The single machine early/tardy problem
Management Science
Scheduling with release dates on a single machine to minimize total weighted completion time
Discrete Applied Mathematics
One-machine rescheduling heuristics with efficiency and stability as criteria
Computers and Operations Research
A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem
Computers and Operations Research
Computers and Industrial Engineering
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
On the Convergence of a Population-Based Global Optimization Algorithm
Journal of Global Optimization
Minimizing the earliness-tardiness costs on a single machine
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
A global optimization method for solving fuzzy relation equations
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Computers and Industrial Engineering
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Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Because there is little research in solving the combinatorial optimization problem (COP) by EM, the paper attempts to employ the random-key concept enabling EM to solve COP in single machine scheduling problem. We present a hybrid algorithm that combines the EM methodology and genetic operators to obtain the best/optimal schedule for this single machine scheduling problem, which attempts to achieve convergence and diversity effect when they iteratively solve the problem. The objective in our problem is minimization of the sum of earliness and tardiness. This hybrid algorithm was tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm.