https://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf

The irace package: Iterated racing for automatic algorithm configuration

The Globalization of Ethics: Universal Declaration of Human Rights

Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154---160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

Biased random-key genetic algorithms for combinatorial optimization

Book Review: Transforming Disability into Ability. Policies to Promote Work and Income Security for Disabled People

This paper presents a review and classification of the literature regarding workforce planning problems incorporating skills. In many cases, technical research regarding workforce planning focuses very hard on the mathematical model and neglects the real life implications of the simplifications that were needed for the model to perform well. On the other hand, many managerial studies give an extensive description of the human implications of certain management decisions in particular cases, but fail to provide useful mathematical models to solve workforce planning problems. This review will guide the operations researcher in his search to find useful papers and information regarding workforce planning problems incorporating skills. We not only discuss the differences and similarities between different papers, but we also give an overview of the managerial insights. The objective is to present a combination of technical and managerial knowledge to encourage the production of more realistic and useful solution techniques.

/pdf/workforce-planning-incorporating-skills-state-of-the-art-2qgte9yu0o.pdf

Workforce Planning Incorporating Skills: State of the Art

A heuristic and a branch-and-bound algorithm for the Assembly Line Worker Assignment and Balancing Problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.

Simple heuristics for the assembly line worker assignment and balancing problem

An iterated tabu search for the multi-compartment vehicle routing problem

Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem

Population growth and the massive production of automotive vehicles have lead to the increase of traffic congestion problems. Traffic congestion today is not limited to large metropolitan areas, but is observed even in medium-sized cities and highways. Traffic engineering can contribute to lessen these problems. One possibility, explored in this paper, is to assign tolls to streets and roads, with the objective of inducing drivers to take alternative routes, and thus better distribute traffic across the road network. This assignment problem is often referred to as the tollbooth problem and it is NP-hard. In this paper, we propose mathematical formulations for two versions of the tollbooth problem that use piecewise-linear functions to approximate congestion cost. We also apply a biased random-key genetic algorithm on a set of real-world instances, analyzing solutions when computing shortest paths according to two different weight functions. Experimental results show that the proposed piecewise-linear functions approximate the original convex function quite well and that the biased random-key genetic algorithm produces high-quality solutions.

/pdf/on-the-minimization-of-traffic-congestion-in-road-networks-496h5ffhpn.pdf

Marcus Ritt

Papers

A heuristic and a branch-and-bound algorithm for the Assembly Line Worker Assignment and Balancing Problem

Simple heuristics for the assembly line worker assignment and balancing problem

An iterated tabu search for the multi-compartment vehicle routing problem

Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem

On the minimization of traffic congestion in road networks with tolls