A new model of parallel-machine scheduling with integral-based learning effect

Przybylski, Bartłomiej

doi:10.1016/j.cie.2018.05.035

Cited by 21 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Literature Reviewmentioning

confidence: 99%

“…In the parallel machine scheduling model, the total weighted earliness and tardiness instead of job tardiness are considered. Przybylski (2018) introduced a new model of parallel-machine scheduling with job processing times described by proper Riemann integrals of a given function. Gedik et al (2016) proposed a constraint programming model and logic-based Benders algorithms to make the best decisions for scheduling nonidentical jobs with availability intervals and sequence-dependent setup times on unrelated parallel machines in a fixed planning horizon.…”

Section: Multiproject Scheduling and Resource Managementmentioning

confidence: 99%

See 1 more Smart Citation

A multiproject scheduling and resource management model in projects construction

Cheraghi

Zohrehvandi

Vanhoucke

et al. 2022

ECAM

View full text Add to dashboard Cite

PurposeThis research presents a multiproject scheduling and resource management (MPSRM) model that includes an M/M/c/n queue system, a p-hub median model, a parallel machine scheduling and a hub location problem solution method. This research aims to design a project network and then sequence raw materials delivery to hub factories.Design/methodology/approachThis research is implemented as a case study in construction and industrial company. It considers several mines to supply raw materials for production in hub factories. In the sequencing phase, a parallel mine scheduling problem specifies the delivery of raw materials to hub factories. Furthermore, a multisource project scheduling model is studied and designed for building project-oriented companies.FindingsThe results suggest that the proposed MPSRM model in this study significantly reduces project transportation costs. Therefore, creating accurate planning in projects, especially in construction projects where transportation plays an important role, can help reduce time and costs. Researchers, project managers and those dealing with projects can use this model to exploit their projects.Research limitations/implicationsWhen the number of construction projects in a construction company increases in a region, choosing an appropriate strategy to supply resources and raw materials becomes very important in terms of profit and loss and project completion as scheduled. An increase in transportation demands alongside the economic development on the one hand and providing competitive transportation services, on the other hand, have increasingly spotlighted the significance of hub networks in transportation systems. In addition, in this research, there is no suitable access road from the mines to the project sites, and considering the workload, if any of the projects become a hub, a proper road should be built.Originality/valueTo the best of the knowledge, there is no outstanding research in which a p-hub median location problem by considering queuing model and different transportation modes and a parallel machine scheduling problem are studied simultaneously.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Multiproject Scheduling and Resource Managementmentioning

confidence: 99%

A multiproject scheduling and resource management model in projects construction

Cheraghi

Zohrehvandi

Vanhoucke

et al. 2022

ECAM

View full text Add to dashboard Cite

show abstract

“…Other studies have validated DeJong's learning model, e.g., Okolowski and Gawiejnowicz [6] and Ji et al [7,8]. More recent papers which have considered scheduling problems with learning effect include studies of Wang and Wang and Wang [9,10]; Wang et al [11]; Xu et al [12]; Chen et al [13]; Toksari and Arik [14]; Bai et al [15]; Mustu and Eren [16]; Pei et al [17]; Zhang et al [18]; Wang and Wang [19,20]; Wang et al [21], and Przybylski [22].…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

Sun

Lei

Huo

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, we introduce a group scheduling model with time-dependent and position-dependent DeJong's learning effect. e objectives of scheduling problems are to minimize makespan, the total completion time, and the total weighted completion time, respectively. We show that the problems remain solvable in polynomial time under the proposed model.Example 2. As seen in Example 1, we change the objective to the total completion time minimization. By Algorithm 2, we solve the problem as follows:Step 1: In group G 1 , the optimal job sequence is J 11 ⟶ J 12 . In group G 2 , the optimal job sequence is J 23 ⟶ J 22 ⟶ J 21 .Step 2: To groups G 1 and G 2 , calculate

show abstract

“…Considering the effect of learning alone on scheduling problems, one can refer to the articles of Lee et al [13] by examining the problem of uniform parallel machine scheduling; Goli et al [14] considered both the effect of learning and the delivery time of goods; Przybylski [15] considered the effect of integral learning; Expósito-Izquierdo et al [16] noted the addition of sequence-dependent setup times. Taking into account both the learning effect and the effect of degradation in the deterministic environment, Toksarı and Güner [17] investigated the problem of identical parallel machine scheduling with the aim of minimizing ET and nonlinear degradation coefficients and common delivery times.…”

Section: Introductionmentioning

confidence: 99%

A New Approach Based on the Learning Effect for Sequence‐Dependent Parallel Machine Scheduling Problem under Uncertainty

Ebrahimi

Dinari

Samaei

et al. 2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Production system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing tasks in the production system. In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. As one of the most important assumptions, the sequence-dependent setup time is of concern. In other words, jobs are processed sequentially, and there is a sequence-dependent setup time. Moreover, the processing time and delivery due date are considered uncertain, and a fuzzy conversion method is used to deal with this uncertainty. The proposed mathematical model is a multiobjective one and tries to minimize speed and completion time. In order to optimize this mathematical model, the genetic algorithm (GA) and variable neighborhood search (VNS) algorithms have been used. A new hybrid algorithm has also been developed for this problem. The results show that the hybrid algorithm can provide more substantial results than classical algorithms. Moreover, it is revealed that a large percentage of Pareto solutions in the proposed algorithm have a generation time of more than 80% of the algorithm’s execution time.

show abstract

A new model of parallel-machine scheduling with integral-based learning effect

Cited by 21 publications

References 19 publications

A multiproject scheduling and resource management model in projects construction

A multiproject scheduling and resource management model in projects construction

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

A New Approach Based on the Learning Effect for Sequence‐Dependent Parallel Machine Scheduling Problem under Uncertainty

Contact Info

Product

Resources

About