This article investigates the firms' optimal quality information disclosure strategies in a supply chain, wherein the supplier may encroach into the retail channel to sell products directly to end consumers. We consider two disclosure formats, namely, retailer disclosure (R‐C) and supplier disclosure (S‐C), and examine the optimal disclosure format from each firm's perspective. We show that either firm prefers to delegate the disclosure option to its partner when the supplier cannot encroach. However, the threat of supplier encroachment dramatically alters the firm's preference of disclosure. The supplier may prefer the S‐C format to the R‐C format when the entry cost is low and the disclosure cost is high to achieve a higher quality information transparency. Meanwhile, the retailer may prefer the R‐C format to the S‐C format when the entry cost is intermediate to deter the possible encroachment of the supplier. In this sense, the firms' preferences of disclosure format can be aligned due to the threat of supplier encroachment. The consumer surplus is always higher under the S‐C format while either disclosure format can lead to a higher social welfare. We also consider an alternative scenario under which the supplier encroaches after the product quality information is disclosed. An interesting observation appears that the supplier may encroach when the product quality is low but foregoes encroachment when the product quality gets higher.
{"title":"Information disclosure with endogenous channel structure","authors":"Yuan Jiang, X. Guan, Yiwen Bian, Song Huang","doi":"10.1002/nav.22080","DOIUrl":"https://doi.org/10.1002/nav.22080","url":null,"abstract":"This article investigates the firms' optimal quality information disclosure strategies in a supply chain, wherein the supplier may encroach into the retail channel to sell products directly to end consumers. We consider two disclosure formats, namely, retailer disclosure (R‐C) and supplier disclosure (S‐C), and examine the optimal disclosure format from each firm's perspective. We show that either firm prefers to delegate the disclosure option to its partner when the supplier cannot encroach. However, the threat of supplier encroachment dramatically alters the firm's preference of disclosure. The supplier may prefer the S‐C format to the R‐C format when the entry cost is low and the disclosure cost is high to achieve a higher quality information transparency. Meanwhile, the retailer may prefer the R‐C format to the S‐C format when the entry cost is intermediate to deter the possible encroachment of the supplier. In this sense, the firms' preferences of disclosure format can be aligned due to the threat of supplier encroachment. The consumer surplus is always higher under the S‐C format while either disclosure format can lead to a higher social welfare. We also consider an alternative scenario under which the supplier encroaches after the product quality information is disclosed. An interesting observation appears that the supplier may encroach when the product quality is low but foregoes encroachment when the product quality gets higher.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"59 1","pages":"105 - 120"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88752063","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}
Reward‐based crowdfunding with the all‐or‐nothing mechanism helps cash‐strapped creators raise funds from potential consumers to develop new products. However, this mechanism may hurt the creator in the long run because possible buying frenzies of strategic consumers will cannibalize the demand for spot sales if the project succeeds with overfunding. Through a two‐period model incorporating a crowdfunding period and a spot sales period, we find that strategic consumers' purchasing decisions depend on the probability that they will like the product in spot sales. Moreover, we show that crowdfunding cannot be used to finance when the setup cost that a creator needs to pay for the production is sufficiently high. In addition, for creators who can use crowdfunding to finance, contrary to the intuition that they should not take risks when the market uncertainty is high, we find the opposite results when we take the joint effect of the setup cost and market uncertainty into consideration. To be specific, when the market uncertainty is high and the setup cost is higher than a threshold, the creator can optimally choose the risky strategy. Furthermore, the creator may benefit from market uncertainty when a high setup cost is required to launch the product.
{"title":"A cash‐strapped creator's reward‐based crowdfunding strategies with spot sales","authors":"Xiaolong Guo, Qian Gao, Tao Li, Yugang Yu","doi":"10.1002/nav.22077","DOIUrl":"https://doi.org/10.1002/nav.22077","url":null,"abstract":"Reward‐based crowdfunding with the all‐or‐nothing mechanism helps cash‐strapped creators raise funds from potential consumers to develop new products. However, this mechanism may hurt the creator in the long run because possible buying frenzies of strategic consumers will cannibalize the demand for spot sales if the project succeeds with overfunding. Through a two‐period model incorporating a crowdfunding period and a spot sales period, we find that strategic consumers' purchasing decisions depend on the probability that they will like the product in spot sales. Moreover, we show that crowdfunding cannot be used to finance when the setup cost that a creator needs to pay for the production is sufficiently high. In addition, for creators who can use crowdfunding to finance, contrary to the intuition that they should not take risks when the market uncertainty is high, we find the opposite results when we take the joint effect of the setup cost and market uncertainty into consideration. To be specific, when the market uncertainty is high and the setup cost is higher than a threshold, the creator can optimally choose the risky strategy. Furthermore, the creator may benefit from market uncertainty when a high setup cost is required to launch the product.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"53 1","pages":"1080 - 1095"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88884820","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}
Zhe Zhang, Xiaoling Song, Xue Gong, Yong Yin, Benjamin Lev, Xiaoyang Zhou
Motivated by a practical production scheduling problem at a factory, this article studies scheduling problems in seru production system (SPS). Seru is a relatively new‐type production mode originating in Japan and has brought inspiring benefits to production practice. Following the just‐in‐time philosophy of SPS, the objective of seru scheduling problem is to minimize the sum of earliness and tardiness penalties. Two common due date types of job are considered, and the seru scheduling problem is formulated as a 0–1 quadratic programming model with linear constraints that is then reformulated using convex reformulation methods to ensure convexity. Computational experiments are implemented. Experimental results indicate that the proposed exact solution method can obtain approximate optimal solutions efficiently and effectively for seru scheduling problems.
{"title":"An exact quadratic programming approach based on convex reformulation for seru scheduling problems","authors":"Zhe Zhang, Xiaoling Song, Xue Gong, Yong Yin, Benjamin Lev, Xiaoyang Zhou","doi":"10.1002/nav.22078","DOIUrl":"https://doi.org/10.1002/nav.22078","url":null,"abstract":"Motivated by a practical production scheduling problem at a factory, this article studies scheduling problems in seru production system (SPS). Seru is a relatively new‐type production mode originating in Japan and has brought inspiring benefits to production practice. Following the just‐in‐time philosophy of SPS, the objective of seru scheduling problem is to minimize the sum of earliness and tardiness penalties. Two common due date types of job are considered, and the seru scheduling problem is formulated as a 0–1 quadratic programming model with linear constraints that is then reformulated using convex reformulation methods to ensure convexity. Computational experiments are implemented. Experimental results indicate that the proposed exact solution method can obtain approximate optimal solutions efficiently and effectively for seru scheduling problems.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"12 1","pages":"1096 - 1107"},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73359459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem of maximizing total early work in a two‐machine flow‐shop, in which n jobs are to be scheduled subject to a common due date d, has been recently studied in the scheduling literature. An O(n2d4) time dynamic programming algorithm was presented first for the weighted case, and then for the unweighted case another O(n2d2) running time dynamic programming algorithm was proposed and converted into an On4ε2$$ Oleft(frac{n^4}{varepsilon^2}right) $$ time fully polynomial time approximation scheme (FPTAS). By establishing new problem properties, we present an O(nd2) time dynamic programming algorithm and an On3ε2$$ Oleft(frac{n^3}{varepsilon^2}right) $$ time FPTAS for the unweighted problem. We generalize the problem to a distributed setting of m parallel two‐machine flow‐shops, develop an O(nd3m) time dynamic programming algorithm, an On3m+1ε3m$$ Oleft(frac{n^{3m+1}}{varepsilon^{3m}}right) $$ time FPTAS, and three integer linear programming (ILP) formulations for it. Computational experiments are conducted to appraise the proposed ILP models.
{"title":"Maximizing total early work in a distributed two‐machine flow‐shop","authors":"A. Dolgui, M. Kovalyov, B. Lin","doi":"10.1002/nav.22076","DOIUrl":"https://doi.org/10.1002/nav.22076","url":null,"abstract":"The problem of maximizing total early work in a two‐machine flow‐shop, in which n jobs are to be scheduled subject to a common due date d, has been recently studied in the scheduling literature. An O(n2d4) time dynamic programming algorithm was presented first for the weighted case, and then for the unweighted case another O(n2d2) running time dynamic programming algorithm was proposed and converted into an On4ε2$$ Oleft(frac{n^4}{varepsilon^2}right) $$ time fully polynomial time approximation scheme (FPTAS). By establishing new problem properties, we present an O(nd2) time dynamic programming algorithm and an On3ε2$$ Oleft(frac{n^3}{varepsilon^2}right) $$ time FPTAS for the unweighted problem. We generalize the problem to a distributed setting of m parallel two‐machine flow‐shops, develop an O(nd3m) time dynamic programming algorithm, an On3m+1ε3m$$ Oleft(frac{n^{3m+1}}{varepsilon^{3m}}right) $$ time FPTAS, and three integer linear programming (ILP) formulations for it. Computational experiments are conducted to appraise the proposed ILP models.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"26 1","pages":"1124 - 1137"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91062690","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}
Nested simulation is typically used to estimate the functional of a conditional expectation. Considerable research has been performed on point estimation for various functionals. However, the quantification of the statistical uncertainty in the point estimator, for instance, using confidence intervals (CIs), has not been extensively investigated. In this article, we establish central limit theorems with the asymptotically optimal convergence rate of Γ−1/3$$ {Gamma}^{-1/3} $$ for nested simulation with different forms of functionals, where Γ$$ Gamma $$ denotes the total computational effort. Based on these theorems, we develop a unified CI framework that can ensure that both the mean squared error of the point estimator and CI width attain the optimal convergence rate. Numerical examples are presented, and the results are found to be consistent with the theoretical results. Experimental results demonstrate that the proposed framework outperforms the existing methods for CI construction in terms of the CI widths and convergence rates.
{"title":"Technical note—Constructing confidence intervals for nested simulation","authors":"Hong-Fa Cheng, Xiaoyu Liu, Kun Zhang","doi":"10.1002/nav.22075","DOIUrl":"https://doi.org/10.1002/nav.22075","url":null,"abstract":"Nested simulation is typically used to estimate the functional of a conditional expectation. Considerable research has been performed on point estimation for various functionals. However, the quantification of the statistical uncertainty in the point estimator, for instance, using confidence intervals (CIs), has not been extensively investigated. In this article, we establish central limit theorems with the asymptotically optimal convergence rate of Γ−1/3$$ {Gamma}^{-1/3} $$ for nested simulation with different forms of functionals, where Γ$$ Gamma $$ denotes the total computational effort. Based on these theorems, we develop a unified CI framework that can ensure that both the mean squared error of the point estimator and CI width attain the optimal convergence rate. Numerical examples are presented, and the results are found to be consistent with the theoretical results. Experimental results demonstrate that the proposed framework outperforms the existing methods for CI construction in terms of the CI widths and convergence rates.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"143 1-2 1","pages":"1138 - 1149"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90668645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The public sector is characterized by hierarchical and interdependent organizations. For defense and security applications in particular, a higher authority is generally responsible for allocating resources among subordinate organizations. These subordinate organizations conduct long‐term planning based on both uncertain resources and an uncertain operating environment. This article develops a modeling framework and multiple solution methodologies for subordinate organizations to use under such conditions. We extend the adversarial risk analysis approach to a stochastic game via a decomposition into a Markov decision process. This allows the subordinate organization to encode its beliefs in a Bayesian manner such that long‐term policies can be built to maximize its expected utility. The modeling framework we develop is illustrated in a realistic counter‐terrorism use case, and the efficacy of our solutions are evaluated via comparisons to alternatively constructed policies.
{"title":"Defense and security planning under resource uncertainty and multi‐period commitments","authors":"William N. Caballero, David Banks, Kerui Wu","doi":"10.1002/nav.22071","DOIUrl":"https://doi.org/10.1002/nav.22071","url":null,"abstract":"The public sector is characterized by hierarchical and interdependent organizations. For defense and security applications in particular, a higher authority is generally responsible for allocating resources among subordinate organizations. These subordinate organizations conduct long‐term planning based on both uncertain resources and an uncertain operating environment. This article develops a modeling framework and multiple solution methodologies for subordinate organizations to use under such conditions. We extend the adversarial risk analysis approach to a stochastic game via a decomposition into a Markov decision process. This allows the subordinate organization to encode its beliefs in a Bayesian manner such that long‐term policies can be built to maximize its expected utility. The modeling framework we develop is illustrated in a realistic counter‐terrorism use case, and the efficacy of our solutions are evaluated via comparisons to alternatively constructed policies.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"16 1","pages":"1009 - 1026"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75430921","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}
Carri W. Chan, Vahid Sarhangian, Prem M. Talwai, K. Gogia
Emergency departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8–12‐h shifts, providing partial flexibility in adjusting staffing levels. In this work, we propose a stochastic queueing network model of patient flow in the ED and study an associated fluid control problem to guide the reassignment decision for two types of nursing staff. We propose a heuristic solution approach and investigate its performance both analytically and using simulation. Analytical results and simulation experiments suggest a significant reduction of waiting times in parameter regimes relevant to the ED setting. We further implement the staffing approach at a large ED. This pilot study highlights several challenges of implementing operational interventions in the ED, including the difficulty of establishing a clean statistical environment in such setting. Despite these challenges, we find that guiding reassignment decisions using our approach is associated with significant improvements to patient flow including a reduction in average total ED length‐of‐stay of 1.7 h.
{"title":"Utilizing partial flexibility to improve emergency department flow: Theory and implementation","authors":"Carri W. Chan, Vahid Sarhangian, Prem M. Talwai, K. Gogia","doi":"10.1002/nav.22073","DOIUrl":"https://doi.org/10.1002/nav.22073","url":null,"abstract":"Emergency departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8–12‐h shifts, providing partial flexibility in adjusting staffing levels. In this work, we propose a stochastic queueing network model of patient flow in the ED and study an associated fluid control problem to guide the reassignment decision for two types of nursing staff. We propose a heuristic solution approach and investigate its performance both analytically and using simulation. Analytical results and simulation experiments suggest a significant reduction of waiting times in parameter regimes relevant to the ED setting. We further implement the staffing approach at a large ED. This pilot study highlights several challenges of implementing operational interventions in the ED, including the difficulty of establishing a clean statistical environment in such setting. Despite these challenges, we find that guiding reassignment decisions using our approach is associated with significant improvements to patient flow including a reduction in average total ED length‐of‐stay of 1.7 h.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"24 1","pages":"1047 - 1062"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74421119","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}
Advances in image acquisition technology have made it convenient and economic to collect large amounts of image data. In manufacturing and service industries, images are increasingly used for quality control purposes because of their ability to quickly provide information about product geometry, surface defects, and nonconforming patterns. In production line monitoring, image data often take the form of image streams in the sense that images from the process are being collected over time. In such applications, a fundamental task is to properly analyze image data streams. This image monitoring problem is challenging for several reasons. First, images often have complicated structures such as edges and singularities, which render many traditional smoothing methods inapplicable. Second, a typical grayscale image contains tens of thousands of pixels, so the data is high‐dimensional. It has been shown in the statistical process control (SPC) literature that conventional multivariate control charts have limited power of detecting process shifts when the data dimension is high. In this article, we propose to transform images using a two‐dimensional wavelet basis and monitor the wavelet coefficients by sparse learning‐based multivariate control charts. By adapting the sparse learning algorithm to our quality control problem, the proposed method is able to detect shifts in the wavelet coefficients in a timely fashion and simultaneously identify those shifted coefficients. Combining this feature with the localization property of the wavelet basis, our method also enables accurate diagnosis of faulty image regions. In addition, the proposed charting statistics have explicit formulas, so they are easy to compute. Theoretical justifications and numerical comparisons with an existing method show that our method works well in applications.
{"title":"Statistical quality control using image intelligence: A sparse learning approach","authors":"Yicheng Kang","doi":"10.1002/nav.22069","DOIUrl":"https://doi.org/10.1002/nav.22069","url":null,"abstract":"Advances in image acquisition technology have made it convenient and economic to collect large amounts of image data. In manufacturing and service industries, images are increasingly used for quality control purposes because of their ability to quickly provide information about product geometry, surface defects, and nonconforming patterns. In production line monitoring, image data often take the form of image streams in the sense that images from the process are being collected over time. In such applications, a fundamental task is to properly analyze image data streams. This image monitoring problem is challenging for several reasons. First, images often have complicated structures such as edges and singularities, which render many traditional smoothing methods inapplicable. Second, a typical grayscale image contains tens of thousands of pixels, so the data is high‐dimensional. It has been shown in the statistical process control (SPC) literature that conventional multivariate control charts have limited power of detecting process shifts when the data dimension is high. In this article, we propose to transform images using a two‐dimensional wavelet basis and monitor the wavelet coefficients by sparse learning‐based multivariate control charts. By adapting the sparse learning algorithm to our quality control problem, the proposed method is able to detect shifts in the wavelet coefficients in a timely fashion and simultaneously identify those shifted coefficients. Combining this feature with the localization property of the wavelet basis, our method also enables accurate diagnosis of faulty image regions. In addition, the proposed charting statistics have explicit formulas, so they are easy to compute. Theoretical justifications and numerical comparisons with an existing method show that our method works well in applications.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"34 1","pages":"1008 - 996"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81192248","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}
With growing air traffic demand and the required airport infrastructure lagging behind by at least a decade, it has become imperative for air traffic controllers to efficiently squeeze the available capacity at an airport in order to minimize aircraft delays. It has been well documented that the two major bottlenecks affecting the smooth functioning of air traffic operations at an airport are runways and taxiways. The key problem involving these resources includes the scheduling of flights on the runway, and the determination of the taxiway paths to be traversed by flights from their assigned gates to the runway. We address this problem by modeling an integrated runway scheduling and taxiway routing problem as a 0–1 mixed‐integer program (MIP) in a free‐path setting where any feasible taxiway route can potentially be assigned to a flight. As a direct application of this MIP model is not suitable for solving large‐scale instances, we develop a three‐step group‐and‐release strategy that first segregates the flights based on their allocated gates and associated ramps, and then solves the MIP model for each ramp to determine the taxiway path for each flight. In the final step, the path for each flight is fixed, and a sequencing problem over all flights is solved to determine high quality, feasible solutions. The performance of the proposed methodology is benchmarked against three algorithms, namely: (i) constraint‐generation; (ii) sequential two‐stage algorithm; and (iii) FCFS algorithm. Our numerical experiments, based on actual flight data from Changi airport (Singapore), show that, on average, the optimality gap as well as the computational time is considerably reduced for our strategy as compared to existing methods, thereby highlighting the efficacy of the proposed approach in solving realistic instances.
{"title":"A 0–1 mixed‐integer program‐based group‐and‐release strategy for solving the integrated runway scheduling and taxiway routing problem","authors":"J. Desai, S. Srivathsan, Chuhang Yu, Dong Zhang","doi":"10.1002/nav.22072","DOIUrl":"https://doi.org/10.1002/nav.22072","url":null,"abstract":"With growing air traffic demand and the required airport infrastructure lagging behind by at least a decade, it has become imperative for air traffic controllers to efficiently squeeze the available capacity at an airport in order to minimize aircraft delays. It has been well documented that the two major bottlenecks affecting the smooth functioning of air traffic operations at an airport are runways and taxiways. The key problem involving these resources includes the scheduling of flights on the runway, and the determination of the taxiway paths to be traversed by flights from their assigned gates to the runway. We address this problem by modeling an integrated runway scheduling and taxiway routing problem as a 0–1 mixed‐integer program (MIP) in a free‐path setting where any feasible taxiway route can potentially be assigned to a flight. As a direct application of this MIP model is not suitable for solving large‐scale instances, we develop a three‐step group‐and‐release strategy that first segregates the flights based on their allocated gates and associated ramps, and then solves the MIP model for each ramp to determine the taxiway path for each flight. In the final step, the path for each flight is fixed, and a sequencing problem over all flights is solved to determine high quality, feasible solutions. The performance of the proposed methodology is benchmarked against three algorithms, namely: (i) constraint‐generation; (ii) sequential two‐stage algorithm; and (iii) FCFS algorithm. Our numerical experiments, based on actual flight data from Changi airport (Singapore), show that, on average, the optimality gap as well as the computational time is considerably reduced for our strategy as compared to existing methods, thereby highlighting the efficacy of the proposed approach in solving realistic instances.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"99 1","pages":"939 - 957"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88107521","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 consider an integrated production and delivery scheduling problem with non‐stationary demand in a two‐stage supply chain, where orders arrive dynamically and the demand is time‐varying. Orders should be first processed on identical machines and then delivered to a single next‐stage destination by the transporters with fixed departure times. The objective is to minimize the order waiting time via production‐delivery scheduling. We formulate the problem into a Markov decision process model and develop an approximate dynamic programming (ADP) method. To shrink action (decision) space, we propose the shorter processing time first and first completion first delivery (SPTm/FCFD) principle to determine order processing sequences and order delivery, and then we establish two constraints to eliminate a fraction of inferior actions. Based on the SPTm/FCFD principle, we propose the SPT/FCFD rule, and show its optimality for two scenarios. In addition, we deploy five basis functions to approximate the value function. The superior performance of ADP policy is validated via numerical experiments, compared with four benchmark policies. We also empirically study the impact of demand features on the waiting time, and results show that these features significantly affect the performances of all polices. In practice, it is suggested to postpone the peak demand, when total demand exceeds the available production capacity.
{"title":"An approximate dynamic programming approach for production‐delivery scheduling under non‐stationary demand","authors":"Haitao Liu, Yuan Wang, L. Lee, E. P. Chew","doi":"10.1002/nav.22037","DOIUrl":"https://doi.org/10.1002/nav.22037","url":null,"abstract":"We consider an integrated production and delivery scheduling problem with non‐stationary demand in a two‐stage supply chain, where orders arrive dynamically and the demand is time‐varying. Orders should be first processed on identical machines and then delivered to a single next‐stage destination by the transporters with fixed departure times. The objective is to minimize the order waiting time via production‐delivery scheduling. We formulate the problem into a Markov decision process model and develop an approximate dynamic programming (ADP) method. To shrink action (decision) space, we propose the shorter processing time first and first completion first delivery (SPTm/FCFD) principle to determine order processing sequences and order delivery, and then we establish two constraints to eliminate a fraction of inferior actions. Based on the SPTm/FCFD principle, we propose the SPT/FCFD rule, and show its optimality for two scenarios. In addition, we deploy five basis functions to approximate the value function. The superior performance of ADP policy is validated via numerical experiments, compared with four benchmark policies. We also empirically study the impact of demand features on the waiting time, and results show that these features significantly affect the performances of all polices. In practice, it is suggested to postpone the peak demand, when total demand exceeds the available production capacity.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"15 1","pages":"511 - 528"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90850544","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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