Pub Date : 2024-11-08DOI: 10.1016/j.cor.2024.106893
Mohammad H. Shekarriz , Dhananjay Thiruvady , Asef Nazari , Rhyd Lewis
For , a -happy vertex in a coloured graph has at least same-colour neighbours, and a -happy colouring (aka soft happy colouring) of is a vertex colouring that makes all the vertices -happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vertices. Graphs with community structures can be modelled by random graph models such as the stochastic block model (SBM). In this paper, we present several theorems showing that both of these notions are related, with numerous real-world applications. We show that, with high probability, communities of graphs in the stochastic block model induce -happy colouring on all vertices if certain conditions on the model parameters are satisfied. Moreover, a probabilistic threshold on is derived so that communities of a graph in the SBM induce a -happy colouring. Furthermore, the asymptotic behaviour of -happy colouring induced by the graph’s communities is discussed when is less than a threshold. We develop heuristic polynomial-time algorithms for soft happy colouring that often correlate with the graphs’ community structure. Finally, we present an experimental evaluation to compare the performance of the proposed algorithms thereby demonstrating the validity of the theoretical results.
对于 0<ρ≤1，彩色图 G 中的ρ-快乐顶点 v 至少有ρ⋅deg(v) 个同色相邻顶点，G 的ρ-快乐着色（又称软快乐着色）是使所有顶点都ρ-快乐的顶点着色。社群是一个子图，其顶点之间的相邻关系多于其他顶点之间的相邻关系。具有群落结构的图可以用随机图模型来建模，如随机块模型（SBM）。在本文中，我们提出了几个定理，说明这两个概念是相关的，并在现实世界中有着大量应用。我们证明，如果模型参数的某些条件得到满足，随机块模型中的图群落很有可能会在所有顶点上诱发 ρ 快乐着色。此外，还推导出了ρ的概率阈值，从而使随机块模型中的图群落诱发ρ-快乐着色。此外，我们还讨论了当 ρ 小于阈值时，图的群落诱导的 ρ 快乐着色的渐近行为。我们为软快乐着色开发了启发式多项式时间算法，这种算法通常与图的群落结构相关。最后，我们通过实验评估来比较所提算法的性能，从而证明理论结果的正确性。
{"title":"Soft happy colourings and community structure of networks","authors":"Mohammad H. Shekarriz , Dhananjay Thiruvady , Asef Nazari , Rhyd Lewis","doi":"10.1016/j.cor.2024.106893","DOIUrl":"10.1016/j.cor.2024.106893","url":null,"abstract":"<div><div>For <span><math><mrow><mn>0</mn><mo><</mo><mi>ρ</mi><mo>≤</mo><mn>1</mn></mrow></math></span>, a <span><math><mi>ρ</mi></math></span>-happy vertex <span><math><mi>v</mi></math></span> in a coloured graph <span><math><mi>G</mi></math></span> has at least <span><math><mrow><mi>ρ</mi><mi>⋅</mi><mo>deg</mo><mrow><mo>(</mo><mi>v</mi><mo>)</mo></mrow></mrow></math></span> same-colour neighbours, and a <span><math><mi>ρ</mi></math></span>-happy colouring (aka soft happy colouring) of <span><math><mi>G</mi></math></span> is a vertex colouring that makes all the vertices <span><math><mi>ρ</mi></math></span>-happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vertices. Graphs with community structures can be modelled by random graph models such as the stochastic block model (SBM). In this paper, we present several theorems showing that both of these notions are related, with numerous real-world applications. We show that, with high probability, communities of graphs in the stochastic block model induce <span><math><mi>ρ</mi></math></span>-happy colouring on all vertices if certain conditions on the model parameters are satisfied. Moreover, a probabilistic threshold on <span><math><mi>ρ</mi></math></span> is derived so that communities of a graph in the SBM induce a <span><math><mi>ρ</mi></math></span>-happy colouring. Furthermore, the asymptotic behaviour of <span><math><mi>ρ</mi></math></span>-happy colouring induced by the graph’s communities is discussed when <span><math><mi>ρ</mi></math></span> is less than a threshold. We develop heuristic polynomial-time algorithms for soft happy colouring that often correlate with the graphs’ community structure. Finally, we present an experimental evaluation to compare the performance of the proposed algorithms thereby demonstrating the validity of the theoretical results.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106893"},"PeriodicalIF":4.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652251","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 : 2024-11-07DOI: 10.1016/j.cor.2024.106898
Banu Soylu , Betül Yıldırım
Relocation involves the repositioning of idle Emergency Service (ES) vehicles among stations in order to reduce the response time. It is well-known in the literature that relocating idle vehicles provides better coverage in the network, which in turn reduces the response time to the next call. In classical emergency service networks, idle vehicles can be relocated between any two stations. This can cause long delays and increase the response times. In this study, we proposed for the first time a hub-and-spoke network to efficiently realize the relocation of idle vehicles. The proposed hub-and-spoke structure consolidates relocations among hubs, while hub-spoke relocations are implemented as needed. Such a structure helps to better organize the simultaneous movements of ES vehicles for relocation. We have developed a mathematical model to maximize the expected safely covered population. The model provides both the hub-spoke topology and the relocation plan (a compliance table), which shows the desired stations of idle vehicles depending on the system state. In the literature, the relocation plan does not show the relocation paths (movements) of the vehicles. We have presented an exact algorithm that computes the relocation paths for all possible call cases and system levels in advance. This helps the dispatcher to manage the system effectively. We performed a detailed simulation study for ES vehicles of a natural gas distributor to demonstrate the real-life suitability of the proposed system. Compared to the classical relocation network structure, the proposed system has improved the response time, relocation time, and travel time especially when the system is busy.
重新定位涉及在站点之间重新定位闲置的紧急服务（ES）车辆，以缩短响应时间。众所周知，重新定位闲置车辆可提高网络的覆盖率，从而缩短对下一个呼叫的响应时间。在传统的紧急服务网络中，闲置车辆可以在任意两个站点之间重新定位。这会造成长时间的延迟，增加响应时间。在本研究中，我们首次提出了一种集线器-辐条网络，以有效实现闲置车辆的重新定位。所提出的 "枢纽-辐条 "结构在枢纽之间整合搬迁，而 "枢纽-辐条 "搬迁则根据需要实施。这种结构有助于更好地组织 ES 车辆同时移动，进行重新安置。我们建立了一个数学模型，以最大限度地提高预期安全覆盖人口。该模型提供了轮辐式拓扑结构和搬迁计划（合规表），根据系统状态显示了闲置车辆的理想站点。在文献中，搬迁计划并不显示车辆的搬迁路径（移动）。我们提出了一种精确算法，可以提前计算所有可能的呼叫情况和系统级别的重新安置路径。这有助于调度员有效地管理系统。我们对一家天然气分销商的 ES 车辆进行了详细的模拟研究，以证明所提系统在现实生活中的适用性。与传统的搬迁网络结构相比，建议的系统改善了响应时间、搬迁时间和旅行时间，尤其是在系统繁忙时。
{"title":"A hub-and-spoke network design for relocating emergency service vehicles","authors":"Banu Soylu , Betül Yıldırım","doi":"10.1016/j.cor.2024.106898","DOIUrl":"10.1016/j.cor.2024.106898","url":null,"abstract":"<div><div>Relocation involves the repositioning of idle Emergency Service (ES) vehicles among stations in order to reduce the response time. It is well-known in the literature that relocating idle vehicles provides better coverage in the network, which in turn reduces the response time to the next call. In classical emergency service networks, idle vehicles can be relocated between any two stations. This can cause long delays and increase the response times. In this study, we proposed for the first time a hub-and-spoke network to efficiently realize the relocation of idle vehicles. The proposed hub-and-spoke structure consolidates relocations among hubs, while hub-spoke relocations are implemented as needed. Such a structure helps to better organize the simultaneous movements of ES vehicles for relocation. We have developed a mathematical model to maximize the expected safely covered population. The model provides both the hub-spoke topology and the relocation plan (a compliance table), which shows the desired stations of idle vehicles depending on the system state. In the literature, the relocation plan does not show the relocation paths (movements) of the vehicles. We have presented an exact algorithm that computes the relocation paths for all possible call cases and system levels in advance. This helps the dispatcher to manage the system effectively. We performed a detailed simulation study for ES vehicles of a natural gas distributor to demonstrate the real-life suitability of the proposed system. Compared to the classical relocation network structure, the proposed system has improved the response time, relocation time, and travel time especially when the system is busy.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106898"},"PeriodicalIF":4.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704703","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 : 2024-11-06DOI: 10.1016/j.cor.2024.106886
Marc Goerigk , Jannis Kurtz
We study iterative constraint and variable generation methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. The goal of this work is to find a set of starting scenarios that provides strong lower bounds early in the process. To this end we define the Relevant Scenario Recognition Problem (RSRP) which finds the optimal choice of scenarios which maximizes the corresponding objective value. We show for classical and two-stage robust optimization that this problem can be solved in polynomial time if the number of selected scenarios is constant and NP-hard if it is part of the input. Furthermore, we derive a linear mixed-integer programming formulation for the problem in both cases.
Since solving the RSRP is not possible in reasonable time, we propose a machine-learning-based heuristic to determine a good set of starting scenarios. To this end, we design a set of dimension-independent features, and train a Random Forest Classifier on already solved small-dimensional instances of the problem. Our experiments show that our method is able to improve the solution process even for larger instances than contained in the training set, and that predicting even a small number of good starting scenarios can considerably reduce the optimality gap. Additionally, our method provides a feature importance score which can give new insights into the role of scenario properties in robust optimization.
{"title":"Data-driven prediction of relevant scenarios for robust combinatorial optimization","authors":"Marc Goerigk , Jannis Kurtz","doi":"10.1016/j.cor.2024.106886","DOIUrl":"10.1016/j.cor.2024.106886","url":null,"abstract":"<div><div>We study iterative constraint and variable generation methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. The goal of this work is to find a set of starting scenarios that provides strong lower bounds early in the process. To this end we define the <em>Relevant Scenario Recognition Problem</em> (RSRP) which finds the optimal choice of scenarios which maximizes the corresponding objective value. We show for classical and two-stage robust optimization that this problem can be solved in polynomial time if the number of selected scenarios is constant and NP-hard if it is part of the input. Furthermore, we derive a linear mixed-integer programming formulation for the problem in both cases.</div><div>Since solving the RSRP is not possible in reasonable time, we propose a machine-learning-based heuristic to determine a good set of starting scenarios. To this end, we design a set of dimension-independent features, and train a Random Forest Classifier on already solved small-dimensional instances of the problem. Our experiments show that our method is able to improve the solution process even for larger instances than contained in the training set, and that predicting even a small number of good starting scenarios can considerably reduce the optimality gap. Additionally, our method provides a feature importance score which can give new insights into the role of scenario properties in robust optimization.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106886"},"PeriodicalIF":4.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652246","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 : 2024-11-06DOI: 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}
Pub Date : 2024-11-03DOI: 10.1016/j.cor.2024.106888
Jan Bierbüße , Lars Mönch , Alexander Biele
A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.
{"title":"Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing","authors":"Jan Bierbüße , Lars Mönch , Alexander Biele","doi":"10.1016/j.cor.2024.106888","DOIUrl":"10.1016/j.cor.2024.106888","url":null,"abstract":"<div><div>A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106888"},"PeriodicalIF":4.1,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164658","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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