Computers & Operations Research最新文献

{"title":"Corporate risk stratification through an interpretable autoencoder-based model","authors":"","doi":"10.1016/j.cor.2024.106884","DOIUrl":"10.1016/j.cor.2024.106884","url":null,"abstract":"<div><div>In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult to understand even for experts, our model provides an easily interpretable visualization of balance sheets, projecting each company in a bi-dimensional space according to an autoencoder-based dimensionality reduction matched with a Nearest-Neighbor-based default density estimation. In the resulting space, the distress zones, where the default intensity is high, appear as homogeneous clusters directly identified. Our empirical experiments provide evidence of the interpretability, forecasting ability, and robustness of the bi-dimensional space.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re-direction in queueing networks with two customer types: The inter-departure analysis","authors":"","doi":"10.1016/j.cor.2024.106867","DOIUrl":"10.1016/j.cor.2024.106867","url":null,"abstract":"<div><div>Re-direction occurs when a customer arriving at a station in a queuing network has to be re-directed to a downstream station to complete service. Re-direction is extremely common in practice and occurs for a variety of reasons, ranging from incorrect initial station assignment to cases where the initial station only provides part of the service. <em>Gatekeeper</em> stations (e.g., information desks) is a special case of re-direction. We consider re-direction in a queueing network consisting of single-server stations serving two customer types with different service time requirements. The behavior of such queueing networks is quite complex: even when all external arrivals and all services are Markovian, the customers’ inter-departure distribution, and hence their arrival process to downstream stations, is non-Markovian. Thus, product-form representation does not hold for such networks. Our analysis focuses on the key building block: the inter-departure process from a station serving two distinct customer types and routing them to two different downstream service paths. Using a novel approach, we obtain a very accurate phase-type representation of the inter-departure process under equilibrium. We show that the resulting methodology has significant advantages over both simulation modeling (our method is much faster) and the available approximation techniques (our method is more accurate). Finally, we demonstrate an interesting phenomenon: even when the station merely re-directs one of the customer types (providing no service and seemingly useless waits), it can serve as a “regulator”, reducing the variability of the downstream arrival process. We show that, under some conditions, this can improve the overall system performance.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep reinforcement learning hyperheuristic for the covering tour problem with varying coverage","authors":"","doi":"10.1016/j.cor.2024.106881","DOIUrl":"10.1016/j.cor.2024.106881","url":null,"abstract":"<div><div>Covering Tour Problem (CTP) is a combinatorial optimization problem in which the objective is to identify a minimum-cost tour that satisfies the coverage of a certain subset of nodes in a graph. The Covering Tour Problem with Varying Coverage (CTP-VC) is an extension of this problem in which the coverage radius is dependent on the amount of time spent at each node. In this paper, we propose a novel approach to address the CTP-VC using a Deep Reinforcement Learning Hyperheuristic (DRLH). This study includes experiments on the existing Adaptive Metaheuristic to solve CTP-VC, to enhance its solution quality. Further, new heuristics and three selection methods, namely Uniform Random Selection (URS), adaptive Metaheuristic (AMH), and the proposed DRLH are introduced. We detail the computational setup, including the instance sets utilized, the training process for the DRLH agent, and the validation procedures for model selection. Through extensive experimentation and analysis, we evaluate the performance of different selection methods, assess the solution quality of the DRLH approach, investigate the robustness of selection methods, examine heuristic selection frequency, and analyze solution convergence. Our results demonstrate the efficacy of the DRLH approach in tackling the CTP-VC, offering promising insights for future research in the interface of combinatorial optimization and reinforcement learning methodologies.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi objective optimization of human–robot collaboration: A case study in aerospace assembly line","authors":"","doi":"10.1016/j.cor.2024.106874","DOIUrl":"10.1016/j.cor.2024.106874","url":null,"abstract":"<div><div>Collaborative robotics is becoming increasingly prevalent in industry 5.0, leading to a growing need to improve interactions and collaborations between humans and robots. However, the current approach to defining the sharing of responsibilities between humans and robots is empirical and uses the robot as an active fixture of parts, which is a sub-optimal method for establishing efficient collaboration. This article focuses on optimizing human–robot collaboration on an assembly line within the aerospace industry based on a real-world use case. The methodology adopted in this research entails employing the multi-objective optimization (MOO) method to effectively tackle both the reduction of makespan and the mitigation of working difficulty. Two techniques have been compared for implementation: the weighted sum and the <span><math><mi>ɛ</mi></math></span>-constraint methods, which allow the generation of solutions addressing multiple objectives simultaneously. The results offer chief robotics officers a new tool to design collaboration patterns between humans and robots, with practical implications for real industrial applications. This solution produces several results, including improving company competitiveness and productivity, while maintaining the central role of humans within the company and improving its well-being.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577961","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":"Arc-flow formulation and branch-and-price-and-cut algorithm for the bin-packing problem with fragile objects","authors":"","doi":"10.1016/j.cor.2024.106878","DOIUrl":"10.1016/j.cor.2024.106878","url":null,"abstract":"<div><div>This study introduces an arc-flow formulation and the first branch-and-price-and-cut (BPC) algorithm designed to solve the bin-packing problem with fragile objects (BPPFO). This variant of the bin-packing problem originates in the field of telecommunications, particularly in the allocation of cellular calls to frequency channels. The arc-flow formulation is inspired by previous studies and modifies the graph construction method to accommodate fragility constraints. We proved the correctness of this formulation and demonstrated its superiority in instances with small maximum fragility through extensive experiments. The proposed BPC algorithm leverages advanced cutting and packing techniques and incorporates innovative elements such as problem reduction, additional cutting planes, and a label-setting-based exact pricing algorithm. The experimental results demonstrate that the proposed BPC algorithm is highly competitive with the state-of-the-art algorithm for solving the BPPFO and can successfully solve several previously unsolved instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553258","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":"Cross regional online food delivery: Service quality optimization and real-time order assignment","authors":"","doi":"10.1016/j.cor.2024.106877","DOIUrl":"10.1016/j.cor.2024.106877","url":null,"abstract":"<div><div>Online food delivery (OFD) represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. With diverse order fulfillment features and increasing expectations for service quality, the task of effectively assigning riders for timely long-distance, cross-regional deliveries presents a significant engineering challenge. Previous studies often relied on traditional rider allocation methods that fail to account for varying capacities, or they utilized non-intelligent systems that did not adequately address fluctuating order demands and service delays. In this study, we introduce a robust Mixed Integer Linear Programming (MILP) optimization framework designed to minimize the total service time and delivery cost for cross-regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. To enhance the predictive accuracy of our model, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands accurately, reflecting the dynamic nature of the marketplace. Additionally, Extreme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and resource allocation within the MILP framework. These machine learning techniques significantly bolster the MILP framework by providing detailed, accurate predictions that improve decision-making processes and adaptability to real-time conditions. Acknowledging the complexity of this optimization problem, we further enhance our approach by integrating a meta-heuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehicles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders, outperforming existing state-of-the-art methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553259","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":"Algorithms for the global domination problem","authors":"","doi":"10.1016/j.cor.2024.106876","DOIUrl":"10.1016/j.cor.2024.106876","url":null,"abstract":"<div><div>A dominating set <span><math><mi>D</mi></math></span> in a graph <span><math><mi>G</mi></math></span> is a subset of its vertices such that every its vertex that does not belong to set <span><math><mi>D</mi></math></span> is adjacent to at least one vertex from set <span><math><mi>D</mi></math></span>. A set of vertices of graph <span><math><mi>G</mi></math></span> is a global dominating set if it is a dominating set for both, graph <span><math><mi>G</mi></math></span> and its complement. The objective is to find a global dominating set with the minimum cardinality. Neither exact nor approximation algorithm existed for the problem known to be <span><math><mrow><mi>N</mi><mi>P</mi></mrow></math></span>-hard. We show that it remains <span><math><mrow><mi>N</mi><mi>P</mi></mrow></math></span>-hard for restricted types of graphs. At the same time, we specify some families of graphs for which the three heuristics, that we propose here, are optimal. Given the complexity status of the problem, our aim was the development of powerful heuristic algorithms that work well in practice for large-scaled instances. To measure the efficiency of our heuristics, we formulated the problem as an integer linear program (ILP) and also we developed an alternative implicit enumeration (IE) algorithm obtaining guaranteed optimal solutions for the existing benchmark instances with up to 8000 vertices. Remarkably, for 56.75% of these instances, at least one of our heuristics also created an optimal solution, where an average absolute error for the remaining instances was a single vertex. The average approximation ratio was 1.005, whereas for the largest benchmark instances with up to 25000 vertices our heuristics delivered solutions in less than 2 min.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553257","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 mathematical formulations for parallel-batching processing machine scheduling problems","authors":"","doi":"10.1016/j.cor.2024.106859","DOIUrl":"10.1016/j.cor.2024.106859","url":null,"abstract":"<div><div>We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jobs simultaneously if jobs with non-identical sizes can be packed in a capacitated machine. In this paper, we tackle single- and parallel-machine BPMPs, and other interesting problem variants that aim at minimizing the makespan. We develop novel formulations along with valid inequalities and an algorithm framework that makes use of dual information and bounding techniques to achieve efficiency when instances are intractable. Extensive computational experiments on benchmark instances show that our approaches achieve state-of-the-art results and prove the optimality of intractable instances in the literature.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572184","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":"The quadratic knapsack problem with setup","authors":"","doi":"10.1016/j.cor.2024.106873","DOIUrl":"10.1016/j.cor.2024.106873","url":null,"abstract":"<div><div>The Quadratic Knapsack Problem is a well-known generalization of the classical 0-1 knapsack problem, in which any pair of items produces a pairwise profit if both are selected. Another relevant generalization of the knapsack problem is the Knapsack Problem with Setup, in which the items are partitioned into classes, the items of a class can only be inserted into the knapsack if the corresponding class is activated, and activating a class involves a setup cost and a setup capacity reduction.</div><div>Despite a rich literature on these two problems, their obvious generalization, i.e., the Quadratic Knapsack Problem with Setup, was never investigated so far. We discuss applications, mathematical models, deterministic matheuristic algorithms, and computationally evaluate their performance.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536185","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":"Satellite Scheduling Problems: A survey of applications in Earth and outer space observation","authors":"","doi":"10.1016/j.cor.2024.106875","DOIUrl":"10.1016/j.cor.2024.106875","url":null,"abstract":"<div><div>With the growing interest in leveraging space technologies to provide both knowledge and services, the need for efficient space mission management also increases. Among all the related problems, the scheduling of tasks performed by observation satellites is not only crucial for the astrophysical community, but it also poses challenging optimization problems, which have been studied for nearly 30 years. The aim of this survey is to provide a comprehensive overview of Satellite Scheduling Problems (SSPs), with a particular focus on applications. First, we propose a novel literature classification of SSPs based on the main variants that have been defined over the years. We address both imaging and communication tasks in the context of Earth-centered missions and, for the first time, of outer space missions. Then, for each class of problems we provide a review of the main contributions available in the literature, offering insights about solution methodologies. Finally, we outline some promising future research directions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553261","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}