A Dual Fitness Function Genetic Algorithm: Application on Deterministic Identical Machine Scheduling
نویسندگان
چکیده
In this paper a genetic algorithm (GA) with dualfitness function is proposed and applied to solve the deterministic identical machine scheduling problem. The mating fitness function value was used to determine the mating for chromosomes, while the selection fitness function value was used to determine their survivals. The performance of this algorithm was tested on deterministic identical machine scheduling using simulated data. The results obtained from the proposed GA were compared with classical GA and integer programming (IP). Results showed that dual-fitness function GA outperformed the classical single-fitness function GA with statistical significance for large problems and was competitive to IP, particularly when large size problems were used. Keywords—Machine scheduling, Genetic algorithms, Due dates, Number of tardy jobs, Number of early jobs, Integer programming, Dual Fitness functions.
منابع مشابه
A New ILP Model for Identical Parallel-Machine Scheduling with Family Setup Times Minimizing the Total Weighted Flow Time by a Genetic Algorithm
This paper presents a novel, integer-linear programming (ILP) model for an identical parallel-machine scheduling problem with family setup times that minimizes the total weighted flow time (TWFT). Some researchers have addressed parallel-machine scheduling problems in the literature over the last three decades. However, the existing studies have been limited to the research of independent jobs,...
متن کاملA New Hybrid Meta-Heuristics Approach to Solve the Parallel Machine Scheduling Problem Considering Human Resiliency Engineering
This paper proposes a mixed integer programming model to solve a non-identical parallel machine (NIPM) scheduling with sequence-dependent set-up times and human resiliency engineering. The presented mathematical model is formulated to consider human factors including Learning, Teamwork and Awareness. Moreover, processing time of jobs are assumed to be non-deterministic and dependent to their st...
متن کاملAn Efficient Bi-objective Genetic Algorithm for the Single Batch-Processing Machine Scheduling Problem with Sequence Dependent Family Setup Time and Non-identical Job Sizes
This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for t...
متن کاملDesign of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness and Machine Deteriorating Costs with Deteriorating Jobs in a Batched Delivery System
This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizin...
متن کاملA Genetic Algorithm for Job Shop Scheduling with Load Balancing
This paper deals with the load-balancing of machines in a real-world job-shop scheduling problem, with identical machines. The load-balancing algorithm allocates jobs, split into lots, on identical machines, with objectives to reduce job total throughput time and to improve machine utilization. A genetic algorithm is developed, whose fitness function evaluates the load-balancing in the generate...
متن کامل