Computers & Operations Research最新文献

{"title":"Maximal covering location with partial coverage for second-level specialized health care services","authors":"Rodolfo Mendoza-Gómez ,&nbsp;Roger Z. Ríos-Mercado","doi":"10.1016/j.cor.2024.106725","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106725","url":null,"abstract":"<div><p>The lack of access to Second-level Health Care Services (SHCS) in developing countries is primarily due to the scarcity of facilities and the limited investment of resources in the public sector. Access to these services directly relates to the distance the population travels to these facilities. In that sense, a maximal covering location problem can be helpful to maximize the impact of decisions related to the location of new SHCS. In this paper, we propose a model to guide the location of additional sites where second-level services can be installed in a network of public hospitals. The partial coverage and variable radius are considered in the problem to assess a large territory with different characteristics and population densities. The system is composed of multiple institutions that supply differentiated varying levels of coverage concerning their own demand and external demand. The objective of the problem is to improve the demand coverage in the system by locating new sites, since there are already sites offering different services. A case study in the Mexican public health system is conducted to assess four specialized SHCS. The obtained results evidence for the benefit of using optimization tools in the resource planning of SHCS.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324954","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":"Dispersed starting solutions in facility location: The case of the planar p-median problem","authors":"Zvi Drezner ,&nbsp;Jack Brimberg ,&nbsp;Anita Schöbel","doi":"10.1016/j.cor.2024.106726","DOIUrl":"10.1016/j.cor.2024.106726","url":null,"abstract":"<div><p>There are many planar multiple facilities location problems for which the optimal locations tend to be spread out. The most popular of these is the planar <span><math><mi>p</mi></math></span>-median problem. With this in mind, we propose several procedures to generate sparse configurations as starting solutions. The proposed procedures are easy to implement, and can be used as modules combined in different sequences within heuristics such as a recent trajectory-based procedure that we tested in this paper.</p><p>The procedures are tested experimentally on a set of 24 large problem instances with up to 10,000 demand points and 100 facilities. We are able to demonstrate that the sparse starting solutions generated by the new procedures lead to significant improvements of final <span><math><mi>p</mi></math></span>-median solutions.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0305054824001989/pdfft?md5=3e8bf98cdadb007c46b68379ed127281&pid=1-s2.0-S0305054824001989-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409729","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":"Assigning multi-skill configurations to multiple servers with a Scenario-Based Planning and Recombination Approach","authors":"Beatrice Bolsi ,&nbsp;Thiago Alves de Queiroz ,&nbsp;Vinícius Loti de Lima ,&nbsp;Arthur Kramer ,&nbsp;Manuel Iori","doi":"10.1016/j.cor.2024.106719","DOIUrl":"10.1016/j.cor.2024.106719","url":null,"abstract":"<div><p>This work deals with a dynamic problem arising from an outpatient healthcare facility. Patients with varying priorities arrive throughout the day, each with specific service requests that must be satisfied within target times. Failure to meet these targets incurs weighted tardiness penalties. Additionally, patients may choose to leave the system if subjected to prolonged waiting times, leading to further weighted penalties. The outpatient facility is equipped with multiple identical servers, each capable of providing a finite subset of services, referred to as configurations. The objective is to dynamically assign configurations, selected from a predefined set, to servers by minimizing the sum of weighted tardiness and abandonment penalties. Assignments are not fixed statically but can be dynamically changed over time to cope with the service requests. To address this problem, we propose a Scenario-Based Planning and Recombination Approach (SBPRA) that integrates an inner Reduced Variable Neighborhood Search. Differently from the traditional Scenario-Based Planning Approach (SBPA), which makes decisions based only on the solutions of individual scenarios, our approach solves an optimization problem to produce an additional solution that offers the best balance among the scenario solutions. Extensive tests on realistic instances show that SBPRA generates solutions that are 38% on average more effective than those generated by SBPA. Overall, the proposed approach can optimize resource allocation, mitigate the impact of patient abandonment, and improve the performance of the outpatient healthcare facility.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141414425","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":"Approximate linear programming for a queueing control problem","authors":"Saied Samiedaluie ,&nbsp;Dan Zhang ,&nbsp;Rui Zhang","doi":"10.1016/j.cor.2024.106711","DOIUrl":"10.1016/j.cor.2024.106711","url":null,"abstract":"<div><p>Admission decisions for loss systems accessed by multiple customer classes are a classical queueing control problem with a wide variety of applications. When a server is available, the decision is whether to admit an arriving customer and collect a lump-sum revenue. The system can be modeled as a continuous-time infinite-horizon dynamic program, but suffers from the curse of dimensionality when different customer classes have different service rates. We use approximate linear programming to solve the problem under three approximation architectures: affine, separable piecewise linear and finite affine. The finite affine approximation is a recently proposed generalization of the affine approximation, which allows for non-stationary parameters. For both affine and finite affine approximations, we derive equivalent, but more compact, formulations that can be efficiently solved. We propose a column generation algorithm for the separable piecewise linear approximation. Our numerical results show that the finite affine approximation can obtain the tightest bounds for 75% of the instances among the three approximations. Especially, when the number of servers is large and/or the load on the system is high, the finite affine approximation always achieves the tightest bounds. Regarding policy performance, the finite affine approximation has the best performance on average compared to the other two approximations and the achievable performance region method (Bertsimaset al., 1994, Kumar and Kumar, 1994). Furthermore, the finite affine approximation is 4 to 5 orders of magnitude faster than the achievable performance region method and the separable piecewise linear approximation for large-scale instances. Therefore, considering bounds, policy performance, and computational efficiency, the finite affine approximation emerges as a competitive approximation architecture for the class of problems studied here.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280608","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-biased genetic algorithm for packing unequal rectangles into a fixed-size circle","authors":"Qiang Luo,&nbsp;Yunqing Rao,&nbsp;Piaoruo Yang,&nbsp;Xusheng Zhao","doi":"10.1016/j.cor.2024.106716","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106716","url":null,"abstract":"<div><p>This study addresses the two-dimensional circular knapsack packing problem, which packs unequal rectangles into a circular container to maximize the number or the area of items packed. A biased genetic algorithm hybridized with a local search algorithm is proposed to solve the problem. The algorithm has a powerful global searching ability and is responsible for exploration, and a local search is applied for exploitation. Therefore, the proposed approach has an excellent search ability that can balance intensification and diversification well. A decoding procedure is proposed to transform the chromosome into a packing layout. The procedure first produces several initial layouts that contain a few rectangles, forms a complete layout for each initial layout, and selects the best one as the final packing layout. Three new types of initial layouts are considered. A new set of evaluation rules for the placement position and a random selection method are proposed. Computational experiments using two benchmark datasets showed that the evolutionary algorithm could provide better solutions than state-of-the-art algorithms from the literature, with 64 new best solutions out of 108 benchmark instances.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291847","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 maximal covering location disruption problem","authors":"Brian J. Lunday","doi":"10.1016/j.cor.2024.106721","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106721","url":null,"abstract":"<div><p>This research sets forth and examines a new sequential, competitive location problem. The <em>maximal covering location disruption problem</em> is a zero-sum Stackelberg game comprised of two stages. A leader denies access to at most <span><math><mi>q</mi></math></span> out of <span><math><mi>n</mi></math></span> possible facility locations in the first stage and, in the second stage, a follower solves a maximal covering location problem while emplacing at most <span><math><mi>p</mi></math></span> facilities. Identifying this problem as both relevant and unaddressed in the current literature, this research examines properties of the bilevel programming formulation to inform heuristic development, subsequently evaluating the efficacy and efficiency of two variants each of an iterative, bounding heuristic (IBH) and a reformulation-based construction heuristic (RCH) over a two sets collectively consisting of 2160 test instances representing a breadth of relative parametric values. Although we illustrate that each heuristic may not identify an optimal solution, computational testing demonstrates the superlative and generally excellent performance of the RCH variants. For the 12.4% of instances for which the RCH does not readily verify the optimality of its solution, lower-bounding procedures characterize solution quality. Both of the RCH variants attain solutions with an average 4.08% relative optimality gap, and they scaled well over different parametric value combinations, solving instances in an average of 98.0 and 123.6 seconds, respectively.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250126","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":"Extremal behavior of the Greedy algorithm for a triangle scheduling problem","authors":"János Balogh ,&nbsp;József Békési ,&nbsp;Nóra Büki ,&nbsp;György Dósa ,&nbsp;Zsolt Tuza","doi":"10.1016/j.cor.2024.106718","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106718","url":null,"abstract":"<div><p>We study the mixed-criticality scheduling problem, where the goal is to schedule jobs with different criticality levels on a single machine. As shown by Dürr et al. (2018), the problem can be treated as a specific 1-dimensional triangle scheduling problem. In that paper a new Greedy algorithm was defined, and the authors proved that its approximation ratio lies between 1.05 and 3/2. In this paper we present a quadratic integer programming model, which can be used to computationally analyze the algorithm for inputs with small sizes. The model simulates the behavior of the algorithm and it compares the makespan with the optimal one. Using this model, we found sequences extendable to longer series, giving a lower bound of 1.27 for the Greedy algorithm. Also, the optimum on problem instances consisting of intervals of natural numbers is analyzed and a closed formula is determined. In this way, we detected two input classes where, in one of them, Greedy is far from optimal (we think that this could be the worst case), and in the other one it is optimal.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324957","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":"An iterated local search matheuristic approach for the multi-vehicle inventory routing problem","authors":"Demetrio Laganà ,&nbsp;Enrico Malaguti ,&nbsp;Michele Monaci ,&nbsp;Roberto Musmanno ,&nbsp;Paolo Paronuzzi","doi":"10.1016/j.cor.2024.106717","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106717","url":null,"abstract":"<div><p>The multi-vehicle inventory routing problem considers an integrated system in which a supplier must satisfy deterministic demands from a set of customers over a finite and discrete time horizon. A limited inventory capacity is available at the customers, and a deterministic amount of product is available at the supplier in each period to fulfill customer demands with a homogeneous fleet of vehicles. The supplier decides when to resupply the customers, the quantities of product to deliver, and the routes to serve the customers. The aim is to find the best supply policy, which minimizes the total inventory and routing costs while ensuring that no stock-out occurs at the customers, while respecting the capacity of each vehicle. The problem has attracted significant attention in recent decades due to its wide applicability in fields where both inventory and routing aspects are addressed together. In this work, we present a matheuristic algorithm based on a mathematical formulation in which we associate a decision variable with each route and period. The size of this set of decision variables is clearly exponential; therefore, we devise a column generation approach in which we heuristically generate a subset of these variables within an iterated local search framework. Each iteration of the algorithm is composed by two phases: in the first one, the current model is solved by means of a general-purpose solver, whereas in the second one the model is updated by replacing some variables with a suitable subset of new variables. Once a local optimum is reached, a diversification step takes place in which the set of columns is expanded to possibly define a new starting solution. We have compared our approach with state-of-the-art algorithms on a large benchmark of instances from the literature. Our computational results show that the proposed algorithm outperforms most of the existing heuristic methods.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0305054824001898/pdfft?md5=6d62f88a450ed253dd0f163cbc9bd748&pid=1-s2.0-S0305054824001898-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250127","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":"Non-identical parallel machines batch processing problem to minimize the makespan: Models and algorithms","authors":"Pedram Beldar ,&nbsp;Maria Battarra ,&nbsp;Gilbert Laporte","doi":"10.1016/j.cor.2024.106708","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106708","url":null,"abstract":"<div><p>This paper studies a parallel heterogeneous machine batching and scheduling problem in which weighted jobs are first batched, and the batches are then assigned and sequenced on machines of varying capacities. The duration of a batch is the longest time needed to process a job, and the objective is that of minimizing the makespan, or the sum of the batches durations on the machine finishing last. The authors develop polynomial-size mathematical formulations and a variable neighborhood search metaheuristic. Extensive computational results suggest that a flow-based formulation outperforms a compact formulation, despite its larger number of variables. The metaheuristic is capable of producing high-quality solutions within a limited computing time.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0305054824001801/pdfft?md5=88796d6a436ba002b921ab8f465240ec&pid=1-s2.0-S0305054824001801-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141239864","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":"Sparse dynamic discretization discovery via arc-dependent time discretizations","authors":"Madison Van Dyk ,&nbsp;Jochen Koenemann","doi":"10.1016/j.cor.2024.106715","DOIUrl":"https://doi.org/10.1016/j.cor.2024.106715","url":null,"abstract":"<div><p>While many time-dependent network design problems can be formulated as time-indexed formulations with strong relaxations, the size of these formulations depends on the discretization of the time horizon and can become prohibitively large. The recently-developed dynamic discretization discovery (DDD) method allows many time-dependent problems to become more tractable by iteratively solving instances of the problem on smaller networks where each node has its own discrete set of departure times. However, in the current implementation of DDD, all arcs departing a common node share the same set of departure times. This causes DDD to be ineffective for solving problems where all near-optimal solutions require many distinct departure times at the majority of the high-degree nodes in the network. Region-based networks are one such structure that often leads to many high-degree nodes, and their increasing popularity underscores the importance of tailoring solution methods for these networks.</p><p>To improve methods for solving problems that require many departure times at nodes, we develop a DDD framework where the set of departure times is determined on the arc level rather than the node level. We apply this arc-based DDD method to a temporal variant of the service network design problem (SND). We show that an arc-based approach is particularly advantageous when instances arise from region-based networks, and when candidate paths are fixed in the base graph for each commodity. Moreover, our algorithm builds upon the existing DDD framework and achieves these improvements with only benign modifications to the original implementation.</p></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0305054824001874/pdfft?md5=56651e7beedd12008c453ca54c4ce4ae&pid=1-s2.0-S0305054824001874-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444670","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}