Heuristics to minimize makespan of parallel batch processing machines

Damodaran, Purushothaman; Chang, Ping-Yu

doi:10.1007/s00170-007-1042-8

Cited by 56 publications

(9 citation statements)

References 23 publications

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“…According to the computational experiments, the HGH outperforms the SA method (Chang et al, 2004). Shao et al (2008) applied the neural network (NN) approach to the problem; comparative experiments with the heuristics of Damodaran and Chang (2008) verified the better performance of the NN method. developed an ACO and a GA algorithm for solving the batch scheduling problem on parallel BPMs with dynamic job arrivals.…”

Section: Bpm Problemmentioning

confidence: 94%

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Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities

Jia

Leung

2015

International Journal of Production Economics

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Section: Bpm Problemmentioning

confidence: 94%

“…Comparing with the results of CPLEX, the SA method is found to gain better solutions with less running time. Damodaran and Chang (2008) provided heuristics to minimize the makespan on parallel BPMs. Two heuristics are used to batch the jobs, where the jobs are all first sorted in descending order of their processing times.…”

Section: Bpm Problemmentioning

confidence: 99%

Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities

Jia

Leung

2015

International Journal of Production Economics

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“…Constraint (5) means that the size of any batch of jobs in the first stage cannot exceed the maximum capacity of the transport vehicle. Constraint (6) means that there are n jobs. Constraint (7) means that the processing time of a batch depends on the one with the largest processing time in the batch.…”

Section: ‫ݎ‬mentioning

confidence: 99%

“…Marimuthu [5] uses ant colony optimization algorithm to optimize the scheduling of jobs, and studies the batch optimization scheduling problem of flow shop. For parallel batch scheduling problem, Purushothaman Damodaran et al [6] proposed a variety of heuristic algorithms to solve parallel batch scheduling problems of different size jobs, and proved the advantages of the heuristic algorithm. Ali Husseinzadeh Kashan et al [7] proposed a discrete particle swarm optimization algorithm to minimize the manufacturing time span, and mixed with local search heuristic to solve parallel batch scheduling problem with inconsistent size of job.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Gs-Ga Algorithm for Parallel Machine Scheduling Problem with Different Transport Vehicles

Zhang

2022

Advances in Transdisciplinary Engineering

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This paper studies the scheduling problem of the same kind of parallel machines with different transportation vehicle capacity. The scheduling problem model is composed of a single warehouse and multiple manufacturers located in different geographical locations. Each manufacturer has the same batch processor, and several vehicles assemble the jobs in the warehouse in batches, transport them to different manufacturers and return them. The manufacturer of vehicle transportation depends on the destination of the jobs when the code is generated. At the manufacturer’s place, because the capacity of the vehicle may exceed the maximum capacity of the batch processor, the job will be re batch for processing after it arrives. Through modeling, a 2n dimensional code is designed, and the gravitational search algorithm is introduced. The mutation and crossover process of genetic algorithm are added. A hybrid genetic algorithm based on gravitational search is proposed. The algorithm is used to optimize the sum of manufacturing time span and transportation cost. The effectiveness of the algorithm is verified by different scale simulation experiments.

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“…Chang et al, (2004) proposed a simulated annealing algorithm to minimize makespan in identical parallel batch-processing machines setting with arbitrary job size. Damodaran and Chang (2008) presented the heuristic algorithms to minimize Makespan on the identical parallel batch processing machines; they compared the performance of the proposed heuristics with a simulated annealing approach. Kashan et al, (2008) and Damodaran et al, (2009) proposed the heuristics and meta-heuristic algorithms to minimize makespan in the identical parallel batch processing machines with arbitrary job sizes.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times

et al. 2021

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This paper considers the minimization of makespan in the unrelated parallel batch processing machines scheduling problem with considering non-identical job size and dynamic job ready time. The considered unrelated machines have different capacity and different processing speed. Each machine processes a number of the jobs as a batch at the same time so that the machine’s capacity is not exceeded. The batch processing time and the batch ready time are equal to the largest processing time and the largest ready time of jobs in the same batch, respectively. In this paper, a Mixed Integer Linear Programming (MILP) model, two categories of the heuristic procedures (six heuristics) and a meta-heuristic algorithm are proposed to solve the problem. A lower bound is also presented by relaxing of the original problem to evaluate the quality of the proposed algorithms. The computational experiments show the performance of the proposed algorithms under the considered measures.

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Heuristics to minimize makespan of parallel batch processing machines

Cited by 56 publications

References 23 publications

Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities

Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities

Hybrid Gs-Ga Algorithm for Parallel Machine Scheduling Problem with Different Transport Vehicles

Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times

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