Simulation for manufacturing system design and operation: Literature review and analysis

Negahban, Ashkan; Smith, Jeffrey S.

doi:10.1016/j.jmsy.2013.12.007

Cited by 440 publications

(228 citation statements)

References 242 publications

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“…In this context, the introduction of uncertainty with simulation makes the situation closer to reality and with the definition of robustness it is possible to achieve a solution that might be not mathematically optimal but more resilient to disruption in the real system. DES has been widely used in many fields, from manufacturing to transportation, already since the last decades, and it has been proven to be useful for what-if analysis, scenario testing, and as a decision support tool (Banks 1998, Brunner, Cross, McGhee, Levis, and Whitney 1998, Negahban and Smith 2014. In DES, time passes according to the occurrence of specific events and the sequence of actions that the entities make are already known in advance.…”

Section: Simulation Environmentmentioning

confidence: 99%

A down to earth solution: Applying a robust simulation-optimization approach to resolve aviation problems

Scala¹,

Mujica²,

Delahaye³

2017

2017 Winter Simulation Conference (WSC)

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To cite this version:Paolo Scala, Daniel Delahaye, Miguel Mujica. A down to earth solution: applying a robust simulationoptimization approach to resolve aviation problems. ABSTRACTThis paper deals with the improvement of the robustness of heuristic solutions for aviation systems affected by uncertainty when the resolution of conflicts is implemented. A framework that includes the use of optimization and simulation is described which in turn generates pseudo-optimal schedules. The initial solution is progressively improved by iteratively evaluating the uncertainty in the generated solutions and calibrating in accordance with the objective function. Simulation is used for testing the feasibility of a solution generated by an optimization algorithm in an environment characterized by uncertainty. The results show that the methodology is able to improve solutions for the scenarios with uncertainty, thus making them excellent candidates for being implemented in real environments.

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Section: Simulation Environmentmentioning

confidence: 99%

A down to earth solution: Applying a robust simulation-optimization approach to resolve aviation problems

Scala¹,

Mujica²,

Delahaye³

2017

2017 Winter Simulation Conference (WSC)

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“…Applicability of systematic studies to operational fields has been recently acknowledged in the manufacturing [9] and industrial software processes [10]. With respect to the scheduling field, a SLR has been conducted for staffing and scheduling problems in software projects, where 52 papers have been extracted and analyzed with the purpose of identifying main issues in adopting resource scheduling features in industry.…”

Section: A Systematic Mapping and Implications In Schedulingmentioning

confidence: 99%

Heuristic Optimization for the Resource Constrained Project Scheduling Problem: a Systematic Mapping

Ciupe

Meza

Orza

2016

Annals of Computer Science and Information Systems

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“…However, given the complexity and importance of material handling systems (MHS) for their impact on the performance of MS [8], it is necessary to go a step further with data-driven simulation. For this purpose, in this paper we suggest the conception of simulation models as a 4-layer architecture (i.e.…”

Section: Introductionmentioning

confidence: 99%

Using 4-Layer Architecture to Simulate Product and Information Flows in Manufacturing Systems

Saez-Mas¹,

García-Sabater²,

Morant-Llorca³

2018

Int. j. simul. model.

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This work illustrates the application of novel simulation architecture with two case studies where the proposed architecture, the so-called 4-layer, allowed us to address the complexity of the analysed systems. The fundamental objective of this work is to show the structure of layers, how layers interact with one another and with the user, and what benefits this separation proposes. The first case study deals with moving car bodies from the paint plant to the assembly line through a sequencing system that involves distributed decision-making processes in an ASRS. The second case study focuses on analysing a layout of a section used to assemble the engine and transmission set, where the quality of the material flow is evaluated. The work highlights some of the advantages of modelling with 4-layer architecture, and explains the key processes that connect different elements.

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Simulation for manufacturing system design and operation: Literature review and analysis

Cited by 440 publications

References 242 publications

A down to earth solution: Applying a robust simulation-optimization approach to resolve aviation problems

A down to earth solution: Applying a robust simulation-optimization approach to resolve aviation problems

Heuristic Optimization for the Resource Constrained Project Scheduling Problem: a Systematic Mapping

Using 4-Layer Architecture to Simulate Product and Information Flows in Manufacturing Systems

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