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Learning path generation involves the computation of learning trajectories to personalize academic instruction to prevent school problems. The Educational Planning Problem ($EPP$ EPP) considers generating personalized learning paths by scheduling activities that satisfy expected grades while minimizing plans makespan. In this work, we propose two scheduling models with two new activity duration functions incorporating student stress and sequence dependence to mimic learning effects and increase personalization. We also developed a metaheuristic algorithm to solve real-size instances composed of a greedy randomized adaptive search procedure enhanced by a variable neighborhood search. Experimental results validate the efficiency of our approach and the importance of considering stress in learning trajectories personalization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two hybrid flow shop scheduling lines must be coordinated to assemble batches of terminated products at their last stage. Each product is thus composed of two jobs, each produced in one of the lines. The set of jobs is to be processed in a series of stages to minimize the makespan of the scheduling, but jobs forming a product must arrive at the assembly line simultaneously. We propose a mixed integer linear programming model. Then, based on the model, we propose a pull-matheuristic algorithm. Finally, we present two metaheuristics, a greedy randomized adaptive search procedure and a biased random key genetic algorithm, and compare all the methodologies with real-based instances of a production scheduling problem in the automobile manufacturing industry. The greedy algorithm yields high-quality solutions, while the genetic one offers the best computational times. [ABSTRACT FROM AUTHOR]