Pub Date : 2024-11-06DOI: 10.1016/j.cor.2024.106890
Ziru Lin , Xiaofeng Xu , Emrah Demir , Gilbert Laporte
This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.
{"title":"Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services","authors":"Ziru Lin , Xiaofeng Xu , Emrah Demir , Gilbert Laporte","doi":"10.1016/j.cor.2024.106890","DOIUrl":"10.1016/j.cor.2024.106890","url":null,"abstract":"<div><div>This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106890"},"PeriodicalIF":4.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.cor.2024.106887
Aliaa Alnaggar, Fatimah Faiza Farrukh
This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model that predicts future hospital occupancy levels considering spatial and temporal patterns in time-series datasets over a network of hospitals. The predictions of the STNN model are then used in the construction of the ambiguity set of the DRO model. To address computational challenges associated with two-stage DRO, we employ the linear-decision-rules technique to derive a tractable mixed-integer linear programming approximation. Extensive computational experiments conducted on real-world data demonstrate the superiority of the STNN model in minimizing forecast errors. Compared to neural network models built for each individual hospital, the proposed STNN model achieves a 53% improvement in average root mean square error. Furthermore, the results demonstrate the value of incorporating spatiotemporal dependencies of demand uncertainty in the DRO model, as evidenced by out-of-sample analysis conducted with both ground truth data and under perfect information scenarios.
{"title":"Distributionally robust hospital capacity expansion planning under stochastic and correlated patient demand","authors":"Aliaa Alnaggar, Fatimah Faiza Farrukh","doi":"10.1016/j.cor.2024.106887","DOIUrl":"10.1016/j.cor.2024.106887","url":null,"abstract":"<div><div>This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model that predicts future hospital occupancy levels considering spatial and temporal patterns in time-series datasets over a network of hospitals. The predictions of the STNN model are then used in the construction of the ambiguity set of the DRO model. To address computational challenges associated with two-stage DRO, we employ the linear-decision-rules technique to derive a tractable mixed-integer linear programming approximation. Extensive computational experiments conducted on real-world data demonstrate the superiority of the STNN model in minimizing forecast errors. Compared to neural network models built for each individual hospital, the proposed STNN model achieves a 53% improvement in average root mean square error. Furthermore, the results demonstrate the value of incorporating spatiotemporal dependencies of demand uncertainty in the DRO model, as evidenced by out-of-sample analysis conducted with both ground truth data and under perfect information scenarios.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106887"},"PeriodicalIF":4.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.cor.2024.106891
Igor Granado , Elsa Silva , Maria Antónia Carravilla , José Fernando Oliveira , Leticia Hernando , Jose A. Fernandes-Salvador
Nowadays, the world’s fishing fleet uses 20% more fuel to catch the same amount of fish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange for a significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.
{"title":"A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem","authors":"Igor Granado , Elsa Silva , Maria Antónia Carravilla , José Fernando Oliveira , Leticia Hernando , Jose A. Fernandes-Salvador","doi":"10.1016/j.cor.2024.106891","DOIUrl":"10.1016/j.cor.2024.106891","url":null,"abstract":"<div><div>Nowadays, the world’s fishing fleet uses 20% more fuel to catch the same amount of fish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange for a significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106891"},"PeriodicalIF":4.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.cor.2024.106889
Shaowen Yao , Tai Zhang , Hao Zhang , Jian Qiu , Jiewu Leng , Qiang Liu , Lijun Wei
The distributor’s online pallet loading problem (DPLP), which involves efficiently packing a set of cuboid boxes with various dimensions and unknown order into a minimum number of given sized pallet, is extensively employed in industrial automation and has recently garnered significant attention from the research community. However, the existing online approaches face challenges due to the unpredictable arrival order of boxes and the requirement for fast processing. To address this issue, we introduce a new variant of DPLP, the online pallet loading problem with buffer area, by introducing a buffer area with a predefined capacity within the stacking region. The arrival boxes are initially positioned within the buffer area until the total number of boxes reaches the maximum capacity of the buffer area, then, the boxes in the buffer area are selected and packed into the pallet. We propose a greedy search heuristic to solve the online DPLP with buffer area. Our approach uses an open space approach to represent the residual space and combine simple and guillotine blocks to generate blocks. By minimizing the amount of waste, we select the appropriate box placement. Extensive experimental tests on classical and practical instances shows that our method improves pallet utilization by more than 15% compared to purely online algorithms. Compared with other state-of-the-art algorithms, our method improves the average improvement by 8%. Moreover, our approach exhibits a certain level of generalizability and holds substantial practical value for real-world applications.
{"title":"The semi-online robotic pallet loading problem","authors":"Shaowen Yao , Tai Zhang , Hao Zhang , Jian Qiu , Jiewu Leng , Qiang Liu , Lijun Wei","doi":"10.1016/j.cor.2024.106889","DOIUrl":"10.1016/j.cor.2024.106889","url":null,"abstract":"<div><div>The distributor’s online pallet loading problem (DPLP), which involves efficiently packing a set of cuboid boxes with various dimensions and unknown order into a minimum number of given sized pallet, is extensively employed in industrial automation and has recently garnered significant attention from the research community. However, the existing online approaches face challenges due to the unpredictable arrival order of boxes and the requirement for fast processing. To address this issue, we introduce a new variant of DPLP, the online pallet loading problem with buffer area, by introducing a buffer area with a predefined capacity within the stacking region. The arrival boxes are initially positioned within the buffer area until the total number of boxes reaches the maximum capacity of the buffer area, then, the boxes in the buffer area are selected and packed into the pallet. We propose a greedy search heuristic to solve the online DPLP with buffer area. Our approach uses an open space approach to represent the residual space and combine simple and guillotine blocks to generate blocks. By minimizing the amount of waste, we select the appropriate box placement. Extensive experimental tests on classical and practical instances shows that our method improves pallet utilization by more than 15% compared to purely online algorithms. Compared with other state-of-the-art algorithms, our method improves the average improvement by 8%. Moreover, our approach exhibits a certain level of generalizability and holds substantial practical value for real-world applications.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106889"},"PeriodicalIF":4.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.cor.2024.106880
Simon Risanger, Steffen J.S. Bakker, Stein-Erik Fleten, Asgeir Tomasgard
Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid’s physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of-concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.
{"title":"Flexible electricity market clearing zones","authors":"Simon Risanger, Steffen J.S. Bakker, Stein-Erik Fleten, Asgeir Tomasgard","doi":"10.1016/j.cor.2024.106880","DOIUrl":"10.1016/j.cor.2024.106880","url":null,"abstract":"<div><div>Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid’s physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of-concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106880"},"PeriodicalIF":4.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.cor.2024.106882
Haifeng Zhang , Kai Yang , Jianjun Dong , Lixing Yang
The widening use of hub networks in urban agglomeration freight systems requires several actual extensions in conventional hub network design problems. For this purpose, we introduce a two-stage robust multimodal hub network design problem for the urban agglomeration freight system by considering incomplete hub network topology, multiple transportation modes, travel time limit and discuss the uncertainty in the constructed network from the demand point of view. Particularly, we model the demand uncertainty for the considered problem in two different ways. The basic model supposes that interval-budgeted uncertainty set is adopted to characterize uncertain demand, while the expanded model additionally considers possible states of the uncertain demand and weights summation of performances over multiple uncertainty sets, namely state-wise budgeted uncertainty set. By using a min–max criterion, we develop the path-based mixed-integer programming formulations for the proposed problem, which can significantly decrease the number of required integer variables and constraints. To handle large-sized problems, we propose an improved Benders decomposition algorithm, in which the master problem is implemented in a branch-and-bound framework and the subproblem is optimality solved by a customized two-step strategy. In addition to evaluating on the standard CAB, TR and AP datasets, we conduct a real-world case study of the Beijing–Tianjin–Hebei urban agglomeration freight system to explore the effect of incorporating uncertainty and showcase the superior performance of the proposed methods.
随着枢纽网络在城市群货运系统中的应用越来越广泛，需要对传统的枢纽网络设计问题进行一些实际扩展。为此，我们通过考虑不完整的枢纽网络拓扑结构、多种运输方式、旅行时间限制等因素，引入了城市集群货运系统的两阶段鲁棒多式联运枢纽网络设计问题，并从需求角度讨论了所构建网络的不确定性。特别是，我们用两种不同的方法为所考虑问题的需求不确定性建模。基本模型假定采用区间预算不确定性集来描述不确定需求，而扩展模型则额外考虑了不确定需求的可能状态，并对多个不确定性集的性能进行加权求和，即状态预算不确定性集。通过使用最小-最大准则，我们为所提问题开发了基于路径的混合整数编程公式，从而大大减少了所需整数变量和约束条件的数量。为了处理大型问题，我们提出了一种改进的 Benders 分解算法，其中主问题在分支与边界框架中实现，子问题通过定制的两步策略优化求解。除了在标准 CAB、TR 和 AP 数据集上进行评估外，我们还对京津冀城市群货运系统进行了实际案例研究，以探索纳入不确定性的影响，并展示所提方法的优越性能。
{"title":"Two-stage robust multimodal hub network design under budgeted demand uncertainty: A Benders decomposition approach and a case study","authors":"Haifeng Zhang , Kai Yang , Jianjun Dong , Lixing Yang","doi":"10.1016/j.cor.2024.106882","DOIUrl":"10.1016/j.cor.2024.106882","url":null,"abstract":"<div><div>The widening use of hub networks in urban agglomeration freight systems requires several actual extensions in conventional hub network design problems. For this purpose, we introduce a two-stage robust multimodal hub network design problem for the urban agglomeration freight system by considering incomplete hub network topology, multiple transportation modes, travel time limit and discuss the uncertainty in the constructed network from the demand point of view. Particularly, we model the demand uncertainty for the considered problem in two different ways. The basic model supposes that interval-budgeted uncertainty set is adopted to characterize uncertain demand, while the expanded model additionally considers possible states of the uncertain demand and weights summation of performances over multiple uncertainty sets, namely state-wise budgeted uncertainty set. By using a min–max criterion, we develop the path-based mixed-integer programming formulations for the proposed problem, which can significantly decrease the number of required integer variables and constraints. To handle large-sized problems, we propose an improved Benders decomposition algorithm, in which the master problem is implemented in a branch-and-bound framework and the subproblem is optimality solved by a customized two-step strategy. In addition to evaluating on the standard CAB, TR and AP datasets, we conduct a real-world case study of the Beijing–Tianjin–Hebei urban agglomeration freight system to explore the effect of incorporating uncertainty and showcase the superior performance of the proposed methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106882"},"PeriodicalIF":4.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult to understand even for experts, our model provides an easily interpretable visualization of balance sheets, projecting each company in a bi-dimensional space according to an autoencoder-based dimensionality reduction matched with a Nearest-Neighbor-based default density estimation. In the resulting space, the distress zones, where the default intensity is high, appear as homogeneous clusters directly identified. Our empirical experiments provide evidence of the interpretability, forecasting ability, and robustness of the bi-dimensional space.
{"title":"Corporate risk stratification through an interpretable autoencoder-based model","authors":"Alessandro Giuliani , Roberto Savona , Salvatore Carta , Gianmarco Addari , Alessandro Sebastian Podda","doi":"10.1016/j.cor.2024.106884","DOIUrl":"10.1016/j.cor.2024.106884","url":null,"abstract":"<div><div>In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult to understand even for experts, our model provides an easily interpretable visualization of balance sheets, projecting each company in a bi-dimensional space according to an autoencoder-based dimensionality reduction matched with a Nearest-Neighbor-based default density estimation. In the resulting space, the distress zones, where the default intensity is high, appear as homogeneous clusters directly identified. Our empirical experiments provide evidence of the interpretability, forecasting ability, and robustness of the bi-dimensional space.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106884"},"PeriodicalIF":4.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.cor.2024.106879
Guiyu Li, Hongbo Duan
There are plenty of uncertainties in the integrated climate-economic system including parameter uncertainty and model uncertainty, which significantly challenges the assessment of climate goals committed in the Paris Agreement pledges. In this study, we develop a robustness assessment framework of climate policy by effectively coupling the distributionally robust optimization (DRO) methodology with integrated assessment models (IAMs), termed DRO-IAMS framework, where “S” emphasizes the multiple IAMs being incorporated. Our approach determines a safeguarding probability for the achievement of carbon-neutrality target through the worst-case Conditional Value-at-Risk (CVaR) criterion by effectively capturing the fat-tail effect and exploiting its tractability. Leveraging a discrete support of uncertain parameters over which the objective value of global temperature increase (GTI) can be readily accessible using the IAMs, our developed DRO-IAMS framework effectively circumvents the difficulty in utilizing analytically the black-box-featured IAMs, and achieves a comprehensive and more flexible fashion in integrating the DRO (e.g, moment, -divergence, and Wasserstein ambiguity sets) and IAMs (e.g., DICE, FUND, and E3METL) to cope with parameter- and model uncertainties in climate policy assessment. Our results suggest that parameter uncertainty and model uncertainty — as critical issues that can have significant impacts on the warming and economic performance of policies — could incur biased assessment for the realization of climate targets. Our proposed DRO-IAMS approach — by its design — is shown to be able to effectively mitigate such issues by pursuing stricter mitigation efforts, and can produce more reliable assessments for typical climate policies than the common sampling-based approaches.
{"title":"Robustness assessment of climate policies towards carbon neutrality: A DRO-IAMS approach","authors":"Guiyu Li, Hongbo Duan","doi":"10.1016/j.cor.2024.106879","DOIUrl":"10.1016/j.cor.2024.106879","url":null,"abstract":"<div><div>There are plenty of uncertainties in the integrated climate-economic system including parameter uncertainty and model uncertainty, which significantly challenges the assessment of climate goals committed in the Paris Agreement pledges. In this study, we develop a robustness assessment framework of climate policy by effectively coupling the distributionally robust optimization (DRO) methodology with integrated assessment models (IAMs), termed DRO-IAMS framework, where “S” emphasizes the multiple IAMs being incorporated. Our approach determines a safeguarding probability for the achievement of carbon-neutrality target through the worst-case Conditional Value-at-Risk (CVaR) criterion by effectively capturing the fat-tail effect and exploiting its tractability. Leveraging a discrete support of uncertain parameters over which the objective value of global temperature increase (GTI) can be readily accessible using the IAMs, our developed DRO-IAMS framework effectively circumvents the difficulty in utilizing analytically the black-box-featured IAMs, and achieves a comprehensive and more flexible fashion in integrating the DRO (<em>e.g</em>, moment, <span><math><mi>ϕ</mi></math></span>-divergence, and Wasserstein ambiguity sets) and IAMs (<em>e.g.</em>, DICE, FUND, and E3METL) to cope with parameter- and model uncertainties in climate policy assessment. Our results suggest that parameter uncertainty and model uncertainty — as critical issues that can have significant impacts on the warming and economic performance of policies — could incur biased assessment for the realization of climate targets. Our proposed DRO-IAMS approach — by its design — is shown to be able to effectively mitigate such issues by pursuing stricter mitigation efforts, and can produce more reliable assessments for typical climate policies than the common sampling-based approaches.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106879"},"PeriodicalIF":4.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Re-direction occurs when a customer arriving at a station in a queuing network has to be re-directed to a downstream station to complete service. Re-direction is extremely common in practice and occurs for a variety of reasons, ranging from incorrect initial station assignment to cases where the initial station only provides part of the service. Gatekeeper stations (e.g., information desks) is a special case of re-direction. We consider re-direction in a queueing network consisting of single-server stations serving two customer types with different service time requirements. The behavior of such queueing networks is quite complex: even when all external arrivals and all services are Markovian, the customers’ inter-departure distribution, and hence their arrival process to downstream stations, is non-Markovian. Thus, product-form representation does not hold for such networks. Our analysis focuses on the key building block: the inter-departure process from a station serving two distinct customer types and routing them to two different downstream service paths. Using a novel approach, we obtain a very accurate phase-type representation of the inter-departure process under equilibrium. We show that the resulting methodology has significant advantages over both simulation modeling (our method is much faster) and the available approximation techniques (our method is more accurate). Finally, we demonstrate an interesting phenomenon: even when the station merely re-directs one of the customer types (providing no service and seemingly useless waits), it can serve as a “regulator”, reducing the variability of the downstream arrival process. We show that, under some conditions, this can improve the overall system performance.
{"title":"Re-direction in queueing networks with two customer types: The inter-departure analysis","authors":"Opher Baron , Oded Berman , Dmitry Krass , Eliran Sherzer","doi":"10.1016/j.cor.2024.106867","DOIUrl":"10.1016/j.cor.2024.106867","url":null,"abstract":"<div><div>Re-direction occurs when a customer arriving at a station in a queuing network has to be re-directed to a downstream station to complete service. Re-direction is extremely common in practice and occurs for a variety of reasons, ranging from incorrect initial station assignment to cases where the initial station only provides part of the service. <em>Gatekeeper</em> stations (e.g., information desks) is a special case of re-direction. We consider re-direction in a queueing network consisting of single-server stations serving two customer types with different service time requirements. The behavior of such queueing networks is quite complex: even when all external arrivals and all services are Markovian, the customers’ inter-departure distribution, and hence their arrival process to downstream stations, is non-Markovian. Thus, product-form representation does not hold for such networks. Our analysis focuses on the key building block: the inter-departure process from a station serving two distinct customer types and routing them to two different downstream service paths. Using a novel approach, we obtain a very accurate phase-type representation of the inter-departure process under equilibrium. We show that the resulting methodology has significant advantages over both simulation modeling (our method is much faster) and the available approximation techniques (our method is more accurate). Finally, we demonstrate an interesting phenomenon: even when the station merely re-directs one of the customer types (providing no service and seemingly useless waits), it can serve as a “regulator”, reducing the variability of the downstream arrival process. We show that, under some conditions, this can improve the overall system performance.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"173 ","pages":"Article 106867"},"PeriodicalIF":4.1,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.cor.2024.106881
Parisa Torabi , Ahmad Hemmati , Anna Oleynik , Guttorm Alendal
Covering Tour Problem (CTP) is a combinatorial optimization problem in which the objective is to identify a minimum-cost tour that satisfies the coverage of a certain subset of nodes in a graph. The Covering Tour Problem with Varying Coverage (CTP-VC) is an extension of this problem in which the coverage radius is dependent on the amount of time spent at each node. In this paper, we propose a novel approach to address the CTP-VC using a Deep Reinforcement Learning Hyperheuristic (DRLH). This study includes experiments on the existing Adaptive Metaheuristic to solve CTP-VC, to enhance its solution quality. Further, new heuristics and three selection methods, namely Uniform Random Selection (URS), adaptive Metaheuristic (AMH), and the proposed DRLH are introduced. We detail the computational setup, including the instance sets utilized, the training process for the DRLH agent, and the validation procedures for model selection. Through extensive experimentation and analysis, we evaluate the performance of different selection methods, assess the solution quality of the DRLH approach, investigate the robustness of selection methods, examine heuristic selection frequency, and analyze solution convergence. Our results demonstrate the efficacy of the DRLH approach in tackling the CTP-VC, offering promising insights for future research in the interface of combinatorial optimization and reinforcement learning methodologies.
{"title":"A deep reinforcement learning hyperheuristic for the covering tour problem with varying coverage","authors":"Parisa Torabi , Ahmad Hemmati , Anna Oleynik , Guttorm Alendal","doi":"10.1016/j.cor.2024.106881","DOIUrl":"10.1016/j.cor.2024.106881","url":null,"abstract":"<div><div>Covering Tour Problem (CTP) is a combinatorial optimization problem in which the objective is to identify a minimum-cost tour that satisfies the coverage of a certain subset of nodes in a graph. The Covering Tour Problem with Varying Coverage (CTP-VC) is an extension of this problem in which the coverage radius is dependent on the amount of time spent at each node. In this paper, we propose a novel approach to address the CTP-VC using a Deep Reinforcement Learning Hyperheuristic (DRLH). This study includes experiments on the existing Adaptive Metaheuristic to solve CTP-VC, to enhance its solution quality. Further, new heuristics and three selection methods, namely Uniform Random Selection (URS), adaptive Metaheuristic (AMH), and the proposed DRLH are introduced. We detail the computational setup, including the instance sets utilized, the training process for the DRLH agent, and the validation procedures for model selection. Through extensive experimentation and analysis, we evaluate the performance of different selection methods, assess the solution quality of the DRLH approach, investigate the robustness of selection methods, examine heuristic selection frequency, and analyze solution convergence. Our results demonstrate the efficacy of the DRLH approach in tackling the CTP-VC, offering promising insights for future research in the interface of combinatorial optimization and reinforcement learning methodologies.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106881"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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