An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – A case study

Marichelvam, M.K.; Mani, Geetha; Tosun, Ömür

doi:10.1016/j.cor.2019.104812

Cited by 122 publications

(49 citation statements)

References 44 publications

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“…Andrade-Pineda et al (2019) studied a novel dual-resource constrained flexible job-shop problem and considered the influence of workers' proficiency. Marichelvam et al (2020) studied a multi-stage hybrid flow shop scheduling problem with identical parallel machines at each stage with the effect of human factors under consideration. The learning and forgetting effects of labors at different skill levels were considered.…”

Section: Literature Reviewmentioning

confidence: 99%

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Peng

Xiao

et al. 2020

J Intell Manuf

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Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource.

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Section: Literature Reviewmentioning

confidence: 99%

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Peng

Xiao

et al. 2020

J Intell Manuf

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“…One impact is the amount of makespan in the production system can increase. Makespan is the total work completion time, starting from the first sequence done by the machine to the last sequence on the machine [8,9,10,11]. The makespan's size will also make the cost of electrical energy in the production machine expended to be large.…”

Section: Introductionmentioning

confidence: 99%

Flow Shop Scheduling Based on Palmer-Neh, Gupta-Neh and Dannenbring-Neh Algorithms to Minimize the Energy Cost

Masrikhan

Kurniawati

2021

Sinergi

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In the manufacturing industry, the most widely used equipment is equipment that uses electricity. Electricity cost is one of the highest operational production costs after labor cost. So, it is very important to save and optimize the use of electrical equipment. One of the manufacturing industries is Taru Martani, Ltd. This research aims to minimize the energy cost by proposing three hybrid algorithms, namely Palmer-NEH, Gupta-NEH, and Dannenbring-NEH methods. Some scheduling evaluation is done using the Efficiency Index (EI) and Relative Error (RE) parameters. It is concluded that the Palmer-NEH and Gupta-NEH methods are the best methods with the lowest energy cost compared with company's actual method and the Dannenbring-NEH method. Based on the Palmer-NEH and Gupta-NEH methods, both methods can save the makespan up to 399.13 minutes or 6.65 hours compared with the company's actual method. With these methods, the company is also able to save the production cost by Rp. 818,043.00.

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“…In reference [11], the iterative greedy algorithm and a variable block heuristic algorithm were fused to solve the HFSP with minimum total flow time, yet only a few solutions were given. In reference [12], an improved particle swarm optimization algorithm was developed based on constructing heuristic algorithm to solve HFSP with minimum maximum completion time and total flow time. However the Pareto front end was not uniform enough.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

Chen

et al. 2020

IEEE Access

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Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution. INDEX TERMS Hybrid flowshop scheduling problem, multi-objective meta-heuristic algorithm, rider optimization algorithm, labor right.

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An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – A case study

Cited by 122 publications

References 44 publications

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Flow Shop Scheduling Based on Palmer-Neh, Gupta-Neh and Dannenbring-Neh Algorithms to Minimize the Energy Cost

A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

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