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Scheduling technicians and tasks through an adaptive multi-objective biased random-key genetic algorithm

ANNALS OF OPERATIONS RESEARCH 2025年第2期346卷 945-980页

作者： Damm, R. B. Chaves, A. A. Riveaux, J. A. Ronconi, D. P. Univ Sao Paulo Polytech Sch Dept Prod Engn Ave Prof Luciano Gualberto1380Cidade Univ BR-05508010 Sao Paulo SP Brazil Univ Fed Sao Paulo Ave Cesare Mansueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil

This work addresses a practical problem concerning the daily scheduling of tasks for field technicians and route planning, taking into account time windows, task priority, technicians' skills, working hours, and lunch breaks. In line with the demands and expectations of large cities' customers, we pursue two goals simultaneously: to maximize the weighted sum of the attended tasks and to perform the highest-priority tasks as soon as possible within their time windows. This is done without disregarding the fact that more efficient routes reduce fuel consumption. This paper presents a bi-objective mixed integer programming model for the problem and introduces an innovative approach that combines a multi-objective BRKGA with the Q-Learning method. Q-Learning is a reinforcement learning method that controls the parameters of the BRKGA during the evolutionary process, learning the best configuration based on rewards. Extensive numerical experiments and comparisons with the Nondominated Sorting genetic algorithm II and the Strength Pareto Evolutionary algorithm 2 show that the proposed multi-objective biased random-key genetic algorithm outperforms the other approaches in instances with up to 200 tasks and 30 technicians, which are typical instances encountered in practice. The developed approach facilitates parameter calibration and consistently attains a substantial portion of the Pareto frontier in the multi-objective STRSP.

关键词： Routing and scheduling technicians Multi-objective biased random-key genetic algorithm Q-Learning

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biased random-key genetic algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems

Biased Random-Key Genetic Algorithm Applied to the Optimal R...

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IEEE-PES Transmission and Distribution Conference and Exhibition - Latin America (T&D-LA)

作者： Vargas, Rommel Romero, Ruben Franco, John F. Sao Paulo State Univ UNESP Dept Elect Engn BR-15385000 Ilha Solteira Brazil Sao Paulo State Univ UNESP Sch Energy Engn BR-19274000 Rosana Brazil

ISBN: (纸本)9781538658444

The optimal reconfiguration of radial Electrical Distribution Systems (EDSs) is a classical optimization problem that deals with the operation of the system and is of great interest to the electricity sector. Although there is a large number of approaches in the specialized literature to solve this problem, the solution of the reconfiguration problem for large-scale EDSs is still difficult. This paper proposes a method to solve the reconfiguration problem of EDSs that is based on the specialized metaheuristic biased random-key genetic algorithm, which showed excellent performance on the solution of complex problems in operational research. Tests carried out using a wellknown EDS demonstrate the efficiency of the proposed method.

关键词： biased random-key genetic algorithm electrical distribution systems power losses reconfiguration

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A Python/C plus plus library for bound-constrained global optimization using a biased random-key genetic algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2015年第3期30卷 710-728页

作者： Silva, R. M. A. Resende, M. G. C. Pardalos, P. M. Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (J Heuristics 17:487-525, 2011b) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (ORSA J Comput 6:154-160, 1994), differing in the way solutions are combined to produce offspring. After a brief introduction to the BRKGA, including a description of the local search procedure used in its decoder, we show how to download, install, configure, and use the library through an illustrative example.

关键词： biased random-key genetic algorithm Global optimization Multimodal functions Continuous optimization Heuristic Stochastic algorithm Stochastic local search Nonlinear programming

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A biased random-key genetic algorithm for the set orienteering problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期292卷 830-854页

作者： Carrabs, Francesco Univ Salerno Dept Math I-84084 Fisciano Italy

This paper addresses the Set Orienteering Problem which is a generalization of the Orienteering Problem where the customers are grouped in clusters, and the profit associated with each cluster is collected by visiting at least one of the customers in the respective cluster. The problem consists of finding a tour that maximizes the collected profit but, since the cost of the tour is limited by a threshold, only a subset of clusters can usually be visited. We propose a biased random-key genetic algorithm for solving the Set Orienteering Problem in which three local search procedures are applied to improve the fitness of the chromosomes. In addition, we introduced three rules useful to reduce the size of the instances and to speed up the resolution of the problem. Finally, a hashtable is used to quickly retrieve the information that are required several times during the computation. The computational results, carried out on benchmark instances, show that our algorithm is significantly faster than the other algorithms, proposed in the literature, and it provides solutions very close to the best-known ones. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Metaheuristics biased random-key genetic algorithm Orienteering problem Routing

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A biased random-key genetic algorithm for the container pre-marshalling problem

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COMPUTERS & OPERATIONS RESEARCH 2016年 75卷 83-102页

作者： Hottung, Andre Tierney, Kevin Univ Paderborn Decis Support & Operat Res Lab Warburger Str 100 D-33098 Paderborn Germany

The container pre-marshalling problem (CPMP) is performed at container terminals around the world to re-order containers so that they can be more efficiently transferred through the terminal. We introduce a novel decoder for a biased random-key genetic algorithm (BRKGA) that solves the CPMP. The decoder consists of a construction algorithm that learns how to best apply single and compound containers moves to quickly sort a bay of containers. Our approach finds better solutions than the state-of-the-art method on many instances of the standard pre-marshalling benchmarks in less computational time. Furthermore, we perform a computational analysis of different components of the BRKGA decoder to determine what types of heuristics work best for pre-marshalling problems, as well as conduct a feature space analysis of different pre-marshalling approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Container pre-marshalling Maritime applications biased random-key genetic algorithm

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A biased random-key genetic algorithm for the minimization of open stacks problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2016年第1-2期23卷 25-46页

作者： Goncalves, Jose Fernando Resende, Mauricio G. C. Costa, Miguel Dias Univ Porto LIAAD INESC TEC Fac Econ Porto P-4200464 Oporto Portugal AT&T Labs Res Florham Pk NJ 07932 USA Univ Porto Fac Econ Porto P-4200464 Oporto Portugal

This paper describes a biased random-key genetic algorithm (BRKGA) for the minimization of the open stacks problem (MOSP). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.

关键词： minimization of open stacks problem cutting pattern biased random-key genetic algorithm random keys

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A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期286卷 867-882页

作者： Goncalves, Jose Fernando Waescher, Gerhard Univ Porto INESC TEC LIAAD Fac Econ Rua Dr Roberto Frias S-N P-4200464 Porto Portugal Otto von Guericke Univ Fac Econ & Management Magdeburg Germany Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing Peoples R China

This paper addresses a two-dimensional (2D) non-guillotine cutting problem, where a set of small rectangular items of given types has to be cut from a large rectangular stock plate having defective regions so as to maximize the total value of the rectangles cut. The number of small items of each item type which can be cut from the large object is unrestricted. A novel MIP model and a hybrid approach combining a novel placement procedure with a biased random-key genetic algorithm (BRKGA) are presented. The parameters used by the novel placement procedure for the development of a cutting plan are evolved by the BRKGA. The management of the free spaces and of the defects uses a maximal-space representation. The approach is evaluated and compared to other approaches by means of a series of detailed numerical experiments using 5414 benchmark instances taken from the literature. The experimental results validate the quality of the solutions and the effectiveness of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.y

关键词： Cutting Packing Two-dimensional Non-guillotine defects BRKGA 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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A biased random-key genetic algorithm for the chordal completion problem

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RAIRO-OPERATIONS RESEARCH 2023年第3期57卷 1559-1578页

作者： Silva, Samuel E. Ribeiro, Celso C. Souza, Ueverton dos Santos Univ Fed Fluminense Inst Comp BR-24210240 Niteroi RJ Brazil

A graph is chordal if all its cycles of length greater than or equal to four contain a chord, i.e., an edge connecting two nonconsecutive vertices of the cycle. Given a graph G = (V, E), the chordal completion problem consists in finding the minimum set of edges to be added to G to obtain a chordal graph. It has applications in sparse linear systems, database management and computer vision programming. In this article, we developed a biased random-key genetic algorithm (BRKGA) for solving the chordal completion problem, based on the strategy of manipulating permutations that represent perfect elimination orderings of triangulations. Computational results show that the proposed heuristic improve the results of the constructive heuristics fill-in and min-degree. We also developed a strategy for injecting externally constructed feasible solutions coded as random keys into the initial population of the BRKGA that significantly improves the solutions obtained and may benefit other implementations of biased random-key genetic algorithms.

关键词： chordal completion Chordal graphs biased random-key genetic algorithm BRKGA Metaheuristics Fill-in Combinatorial optimization

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A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model

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JOURNAL OF GLOBAL OPTIMIZATION 2020年第4期77卷 949-973页

作者： Pinto, Bruno Q. Ribeiro, Celso C. Rosseti, Isabel Noronha, Thiago F. Inst Fed Educ Ciencia & Tecnol Triangulo Mineiro BR-38411104 Uberlandia MG Brazil Univ Fed Fluminense Inst Comp BR-24210240 Niteroi RJ Brazil Univ Fed Minas Gerais Dept Comp Sci BR-31270901 Belo Horizonte MG Brazil

The problem of routing and wavelength assignment in optical networks consists in minimizing the number of wavelengths that are needed to route a set of demands, such that demands routed using lightpaths that share common links are assigned to different wavelengths. We present a biased random-key genetic algorithm for approximately solving the problem of routing and wavelength assignment of sliding scheduled lightpath demands in optical networks. In this problem variant, each demand is characterized not only by a source and a destination, but also by a duration and a time window in which it has to be met. Computational experiments show that the numerical results obtained by the proposed heuristic improved upon those obtained by a multistart constructive heuristic. In addition, the biased random-key genetic algorithm obtained much better results than an existing algorithm for the problem, finding solutions that use roughly 50% of the number of wavelengths determined by the latter.

关键词： biased random-key genetic algorithm Metaheuristics Routing and wavelength assignment Sliding scheduled lightpath demands Scheduled lightpath demands Optical networks

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