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15th IFAC Symposium on Information Control Problems in Manufacturing

作者： Goncalves, Jose Fernando Univ Porto Fac Econ INESC TEC LIAAD Rua Campo Alegre 823 P-4100 Oporto Portugal

This paper deals with a very common problem in the home-textile industry. Given a set of orders of small rectangles of fabric the problem consists of determining the lengths and widths of a set of large rectangles of fabric to be produced and the corresponding cutting patterns. The objective is to minimize the total quantity of fabric necessary to satisfy all orders. The approach proposed uses a biased random-key genetic algorithm for generating sets of cutting patterns which are the input to a sequential heuristic procedure which generates a solution. Experimental tests based on a set of 100 random generated problems with known optimal solution validate quality of the approach. (C) 2013, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved,

关键词： biased random-key genetic algorithm Cutting pattern Cutting problem Sequential heuristic procedure random-keys

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Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

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COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Bierbuesse, Jan Moench, Lars Biele, Alexander Univ Hagen Dept Math & Comp Sci D-58097 Hagen Germany Airbus Operat GmbH Programme & Integrated Planning D-21129 Hamburg Germany

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

关键词： Multi-mode Time-constrained Project Scheduling Problem Generalized Temporal Constraints biased random-key genetic algorithm Aircraft Manufacturing Problem Instances

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The capacitated family traveling salesperson problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第4期31卷 2123-2153页

作者： Dominguez-Casasola, Saul Gonzalez-Velarde, Jose Luis Rios-Solis, Yasmin a. Reyes-Vega, Kevin Alain Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

The capacitated family traveling salesperson problem (CFTSP) is about a graph in which nodes are partitioned into disjoint and differently weighted families. The objective is to find the shortest route that visits a given number of nodes in each family with a set of capacitated agents. The CFTSP is a new variant of the family traveling salesman problem (FTSP). Nevertheless, existing exact and metaheuristic methods for the FSTP cannot be straightforwardly applied due to the feasibility condition that requires agents to stay within their capacity. Thus, we propose integer linear programming formulations with five different subtour inequalities sets that are tested and compared with the classical approaches. In addition, we propose a biased random-key genetic algorithm with four decoder algorithms that find high-quality solutions in short computational times. Based on our experiments, we have found that our methods are effective in addressing subtour avoidance and ensuring feasible assignments.

关键词： metaheuristics integer programming capacitated family traveling salesperson problem traveling salesman problem biased random-key genetic algorithm

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A flow-based model for the multivehicle covering tour problem with route balancing

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第5期31卷 2930-2955页

作者： Ota, Cristina Teruko Fiorotto, Diego Jacinto Ghidini, Carla Taviane Lucke da Silva de Oliveira, Washington Alves Univ Estadual Campinas UNICAMP Fac Ciencias Aplicadas R Pedro Zaccaria 1300 BR-13484350 Limeira SP Brazil

This paper introduces a new flow-based mathematical formulation for the multivehicle covering tour problem. This problem determines a set of vehicle routes for the available fleet;therefore, the total distance traveled is minimal. In the proposed mathematical formulation, each vertex represents a location of interest, and these vertices belong to three sets: vertices that can be visited;vertices that must be visited;and vertices that must be covered, in the sense that each vertex must be close to a visited vertex in a route. The proposed formulation aims to find solutions with balanced routes (all the routes with an equal or similar number of visited nodes) and extends the previous formulations proposed in the literature by considering a two-index vehicle flow formulation with a decision variable to find the balanced routes. It constitutes an NP-hard integer programming problem based on a two-commodity flow model, which is solved by applying the branch-and-cut and biased random-key genetic algorithms. This paper reports computational results for some adapted instances from TSPLIB to evaluate the efficiency of the proposed model. The computational results validate the branch-and-cut and genetic algorithms, whereby the latter performs better in large-size instances with reduced computational runtime. Finally, the used approaches obtained competitive solutions compared to the literature.

关键词： vehicle routing covering tour problem branch-and-cut biased random-key genetic algorithm

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A biased random-key genetic algorithm for the Steiner triple covering problem

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OPTIMIZATION LETTERS 2012年第4期6卷 605-619页

作者： Resende, Mauricio G. C. Toso, Rodrigo F. Goncalves, Jose Fernando Silva, Ricardo M. A. AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA NIAAD Fac Econ Porto P-4200464 Oporto Portugal Univ Fed Pernambuco CIn Recife PE Brazil

We present a biased random-key genetic algorithm (BRKGA) for finding small covers of computationally difficult set covering problems that arise in computing the 1-width of incidence matrices of Steiner triple systems. Using a parallel implementation of the BRKGA, we compute improved covers for the two largest instances in a standard set of test problems used to evaluate solution procedures for this problem. The new covers for instances A(405) and A(729) have sizes 335 and 617, respectively. On all other smaller instances our algorithm consistently produces covers of optimal size.

关键词： Steiner triple covering Set covering genetic algorithm biased random-key genetic algorithm random keys Combinatorial optimization Heuristics Metaheuristics

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Superior genetic algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics

Superior Genetic Algorithms for the Target Set Selection Pro...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Doerr, Benjamin Krejca, Martin S. Nguyen Vu Ecole Polytech CNRS Inst Polytech Paris Lab Informat LIX Palaiseau France Ecole Polytech Inst Polytech Paris Palaiseau France

ISBN: (纸本)9798400704949

The target set selection problem (TSS) asks for a set of vertices such that an influence spreading process started in these vertices reaches the whole graph. The current state of the art for this NP-hard problem are three recently proposed randomized search heuristics, namely a biased random-key genetic algorithm (BRKGA) obtained from extensive parameter tuning, a max-min ant system (MMAS), and a MMAS using Q-learning with a graph convolutional network. We show that the BRKGA with two simple modifications and without the costly parameter tuning obtains significantly better results. Our first modification is to simply choose all parameters of the BRKGA in each iteration randomly from a power-law distribution. The resulting parameterless BRKGA is already competitive with the tuned BRKGA, as our experiments on the previously used benchmarks show. We then add a natural greedy heuristic, namely to repeatedly discard small-degree vertices that are not necessary for reaching the whole graph. The resulting algorithm consistently outperforms all of the state-of-the-art algorithms. Besides providing a superior algorithm for the TSS problem, this work shows that randomized parameter choices and elementary greedy heuristics can give better results than complex algorithms and costly parameter tuning.

关键词： Target set selection combinatorial optimization biased random-key genetic algorithm parameter tuning

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A Hybrid BRKGA for Joint Scheduling Production, Transport, and Storage/Retrieval in Flexible Job Shops

A Hybrid BRKGA for Joint Scheduling Production, Transport, a...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Homayouni, S. Mahdi Fontes, Dalila B. M. M. Fontes, Fernando A. C. C. CESE INESC TEC Campus FEUPRua Dr Roberto P-4200465 Porto Portugal Univ Porto Fac Econ INESC TEC LIAAD Porto Portugal Univ Porto Fac Engn SYSTEC ARISE Porto Portugal

ISBN: (纸本)9798400701207

This paper addresses the joint scheduling of production operations, transport tasks, and storage/retrieval activities in flexible job shop systems where the production operations and transport tasks can be done by one of the several resources available. Jobs need to be retrieved from storage and delivered to a load/unload area, from there, they are transported to and between the machines where their operations are processed on. Once all operations of a job are processed, the job is taken back to the load/unload area and then returned to the storage cell. Therefore, the problem under study requires, concurrently, solving job routing, machine scheduling, transport allocation, vehicle scheduling, and shuttle schedule. To this end, we propose a hybrid biased random-key genetic algorithm (BRKGA) in which the mutation operator resorts to six local search heuristics. The computational experiments conducted on a set of benchmark instances show the effectiveness of the proposed mutation operator.

关键词： flexible job shop scheduling problem joint scheduling biased random-key genetic algorithm joint scheduling storage/ retrieval

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A BRKGA with Implicit Path-Relinking for the Vehicle Routing Problem with Occasional Drivers and Time Windows 1

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14th International Conference on Metaheuristics (MIC)

作者： Festa, Paola Guerriero, Francesca Resende, Mauricio G. C. Scalzo, Edoardo Univ Napoli Federico II Dept Math & Applicat I-80138 Naples Italy Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende CS Italy Amazon Com Inc 333 108th Ave NE Bellevue WA 98004 USA Univ Washington 3900 E Stevens Way NE Seattle WA 98195 USA

ISBN: (数字)9783031265044

ISBN: (纸本)9783031265037;9783031265044

This paper describes a biased random-key genetic algorithm (BRKGA) with implicit path-relinking for the Vehicle Routing Problem with Occasional Drivers (VRPOD). After a review of the relevant literature, the paper describes a proposed decoder and how BRKGA parameters are set. Experimental results show the efficacy of the proposed approach.

关键词： Occasional drivers Vehicle routing problem biased random-key genetic algorithm Restart strategy

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The P-Next Center Problem with Capacity and Coverage Radius Constraints: Model and Heuristics 14th

The P-Next Center Problem with Capacity and Coverage Radius ...

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14th International Conference on Metaheuristics (MIC)

作者： Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rua Marques Sae Vicente 225 BR-22453900 Rio de Janeiro RJ Brazil AT&T Labs Res 200 South Laurel Ave Middletown NJ 07748 USA

ISBN: (纸本)9783031265037;9783031265044

This paper introduces a novel problem of facility location, called the p-next center problem with capacity and coverage radius constraints. We formulate a mixed integer programming model for this problem, and compare the results found by CPLEX with three biased random-key genetic algorithms variants. We also propose several instances for this problem, based on existing ones for the p-next center problem. Additionally, we analyze the effect of the radius and demand on instance difficulty. We also observe the performance gains with a relaxed capacity and demand constraint, i.e., permitting demand to be unmet by the model. Results point that the BRKGA variants had significantly better performance than CPLEX, and similar performances among themselves. Of those, BRKGA-FI was shown to have slightly better results than the other variants.

关键词： Facility location biased random-key genetic algorithm Metaheuristics

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Heuristic approaches for scheduling of a two-machine flowshop with outsourcing lead-time

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ENGINEERING OPTIMIZATION 2024年第12期56卷 2431-2449页

作者： Kim, Eun-Seok Lee, Ik Sun Queen Mary Univ London Sch Business & Management London England Dong A Univ Sch Business Busan South Korea

This article studies a scheduling problem of a two-machine flowshop , considering outsourcing lead-time, where outsourcing a job incurs outsourcing cost as well as outsourcing lead-time. Each job has an option of being outsourced, i.e. the first operation of the outsourced job is handled by an external subcontractor while the second operation must be processed in-house after its delivery. The objective is to find a schedule for minimizing the weighted sum of the outsourcing costs and the total completion time. For the problem, the subset of jobs to be outsourced and the processing order of jobs are interwoven, implying that decisions have to be made simultaneously. This study examines the properties of the optimal solution and proposes various heuristics based on iterative local search and the biased random-key genetic algorithm. Extensive computational experiments are carried out in various settings.

关键词： Scheduling outsourcing two-machine flowshop heuristic biased random-key genetic algorithm

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