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Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that there is no efficient algorithm to reach the optimal solution of the problem. To minimize the make-span of large permutation flow-shop scheduling problems in which there are sequence-dependent setup times on each machine, this paper develops one novel hybrid genetic algorithms (HGA). Proposed HGA apply a modified approach to generate the population of initial chromosomes and also use an improved heuristic called the iterated swap procedure to improve them. Also the author uses three genetic operators to make good new offspring. The results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of the solutions. [ABSTRACT FROM AUTHOR]

Electromagnetism algorithm is a meta-heuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper presents a novel hybrid electromagnetism algorithm called SA_EM to solve the multi-depot periodic vehicle routing problem (MDPVRP). The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the electromagnetism (EM) with simulated annealing (SA). Moreover, during implementing the hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The objective consists of two terms as follows: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situation and have little attention in the literature. Finally, the experimental results have shown that the proposed hybrid method is competitive to solve the vehicle routing problem compared with the best existing methods in terms of solution quality. [ABSTRACT FROM AUTHOR]

In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics. [ABSTRACT FROM AUTHOR]

Single-machine capacitated lot-sizing and scheduling problem (SMCLSP) is one of the most famous fields of research in scheduling area. What makes this problem particularly difficult to solve in many real situations is large and sequence-dependent setups and also multiple criterions that must be considered. In this paper, such a problem is formulated and a hybrid simulated annealing for this problem is presented. Also, lower bounds are obtained from solving the problem relaxation, and they are compared with the optimal solutions to estimate the goodness of them. As decision criteria, all of important costs imposed by the solution are considered and also infeasible sequences are mentioned. There is a ceiling value for the manufacturer's cost of each job and according to it, some sequences between jobs are maybe infeasible. It showed the superiority of hybrid simulated annealing (HSA) compared with one of the efficient recent heuristics. The average deviation of the HSA from the corresponding optimal solution for small-size problems ranges from 0% to 10% and from the corresponding lower bound ranges for large-size problems ranges from 11% to 21%. The presented HSA is capable to solve large instances that are mostly compatible with the real-world problems. [ABSTRACT FROM AUTHOR]