Computers & Operations Research最新文献

{"title":"Bidding in day-ahead electricity markets: A dynamic programming framework","authors":"Jérôme De Boeck ,&nbsp;Bernard Fortz ,&nbsp;Martine Labbé ,&nbsp;Étienne Marcotte ,&nbsp;Patrice Marcotte ,&nbsp;Gilles Savard","doi":"10.1016/j.cor.2025.107024","DOIUrl":"10.1016/j.cor.2025.107024","url":null,"abstract":"<div><div>Strategic bidding problems have gained a lot of attention with the introduction of deregulated electricity markets where producers and retailers trade electricity in a day-ahead market run by a Market Operator (MO). All actors propose bids composed of a unit production price and a quantity of electricity to the MO. Based on these bids, the MO selects the most interesting ones and defines the spot price of electricity at which all actors are paid. As the bids of all actors determine the price of electricity, a bidding Generation Company (GC) faces a high risk regarding its profit when placing bids as the bids of competitors are not known in advance. This paper proposes a novel dynamic programming framework for a GC’s Stochastic Bidding Problem (SBP) in the day-ahead market considering uncertainty over the competitor bids. We prove this problem is NP-hard and study two variants of this problem solved with the dynamic programming framework. Firstly, a relaxation provides an upper bound solved in polynomial time (SBP-R). Secondly, we consider a bidding problem using fixed bidding quantities (SBP-Q) that has previously been solved through heuristic methods. We prove that SBP-Q is NP-hard and solve it to optimality in pseudo-polynomial time. SBP-Q is solved on much larger instances than in previous studies. We show on realistic instances that its optimal value is typically under 1% of the optimal value of SBP by using the upper bound provided by SBP-R.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107024"},"PeriodicalIF":4.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474346","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 hyper-heuristic based on surrogate genetic programming for the three-dimensional spatial resource-constrained project scheduling problem under uncertain environments","authors":"Lubo Li ,&nbsp;Jingwen Zhang ,&nbsp;Haohua Zhang ,&nbsp;Roel Leus","doi":"10.1016/j.cor.2025.107013","DOIUrl":"10.1016/j.cor.2025.107013","url":null,"abstract":"<div><div>For a class of large and complex engineering projects with limited construction sites, three-dimensional (3D) spatial resources usually become a bottleneck that hinders their smooth implementation. A project schedule is easily disturbed by space conflicts and uncertain environments if these factors are not considered in advance. Firstly, we extend the traditional resource-constrained project scheduling problem (RCPSP) by considering 3D spatial resource constraints under uncertain environments, and propose a new three-dimensional spatial resource-constrained project scheduling problem with stochastic activity durations (3D-sRCPSPSAD). The activity schedule and the space allocation need to be decided simultaneously, so we design the first-fit and the best-fit strategies, and integrate them into the traditional resource-based policy to schedule activities and allocate 3D space. Secondly, a novel hyper-heuristic based on surrogate genetic programming (HH-SGP) is designed to evolve rules automatically for the 3D-sRCPSPSAD. The main goal of the surrogate model in HH-SGP is to construct an approximate model of the fitness function based on the random forest technique. Therefore, it can be used as an efficient alternative to the more expensive fitness function in the evolutionary process. More importantly, the weak elitism mechanism and other modified techniques are designed to improve the performance of HH-SGP. Thirdly, we configure the parameters of 3D spatial resources and generate numerical instances. Finally, from the aspects of solution quality and stability, we verify the efficiency, quality and convergence rate of HH-SGP under different uncertain environments. The effectiveness of the surrogate model, and the performance of the first-fit and the best-fit strategies are also analyzed through extensive numerical experiments. The results indicate that our designed HH-SGP algorithm performs better than traditional heuristics for the 3D-sRCPSPSAD, and the performance of the fitness function surrogate model in HH-SGP is generally better than without it. This study can also help project practitioners schedule activities and allocate spatial resources more effectively under various uncertain scenarios.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107013"},"PeriodicalIF":4.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474345","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":"General Polyhedral Approximation of two-stage robust linear programming for budgeted uncertainty","authors":"Lukas Grunau ,&nbsp;Tim Niemann ,&nbsp;Sebastian Stiller","doi":"10.1016/j.cor.2025.107014","DOIUrl":"10.1016/j.cor.2025.107014","url":null,"abstract":"<div><div>We consider two-stage robust linear programs with uncertain righthand side. We develop a General Polyhedral Approximation (GPA), in which the uncertainty set <span><math><mi>U</mi></math></span> is substituted by a finite set of polytopes derived from the vertex set of an arbitrary polytope that dominates <span><math><mi>U</mi></math></span>. The union of the polytopes need not contain <span><math><mi>U</mi></math></span>. We analyze and computationally test the performance of GPA for the frequently used budgeted uncertainty set <span><math><mi>U</mi></math></span> (with <span><math><mi>m</mi></math></span> rows). For budgeted uncertainty affine policies are known to be best possible approximations (if coefficients in the constraints are nonnegative for the second-stage decision). In practice calculating affine policies typically requires inhibitive running times. Therefore an approximation of <span><math><mi>U</mi></math></span> by a single simplex has been proposed in the literature. GPA maintains the low practical running times of the simplex based approach while improving the quality of approximation by a constant factor. The generality of our method allows to use any polytope dominating <span><math><mi>U</mi></math></span> (including the simplex). We provide a family of polytopes that allows for a trade-off between running time and approximation factor. The previous simplex based approach reaches a threshold at <span><math><mrow><mi>Γ</mi><mo>&gt;</mo><msqrt><mrow><mi>m</mi></mrow></msqrt></mrow></math></span> after which it is not better than a quasi nominal solution. Before this threshold, GPA significantly improves the approximation factor. After the threshold, it is the first fast method to outperform the quasi nominal solution. We exemplify the superiority of our method by a fundamental logistics problem, namely, the Transportation Location Problem, for which we also specifically adapt the method and show stronger results.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107014"},"PeriodicalIF":4.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474281","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":"Predictive model-based multi-objective optimization with life-long meta-learning for designing unreliable production systems","authors":"Ehsan Mahmoodi ,&nbsp;Masood Fathi ,&nbsp;Amos H.C. Ng ,&nbsp;Alexandre Dolgui","doi":"10.1016/j.cor.2025.107011","DOIUrl":"10.1016/j.cor.2025.107011","url":null,"abstract":"<div><div>Owing to the realization of advanced manufacturing systems, manufacturers have more flexibility in improving their processes through design decisions. Design decisions in production lines primarily involve two complex problems: buffer and resource allocation (B&amp;RA). The main aim of B&amp;RA is to determine the best location and size of buffers in the production line and optimally allocate production resources, such as operators and machines, to workstations. Inspired by a real-world case from the marine engine production industry, this study addresses B&amp;RA in high-mix, low-volume hybrid flow shops (HFSs) with feed-forward quality inspection. These HFSs can be characterized by uncertainties in demand, material handling, processing times, and quality control. In this study, the production environment is modeled via discrete-event simulation, which reflects the features of the actual system without requiring unreasonable or restrictive assumptions. To replace the expensive simulation runs, five widely used regressor machine learning algorithms in manufacturing are trained on data sampled from the simulation model, and the best-performing algorithm is selected as the predictive model. To obtain high-quality solutions, the predictive model is coupled with an enhanced non-dominated sorting genetic algorithm (En-NSGA-II) that incorporates lifelong meta-learning and features a customized representation and a variable neighborhood search. Additionally, a post-optimality analysis using a pattern-mining algorithm is performed to generate knowledge for allocating buffers and operators based on the optimization results, thus providing promising managerial insights.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107011"},"PeriodicalIF":4.1,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437255","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":"Model-based algorithms for the 0-1 Time-Bomb Knapsack Problem","authors":"Roberto Montemanni ,&nbsp;Derek H. Smith","doi":"10.1016/j.cor.2025.107010","DOIUrl":"10.1016/j.cor.2025.107010","url":null,"abstract":"<div><div>A stochastic version of the 0–1 Knapsack Problem recently introduced in the literature and named the 0–1 Time-Bomb Knapsack Problem is the topic of the present work. In this problem, in addition to profit and weight, each item is characterized by a probability of exploding, and therefore destroying all the contents of the knapsack, in case it is loaded. The optimization aims at maximizing the expected profit of the selected items, which takes into account also the probabilities of explosion, while fulfilling the capacity constraint. The problem has real-world applications in logistics and cloud computing.</div><div>In this work, two model-based algorithms are introduced. They are based on partial linearizations of a non-linear model describing the problem. Extensive computational results on the instances available in the literature are presented to position the new methods as the best-performing ones, while comparing against those previously proposed.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107010"},"PeriodicalIF":4.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420803","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":"An exact method and a heuristic for last-mile delivery drones routing with centralized graph-based airspace control","authors":"Jorge Luiz Franco ,&nbsp;Vitor Venceslau Curtis ,&nbsp;Edson Luiz França Senne ,&nbsp;Filipe Alves Neto Verri","doi":"10.1016/j.cor.2025.107006","DOIUrl":"10.1016/j.cor.2025.107006","url":null,"abstract":"<div><div>The increasing demand for efficient last-mile delivery, driven by the rise of e-commerce, has intensified the need for innovative solutions to manage the complexities of urban logistics. Among the most pressing challenges are the Multi-Agent Pathfinding (MAPF) problem and collision avoidance, both of which are NP-hard and critical for the safe and efficient operation of drones. Collision avoidance is particularly challenging due to the expected high density of drones in future urban environments, making it a problem that remains largely unsolved. Traditional approaches often rely on heuristic and metaheuristic methods to manage these complexities, as large instances are beyond the reach of exact methods. Additionally, distributed relaxations to these problems can lead to suboptimal outcomes and highlights the need for a more centralized and controlled solution. This research adopts a graph-based representation of the delivery area, transforming the centralized Last-Mile Delivery Drones (LMDD) problem into a network flow optimization problem. We propose two graph-based novelty methods in LMDD, a purely exact, NP-hard Mixed Integer Linear Programming (MILP) solution that is evaluated against a heuristic. The complexity of the heuristic is bounded by <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>P</mi></mrow><mrow><mn>1</mn><mo>.</mo><mn>5</mn></mrow></msup><mi>K</mi><mo>)</mo></mrow></mrow></math></span>, where <span><math><mi>P</mi></math></span> represents the number of permits and <span><math><mi>K</mi></math></span> is the number of drones. In contrast, the complexity of the MILP model is approximated by <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>K</mi></mrow><mrow><mn>7</mn></mrow></msup><msup><mrow><mi>P</mi></mrow><mrow><mn>5</mn><mo>.</mo><mn>25</mn></mrow></msup><msup><mrow><mn>2</mn></mrow><mrow><msup><mrow><mi>K</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>P</mi><msqrt><mrow><mi>P</mi></mrow></msqrt></mrow></msup><mo>)</mo></mrow></mrow></math></span>, making it intractable for larger instances. The findings from simulations indicate that the graph-based heuristic effectively balances computational efficiency and operational reliability, making it a viable solution for real-world LMDD applications, where large instances and practical execution times are required. This research significantly contributes to the fields of drone logistics and transportation by providing a scalable method for optimizing LMDD paths.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107006"},"PeriodicalIF":4.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403707","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":"Revisiting the parallel tempering algorithm: High-performance computing and applications in operations research","authors":"André Luís Barroso Almeida ,&nbsp;Joubert de Castro Lima ,&nbsp;Marco Antonio Moreira Carvalho","doi":"10.1016/j.cor.2025.107000","DOIUrl":"10.1016/j.cor.2025.107000","url":null,"abstract":"<div><div>This study explores the parallel tempering method, an approach that has shown promising results in simulation and is suited for modern multiprocessor platforms. Though relatively unexplored in operations research, the algorithm has significant potential. The study evaluates a newly developed single-node CPU-based parallel implementation of parallel tempering in three case studies involving challenging operations research problems. The evaluation considers both the quality of solutions and response time. The study also proposes an API containing the implementation of multi-core parallel tempering to encourage its use and facilitate future implementations. Evaluation results confirm the effectiveness of parallel tempering, demonstrating significant performance against state-of-the-art methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107000"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420801","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":"Reinforcement learning-enhanced variable neighborhood search strategies for the k-clustering minimum biclique completion problem","authors":"Juntao Zhao ,&nbsp;Mhand Hifi","doi":"10.1016/j.cor.2025.107008","DOIUrl":"10.1016/j.cor.2025.107008","url":null,"abstract":"<div><div>In scenarios where different groups of users need to receive the same broadcast or in social media platforms where user interactions form distinct clusters, addressing the problems of minimizing communication channels or understanding user communities can be critical. These problems are modeled as the <span><math><mi>k</mi></math></span>-clustering minimum biclique completion problem, which is recognized as an NP-hard combinatorial optimization problem. This paper presents a novel approach to solving such a problem through reinforcement learning-enhanced variable neighborhood search. The proposed method features an innovative strategy based on probability learning. It integrates a range of neighborhood techniques with a tabu strategy to enable an exploration of the search space. A key aspect of the method is its implementation of a perturbation procedure through probability learning, which significantly enhances the iterative process by guiding the search towards previously unexplored and promising regions. Experimental evaluations on benchmark instances from existing literature highlight the method’s robustness and high competitiveness. The results reveal its superior performance compared to leading solvers such as Cplex and other recently published methods. Additionally, statistical analyses, including the sign test and the Wilcoxon signed-rank test, are conducted to determine the most effective approach among those tested. These analyses confirm that the method not only achieves new performance bounds but also shows its ability to deliver promising solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107008"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420906","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 cooperative co-evolutionary algorithm with context vector enhancement strategy for feature selection on high-dimensional classification","authors":"Zhaoyang Zhang,&nbsp;Jianwu Xue","doi":"10.1016/j.cor.2025.107009","DOIUrl":"10.1016/j.cor.2025.107009","url":null,"abstract":"<div><div>Feature selection (FS) in high-dimensional classification is challenging due to the exponential increase in the number of possible feature subsets and the increased risk of model overfitting. Focusing on this challenge, the cooperative co-evolutionary algorithm (CCEA), which adopts a divide-and-conquer strategy, has been successfully applied to FS. However, existing CCEA based FS methods risk getting trapped in pseudo-optimum. To address this issue, we propose a novel CCEA based FS method, which can manipulate the context vector to escape pseudo-optimum. Specifically, competitive swarm optimizer (CSO), a variant of particle swarm optimization (PSO), is extended into CCEA based CSO due to its high performance in high-dimensional problems. This forms the foundation of the proposed method. Subsequently, a non-parametric space division strategy is proposed, which enables the proposed method to adaptively handle both low-dimensional and high-dimensional data. Most importantly, a context vector enhancement strategy is proposed, which performs search across all subspaces of the context vector, enabling proposed method to escape pseudo-optimum. Following this, a subpopulation enhancement strategy is proposed, which generates new individuals according to the search results of context vector enhancement strategy to replace individuals with poor fitness, accelerating the evolution of subpopulations. We compare the proposed method with a few state-of-the-art FS methods on 18 datasets with up to 12600 features. Experimental results show that, in most cases, the proposed method is able to find a smaller feature subset with higher classification accuracy.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107009"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420805","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":"Comparison of a rule-based heuristic and a linear programming model for assigning mentees and mentors in a women in technology mentoring programme","authors":"Sarah E. Marshall ,&nbsp;Mahsa Mohaghegh","doi":"10.1016/j.cor.2025.107002","DOIUrl":"10.1016/j.cor.2025.107002","url":null,"abstract":"<div><div>Women remain significantly underrepresented in the fields of science, technology, engineering and mathematics (STEM), but positive mentoring relationships can help mitigate the challenges they face when studying and working in these areas. To support female university students in STEM, the Auckland University of Technology (AUT) established the Women in Tech mentorship programme in 2019. Initially, the matching of mentees and mentors was achieved manually, but as the programme’s popularity grew, this process became increasingly time consuming. This study addresses the challenges associated with assigning mentees to mentors by automating the matching process based on mentee and mentor attributes. A rule-based heuristic is proposed and compared with a linear programming (LP) approach. Numerical experiments were conducted to evaluate the performance of these algorithms across various scenarios. The rule-based heuristic provides a simple and easily understandable way to allocate mentees and mentors that performs nearly as well as an optimal matching provided by the LP approach. Applying these algorithms to real data from the AUT Women in Tech mentorship programme, it was found that they outperformed manual matching in several performance metrics.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107002"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444605","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}