithm. We validate the accuracy of the OQN by simulation and verify the efficiency of the ALNS algorithm by comparing it with Gurobi, a tabu search algorithm, several heuristic assignment rules, and the rule used by our case company, that assigns high demands close to loading stations. The results show that the ALNS solution provides a relatively low throughput time by dispersing destinations with high demands over drop‐off points. In addition, we investigate the effects of different system layouts and travel path topologies. We also show that the ALNS assignment rule produces substantially lower operational costs than the heuristic assignment rules for a given required throughput capacity.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"36 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924792","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}
Evaluating two‐terminal network reliability is a classical problem with numerous applications. Because this problem is ‐Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently; however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of a two‐terminal network in which there are two classes of i.i.d. components. In this setting, we prove that each MC replication to estimate the signature of a multi‐class system entails solving a multi‐objective maximum capacity path problem. For the case of two classes of components, we adapt a Dijkstra's‐like bi‐objective shortest path algorithm from the literature for the purpose of solving the resulting bi‐objective maximum capacity path problem. We perform computational experiments to compare our method's efficiency against intuitive benchmark approaches. Our computational results demonstrate that the bi‐objective optimization approach consistently outperforms the benchmark approaches, thereby enabling a larger number of MC replications and improved accuracy of the reliability estimation. Furthermore, the efficiency gains versus benchmark approaches appear to become more significant as the network increases in size.
评估双终端网络的可靠性是一个经典问题，应用广泛。由于这个问题很复杂，因此涉及大型系统的实际研究通常采用近似或估计系统可靠性的方法，而不是精确地评估系统可靠性。研究人员对破坏谱和生存特征等特征进行了描述，这些特征概括了系统的结构，并产生了评估或近似网络可靠性的程序。如果能高效地计算特征，这些程序就会很有优势；然而，计算特征对于复杂系统来说具有挑战性。基于这一动机，我们考虑使用蒙特卡罗（MC）模拟来估算双终端网络的生存特征，其中有两类 i.i.d. 部件。在这种情况下，我们证明了估计多类系统特征的每次 MC 复制都需要解决一个多目标最大容量路径问题。对于两类组件的情况，我们采用文献中类似于 Dijkstra 的双目标最短路径算法来解决由此产生的双目标最大容量路径问题。我们进行了计算实验，以比较我们的方法与直观基准方法的效率。计算结果表明，双目标优化方法的性能始终优于基准方法，因此可以进行更多的 MC 复制，并提高可靠性估计的准确性。此外，与基准方法相比，随着网络规模的扩大，效率的提高似乎变得更加显著。
{"title":"An optimization‐based Monte Carlo method for estimating the two‐terminal survival signature of networks with two component classes","authors":"Daniel B. Lopes da Silva, K. M. Sullivan","doi":"10.1002/nav.22218","DOIUrl":"https://doi.org/10.1002/nav.22218","url":null,"abstract":"Evaluating two‐terminal network reliability is a classical problem with numerous applications. Because this problem is ‐Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently; however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of a two‐terminal network in which there are two classes of i.i.d. components. In this setting, we prove that each MC replication to estimate the signature of a multi‐class system entails solving a multi‐objective maximum capacity path problem. For the case of two classes of components, we adapt a Dijkstra's‐like bi‐objective shortest path algorithm from the literature for the purpose of solving the resulting bi‐objective maximum capacity path problem. We perform computational experiments to compare our method's efficiency against intuitive benchmark approaches. Our computational results demonstrate that the bi‐objective optimization approach consistently outperforms the benchmark approaches, thereby enabling a larger number of MC replications and improved accuracy of the reliability estimation. Furthermore, the efficiency gains versus benchmark approaches appear to become more significant as the network increases in size.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"6 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796887","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}
Ling Qing, Yunqiang Yin, Dujuan Wang, Yugang Yu, T. C. E. Cheng
We consider multi‐period blood supply chain network design in disaster situations that involve blood donor groups, permanent and temporary blood collection facilities, blood banks, and hospitals. We use a discrete scenario set to model the uncertain blood supply and demand, and the unforeseeable disruptions in permanent blood collection facilities, blood banks, and road links arising from a disaster, where instead of complete failure, disrupted permanent blood collection facilities and blood blanks may only lose part of their capacities. To design a reliable blood supply network to mitigate the possible disruptions, we present a two‐stage adaptive robust model that integrates the location, inventory, and allocation decisions incorporating a blood sharing strategy, where blood can be delivered from a disrupted/non‐disrupted blood bank to disrupted blood banks to enhance the flexibility of the relief network. For this novel problem, we devise an exact algorithm that integrates column‐and‐constraint generation and Benders decomposition and introduce several non‐trivial acceleration techniques to speed up the solution generation process. We conduct extensive numerical studies on random data sets to evaluate the algorithmic performance. We also conduct a case study in Tehran to demonstrate its real‐life applicability and examine the impacts of key model parameters on the solutions. The numerical results verify the benefits of our model over typical benchmarks, that is, deterministic and stochastic models, and the superiority of our solution algorithm over the CPLEX solver and two well‐known solution approaches, that is, column‐and‐constraint generation and Benders decomposition. Finally, based on the numerical results, we derive managerial insights from the analytical findings.
{"title":"A two‐stage adaptive robust model for designing a reliable blood supply chain network with disruption considerations in disaster situations","authors":"Ling Qing, Yunqiang Yin, Dujuan Wang, Yugang Yu, T. C. E. Cheng","doi":"10.1002/nav.22214","DOIUrl":"https://doi.org/10.1002/nav.22214","url":null,"abstract":"We consider multi‐period blood supply chain network design in disaster situations that involve blood donor groups, permanent and temporary blood collection facilities, blood banks, and hospitals. We use a discrete scenario set to model the uncertain blood supply and demand, and the unforeseeable disruptions in permanent blood collection facilities, blood banks, and road links arising from a disaster, where instead of complete failure, disrupted permanent blood collection facilities and blood blanks may only lose part of their capacities. To design a reliable blood supply network to mitigate the possible disruptions, we present a two‐stage adaptive robust model that integrates the location, inventory, and allocation decisions incorporating a blood sharing strategy, where blood can be delivered from a disrupted/non‐disrupted blood bank to disrupted blood banks to enhance the flexibility of the relief network. For this novel problem, we devise an exact algorithm that integrates column‐and‐constraint generation and Benders decomposition and introduce several non‐trivial acceleration techniques to speed up the solution generation process. We conduct extensive numerical studies on random data sets to evaluate the algorithmic performance. We also conduct a case study in Tehran to demonstrate its real‐life applicability and examine the impacts of key model parameters on the solutions. The numerical results verify the benefits of our model over typical benchmarks, that is, deterministic and stochastic models, and the superiority of our solution algorithm over the CPLEX solver and two well‐known solution approaches, that is, column‐and‐constraint generation and Benders decomposition. Finally, based on the numerical results, we derive managerial insights from the analytical findings.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"25 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641421","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 considers a supply chain that consists of a manufacturer and a retailer, who concern their respective profits as well as consumer welfare. Each firm's objective is modelled as a weighted sum of its profit and consumer surplus, with the weight on consumer surplus representing the concern level of the firm. We first examine a push supply chain where the manufacturer determines the wholesale price and the retailer determines the order quantity. We derive the optimal decisions and investigate the impact of the firms' consumer surplus consideration on the interactive decisions of the supply chain members and the overall performance of the supply chain. We show that a higher level of retailer's consumer concern does not necessarily lead to higher consumer surplus because her concern on consumers may be exploited by the manufacturer to improve his objective; and the manufacturer's concern on consumers may not benefit the retailer in terms of her profit, especially when the manufacturer's concern level is relatively low. Nevertheless, compared to the for‐profit supply chain, concern on consumer surplus could be beneficial to both firms' profits as well as consumer surplus, inducing a “win‐win‐win” situation under certain conditions. Furthermore, as a social planner, the government seeks to optimize social welfare by adopting subsidy policies, and we examine two types of intervention policies, that is, subsidizing firms and subsidizing consumers. We show that when subsidizing firms, government's quantity‐based subsidy is always more cost‐effective than sales‐based subsidy. As the firms' concern levels become higher or the demand uncertainty becomes lower, subsidizing consumers can achieve higher social welfare than subsidizing firms. Moreover, we examine the impact of the government's budget constraint and concern level on consumer surplus, and extend the analysis to a pull supply chain to show the robustness of the major findings.
{"title":"Firm decisions and government subsidies in a supply chain with consumer surplus consideration","authors":"Yongbo Xiao, Xiuyi Zhang, Xiaole Wu","doi":"10.1002/nav.22207","DOIUrl":"https://doi.org/10.1002/nav.22207","url":null,"abstract":"This paper considers a supply chain that consists of a manufacturer and a retailer, who concern their respective profits as well as consumer welfare. Each firm's objective is modelled as a weighted sum of its profit and consumer surplus, with the weight on consumer surplus representing the concern level of the firm. We first examine a push supply chain where the manufacturer determines the wholesale price and the retailer determines the order quantity. We derive the optimal decisions and investigate the impact of the firms' consumer surplus consideration on the interactive decisions of the supply chain members and the overall performance of the supply chain. We show that a higher level of retailer's consumer concern does not necessarily lead to higher consumer surplus because her concern on consumers may be exploited by the manufacturer to improve his objective; and the manufacturer's concern on consumers may not benefit the retailer in terms of her profit, especially when the manufacturer's concern level is relatively low. Nevertheless, compared to the for‐profit supply chain, concern on consumer surplus could be beneficial to both firms' profits as well as consumer surplus, inducing a “win‐win‐win” situation under certain conditions. Furthermore, as a social planner, the government seeks to optimize social welfare by adopting subsidy policies, and we examine two types of intervention policies, that is, subsidizing firms and subsidizing consumers. We show that when subsidizing firms, government's quantity‐based subsidy is always more cost‐effective than sales‐based subsidy. As the firms' concern levels become higher or the demand uncertainty becomes lower, subsidizing consumers can achieve higher social welfare than subsidizing firms. Moreover, we examine the impact of the government's budget constraint and concern level on consumer surplus, and extend the analysis to a pull supply chain to show the robustness of the major findings.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"1 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337600","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}
Government regulations on emission control can be broadly divided into two categories: price instruments and quantity instruments. In this paper, we develop a stylized model to compare the two instruments in the presence of market uncertainty. We find that when the emission intensity (i.e., emissions per unit of production) and the market uncertainty are both high or low, the expected social welfare under the price instruments will be higher; otherwise, the performance of the quantity instruments is comparatively better. The results are robust when incorporating firm competition and national/regional pollution damage. Afterward, we demonstrate that the government's quick‐response capability or a hybrid of the price and quantity instruments can improve the expected social welfare, especially for high‐emitting industries when the market uncertainty is intermediate. Lastly, for heterogeneous firms, we find that allowing permit trading in the quantity instrument may not be beneficial when pollution from each firm is more likely to have regional effects.
{"title":"Optimal emission regulation under market uncertainty","authors":"Guokai Li, Pin Gao, Zizhuo Wang","doi":"10.1002/nav.22204","DOIUrl":"https://doi.org/10.1002/nav.22204","url":null,"abstract":"Government regulations on emission control can be broadly divided into two categories: price instruments and quantity instruments. In this paper, we develop a stylized model to compare the two instruments in the presence of market uncertainty. We find that when the emission intensity (i.e., emissions per unit of production) and the market uncertainty are both high or low, the expected social welfare under the price instruments will be higher; otherwise, the performance of the quantity instruments is comparatively better. The results are robust when incorporating firm competition and national/regional pollution damage. Afterward, we demonstrate that the government's quick‐response capability or a hybrid of the price and quantity instruments can improve the expected social welfare, especially for high‐emitting industries when the market uncertainty is intermediate. Lastly, for heterogeneous firms, we find that allowing permit trading in the quantity instrument may not be beneficial when pollution from each firm is more likely to have regional effects.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366640","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}
Class‐based storage policy with optimized contour‐shaped class boundary can significantly improve storage system's performance. Surprisingly, this policy has not been explored in widely used multi‐aisle automated storage and retrieval systems (MA‐AS/RSs), which use one storage and retrieval machine to serve multiple aisles with the help of an aisle‐transfer technique. This paper investigates the two‐class‐based storage policy with contour‐shaped class boundary in MA‐AS/RSs that use a transfer car for aisle transfer. The aim is to optimize the system dimensions and class boundary by minimizing system's expected travel time. Based on the approximation of the MA‐AS/RS with a continuous cube and the proposed hierarchical procedure, analytical expected travel time expressions for systems with any dimensions and class boundary are calculated. In addition, based on several proved properties, closed‐form optimal system dimensions and class boundary are derived. Numerical results show the accuracy of our continuous cubic approximation is sufficient. By measuring the performance using the average of expected travel time over all tested systems with various dimensions, we find that (1) class‐based policy with our optimal class boundary can respectively improve the performance by at least 40%, 10%, and 50% compared to three previous policies in the case of 20/80 ABC curve; and (2) system with our optimal dimensions can improve the performance by about 20%–30%. Several managerial insights for warehouse practitioners are presented.
{"title":"Modeling and optimization of expected travel time for multi‐aisle AS/RSs with two‐class‐based storage policy","authors":"Hu Yu","doi":"10.1002/nav.22202","DOIUrl":"https://doi.org/10.1002/nav.22202","url":null,"abstract":"Class‐based storage policy with optimized contour‐shaped class boundary can significantly improve storage system's performance. Surprisingly, this policy has not been explored in widely used multi‐aisle automated storage and retrieval systems (MA‐AS/RSs), which use one storage and retrieval machine to serve multiple aisles with the help of an aisle‐transfer technique. This paper investigates the two‐class‐based storage policy with contour‐shaped class boundary in MA‐AS/RSs that use a transfer car for aisle transfer. The aim is to optimize the system dimensions and class boundary by minimizing system's expected travel time. Based on the approximation of the MA‐AS/RS with a continuous cube and the proposed hierarchical procedure, analytical expected travel time expressions for systems with any dimensions and class boundary are calculated. In addition, based on several proved properties, closed‐form optimal system dimensions and class boundary are derived. Numerical results show the accuracy of our continuous cubic approximation is sufficient. By measuring the performance using the average of expected travel time over all tested systems with various dimensions, we find that (1) class‐based policy with our optimal class boundary can respectively improve the performance by at least 40%, 10%, and 50% compared to three previous policies in the case of 20/80 ABC curve; and (2) system with our optimal dimensions can improve the performance by about 20%–30%. Several managerial insights for warehouse practitioners are presented.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272215","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}
Reference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around reduced weighted mean absolute percentage error.
参考价格（RP）是消费者对价格的主观感受，对购买决策有重要影响。标准的参考价格公式将参考价格定义为过去价格的指数加权平均值，但忽略了价格下降和价格上涨时权重的不对称性，而这种不对称性可以从价格下降或上涨后几天的需求趋势中观察到。这种疏忽通常会导致对需求的高估，我们通过经验证据对此进行了说明。我们引入了 "带曝光效应的 RP"（RPEE）这一新概念，通过施加与价格对消费者的曝光程度成比例的权重来捕捉 RP 表述中的这种不对称。曝光效应可以通过大多数网络零售平台的点击流数据来衡量。我们建立了一个能解释标准 RP 形成的顾客行为模型，并以一种自然的方式对其进行了扩展，为 RPEE 的使用提供了基础，尤其是对于重复购买较少的产品。然后，我们以经验为基础，为销售数千种产品的电子零售商确立了 RPEE 预测的广泛优势。我们证明，与标准 RP 相比，RPEE 对产品有显著而持续的改进，加权平均绝对百分比误差大约减少了。
{"title":"Forecasting using reference prices with exposure effect","authors":"Opher Baron, Chang Deng, Simai He, Hongsong Yuan","doi":"10.1002/nav.22190","DOIUrl":"https://doi.org/10.1002/nav.22190","url":null,"abstract":"Reference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around \u0000reduced weighted mean absolute percentage error.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"35 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674592","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}
Airport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.
{"title":"Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies","authors":"J. Desai, Guan Lian, S. Srivathsan","doi":"10.1002/nav.22189","DOIUrl":"https://doi.org/10.1002/nav.22189","url":null,"abstract":"Airport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"64 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700462","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":"Editorial: Advancing pandemic preparedness through data analytics and operations research","authors":"Ebru K. Bish, Tinglong Dai, Sanjay Mehrotra","doi":"10.1002/nav.22185","DOIUrl":"https://doi.org/10.1002/nav.22185","url":null,"abstract":"","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"24 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372194","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 a periodic‐review multi‐supplier series inventory system in which the demand is restricted to partial sum uncertainty sets. We present and solve a robust rolling‐horizon model for the system. We propose an induction framework to characterize the closed‐form robust optimal solution of the problem. We show that the robust optimal policy combines the echelon base‐stock policy and a gap‐of‐echelon‐base‐stock policy for the uppermost stage and a modified echelon base‐stock policy for the other downstream stages. The policy structure is easy for the manager to understand and implement in practice. The policy parameters are directly determined by a sequence of nominal partial‐sum demands, and its computation is very effective. In addition, the policy does not rely on complete information about the demand distribution; its solution can be more robust than that of stochastic optimization methods, especially when demand is highly uncertain, and forecasting is difficult. Based on the structure of the robust optimal policy, we design two heuristic policies for the system and evaluate the policies' performance through an extensive numerical study using both synthetic and real data.
{"title":"Robust multi‐echelon inventory management with multiple suppliers","authors":"Liangquan Wang, Chaolin Yang","doi":"10.1002/nav.22147","DOIUrl":"https://doi.org/10.1002/nav.22147","url":null,"abstract":"We study a periodic‐review multi‐supplier series inventory system in which the demand is restricted to partial sum uncertainty sets. We present and solve a robust rolling‐horizon model for the system. We propose an induction framework to characterize the closed‐form robust optimal solution of the problem. We show that the robust optimal policy combines the echelon base‐stock policy and a gap‐of‐echelon‐base‐stock policy for the uppermost stage and a modified echelon base‐stock policy for the other downstream stages. The policy structure is easy for the manager to understand and implement in practice. The policy parameters are directly determined by a sequence of nominal partial‐sum demands, and its computation is very effective. In addition, the policy does not rely on complete information about the demand distribution; its solution can be more robust than that of stochastic optimization methods, especially when demand is highly uncertain, and forecasting is difficult. Based on the structure of the robust optimal policy, we design two heuristic policies for the system and evaluate the policies' performance through an extensive numerical study using both synthetic and real data.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88576876","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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