A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems

Engin, Orhan; Güçlü, Abdullah

doi:10.1016/j.asoc.2018.08.002

Cited by 161 publications

(81 citation statements)

References 55 publications

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“…On the other hand, ACO was first developed in 1992. It is simulated with the search food process of ants to solve complex optimization problems [44]. During the foraging process, each virtual ant in the colony would search the path according to the transition probability and leave some pheromones on their trails.…”

Section: The Concepts Of Pso and Acomentioning

confidence: 99%

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

Lü

Wang

2019

Applied Sciences

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Integrating a partnership with potentially stronger suppliers is widely acknowledged as a contributor to the organizational competitiveness of a supply chain. This paper proposes an event-based model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network in order to understand factors that affect supply chain partnership integration. We develop a multi-objective supply chain partnership integration problem by maximizing trustworthiness, supplier service, qualified products rate and minimizing cost, and then, apply a hybrid algorithm (PSACO) with particle swarm optimization (PSO) and ant colony optimization (ACO) that aims to efficiently solve the problem. It combines the advantages of PSO with reliable global searching capability and ACO with great evolutionary ability and positive feedback. By using the actual data from 1688.com, experimental studies are carried out. The parameter optimizing of the hybrid algorithm is firstly deployed and then we compare the problem solution results of PSACO with the original PSO, ACO. By studying the partnership integration results and implementing analysis of variance (ANOVA) analysis, it shows that the event based model with PSACO approach has validity and superiority over PSO and ACO, and can be served as a tool of decision making for supply chain coordination management in business.

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Section: The Concepts Of Pso and Acomentioning

confidence: 99%

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

Lü

Wang

2019

Applied Sciences

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“…Recently it has been found out that mutation of two or more of these meta-heuristics known as hybrid heuristics can be more efficient than being applied in an isolated manner. As a consequence, a lot of hybrid heuristic based algorithms have been investigated by researchers in the past few years and reported that they can improve the performance especially when dealing with real-world and large-scale problems (Engin & Güçlü, 2018;Kuo et al, 2009;Lin et al, 2015;Ruiz et al, 2019;Zhang et al, 2019;Zhao et al, 2018). The performance of all types of approaches discussed above can be judged by three main factors: solution efficiency, computational efficiency, and ease of application.…”

Section: Introductionmentioning

confidence: 99%

A discrete Jaya algorithm for permutation flow-shop scheduling problem

Mishra

Shrivastava

2020

10.5267/j.ijiec

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Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flowshop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Belabid

Aqil

Allali

2020

Journal of Applied Mathematics

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In this paper, we study the resolution of a permutation flow shop problem with sequence-independent setup time. The objective is to minimize the maximum of job completion time, also called the makespan. In this contribution, we propose three methods of resolution, a mixed-integer linear programming (MILP) model; two heuristics, the first based on Johnson’s rule and the second based on the NEH algorithm; and finally two metaheuristics, the iterative local search algorithm and the iterated greedy algorithm. A set of test problems is simulated numerically to validate the effectiveness of our resolution approaches. For relatively small-size problems, it has been revealed that the adapted NEH heuristic has the best performance than that of the Johnson-based heuristic. For the relatively medium and large problems, the comparative study between the two metaheuristics based on the exploration of the neighborhood shows that the iterated greedy algorithm records the best performances.

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A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems

Cited by 161 publications

References 55 publications

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

A discrete Jaya algorithm for permutation flow-shop scheduling problem

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

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