Algoritmo genético para solucionar el problema de dimensionamiento y programación de lotes con costos de alistamiento dependientes de la secuencia

The main purpose of this paper is to develop a hybrid genetic algorithmin order to determine the lot sizes and their production scheduling in asingle machine manufacturing system for multi-item orders, the objectivefunction minimizes the sum of holding costs, tardy costs and setup costs.The problem considers a set of orders to be processed each one with itsown due date. Each order must be delivered complete. In the schedulingare considered sequence dependent setup times. The proposed hybridgenetic algorithm has embedded a heuristic that is used to calculate itsfitness function. The heuristic method presents a modification on theoptimal timming algorithm in which are involved sequence dependentset up times. A design of experiments is developed in order to assess thealgorithm performance, which is also tested using random-generateddata and results are compared with those generated by an exact method.The results show that the algorithm achieves a good performance in bothsolution quality and time especially for large instances.El objetivo de este artículo es desarrollar un algoritmo genético el cualpermita determinar los tamaños de lote de producción y su programaciónen un sistema de manufactura de una máquina para órdenesmultiproducto, cuya función objetivo minimiza la suma de los costosde inventario por terminaciones tardías y de alistamiento. El problemacontempla un conjunto de órdenes a ser procesadas con sus respectivasfechas de entrega. Cada orden debe ser entregada en su totalidad. Dentrode la programación de los trabajos se consideran tiempos de alistamientodependientes de la secuencia. En la metaheurística implementada se utilizade manera embebida un método heurístico para el cálculo de la funciónde adaptación. El método heurístico presentado es una variación delOptimal Timming Algorithm el cual involucra los tiempos de alistamientodependientes de la secuencia. Se desarrolla un diseño de experimentospara probar el desempeño del algoritmo utilizando instancias generadasde forma aleatoria y comparando sus soluciones contra las encontradaspor un método exacto. Los resultados muestran que el algoritmo lograun buen desempeño tanto en tiempo de ejecución como en calidad de lasolución especialmente en instancias grandes.

A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem

This paper deals with the long-term Military Flight and Maintenance Planning problem. In order to solve this problem efficiently, we propose a new solution approach based on a new Mixed Integer Program and the use of both valid cuts generated on the basis of initial conditions and learned cuts based on the prediction of certain characteristics of optimal or near-optimal solutions. These learned cuts are generated by training a Machine Learning model on the input data and results of 5000 instances. This approach helps to reduce the solution time with little losses in optimality and feasibility in comparison with alternative matheuristic methods. The obtained experimental results show the benefit of a new way of adding learned cuts to problems based on predicting specific characteristics of solutions.French Defense Procurement Agency of the French Ministry of Defense (DGA)

The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm

[EN] The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10^20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Garcia, J.; Martí Albiñana, JV.; Yepes, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics. 8(6):862-01-862-22. https://doi.org/10.3390/math8060862S862-01862-228

Étude de stratégies parallèles de coopération avec POSL

National audienceThe multi-core technology and massive parallel ar-chitectures are nowadays more accessible for a broadpublic through hardware like the Xeon Phi or GPUcards. This architecture strategy has been commonlyadopted by processor manufacturers to stick with Moo-re’s law. However, this new architecture implies newways to design and implement algorithms to exploit itsfull potential. This is in particular true for constraint-based solvers dealing with combinatorial optimizationproblems. In this paper we use Parallel-Oriented Sol-ver Language (POSL), a framework to build intercon-nected meta-heuristic-based solvers working in paral-lel, by using communications operators, to solve ins-tances ofSocial GolfersandCostas Arrayproblemsand measure its performance. We test many differentsolution’s strategies, thanks to a parallel-oriented lan-guage provided, based on operators.La technologie multi-coeur et les architecturesmassivement parallèles sont de plus en plus accessiblesà tous, à travers des matériaux comme le XeonPhi ou les cartes GPU. Cette stratégie d’architecture aété communément adoptée par les producteurs pourfaire face à la loi de Moore. Or, ces nouvelles architecturesimpliquent d’autres manières de concevoir etd’implémenter les algorithmes, pour exploiter complètementleur potentiel, en particulier dans le cas dessolveurs de contraintes traitant de problèmes d’optimisationcombinatoire. Dans cet article on utilise un Langagepour créer des Solveurs Orienté Parallèle (POSLpour Parallel-Oriented Solver Language) : cadre permettantde construire des solveurs basés sur desméta-heuristiques interconnectées travaillant en parallèle,dans le but de résoudre des instances des problèmesSocial Golfers et Costas Array et de mesurersa performance. Nous testons plusieurs stratégiesde résolution, grâce au langage orienté parallèle, basésur des opérateurs, que POSL fournis

Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs

A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem

This paper deals with the long-term Military Flight and Maintenance Planning problem. In order to solve this problem efficiently, we propose a new solution approach based on a new Mixed Integer Program and the use of both valid cuts generated on the basis of initial conditions and learned cuts based on the prediction of certain characteristics of optimal or near-optimal solutions. These learned cuts are generated by training a Machine Learning model on the input data and results of 5000 instances. This approach helps to reduce the solution time with little losses in optimality and feasibility in comparison with alternative matheuristic methods. The obtained experimental results show the benefit of a new way of adding learned cuts to problems based on predicting specific characteristics of solutions

Combining metaheuristics with mathematical programming, constraint programming and machine learning

International audienc

Combining metaheuristics with mathematical programming, constraint programming and machine learning

International audienc