A novel Iterative Optimization-based Simulation (IOS) framework: An effective tool to optimize system’s performance

Dehghanimohammadabadi, Mohammad; Keyser, Thomas K.; Cheraghi, S. Hossein

doi:10.1016/j.cie.2017.06.037

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…In [118], authors have developed an Iterative Optimization Simulation (IOS) framework where optimization is embedded into a simulation system. The framework integrates a simulation manager, optimization manager, and a database manager to transfer data between the latter.…”

Section: Operational Decision Planningmentioning

confidence: 99%

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Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

2020

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Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored.

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Section: Operational Decision Planningmentioning

confidence: 99%

“…However, they differ by the fact that optimization doesn't need simulation to evaluate the solutions generated. IOS was successfully applied by [118] for scheduling problems.…”

Section: -Solution Completion By Simulation (Scs)mentioning

confidence: 99%

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

2020

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“…SBO is also known as simulation optimization, black box optimization, parametric optimization, stochastic optimization, or optimization via simulation 50 . It is a design approach that can be used to generate a number of scenarios from a probabilistic or deterministic model and then select the best alternative solution by applying scheduling decisions and aggressive search approaches to these scenarios to obtain the best solution 11 . SBO is a combination or integration of simulation and optimization/metaheuristic techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Simulation-based optimization (SBO) is the state-of-the-art design technique that combines both simulation and optimization techniques. 11 The application and development of this method is increasing drastically as it is one of the key disruptive technologies shaping the era of the fourth industrial revolution (industry 4.0). 12 The apparel industries are the late-comers adopting disruptive technologies.…”

Section: Introductionmentioning

confidence: 99%

A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is important for the apparel industry to be flexible enough to respond quickly to the unstable customers' demand through continuous improvement of their process efficiency and productivity. This study proposed assembly line balancing problem (ALBP) for complex garment assembly line using simulation-based optimization under stochastic task times. The proposed ALBP solution approach aimed at minimizing the cycle time for a given number of workstations with consideration of constraints on number of resources, precedence relations, and resource types. The empirical study was conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 69 workstations was considered. The discrete event simulation of the trouser assembly line was developed using Arena simulation software. The local optimal solution was obtained from simulation experiments which was adopted for the optimization process. The OptQuest tool was used to solve a single objective optimization with discrete control values. The results showed that the average throughput increased by 30% for local optimal line balancing and 55% for global optimal line balancing. Consequently, the cycle time reduced by 23% and 36%, respectively.

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“…The interactions between different parts of the prototype are illustrated in Figure 3. [45] and Dehghanimohammadabadi and Keyser [46]. CallMATLAB can run a MATLAB script during simulation runs, and the simulation pauses until the MATLAB script is finished.…”

Section: Prototypementioning

confidence: 99%

Concrete Construction: How to Explore Environmental and Economic Sustainability in Cold Climates

Chen

Olofsson

et al. 2020

Sustainability

Self Cite

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In many cold regions around the world, such as northern China and the Nordic countries, on-site concrete is often cured in cold weather conditions. To protect the concrete from freezing or excessively long maturation during the hardening process, contractors use curing measures. Different types of curing measures have different effects on construction duration, cost, and greenhouse gas emissions. Thus, to maximize their sustainability and financial benefits, contractors need to select the appropriate curing measures against different weather conditions. However, there is still a lack of efficient decision support tools for selecting the optimal curing measures, considering the temperature conditions and effects on construction performance. Therefore, the aim of this study was to develop a Modeling-Automation-Decision Support (MADS) framework and tool to help contractors select curing measures to optimize performance in terms of duration, cost, and CO2 emissions under prevailing temperatures. The developed framework combines a concrete maturity analysis (CMA) tool, a discrete event simulation (DES), and a decision support module to select the best curing measures. The CMA tool calculates the duration of concrete curing needed to reach the required strength, based on the chosen curing measures and anticipated weather conditions. The DES simulates all construction activities to provide input for the CMA and uses the CMA results to evaluate construction performance. To analyze the effectiveness of the proposed framework, a software prototype was developed and tested on a case study in Sweden. The results show that the developed framework can efficiently propose solutions that significantly reduce curing duration and CO2 emissions.

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A novel Iterative Optimization-based Simulation (IOS) framework: An effective tool to optimize system’s performance

Cited by 19 publications

References 32 publications

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

A complex garment assembly line balancing using simulation‐based optimization

Concrete Construction: How to Explore Environmental and Economic Sustainability in Cold Climates

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