Dispatching rules are simple but efficient heuristics to solve multi-objective job shop scheduling problems, particularly useful to face the challenges of dynamic shop environments. A promising method to automatically evolve such rules represents genetic programming based hyper-heuristic...

An optimal sizing model for an off-grid hybrid micro-grid system (HMGS) consisting of solar photovoltaic (PV), wind turbine (WT), diesel generator (DG) and battery (BAT) is developed. A tri-objective formulation, considering techno-socio-economic factors, is employed for finding the optimal...

This work aims to reconcile theory and practice, proposing a dynamic model of asset allocation that allows the creation, with the help of genetic algorithms, of reasonable portfolios for investors chasing a variety of objectives over multiple time horizons. Our results are interesting and...

In this paper, we propose a novel operational law for calculating the credibility distribution of monotone functions of independent regular fuzzy numbers to study the project scheduling problem with partially (or fully) fuzzy activity durations. In this regard, we formulate three corresponding...

Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their...

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric (unbalanced) demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric...