Uniform parallel batch machines scheduling considering transportation using a hybrid DPSO-GA algorithm

Jiang, Lu; Pei, Jun; Liu, Xinbao; Pardalos, Pãnos M.; Yang, Yunjie; Qian, Xiaofei

doi:10.1007/s00170-016-9156-5

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…For the problems considering multimachines, Zhou et al [12] proposed an effective differential evolution-based hybrid algorithm to minimize makespan on uniform parallel batch processing machines and the algorithm was evaluated by comparing with a random keys genetic algorithm (RKGA) and a particle swarm optimization (PSO) algorithm. Similar problem was studied by Jiang et al [13] considering batch transportation. A hybrid algorithm combining the merits of discrete particle swarm optimization (DPSO) and genetic algorithm (GA) is proposed to solve this problem.…”

Section: Introductionmentioning

confidence: 76%

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Wang

2018

Mathematical Problems in Engineering

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The problem of minimizing the makespan on single batch processing machine is studied in this paper. Both job sizes and processing time are nonidentical and the processing time of each batch is determined by the job with the longest processing time in the batch. Max-Min Ant System (MMAS) algorithm is developed to solve the problem. A local search method MJE (Multiple Jobs Exchange) is proposed to improve the performance of the algorithm by adjusting jobs between batches. Preliminary experiment is conducted to determine the parameters of MMAS. The performance of the proposed MMAS algorithm is compared with CPLEX as well as several other algorithms including ant cycle (AC) algorithm, genetic algorithm (GA), and two heuristics, First Fit Longest Processing Time (FFLPT) and Best Fit Longest Processing Time (BFLPT), through numerical experiment. The experiment results show that MMAS outperformed others especially for large population size.

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Section: Introductionmentioning

confidence: 76%

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Wang

2018

Mathematical Problems in Engineering

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“…In this paper, the machine coding method is used to determine the processing machine, and because each job needs to be transported to a specific factory by a transport vehicle for processing, the coding length is designed to be equal to twice the length of the number of processed jobs, and the gene string is composed of the serial number of the processing machine including the number and length of the front job and the serial number of the transport vehicle including the number and length of the back job. Referring to the paper of Jiang [14], all the jobs are arranged in descending order of processing time.…”

Section: ‫ݎ‬mentioning

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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“…They developed a mixed integer programming model and an effective differential evolution-based hybrid algorithm. In the work by Jiang et al [21], a hybrid algorithm that combines particle swarm optimization and genetic algorithm was developed for scheduling uniform parallel machines with batch transportation. The study by Zeng et al [22] examined a bi-objective scheduling problem on uniform parallel machines by considering electricity costs under time-dependent or time-of-use electricity tariffs.…”

Section: Literature Reviewmentioning

confidence: 99%

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

Lee

Jang

2019

Sustainability

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We examine a uniform parallel machine scheduling problem with dedicated machines, job splitting, and limited setup resources for makespan minimization. In this problem, machines have different processing speeds, and each job can only be processed at several designated machines. A job can be split into multiple sections and those sections can be processed on multiple machines simultaneously. Sequence-independent setup times are assumed, and setup operations between jobs require setup operators that are limited. For the problem, we first develop a mathematical optimization model and for large-sized problems a constructive heuristic algorithm is proposed. Finally, we show that the algorithm developed is efficient and provides good solutions by experiments with various scenarios.

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Uniform parallel batch machines scheduling considering transportation using a hybrid DPSO-GA algorithm

Cited by 25 publications

References 33 publications

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

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

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

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