Computers & Operations Research最新文献

{"title":"Distributionally robust single machine scheduling with release and due dates over Wasserstein balls","authors":"Haimin Lu,&nbsp;Jiayan Huang,&nbsp;Chenxu Lou,&nbsp;Zhi Pei","doi":"10.1016/j.cor.2024.106892","DOIUrl":"10.1016/j.cor.2024.106892","url":null,"abstract":"<div><div>Single machine scheduling aims at determining the job sequence with the best desired performance, and provides the basic building block for more advanced scheduling problems. In the present study, a single machine scheduling model with uncertain processing time is considered by incorporating the job release time and due date. The job processing time follows unknown probability distribution, and can be estimated via the historical data. To model the uncertainty, the processing time distribution is defined over a Wasserstein ball ambiguity set, which covers all feasible probability distributions within the confidence radius of the empirical distribution, known as the center of the ball. Then a data-driven distributionally robust scheduling model is constructed with individual chance constraints. In particular, two equivalent reformulations are derived with respect to the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm and <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm metrics of the Wasserstein ball, namely, a mixed-integer linear programming and a mixed-integer second order cone programming model, respectively. To accelerate the solving of large-scale instances, a tailored constraint generation algorithm is introduced. In the numerical analysis, the proposed distributionally robust scheduling approach is compared with the state-of-the-art methods in terms of out-of-sample performance.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106892"},"PeriodicalIF":4.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704702","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}

{"title":"A novel mixed-integer linear programming formulation for continuous-time inventory routing","authors":"Akang Wang ,&nbsp;Xiandong Li ,&nbsp;Jeffrey E. Arbogast ,&nbsp;Zachary Wilson ,&nbsp;Chrysanthos E. Gounaris","doi":"10.1016/j.cor.2024.106883","DOIUrl":"10.1016/j.cor.2024.106883","url":null,"abstract":"<div><div>Inventory management, vehicle routing, and delivery scheduling decisions are simultaneously considered in the context of the inventory routing problem. This paper focuses on the continuous-time version of this problem where, unlike its more traditional discrete-time counterpart, the distributor is required to guarantee that inventory levels are maintained within the desired intervals at any moment of the planning horizon. In this work, we develop a compact mixed-integer linear programming formulation to model the continuous-time inventory routing problem. We further discuss means to expedite its solution process, including the adaptation of well-known rounded capacity inequalities to tighten the formulation in the context of a branch-and-cut algorithm. Through extensive computational studies on a suite of 90 benchmark instances from the literature, we show that our branch-and-cut algorithm outperforms the state-of-the-art approach. We also consider a new set of 63 instances adapted from a real-life dataset and show our algorithm’s practical value in solving instances with up to 20 customers to guaranteed optimality.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106883"},"PeriodicalIF":4.1,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652254","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}

{"title":"The min max multi-trip drone location arc routing problem","authors":"Teresa Corberán ,&nbsp;Isaac Plana ,&nbsp;José María Sanchis","doi":"10.1016/j.cor.2024.106894","DOIUrl":"10.1016/j.cor.2024.106894","url":null,"abstract":"<div><div>This paper studies the Min Max Multi-Trip drone Location Arc Routing Problem (MM-MT-dLARP), an arc routing problem that combines trucks and drones. We have a set of lines (usually curved) that have to be flown over by drones to perform a service (inspection, for example). There is a depot from which the trucks leave, each one carrying a drone, and a set of potential launching points where the truck can launch and pick up the drone. Drones have limited autonomy, but they can make several flights. We consider a min–max objective, in which the makespan, or time necessary to complete the service, must be minimized. Using aerial drones instead of ground vehicles allows to travel off the network: drones can enter a line through any of its points, service only a portion of that line and then exit through another of its points, without following the lines of the network. This allows for finding better solutions but also increases the difficulty of the problem. This issue can be addressed by digitizing the MM-MT-dLARP instances, approximating each line by a polygonal chain with a finite number of intermediate points, and requiring that drones can only enter and exit a line through those intermediate points. Thus, an instance of a discrete Min Max Multi-Trip Location Arc Routing Problem (MM-MT-LARP) is obtained. Here, an integer formulation for the MM-MT-LARP is proposed, some families of valid inequalities are proved to be facet-inducing of a relaxed polyhedron, and a branch-and-cut algorithm based on the strengthened formulation is developed. This algorithm has only been applied to small instances without intermediate points on the lines. In addition, we have developed a matheuristic algorithm for the MM-MT-dLARP that combines a construction phase, four local search procedures integrated into a Variable Neighborhood Descent (VND) algorithm, and a set of rules for selecting intermediate points to improve the solutions. We present the results obtained on a set of randomly generated instances involving up to 6 launching points and 88 original lines.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106894"},"PeriodicalIF":4.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652253","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}

{"title":"Facility location and restoration games","authors":"Suzan Iloglu ,&nbsp;Laura A. Albert ,&nbsp;Carla Michini","doi":"10.1016/j.cor.2024.106896","DOIUrl":"10.1016/j.cor.2024.106896","url":null,"abstract":"<div><div>Effective recovery of interdependent infrastructure systems after natural disasters requires coordination between multiple infrastructure owners, such as power and telecommunications utilities. If infrastructure owners make restoration decisions in isolation from one another, then recovery may be piecemeal. A fundamental understanding of these interdependencies can provide insights to incentivize shared restoration that benefit all infrastructure users, with the goal to maximize the social welfare even in a non-cooperative setting. We introduce a non-cooperative facility location and restoration game on a layered network, where each layer belongs to a player, to model the recovery of interdependent infrastructure systems after disasters. The goal of the model is to plan short term post-disaster recovery. The players want to minimize the cost to satisfy their own demand by restoring network components, and each player can serve the other players’ demands if they are paid a fee to do so. We propose exact and approximate algorithms to set incentives (fees) so that the players’ actions at equilibrium are aligned with a social optimum of the system, which minimizes the total cost. We present a case study in which we consider the recovery efforts of telecommunication infrastructure companies and provide results for the facility location and restoration games. The models and proposed algorithms can be used to set policy, inform the structure of inter-agency mutual aid partnerships to support disaster recovery, and negotiate inter-agency usage fees prior to a disaster to ease shared recovery efforts.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"174 ","pages":"Article 106896"},"PeriodicalIF":4.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652247","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}

{"title":"Soft happy colourings and community structure of networks","authors":"Mohammad H. Shekarriz ,&nbsp;Dhananjay Thiruvady ,&nbsp;Asef Nazari ,&nbsp;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>&lt;</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}

{"title":"A hub-and-spoke network design for relocating emergency service vehicles","authors":"Banu Soylu ,&nbsp;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}

{"title":"Data-driven prediction of relevant scenarios for robust combinatorial optimization","authors":"Marc Goerigk ,&nbsp;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}

{"title":"Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services","authors":"Ziru Lin ,&nbsp;Xiaofeng Xu ,&nbsp;Emrah Demir ,&nbsp;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}

{"title":"Distributionally robust hospital capacity expansion planning under stochastic and correlated patient demand","authors":"Aliaa Alnaggar,&nbsp;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}

{"title":"A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem","authors":"Igor Granado ,&nbsp;Elsa Silva ,&nbsp;Maria Antónia Carravilla ,&nbsp;José Fernando Oliveira ,&nbsp;Leticia Hernando ,&nbsp;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}