Minimizing total flow time for the worker assignment scheduling problem in the identical parallel-machine models

Hu, Po-Chieng

doi:10.1007/s00170-003-1989-z

Cited by 13 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our experiment consists three cases based on the number of jobs (n), machines (m), and operators (w)small, medium, and big-sized problem (see Table 1). We generate all parameters from uniform distributions which are U [1,79], U [1,99], U [1,20], and U [3,10] respectively for setup, machining, unloading, and moving time. The solver is time limited in the medium-sized problem for 300 seconds and not run in the big-sized problem despite memory problem.…”

Section: Numerical Examples and Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

Akbar

Irohara

2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

This paper proposes a novel dual resource constrained (DRC) scheduling problem under identical parallel machine environment that consider operator working modes and moving activity between machines with regards to the makespan minimization objective. We define the working modes as all operator activities when the operators interact with the machines such as loading, setup, controlling, and unloading. Firstly, we provide the mathematical model of the problem using Mixed Integer Linear Programming (MILP). We add unloading activity beside setup to be included in the model. Also, we consider the moving activity that is usually neglected in DRC scheduling problem. Moreover, we propose a permutation-based genetic algorithm (PGA) to tackle the computational burden of the bigger size problem. Then, we run a full factorial experiment with replication to compare the solution quality and computational time of our PGA to the solver and random search method. The results show that our proposed PGA could solve the problem in a reasonable time that is faster than the solver with a good quality solution that is better than random search.

show abstract

Section: Numerical Examples and Computational Resultsmentioning

confidence: 99%

“…Most research in DRC scheduling deals in a condition that the operator number is not less than the number of machines, e.g. [3] and [4]. On the next level, we find articles that deal with fewer operators, but they cannot supervise many jobs at the time [5], [6].…”

Section: Introductionmentioning

confidence: 92%

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

Akbar

Irohara

2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…Hu [11] minimizes the total flow time in a parallel machine system assigning jobs and workers to the machines. The author proposes a heuristic solution with two sequential procedures: the former assigning the jobs and the latter the workers to the machines.…”

Section: Related Workmentioning

confidence: 99%

Workforce influence on manufacturing machines schedules

Caricato

Grieco

Arigliano

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This study addresses a parallel machines scheduling problem with sequence-dependent setup times and additional resource constraints related to workforce management. In most industrial cases, the execution of jobs requires the involvement of human resources in addition to machines: this work addresses the many complications due to workforce-specific issues that arise in a real industrial application. This is achieved separating the complex yet classical parallel machines scheduling problem with sequence-dependent setup times from the additional human resources planning problem: the former is formulated and solved through constraint programming, while an ad hoc procedure is provided for the latter. An Italian specialized firm, Prosino S.r.l., provides the industrial case to both validate the adequacy of the adopted method to the actual industrial problem and test the effectiveness of the proposed approach. Computational results obtained over six months of experimentation at the partner firm are presented.

show abstract

“…Behnamian [33] proposed a group competition algorithm based on the variable neighborhood search (VNS) algorithm, considering the influence of worker learning effect and deterioration effect on the HFSP. Hu et al [34] studied the scheduling problem considering worker constraints. In the job scheduling stage, SPT-A heuristic algorithm was used, and in the worker allocation stage, the largest marginal contribution (LMC) method was used to minimize the total flow time.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

Geng

Liu

2020

IEEE Access

View full text Add to dashboard Cite

The classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop scheduling problem with dual resource constraints (DHFSP). Firstly, according to the characteristics of DHFSP and various constraints, the model is built to minimize the maximum completion time (makespan), total tardiness time and workload balance of worker. Then, an improved multi-objective memetic algorithm (IMOMA) is proposed to solve the DHFSP, which mainly includes the improvement of initial population, crossover, mutation and local search. In addition, Taguchi method is used to set parameters. Finally, through numerical experiments, IMOMA is compared with NSGA-II, MODE and MOMVO algorithms. The experimental results show that IMOMA can solve the multi-objective hybrid flow shop scheduling problem with dual resource constraints effectively. In terms of convergence, diversity and dominance of non-dominated solutions, IMOMA is significantly superior to other algorithms, but the distribution uniformity of non-dominated solutions of the four algorithms are not significantly different.

show abstract

Minimizing total flow time for the worker assignment scheduling problem in the identical parallel-machine models

Cited by 13 publications

References 17 publications

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

Workforce influence on manufacturing machines schedules

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

Contact Info

Product

Resources

About