Simulation-Based Multi-Objective Optimization of a Real-World Scheduling Problem

Persson,; Grimm,; Ng, Tsan Sheng; Lezama, A.; Ekberg,; Falk, Volkmar; Stablum,

doi:10.1109/wsc.2006.322952

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…This is essentially taking the discrete derivative, or "slope" of the function for each separate dimensional input parameter, denoted by i [2,4,8]. However, if the simulation output is noisy, then the gradient estimation could also be noisy, possibly making the decision point move in an inappropriate direction [4,7]. It is clear that simulations with a higher number of variable input parameters will require more calculations to appraisal the slope.…”

Section: Gradient and Stochastic Methodsmentioning

confidence: 99%

A Review on Simulation Optimization

Ahmad¹

2017

IJAERS

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Abstract- I. INTRODUCTIONThe mathematical model of a system is concentrated on using simulation; it is known as a simulation model. System behavior at particular estimations of info factors is assessed by running the simulation model for a settled timeframe. A simulation experiment can be characterized as a test or a progression of tests in which significant changes are made to the information factors of a simulation model so that we may observe and recognize the purposes behind changes in the output variable(s). At the point when the quantity of information factors is huge and the simulation model is perplexing, the simulation experiment may turn out to be computationally restrictive. Other than the high computational cost, a much higher expense is brought about when imperfect info variable qualities are chosen. The way toward finding the best info variable qualities from among all potential outcomes without unequivocally evaluating each plausibility is simulation optimization. The goal of simulation optimization is minimizing the assets spent while amplifying the information acquired in a simulation experiment. A general simulation model comprises n input variables

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Section: Gradient and Stochastic Methodsmentioning

confidence: 99%

A Review on Simulation Optimization

Ahmad¹

2017

IJAERS

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“…In our work the performance of the BHS is the source that provides capacity constraint for resource requirement planning. Simulation-based multi-objective optimisation was applied to a scheduling problem in the postal service industry by Persson et al [2]. They modelled a mail sorting process, using the ARENA software package, and applied genetic algorithms to find an optimal mail schedule.…”

Section: Related Literaturementioning

confidence: 99%

Simulation-based Input Loading Condition Optimisation of Airport Baggage Handling Systems

Creighton

Nahavandi

2007

2007 IEEE Intelligent Transportation Systems Conference

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Abstract⎯ Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.

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“…Building on top of a component-based architecture for SO (Persson et al 2006), this paper presents and describes an on-line instrumentation component for a SO system ( Figure 3). This instrumentation component provides runtime visual monitoring, control, and analysis of the SO process.…”

Section: Figure 2: Traditional Black-box Simulation-based Optimizationmentioning

confidence: 99%

“…In short, the problem is to schedule mail batches on sorting machines at the Swedish Postal Services (see Persson et al 2006 for more information).…”

Section: Case Implementationmentioning

confidence: 99%

“…Building on top of a component-based architecture specifically designed for SO (Persson et al 2006), the online monitoring component that connects to the SO system is implemented in C++ using Microsoft Visual Studio .NET. Spreadsheet applications like Microsoft Excel that is commonly used for data input and output in simulation modeling may be used to develop the user interface of the instrumentation component.…”

Section: Case Implementationmentioning

confidence: 99%

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On-Line Instrumentation for Simulation-Based Optimization

Persson

Grimm

2006

Proceedings of the 2006 Winter Simulation Conference

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Traditionally, a simulation-based optimization (SO) system is designed as a black-box in which the internal details of the optimization process is hidden from the user and only the final optimization solutions are presented. As the complexity of the SO systems and the optimization problems to be solved increases, instrumentation -a technique for monitoring and controlling the SO processes -is becoming more important. This paper proposes a white-box approach by advocating the use of instrumentation components in SO systems, based on a component-based architecture. This paper argues that a number of advantages, including efficiency enhancement, gaining insight from the optimization trajectories and higher controllability of the SO processes, can be brought out by an on-line instrumentation approach. This argument is supported by the illustration of an instrumentation component developed for a SO system designed for solving real-world multi-objective operation scheduling problems.

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Simulation-Based Multi-Objective Optimization of a Real-World Scheduling Problem

Cited by 9 publications

References 9 publications

A Review on Simulation Optimization

A Review on Simulation Optimization

Simulation-based Input Loading Condition Optimisation of Airport Baggage Handling Systems

On-Line Instrumentation for Simulation-Based Optimization

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