Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Theory of Lexicographic Multi-Criteria Optimization
ICECCS '96 Proceedings of the 2nd IEEE International Conference on Engineering of Complex Computer Systems
A memetic algorithm for the job-shop with time-lags
Computers and Operations Research
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
SIAM Journal on Optimization
A decision support system for production scheduling in an ion plating cell
Expert Systems with Applications: An International Journal
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
How to Solve It: Modern Heuristics 2e
How to Solve It: Modern Heuristics 2e
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Sequence-dependent group scheduling problem on unrelated-parallel machines
Expert Systems with Applications: An International Journal
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A modified particle swarm optimization for aggregate production planning
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
An initiative was introduced in one of the production facilities of Germany's E.G.O. Group in order to enhance its SAP information system with a custom-made application for production-scheduling optimization. The goal of the optimization is to find a production schedule that satisfies different, contradictory production and business constraints. We show the challenges faced in the application of the multi-objective optimization approach, which is gaining influence in the management of production scheduling. We implement a memetic version of the Indicator-Based Evolutionary Algorithm with customized reproduction operators and local search procedures to find a set of feasible, non-dominated solutions. Such a memetic algorithm was applied to two real order lists from the production company. Additionally, we also lay out an efficient presentation of the multi-objective results for an expert's support in decision making. This provides the management with the possibility to gain additional insights into how the production schedule dynamically reacts to changes in the decision criteria. We show that the multi-objective approach is able to find high-quality solutions, which enables flexibility when it comes to quickly adapting to specific business conditions.