Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Industrial Engineering - Supply chain management
Advanced planning and scheduling with outsourcing in manufacturing supply chain
Computers and Industrial Engineering - Supply chain management
Simulating Gender Separation With Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Supply chain scheduling: sequence coordination
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times
Computers and Operations Research
A dynamic driver management scheme for less-than-truckload carriers
Computers and Operations Research
Integrated process planning and scheduling in a supply chain
Computers and Industrial Engineering
An adaptive genetic algorithm with dominated genes for distributed scheduling problems
Expert Systems with Applications: An International Journal
A genetic algorithm based heuristic to the multi-period fixed charge distribution problem
Applied Soft Computing
Tabu search and lower bounds for a combined production-transportation problem
Computers and Operations Research
Computers and Operations Research
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This study considers the scheduling of products and vehicles in a two-stage supply chain environment. The first stage contains m suppliers with different production speeds, while the second stage is composed of l vehicles, each of which may have a different speed and different transport capacity. In addition, it is assumed that the various output products occupy different percentages of each vehicle's capacity. We model the situation as a mixed integer programming problem, and, to solve it, we propose a gendered genetic algorithm (GGA) that considers two different chromosomes with non-equivalent structures. Our experimental results show that GGA offers better performance than standard genetic algorithms that feature a unique chromosomal structure. In addition, we compare the GGA performance with that of the most similar problem reported to date in the literature as proposed by Chang and Lee [Chang, Y., & Lee, C. (2004). Machine scheduling with job delivery coordination. European Journal of Operational Research, 158(2), 470-487]. The experimental results from our comparisons illustrate the promising potential of the new GGA approach.