Train driver scheduling is the problem of finding an assignment of drivers to cover all train vehicle work, such that cost is minimized while all constraints are satisfied. Relieving of drivers happens mostly at train stops; in many cases the train will stop for several minutes, giving rise to a window of relief opportunities (WRO), but current industry practice is to consider relieving only at...

The use of genetic algorithms (GAs) to solve combinatorial optimization problems often produces a population of infeasible solutions because of optimization problem constraints. A solution pool with a large number of infeasible solutions results in poor search performance of a GA, or worse, the algorithm ceases to run. In such cases, the methods of penalty function and multi-objective optimizat...

In non-parametric statistics, no assumption is made about the probability distribution of the population, and the test statistics are usually based on the rank of the data. In multivariate data analysis, every data item consists of several elements (i.e. is an n-tuple). The concept of data depth in non-parametric multivariate statistics is the generalization of the univariate rank method. Given...

Searches that include both feasible and infeasible solutions have proved to be efficient algorithms for solving some scheduling problems. Researchers conjecture that these algorithms yield two primary benefits: 1) they tend to focus on solutions close to the boundary between feasible and infeasible solutions, where active constraints are likely to yield optimal values, and 2) moves that include...

This paper develops a local reasoning method to check lineartime temporal properties of concurrent programs. In practice, it is often infeasible to model check over the product state space of a concurrent program. The method developed in this paper replaces such global reasoning with checks of (abstracted) individual processes. An automatic refinement step gradually exposes local state if neces...

The minimization of a convex quadratic function under bound constraints is a fundamental building block for solving more complicated optimization problems. The active-set method introduced by Bergounioux et al. [1, 2] has turned out to be a powerful, fast and competitive approach for this problem. Hintermüller et al. [15] provide a theoretical explanation of its efficiency by interpreting it as...

This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of fe...