SOLVING A SINGLE MACHINE SCHEDULING PROBLEM BY A DISCRETE VERSION OF ELECTROMAGNETISM-LIKE METHOD

The electromagnetism-like method (EM) is a population based meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points (i.e., our solutions) towards the optimality. In general, the EM has been initially used for solving continuous optimization problems and could not be applied on combinatorial optimization ones. This paper proposes a discrete binary version of the EM for solving combinatorial optimization problems. To show the efficiency of our proposed EM, we solve a single machine scheduling problem and compare our computational results with the solutions reported in the literature. Finally, we conclude that our proposed method is capable of solving such well-known problems more efficiently than the previous studies.

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Two novel population based algorithms for the single machine scheduling problem with sequence dependent setup times and release times

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2021.100869 ◽

2021 ◽

pp. 100869

Author(s):

Victor Fernandez-Viagas ◽

Antonio Costa

Keyword(s):

Single Machine ◽

Machine Scheduling ◽

Population Based ◽

Single Machine Scheduling ◽

Setup Times ◽

Scheduling Problem ◽

Release Times ◽

Sequence Dependent Setup ◽

Dependent Setup Times ◽

Sequence Dependent Setup Times

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Single Machine Scheduling with Rejection

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2012040103 ◽

2012 ◽

Vol 3 (2) ◽

pp. 42-61 ◽

Cited By ~ 3

Author(s):

Atefeh Moghaddam ◽

Lionel Amodeo ◽

Farouk Yalaoui ◽

Behrooz Karimi

Keyword(s):

Single Machine ◽

Optimization Problems ◽

Machine Scheduling ◽

Single Machine Scheduling ◽

Phase Method ◽

Two Phase ◽

General Technique ◽

Combinatorial Optimization Problems ◽

Traditional Research ◽

The Cost

In this paper, the authors consider a single machine scheduling problem with rejection. In traditional research, it is assumed all jobs must be processed. However, in the real-world situation, certain jobs can be rejected. In this study, the jobs can be either accepted and scheduled or be rejected at the cost of a penalty. Two objective functions are considered simultaneously: (1) minimization of the sum of weighted completion times for the accepted jobs, and (2) minimization of the sum of penalties for the rejected jobs. The authors apply two-phase method (TPM), which is a general technique to solve bi-objective combinatorial optimization problems, to find all supported and non-supported solutions for small-sized problems. The authors present a mathematical model for implementing both phases. On the other hand, three different bi-objective simulated annealing algorithms have also been developed to find a good estimation of Pareto-optimal solutions for large-sized problems. Finally the authors discuss the results obtained from each of these algorithms.

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Discrete Fireworks Algorithm for Single Machine Scheduling Problems

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2016070102 ◽

2016 ◽

Vol 7 (3) ◽

pp. 24-35 ◽

Cited By ~ 3

Author(s):

Mohamed Amine El Majdouli ◽

Abdelhakim Ameur El Imrani

Keyword(s):

Single Machine ◽

Optimization Problems ◽

Continuous Optimization ◽

Machine Scheduling ◽

Single Machine Scheduling ◽

Total Weighted Tardiness ◽

Neighborhood Search ◽

Scheduling Problems ◽

Fireworks Algorithm ◽

Machine Scheduling Problems

Over the recent years, Fireworks Algorithm has recorded an increasing success on solving continuous optimization problems, due to its efficiency, simplicity and more importantly its rapid convergence to good optimums. Thus, the Fireworks Algorithm performance is now widely comparable with the most popular methods in the optimization field such as evolutionary computation and swarm intelligence techniques. This paper introduces a discrete Fireworks Algorithm for combinatorial single machine scheduling problems. Taking advantage of the robust design of the original Fireworks Algorithm, a new adaptation of sparks generation is proposed with a novel use of the control parameters. To verify the explorative performance of the algorithm, a hybridization with Variable Neighborhood Search heuristic is implemented. To validate it, the proposed method is tested with several benchmarks instances of the single machine total weighted tardiness. A comparison with other optimization algorithms is also included. The obtained results exhibit the high performance of the proposed method.

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