Journey to the center of the linear ordering problem

A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the escape technique play a key role in the performance of these algorithms. Of course, a specific strategy has a different effect on distinct problems or instances. In this paper, we focus on a permutation-based combinatorial optimization problem: the linear ordering problem. We provide a theoretical landscape analysis for the adjacent swap, the swap and the insert neighborhoods. By making connections to other different problems found in the Combinatorics field, we prove that there are some moves in the local optima that will necessarily return a worse or equal solution. The number of these non-better solutions that could be avoided by the escape techniques is considerably large with respect to the number of neighbors. This is a valuable information that can be included in any of those algorithms designed to escape from the local optima, increasing their efficiency.TIN2016-78365-R IT1244-1

A review of literature on parallel constraint solving

As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better performance in a Genetic Algorithm (GA). This paper presents an adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator's effectiveness. Best performing operators are given high application rates and low rates are given to the worse performing ones. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators works better than using one operator in isolation. This study makes a contribution to advance our understanding of how to make effective use of crossover operators on this highly-constrained optimisation problem

Diseño de estrategias para el reposicionamiento de unidades en Sistemas Públicos de Bicicletas

RESUMEN: Los Sistemas Públicos de Bicicletas (SPB) son un medio de transporte económico y amigable con el medio ambiente para recorrer distancias medianas y cortas al interior de las ciudades. Estos poseen una demanda asimétrica que los caracteriza generando así abundancia o escasez de bicicletas y puntos de anclajes en las estaciones a lo largo de la operación. Se propone un modelo matemático de optimización de programación binaria que tiene como objetivo agrupar las estaciones de un SPB en zonas de reposicionamiento, teniendo impacto en un horizonte de planeación táctico. El modelo considera aspectos adicionales a los geográficos. Se realizan pruebas con instancias reales de un SPB con 452 estaciones

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Roteamento multicast multisessão: modelos e algoritmos

Multicast Technology has been studied over the last two decades and It has shown to be a good approach to save network resources. Many approaches have been considered to solve the multicast routing problem considering only one session and one source to attending session‘s demand, as well, multiple sessions with more than one source per session. In this thesis, the multicast routing problem is explored taking in consideration the models and the algorithms designed to solve it when where multiple sessions and sources. Two new models are proposed with different focuses. First, a mono-objective model optimizing residual capacity, Z, of the network subject to a budget is designed and the objective is to maximize Z. Second, a multi-objective model is designed with three objective functions: cost, Z and hops counting. Both models consider multisession scenario with one source per session. Besides, a third model is examined. This model was designed to optimize Z in a scenario with multiple sessions with support to more than one source per session. An experimental analysis was realized over the models considered. For each model, a set of algorithms were designed. First, an ACO, a Genetic algorithm, a GRASP and an ILS algorithm were designed to solve the mono-objective model – optimizing Z subject to a budget. Second, a set of algorithm were designed to solve the multi-objective model. The classical approaches were used: NSGA2, ssNSGA2, SMS-EMOA, GDE3 and MOEA/D. In addition, a transgenetic algorithm was designed to solve the problem and it was compared against the classical approaches. This algorithm considers the use of subpopulations during the evolution. Each subpopulation is based on a solution construction operator guided by one of the objective functions. Some solutions are considered as elite solutions and they are considered to be improved by a transposon operator. Eight versions of the transgenetic algorithm were evaluated. Third, an algorithm was designed to solve the problem with multiple sessions and multiple sources per sessions. This algorithm is based on Voronoi Diagrams and it is called MMVD. The algorithm designed were evaluated on large experimental analysis. The sample generated by each algorithm on the instances were evaluated based on non-parametric statistical tests. The analysis performed indicates that ILS and Genetic algorithm have outperformed the ACO and GRASP. The comparison between ILS and Genetic has shown that ILS has better processing time performance. In the multi-objective scenario, the version of Transgenetic called cross0 has shown to be statistically better than the other algorithms in most of the instances based on the hypervolume and addictive/multiplicative epsilon quality indicators. Finally, the MMVD algorithm has shown to be better than the algorithm from literature based on the experimental analysis performed for the model with multiple session and multiple sources per session.A tecnologia multicast tem sido amplamente estudada ao longo dos anos e apresenta-se como uma solução para melhor utilização dos recursos da rede. Várias abordagens já foram avaliadas para o problema de roteamento desde o uso de uma sessão com apenas uma fonte a um cenário com múltiplas sessões e múltiplas fontes por sessão. Neste trabalho, é feito um estudo dos modelos matemáticos para o problema com múltiplas sessões e múltiplas fontes. Dois modelos matemáticos foram propostos: uma versão multissessão mono-objetivo que visa a otimização da capacidade residual sujeito a um limite de custo e uma versão multiobjetivo com três funções-objetivo. Ambos os modelos levam em conta o cenário multissessão com uma fonte por sessão. Além disso, um estudo algorítmico foi realizado sobre um modelo da literatura que utiliza múltiplas fontes por sessão. Três conjuntos de algoritmos foram propostos. O primeiro conjunto trata do problema mono-objetivo proposto e considera as abordagens ACO, Genético, GRASP e ILS. O segundo conjunto consiste dos algoritmos propostos para o modelo multiobjetivo. Foram projetados os seguintes algoritmos: NSGA2, ssNSGA2, GDE3, MOEA/D e SMS-EMOA. Além disso, foi projetado um algoritmo transgenético com subpopulações baseadas em operadores de criação de solução direcionados por objetivos do problema. Também foi utilizado o conceito de soluções de elite. No total, 8 versões do algoritmo transgenético foram avaliadas. O terceiro conjunto de algoritmos consiste da heurística MMVD proposta para o modelo da literatura com múltiplas fontes por sessão. Esta heurística é baseada no uso de diagramas de Voronoi. O processo experimental foi realizado com amplo número de instâncias configuradas de modo a avaliar diferentes situações. Os resultados foram comparados utilizando métodos estatísticos não-paramétricos. A análise final indicou que o ILS e o Genético obtiveram resultados muito similares, entretanto o ILS possui melhor tempo de processamento. A versão cross0 do algoritmo transgenético obteve o melhor resultado em praticamente todos os cenários avaliados. A heurística MMVD obteve excelentes resultados sobre algoritmos da literatura