Computers & Operations Research最新文献

{"title":"New cutting planes for open-pit mine scheduling with multi-period block extraction","authors":"Venkat Akhil Ankem ,&nbsp;Guy Desaulniers ,&nbsp;Michel Gamache ,&nbsp;Vincent Raymond","doi":"10.1016/j.cor.2026.107408","DOIUrl":"10.1016/j.cor.2026.107408","url":null,"abstract":"<div><div>The open-pit mine production scheduling problem (OPMPSP) is a fundamental planning problem in mining engineering. Given a discretized ore body representation known as a block model, the OPMPSP consists in computing the schedule of block excavation (extraction time, quantity, and processing decisions) over a planning horizon while adhering to operational constraints and maximizing the net present value of the profit. This problem is typically modeled using a large-sized mixed-integer linear programming (MILP) formulation. In this paper, we focus on an OPMPSP involving blocks of varying sizes that can be extracted over multiple periods. For this problem, we propose a new MILP formulation that includes two types of scheduling variables, one indicating the starting period of the extraction of a block and the other its ending period. Considering these two variable types allows to introduce new versions of known cutting planes as well as new cut families that are defined for blocks requiring multiple periods to be extracted. All cuts are generated a priori and added to the MILP formulation which is then solved by a commercial MILP solver. Through extensive computational experiments on three real-life OPMPSP instances, we demonstrate that the proposed cuts significantly reduce computational times (by up to 70.3%), making a valuable contribution to large-scale mine planning optimization. This methodology is also integrated into a rolling-horizon heuristic, where the cutting planes can be updated at each iteration.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107408"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035848","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":"Electric vehicle routing with heterogeneous charging stations","authors":"Weiquan Wang ,&nbsp;Yossiri Adulyasak ,&nbsp;Jean-François Cordeau","doi":"10.1016/j.cor.2025.107374","DOIUrl":"10.1016/j.cor.2025.107374","url":null,"abstract":"<div><div>The routing of an electric vehicle often requires planning stops at charging stations to recharge the vehicle’s battery. At the same time, the price of electricity, the charging technology, and the waiting time at a charging station (CS) can impact routing and charging decisions for a fleet of vehicles. We introduce the Electric Vehicle Routing Problem with Heterogeneous Charging Stations (E-VRP-HC). We simultaneously consider charging functions (which are nonlinear in nature), time-dependent waiting functions, and time-of-use electricity pricing at CSs. The advantage of considering this function explicitly is that we can reduce costs by avoiding peak queuing times at CSs or periods with high charging prices. The objective function is to minimize the sum of the route duration costs, charging costs, and vehicle fixed costs. To solve this problem, we propose a path-based mixed-integer linear programming formulation, which does not require time discretization to track charging costs. The formulation can be solved using a general-purpose solver to find optimal solutions for small-sized instances. However, tackling this large-scale problem is challenging given the fact that determining an optimal vehicle’s time schedule for a given route in this context is already a complex optimization problem. To address this, we propose two methods for the time scheduling problem: (i) an exact method that obtains an optimal schedule but is computationally intensive and (ii) a heuristic method that can provide a high-quality solution in a very short time. Building upon these two optimization subroutines, we develop an effective metaheuristic framework to solve large-scale instances. The proposed model and heuristic are validated on two sets of benchmark instances: a newly designed benchmark set, and a real-world dataset from Beijing. The computational results on benchmark instances demonstrate the performance of our approach in handling the complexity of the E-VRP-HC. Numerical tests on the real-world dataset show that considering the time-of-use pricing function and nonlinear charging function can significantly reduce costs. Finally, we provide valuable managerial insights based on the results of our computational experiments.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107374"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035855","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":"Stochastic dynamic inventory-routing: A comprehensive review","authors":"Francisco Maia ,&nbsp;Gonçalo Figueira ,&nbsp;Fábio Neves-Moreira","doi":"10.1016/j.cor.2026.107383","DOIUrl":"10.1016/j.cor.2026.107383","url":null,"abstract":"<div><div>The stochastic dynamic inventory-routing problem (SDIRP) is a fundamental problem within supply chain operations that integrates inventory management and vehicle routing while handling the stochastic and dynamic nature of exogenous factors unveiled over time, such as customer demands, inventory supply and travel times. While practical applications require dynamic and stochastic decision-making, research in this field has only recently experienced significant growth, with most inventory-routing literature focusing on static variants. This paper reviews the current state of research on SDIRPs, identifying critical gaps and highlighting emerging trends in problem settings and decision policies. We extend the existing inventory-routing taxonomies by incorporating additional problem characteristics to better align models with real-world contexts. As a result, we highlight the need to account for further sources of uncertainty, multiple-supplier networks, perishability, multiple objectives, and pickup and delivery operations. We further categorize each study based on its policy design, investigating how different problem aspects shape decision policies. To conclude, we emphasize that large-scale and real-time problems require more attention and can benefit from decomposition approaches and learning-based methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107383"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976360","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":"Hybrid action space approach to traffic signal optimization using deep reinforcement learning","authors":"Jihong Jin ,&nbsp;Shangming Wu ,&nbsp;Pengwei Zhang ,&nbsp;Xiaorui Zhang ,&nbsp;Chaoen Yin ,&nbsp;Changyin Dong","doi":"10.1016/j.cor.2026.107391","DOIUrl":"10.1016/j.cor.2026.107391","url":null,"abstract":"<div><div>Traffic intersections with time-varying demand encounter persistent challenges in achieving adaptive signal timing optimization and dynamic traffic equilibrium. Existing deep reinforcement learning methodologies predominantly adopt discrete action spaces, which constrain the capacity of such approaches to address complex spatiotemporal traffic dynamics. This research proposes a hybrid action space-based Deep Q-Network (H-DQN) framework for traffic signal control, evaluating its optimization effects through multi-dimensional performance metrics under dynamic traffic conditions. Firstly, this study treats phase policy selection and duration setting as two-level synergistic optimization, thereby proposing a refined hybrid action decision-making mechanism that integrates a dual-layer architecture for joint decision-making on signal phases and green durations. Secondly, the state space is defined by integrating lane saturation levels, queuing lengths, and current green phases across all intersection lanes. At the same time, the reward function is constructed based on the temporal variation in vehicle counts between consecutive sampling intervals, enabling dynamic adaptation to real-time traffic changes while ensuring computational efficiency through optimized design. Finally, this study validates the model in the Simulation of Urban MObility (SUMO) simulation environment across three traffic scenarios, which are uniform traffic with stable speed, non-uniform traffic with stable speed, and complex traffic with varying arrival rates. The results show that the proposed H-DQN model significantly improved intersection throughput efficiency across all three traffic scenarios compared to the DQN method, reducing average queue lengths by 21.58%, 49.43%, and 44.92%, respectively. Compared to Q-learning, the reductions in average queue lengths improved by 86.32%, 87.61%, and 84.51%. Meanwhile, the H-DQN model strengthens average vehicle speeds while minimizing delays, fuel consumption, and lane occupancy, thereby achieving substantial improvements in intersection operational efficiency and traffic throughput.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107391"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976357","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":"Novel models and efficient heuristic for the vessel–unmanned surface vehicle routing problem","authors":"Lianhua Tang ,&nbsp;Yantong Li ,&nbsp;Kanglin Liu ,&nbsp;Jean-François Côté ,&nbsp;Leandro C. Coelho","doi":"10.1016/j.cor.2026.107384","DOIUrl":"10.1016/j.cor.2026.107384","url":null,"abstract":"<div><div>This paper addresses the vessel–unmanned surface vehicle (USV) routing problem (VURP), which jointly determines the routes of a vessel and a USV. The problem is applicable to offshore supply, search and rescue, and inspection operations. In the VURP, a vessel carries a loaded USV and the USV departs from the vessel to deliver goods to offshore platforms when in proximity. The objective is to minimize the total routing costs of both vehicles by optimizing their routes along with the USV’s departure and arrival points. We begin by exploiting the structural properties of the problem and propose two enhanced mixed-integer second-order conic programming (MISOCP) formulations, each further strengthened with valid inequalities to improve computational performance. Due to the NP-hard nature of the problem, we develop a tailored adaptive large neighborhood search (ALNS) algorithm designed to handle practical-sized instances. The proposed ALNS employs a two-phase framework to enable a multi-start mechanism: the first phase generates a diverse set of initial solutions using an effective approximation-based scheme, while the second phase iteratively improves these solutions through a problem-specific ALNS procedure. This structure balances global exploration and local intensification, enhancing both solution diversity and robustness. Extensive computational experiments demonstrate the strong performance of the improved formulations and the heuristic method. The integration of USVs significantly reduces vessel travel costs, especially when the USVs have larger capacities. Moreover, numerical results on benchmark instances show that our ALNS outperforms state-of-the-art heuristics, achieving 45 new best solutions among 72 open benchmark cases.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107384"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035854","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":"Optimizing order bundling and dispatching in online food delivery for enhanced delivery efficiency","authors":"Shan Zhu ,&nbsp;Xiangpei Hu ,&nbsp;Yanling Zhuang ,&nbsp;Yufei Yuan ,&nbsp;Wenjie Wang ,&nbsp;Zheng Wang","doi":"10.1016/j.cor.2026.107387","DOIUrl":"10.1016/j.cor.2026.107387","url":null,"abstract":"<div><div>The online food delivery industry is undergoing a rapid global expansion, making it an easily accessible service for a growing number of consumers. With a simple swipe on a smartphone, customers can conveniently order food from a wide range of restaurants through online food delivery platforms such as Uber Eats, Grubhub, Meituan, and Eleme. The core functionality of these platforms is their algorithmic approach to order dispatching, which is the focus of our study. The objective is to optimize the courier-order matching process, thereby minimizing delivery time and distance, enhancing the efficiency and effectiveness of the service. In contrast to the conventional approach of matching a single order to each courier, our study explores a one-to-many courier-order matching process, which we term a concurrent order dispatching process optimizing order bundling, courier matching and route planning jointly. Our study proposes a comprehensive framework that intricately models the concurrent order dispatching process in great detail. Specifically, we construct a mixed-integer programming model and develop a hybrid heuristic algorithm to address the issue in an efficient manner. We introduce a novel order-bundling closeness measurement value to strategically dispatch multiple orders concurrently to each single courier during a designated decision time window. To assess the model’s and algorithm’s efficacy, we conducted experiments on both small-scale synthetic instances and large-scale real cases, utilizing data from a prominent online food delivery platform in China. The computational results demonstrate that our proposed approach yields solutions that are very close to the optimum in small-scale cases, and achieves significant improvements in terms of average delay time reduction and average distance savings in large-scale cases. In particular, our approach can save the average distance per order by 1.8 km and reduce the average delay time per order from 35 min to 10 min, in comparison to existing policies. We seek to ensure that a significant portion of orders are delivered on time, even with a limited number of couriers.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107387"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976356","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":"Numerical methods for stochastic optimization problems with decision-dependent distributions under large delays","authors":"Licheng Deng,&nbsp;Yongchao Liu","doi":"10.1016/j.cor.2026.107392","DOIUrl":"10.1016/j.cor.2026.107392","url":null,"abstract":"<div><div>In this paper, we aim to seek the performative stable solution and the stationary point of the stochastic optimization problem with decision-dependent distributions in a centralized communication setting. We focus on the scenario where there are linearly growing delays in the system with a centralized communication protocol and propose two distributed asynchronous algorithms. For the performative stable solution, we provide a distributed asynchronous algorithm, DASGD-GD, which integrates the distributed asynchronous stochastic gradient descent method with the greedy deployment. We show that the iterates generated by DASGD-GD converge to the performative stable solution with rate <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>k</mi></mrow><mrow><mo>−</mo><mi>α</mi></mrow></msup><mo>)</mo></mrow></mrow></math></span>, where <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span> and depends on the setting of the stochastic optimization problem under large delays. For the stationary point, we provide a distributed asynchronous algorithm, DASGD-AG, which combines the distributed asynchronous stochastic gradient descent method with the adaptive gradient scheme. We show that the iterates generated by DASGD-AG converge to a stationary point with rate <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mo>ln</mo><mi>k</mi></mrow></mfrac><mo>)</mo></mrow></mrow></math></span>. The effectiveness of DASGD-GD and DASGD-AG is further demonstrated numerically with synthetic and real-world data.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107392"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976358","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":"Enhancing matheuristics for a thermal unit commitment problem through kernel search and local branching","authors":"Uriel I. Lezama-Lope ,&nbsp;Roger Z. Ríos-Mercado ,&nbsp;Diana L. Huerta-Muñoz","doi":"10.1016/j.cor.2026.107385","DOIUrl":"10.1016/j.cor.2026.107385","url":null,"abstract":"<div><div>The unit commitment problem is an important problem arising in the planning and optimization of power system operations. The problem is characterized by the need to determine the optimal scheduling of electrical generators to meet fluctuating demand while minimizing production costs. In this study, we address a thermal unit commitment problem under a staircase cost structure. Despite the encouraging outcomes from the reformulation strategies to the mathematical model, this problem still presents significant challenges. Despite the encouraging outcomes from the reformulation strategies to the mathematical model, the unit commitment problem still presents significant challenges. We propose a novel two-phase matheuristic framework that first employs a constructive heuristic to generate high-quality initial solutions, followed by an enhancement phase that leverages mechanisms such as local branching and kernel search. The proposed methods form a hybrid approach, working alongside off-the-shelf solvers on the same mathematical formulation to accelerate the search and obtain solutions near optimality. Experimental results on well-known, highly challenging instances demonstrate the effectiveness and value of each matheuristic component and show that the proposed hybrid approach consistently outperforms solvers working alone as well as existing heuristics, particularly for large-scale instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107385"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145950192","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":"Optimization of fleet search on network of regions","authors":"Ertan Yakıcı ,&nbsp;Levent Eriskin ,&nbsp;Mumtaz Karatas ,&nbsp;Orhan Karasakal","doi":"10.1016/j.cor.2026.107394","DOIUrl":"10.1016/j.cor.2026.107394","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) are widely used in modern military missions, primarily for surveillance, reconnaissance, search and detection, and air-to-ground strikes. The widespread use of UAVs in recent conflicts, such as the Russia–Ukraine war, once again highlighted their growing strategic importance. The complexity of military missions carried out by UAVs, coupled with the need for autonomous and coordinated fleet operations, requires analytical approaches to optimize deployment planning and improve operational efficiency. In this study, we address a UAV deployment planning problem for search and detection missions, in which a homogeneous fleet of UAVs is tasked with searching for hostile assets across a network of disjoint regions. Each region is characterized by an a priori probability of target presence, a search difficulty factor which affects the probability of detection, and known inter-region distances.</div><div>For this purpose, we first develop a mixed-integer nonlinear programming formulation which determines the base locations of UAVs, allocates the limited search time across regions, and sequences the visits to maximize the total time-weighted detection probability mass to achieve the highest probability as much and as early as possible during the operation. Next, we apply a tangent line approximation technique to reformulate the model as a mixed-integer linear programming problem, which we solve using commercial off-the-shelf solvers. We then propose a hybrid heuristic approach based on the ant colony optimization method to generate high-quality solutions. Our computational experiments reveal that the proposed heuristic significantly reduces solution time while maintaining superior performance compared to the linear approximation model.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107394"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975660","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":"Integrated routing optimization of pilotage and tugging services","authors":"Xin Wang ,&nbsp;Yijing Liang ,&nbsp;Haobin Li ,&nbsp;Ek Peng Chew ,&nbsp;Kok Choon Tan","doi":"10.1016/j.cor.2026.107395","DOIUrl":"10.1016/j.cor.2026.107395","url":null,"abstract":"<div><div>Seaports are important connections between inland and maritime transportation. During the vessels’ entering/leaving ports, the pilotage service is necessary to mitigate risk, especially for large vessels and congested ports. In the pilotage process, pilots are transported by pilot boats to board the vessels and provide guidance until the vessels’ arriving/leaving the berths, and tugboats are in charge of providing horsepower for vessels to move safely near the port. In this paper, a joint optimization problem considering the pilotage and tugging services is studied. Realistic constraints, including the multi-waypoints of tugboats, required service time windows of vessels, different types of tugboats and pilots. A mixed integer programming model is introduced, and small-size instances are solved by CPLEX solver. To solve large-scale instances, an adaptive large neighborhood search algorithm with linear programming models (ALNS-LP) together with a tailored feasibility check procedure and cost evaluation process is proposed. Extensive computational experiments are conducted to verify efficiency and effectiveness of the algorithm and obtain some managerial insights for port operators.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"189 ","pages":"Article 107395"},"PeriodicalIF":4.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976361","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}