A new MCTS-based algorithm for multi-objective flexible job shop scheduling problem

Chou, Jen-Jai; Liang, Chao-Chin; Wu, Hung-Chun; Wu, I‐Chen; Wu, Tung-Ying

doi:10.1109/taai.2015.7407061

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…MCTS has rarely been used for combinatorial optimization problems. The application of MCTS algorithms on job shop scheduling problems can be found in [13], [37]. Matsumoto et al applied Single Player MCTS (SP-MCTS) on reentrant scheduling problems [31].…”

Section: Related Workmentioning

confidence: 99%

A 2-Stage Approach for the Nurse Rostering Problem

et al. 2022

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In this paper, we are addressing the NP-hard nurse rostering problem utilizing a 2-stage approach. In stage one, Monte Carlo Tree Search (MCTS) and Hill Climbing (HC) are hybridized in finding a feasible solution (satisfying all the hard constraints). We propose a new constant C value (which balances search diversification and intensification of MCTS) and tree policy/node selection function in the selection procedure of MCTS. In stage two, the feasible solution is further improved using Iterated Local Search (ILS) with Variable Neighbourhood Descent as the local search component. We introduce several unique neighbourhood structures for the ILS. In addition, we propose a novel perturbation strategy to allow the search to escape from local optimum. The proposed methodology is tested on the Shift Scheduling dataset (24 benchmark instances). New best results are reported for seven and two instances for the 10 and 60 minutes run respectively. An in-depth discussion on the attributes of the proposed methodology that lead to its good performance is provided.INDEX TERMS nurse rostering, hill climbing, monte carlo tree search, iterated local search, variable neighbourhood descent.

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Section: Related Workmentioning

confidence: 99%

A 2-Stage Approach for the Nurse Rostering Problem

et al. 2022

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“…Especially hybrid MO-MCTS approaches have demonstrated their abilities by solving the Pareto Kacem benchmark problem [19,22,23]. However, the Kacem benchmark problem does not cover important restrictions (transport, setup) and does not offer process flexibility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Accounting for the fact that only minor improvement is to be expected by a neighborhood search, a pre-selection of the solutions to execute local search on is advisable. In literature [19,22,23] local search is executed on rollout states. To improve the efficiency of the entire optimizer, the following strategies have been developed:…”

Section: Interaction Of Local Search and Base Optimizermentioning

confidence: 99%

Hybrid Monte Carlo tree search based multi-objective scheduling

Hofmann

Liu

May

et al. 2022

Prod. Eng. Res. Devel.

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As markets demand targeted products for highly differentiated use cases, the number of variants in production increases, whilst the volume per variant decreases. Different product variants result in differences in work content on workstation level which cause takt time losses and result in a poor utilization. In this context, matrix-structured production systems with neither temporal nor spacial linkage emerged to reduce the effects of different work content on the entire production system. However, matrix-structured production systems require far more complex production control. To that end, this paper presents a scheduling approach. The proposed scheduling system considers variable process sequences and their allocation to different workstations in order to optimize scheduling objectives. This contribution presents a Monte Carlo tree search based optimizer combined with local search as post optimizer to derive schedules in a short time span to enabling reactive scheduling. The application of the scheduler to a benchmark problem and an industrial scheduling problem demonstrates the quality of the results and illustrates how the scheduler reassigns the work content dynamically.

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“…A MCTS-based algorithm for the multi-objective flexible job shop scheduling problem was presented by Wu et al [33], and it was improved by incorporating the Variable Neighborhood Descent Algorithm and other techniques, like rapid action value, which can estimate the heuristic and transposition table. Chou et al [34] used the improved MCTS algorithm to solve the multi-objective flexible shop scheduling problem, and search the minimum completion time by the adaptive value game comparison. Furuoka and Matsumoto [35] used an MCTS-based algorithm to find good schedules for a re-entrant scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Improved SP-MCTS-Based Scheduling for Multi-Constraint Hybrid Flow Shop

et al. 2020

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As a typical non-deterministic polynomial (NP)-hard combinatorial optimization problem, the hybrid flow shop scheduling problem (HFSSP) is known to be a very common layout in real-life manufacturing scenarios. Even though many metaheuristic approaches have been presented for the HFSSP with makespan criterion, there are limitations of the metaheuristic method in accuracy, efficiency, and adaptability. To address this challenge, an improved SP-MCTS (single-player Monte-Carlo tree search)-based scheduling is proposed for the hybrid flow shop to minimize the makespan considering the multi-constraint. Meanwhile, the Markov decision process (MDP) is applied to transform the HFSSP into the problem of shortest time branch path. The improvement of the algorithm includes the selection policy blending standard deviation, the single-branch expansion strategy and the 4-Rule policy simulation. Based on this improved algorithm, it could accurately locate high-potential branches, economize the resource of the computer and quickly optimize the solution. Then, the parameter combination is introduced to trade off the selection and simulation with the intention of balancing the exploitation and exploration in the search process. Finally, through the analysis of the calculated results, the validity of improved SP-MCTS (ISP-MCTS) for solving the benchmarks is proven, and the ISP-MCTS performs better than the other algorithms in solving large-scale problems.

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A new MCTS-based algorithm for multi-objective flexible job shop scheduling problem

Cited by 4 publications

References 8 publications

A 2-Stage Approach for the Nurse Rostering Problem

A 2-Stage Approach for the Nurse Rostering Problem

Hybrid Monte Carlo tree search based multi-objective scheduling

Improved SP-MCTS-Based Scheduling for Multi-Constraint Hybrid Flow Shop

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