A new hybrid ant colony algorithm for scheduling of no-wait flowshop

Abstract

In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the wellknown flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

Solutions to No‐Wait Flow Shop Scheduling Problem Using the Flower Pollination Algorithm Based on the Hormone Modulation Mechanism

“…According to the scale of the instances, they are divided into 12 groups, with 10 in each group. The proposed algorithm is compared with the ACO-SA [56] algorithm and IIGA [53] algorithm. The ACO-SA algorithm only performed the test of the first 110 instances (Ta001-Ta110) and did not test the 10 instances in 500 × 20 scale (Ta111-Ta120).…”

Solving Permutation Flow Shop Scheduling Problem with Sequence-Independent Setup Time

“…Other nature-inspired metaheuristics are applied for solving flow shop scheduling problem, the artificial bee colony algorithm [19][20][21], ant colony optimization algorithm [22,23], and water wave optimization algorithm [24,25]. e efficiency of these metaheuristics is observed during the resolutions of the flow shop scheduling problems with several jobs and machines.…”

A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

“…en, many scholars have devoted enormous effort and time to the research of FSP. For example, Riahi and Kazemi [6] have proposed a no-wait flow-shop scheduling problem, which is a variant of FSP, considering the makespan and flow time. In the paper, the authors have provided the mathematical formulation and a hybrid metaheuristic algorithm combined ant colony optimization and simulated annealing algorithm.…”

“…Due to the NP-hard of the HFSP, in many prior studies, metaheuristic algorithms have been applied to tackle the HFSP [6,11,13], such as greedy algorithm, simulated annealing algorithm, genetic algorithm, etc. Metaheuristic algorithms can obtain the solution of complexity optimization problem in available time, which have been developed in many fields, i.e., manufacturing industry [15][16][17], airline industry [18][19][20][21], energy sources [22][23][24][25], etc.…”

“…An example of solution presentation. : production A: 2, production B: 2, production C: 3, and production D: An example of correction strategy 6. Scientific Programming…”