A Hyperheuristic Approach for Intercell Scheduling With Single Processing Machines and Batch Processing Machines

Li, Dongni; Li, Miao; Meng, Xianwen; Tian, Yajie

doi:10.1109/tsmc.2014.2332443

Cited by 35 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All reviewed literature has been applied in one or a combination of various platforms such as single machine [49], parallel machine [50], flow shop [51], series batch [52,53], real-time [54], and flexible manufacturing systems scheduling [55]. Between these, the parallel machine scheduling (PMS) problem is an atypical scheduling problem with extensive practical relevance.…”

Section: Simulated Annealingmentioning

confidence: 99%

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

et al. 2021

View full text Add to dashboard Cite

A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.

show abstract

Section: Simulated Annealingmentioning

confidence: 99%

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Six solution representations which encoded in different chromosome sizes are defined and compared in experiments, results show that the hybrid representation maintains a relatively stable good performance regardless of the size value. Li et al [80] studied a complex scheduling problem where various machine types are considered in a multi-stage hybrid flow shop and an ant colony optimization (ACO)-based hyper-heuristic was proposed for the selection of heuristic rules.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

2019

View full text Add to dashboard Cite

In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agentbased hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multiobjective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants. INDEX TERMSScheduling, flexible job shop, multi-agent, hyper-heuristics, genetic programming. NOMENCLATURE NSGAII Nondominated sorting genetic algorithm II. SPEA2 Strength Pareto evolutionary algorithm 2. 2/3/MPGP Multi-objective cooperative coevolution genetic programming with two/three/multiple sub-populations. The associate editor coordinating the review of this manuscript and approving it for publication was Kuo-Ching Ying. 2/3/MTGP Multi-objective genetic programming with single population that an individual contains two/three/multiple sub-trees. OMOPSO Optimized multi-objective particle swarm optimization. SMPSO Speed-constrained multi-objective particle swarm optimization. MAHH Multi-agent based hyper-heuristics.

show abstract

“…The statistics of 32 companies in the United States show that 25.8% to 34.3% of the operations need to be processed in foreign cells [6]. More than 50% of all processing routes need intercell transfers in the Chinese manufacturing industry [7]. In these CMS, parts that need to be processed in multiple cells are known as exceptional elements, while the machines that are used by the parts are called bottleneck machines.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Huang

Yang

2020

Mathematics

View full text Add to dashboard Cite

Based on the non-permutation property of intercell scheduling in flowline manufacturing cells, a hybrid harmony search algorithm is proposed to solve the problem with the makespan criterion. On the basis of the basic harmony search algorithm, the three key elements of memory consideration, pitch adjustment and random selection are discretized and improved to adapt to the operation-based encoding. To compare the performance, different scale cases are generated in both the overall solution and the two-stage solution with the proposed algorithm, the hybrid particle swarm optimization algorithm and the hybrid genetic algorithm. The relative deviation is taken as the performance index. The compared results show that a better solution can be obtained with the proposed algorithm in both the overall solution and the two-stage solution, verifying the superior performance of the proposed algorithm.

show abstract

A Hyperheuristic Approach for Intercell Scheduling With Single Processing Machines and Batch Processing Machines

Cited by 35 publications

References 28 publications

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Contact Info

Product

Resources

About