An effective two-stage iterated greedy algorithm to minimize total tardiness for the distributed flowshop group scheduling problem

Wang, Zhiyuan; Pan, Quan-Ke; Gao, Liang; Wang, Yu‐Long

doi:10.1016/j.swevo.2022.101143

Cited by 34 publications

(6 citation statements)

References 42 publications

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“…They constructed a mixed-integer linear programming model and proposed a cooperative co-evolutionary algorithm. Building upon Pan's work, Wang et al [16] further refined the problem by focusing on tardiness. Their improved iterative greedy algorithm demonstrated promising results in minimizing total tardiness.…”

Section: Literature Reviewmentioning

confidence: 99%

Model and Algorithm for a Two-Machine Group Scheduling Problem with Setup and Transportation Time

Ni,

Dai,

Yuan

et al. 2024

Mathematics

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This paper investigates a two-machine group scheduling problem with sequence-independent setup times and round-trip transportation times, which is derived from the production management requirements of modern steel manufacturing enterprises. The objective is to minimize the makespan. Addressing limitations in prior studies, we consider a critical but largely ignored transportation method, namely round-trip transportation, and restricted transporter capacity between machines. To solve this problem, a mixed-integer programming model is first developed. Then, the problem complexity is analyzed for situations with both single and unlimited transporters. For the NP-hard case of a single transporter, we design an efficient two-stage heuristic algorithm with proven acceptable solution quality bounds. Extensive computational experiments based on steel plant data demonstrate the effectiveness of our approach in providing near-optimal solutions, and the maximum deviation between our algorithm and the optimal solution is 1.38%. This research can provide an operable optimization method that is valuable for group scheduling and transportation scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Model and Algorithm for a Two-Machine Group Scheduling Problem with Setup and Transportation Time

Ni,

Dai,

Yuan

et al. 2024

Mathematics

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“…The greedy algorithm is widely used because the steps are easy to understand and easy to use. Greedy efficiency analysis is also more accessible than other algorithms, such as Divide and Conquer [22]. The Greedy algorithm is usually used for optimization problems because there are many problems that, if explored in detail, will take a lot of time [23].…”

Section: Greedy Algorithmmentioning

confidence: 99%

Application of Artificial Intelligence to the Development of Playing Ability in the Valorant Game

Fernanda

Geovanni

Huda

2022

itsdi

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From children to adults, everyone enjoys playing online games as a form of entertainment. Online games have a more popular market because they can meet other players worldwide connected to the internet. NPCs (players controlled by a computer system) are also available in online games as player substitutes or for skill practice. As a result, we have been interacting with artificial intelligence in the competition and our environment without realizing it. The game's AI (Artificial Intelligent) can offer an experience similar to playing with other players. Artificial intelligence may always influence online games, whether to replace opponents or choose the game's plot. First Person Shooter (FPS) game Valorant was made available for free on the Windows platform by Riot Games. With a CS: GO-like concept (Counter Strike Global Offensive). The amount of money you have available for each round must be calculated at the equipment purchasing phase to produce effective gaming equipment. The Greedy Algorithm is one of the algorithms that can be used to choose the equipment for the money/cred owned. This algorithm will select the most expensive item the player can buy each round so that the player's chances of winning that round increase.

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“…This metaheuristic was first proposed by [84] and since then is one of the best performing metaheuristics in flowshop-based scheduling problems. Examples of state-of-the-art iterated greedy algorithms can be found in [42,[85][86][87][88][89][90].…”

Section: Iterated Greedy Algorithm Igvmentioning

confidence: 99%

The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm

et al. 2022

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In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human supervision and the growing attention paid in the scheduling literature, we are not aware of any previous study on the problem under consideration. To cover this gap, we start theoretically addressing the problem by: proposing three mixed-integer linear programming models to find optimal solutions in the problem; and proposing different decoding procedures to code solutions in approximated procedures. After that, the best decoding procedure is used to propose a new mechanism that generates 896 different dispatching rules, combining different measures, indicators, and sorting criteria. All these dispatching rules are embedded in the traditional NEH algorithm. Finally, an iterated greedy algorithm is proposed to find near-optimal solutions. By doing so, we provide academics and practitioners with efficient methods that can be used to obtain exact solutions of the problem; applied to quickly schedule jobs and react under changes; used for initialisation or embedded in more advanced algorithms; and/or easily updated and implemented in real manufacturing scenarios.

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An effective two-stage iterated greedy algorithm to minimize total tardiness for the distributed flowshop group scheduling problem

Cited by 34 publications

References 42 publications

Model and Algorithm for a Two-Machine Group Scheduling Problem with Setup and Transportation Time

Model and Algorithm for a Two-Machine Group Scheduling Problem with Setup and Transportation Time

Application of Artificial Intelligence to the Development of Playing Ability in the Valorant Game

The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm

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