Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A computational study of vehicle routing applications
A computational study of vehicle routing applications
Solving a Practical Pickup and Delivery Problem
Transportation Science
Hi-index | 0.00 |
Grouping problems arise in many applications, and the aim is to partition a set U of items, into a collection of mutually disjoint subsets or groups. The objective of grouping is to optimize a cost function such as to minimize the number of groups. Problems in this category may come from many different domains such as graph coloring, bin packing, cutting stock, and scheduling. This investigation is related in particular to scheduling transportation events, modeled as a grouping problem, and with the objective to minimize the number of vehicles used and satisfying the customer demand. There is a set of events to be scheduled (items) into a set of vehicles (groups). Of course, there are constraints that forbid assigning all events to a single vehicle. Two different techniques are used in this work to tackle the problem: Grouping Genetic Algorithms and an algorithm based on the heuristic DJD widely used for solving bin packing problems. Both methods were adapted to the problem and compared to each other using a set of randomly generated problem instances designed to comply with real situations.