Modular Framework for Crowd Simulation “Menge” from a Production Warehouse Simulation Perspective

The topic of warehouse optimization is a critical and popular topic within the scientific literature. Particularly, order-picking optimization presents several profound difficulties: modeling, analysis, and data representation. Althoug these are necessary and obvious requirements for any research project, the visualization is an important aspect that has its challenges and complications. Menge is an open-source platform for crowd simulation. It provides an extensible common for research and development. In the present study, it is shown how the simulation platform known as Menge was adapted as a possible complement to the optimization process for the simulated visualization of its results. This chapter presents a solution using Menge to separate the modeling for the routing algorithm from visualization and simulation programming. The purpose of this work is to establish a starting point of industrial simulation in collaboration with algorithms for order-picking optimization in warehouses

Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier

REVISIÓN DE FACTORES QUE AFECTAN LA CALENDARIZACIÓN DE LAS CIRUGÍAS EN LOS QUIRÓFANOS

La distribución de las intervenciones quirúrgicas en diversos quirófanos, supone una programación que contempla diversos factores y recursos. El presente artículo realiza una revisión simplista de tales factores y recursos. Además, abre una discusión sobre los mismos y el impacto que podrían tener dentro del proceso de calendarización

Inferring Parameters of a Relational System of Preferences from Assignment Examples using an Evolutionary Algorithm

Most evolutionary multi-objective algorithms perform poorly in many-objective problems. They normally do not make selective pressure towards the Region of Interest (RoI), the privileged zone in the Pareto frontier that contains solutions important to a DM. Several works have proved that a priori incorporation of preferences improves convergence towards the RoI. The work of (Fernandez, E. Lopez, F. Lopez, & Coello Coello, 2011) uses a binary fuzzy outranking relational system to map many-objective problems into a tri-objective optimization problem that searches the RoI; however, it requires the elicitation of many preference parameters, a very hard task. The use of an indirect elicitation approach overcomes such situation by allowing the parameter inference from a battery of examples. Even though the relational system of Fernandez et al. (2011) is based on binary relations, it is more convenient to elicit its parameters from assignment examples. In this sense, this paper proposes an evolutionary-based indirect parameter elicitation method that uses preference information embedded in assignment examples, and it offers an analysis of their impact in a priori incorporation of DM’s preferences. Results show, through an extensive computer experiment over random test sets, that the method estimates properly the model parameter’s values

Application of the Order-Picking and Self-Organizing Maps Models to Optimize the Supply Chain: A Review of the Literature

At present, the management systems of companies and Industry 4.0 are closely linked. However, technologies that promise to drastically help the efficiency of operations are not implemented in production floors and specifically in warehouses. This chapter shows a methodology of bibliographic search that determines the most recent optimization algorithms and with greater occurrence within the scientific literature. Editorials and scientific journals are also taken into account. The information is analyzed and shown in the conclusion part of the work. The conclusions indicate the hybrid algorithms with greater publication, their characteristics, the different types of metrics used for the implementation of optimization systems in production environments, and the secondary support algorithms used to cover the performance weaknesses of the main algorithms

Distribution of food in a specialized hospital using ambient intelligence to improve a model of macroergonomics

SIDA (Intelligent Food Distribution System, for its acronym in Spanish) is a proposed tool for the distribution of food that can be personalized depending on the medical characteristics of each patient. The target of the tool is to provide foods that contain higher nutrients in the diet set by a hospital. A model of decision trees was based on data from the organization of the United Nations Food and Agriculture Organization (FAO) and used for decision making in the simulated three basic foods based on the diet of Latin American countries typically integrated by rice, potatoes, and lentils from the parameters of fat, energy, and protein, respectively, that contains every type of food