A simulation–optimization strategy to deal simultaneously with tens of decision variables and multiple performance measures in manufacturing

Niño-Pérez, Esmeralda; Mendez-Vazquez, Yaileen M.; Arias-González, Dick E.; Cabrera-Ríos, Mauricio

doi:10.1057/s41273-017-0056-y

Cited by 5 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the combination of simulation and optimization is highly imperative to find an optimal set of values for certain input variables [14][15]. This is called simulation-based optimization which allows a decision-maker to systematically search a large decision space for an optimal or near-optimal system design without being constrained to a few pre-specified alternatives [16][17].…”

Section: Simulation Techniques In Manufacturingmentioning

confidence: 99%

“…By combining various resource constraints and cost factors, project managers can estimate the impact of different scenarios on project expenses. This simulation-based optimization technique is particularly beneficial for optimizing resource allocation and detecting potential bottlenecks that may inflate costs [17].…”

Section: Discrete Event Simulationmentioning

confidence: 99%

“…In order to further increase production processes, other optimization techniques such as Lean manufacturing can be combined with Just-in-Time [17].…”

Section: Just-in-timementioning

confidence: 99%

“…Simulation-based optimization and modelling has been identified as one of the best effective means of finding solution to problems encountered in production dynamic scheduling. In order to minimize production time and ensure optimization of task implementation plan, the combination of simulation-based optimization with lean production in manufacturing system has been the most effective [17]. For the selection of process improvement ideas, discrete event simulation is the best technique to be adopted.…”

Section: Scheduling and Sequencingmentioning

confidence: 99%

See 3 more Smart Citations

Process Optimization in Manufacturing Industries Using Simulation Technologies

Okuyelu,

Doskenov,

Aribike

2024

CJAST

View full text Add to dashboard Cite

The industrial application and importance of simulation technologies for process optimization in manufacturing industries cannot be underestimated. Adequate monitoring of raw materials processing to finished products and their conveyance to the end users is required together with profits and cost optimization to ensure the company attained the set goals. Thus, it is imperative to adopt the use of simulation-based optimization tools. This paper succinctly discussed the importance of simulation-based optimization technologies as effective techniques in manufacturing industries. The concept of process optimization, simulation and simulation techniques as related to manufacturing were discussed. Various techniques of simulation-based optimization in manufacturing were presented. These include Monte Carlo simulation, discrete event simulation and system dynamics. Also, typical examples of manufacturing optimization techniques were stated. These include Just-in-Time, statistical process control, total quality management, six sigma, value stream mapping and computer numerical control. Various applicable areas of simulation-based optimization technologies in manufacturing were also addressed. However, resistance to change, lack of expertise, lack of data, unintended consequences, over-reliance on technology and insufficient testing are the associated problems to simulation-based optimization in manufacturing. In conclusion, relevant simulation-based optimization technologies should be adopted by various manufacturing companies to ensure profit maximization while still keeping the chain between production of good and supply to customers intact.

show abstract

Section: Simulation Techniques In Manufacturingmentioning

confidence: 99%

Section: Discrete Event Simulationmentioning

confidence: 99%

“…In order to further increase production processes, other optimization techniques such as Lean manufacturing can be combined with Just-in-Time [17].…”

Section: Just-in-timementioning

confidence: 99%

Section: Scheduling and Sequencingmentioning

confidence: 99%

See 2 more Smart Citations

Process Optimization in Manufacturing Industries Using Simulation Technologies

Okuyelu,

Doskenov,

Aribike

2024

CJAST

View full text Add to dashboard Cite

show abstract

“…However, despite the success shown by simulation and optimization techniques, researchers have shown their shortcomings when employed separately. On the one hand, as the complexity of a system and its decision variables increase, simulation techniques become impractical [17,18]. On the other hand, most RMS optimization studies have simplified the problem by disregarding the variability and stochasticity of the systems.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

et al. 2023

View full text Add to dashboard Cite

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

show abstract

Simulation-Based Multi-Objective Optimization for Reconfigurable Manufacturing System Configurations Analysis

Díaz

Aslam

et al. 2020

2020 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

A simulation–optimization strategy to deal simultaneously with tens of decision variables and multiple performance measures in manufacturing

Cited by 5 publications

References 21 publications

Process Optimization in Manufacturing Industries Using Simulation Technologies

Process Optimization in Manufacturing Industries Using Simulation Technologies

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

Simulation-Based Multi-Objective Optimization for Reconfigurable Manufacturing System Configurations Analysis

Contact Info

Product

Resources

About