A survey on simulation optimization for the manufacturing system operation

Liu, Ran; Xie, Xiaolei; Yu, Kecheng; Hu, Qiaoyu

doi:10.1080/02286203.2017.1401418

Cited by 43 publications

(19 citation statements)

References 98 publications

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“…Because of the discrete input variables, single objective function, stochastic nature of processing times used in building the simulation model, and objective function has factor (machine number and helper number) interaction, this kind of optimization problem is known as nonlinear stochastic and single objective optimization with discrete control values. 15,50 An optimization process was executed with automatic termination of approximately 100 simulations. Each simulation presented feasible optimal solutions as shown in Figure 11.…”

Section: Optquest Optimizationmentioning

confidence: 99%

“…Therefore, SBO is widely used in evaluating complex systems, and optimizing responses for manufacturing problems. 15 The distinctive hurdles for garment manufacturers are long production lead time, bottlenecking, and low productivity. Moreover, sewing or assembly line is the most critical phenomenon of garment manufacturing as it generally composes of huge number of operations and resources.…”

Section: Introductionmentioning

confidence: 99%

“…However, most practical manufacturing systems are extremely complex that finding optimal decision variables analytically are extremely difficult. Therefore, SBO is widely used in evaluating complex systems, and optimizing responses for manufacturing problems 15 …”

Section: Introductionmentioning

confidence: 99%

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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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Section: Optquest Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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“…Liu et al [22] compiled an overview of different optimization approaches for solving manufacturing planning and scheduling problems. They listed numerous global and local optimization methods, along with application examples and associated constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Ištoković¹,

Perinić²,

Doboviček³

et al. 2019

Adv produc engineer manag

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The problems of determining the order and size of the product batches in the flow shop with multiple processors (FSMP) and sequence-dependent setup times are among the most difficult manufacturing planning tasks. In today's environment, where necessity for survival in the market is to deliver the goods in time, it is crucial to optimize production plans. Inspired by real sector manufacturing system, this paper demonstrates the discrete event simulation (DES) supported by the genetic algorithm (GA) optimization tool. The main aim is to develop the simulation framework as a support for the daily planning of manufacturing with emphasis on determining the size and entry order of the product batches within specific requirements. Procedures are developed within the genetic algorithm, which are implemented in Tecnomatix Plant Simulation software package. A genetic algorithm was used to optimize mean flow time (MFT) and total setup time (TST) performance measures. Primary constraint for on-time delivery was imposed on the model. The research results show that solutions are industrially applicable and provide accurate information on the batch size of the defined products, as well as a detailed schedule and timing of entry into the observed system. Display of the solution, in a simple and concise manner, serves as a tool for manufacturing operations planning.

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“…In particular, two different optimisation packages have been applied there to one small practical problem and the obtained results have been promising enough to conclude that the practice of simulation-based optimisation would grow significantly. This prophecy has fulfilled since and especially recent years yielded in numerous related publications [13]. For example, in [8], a simulation model has been built using a discrete-event simulation software named Anylogic [14], a commercial tool to simulate complex business systems.…”

Section: Related Workmentioning

confidence: 99%

Implementing Digital Twins of Smart Factories with Interval Algebra

Dziurzański

Swan

Indrusiak

et al. 2019

2019 IEEE International Conference on Industrial Technology (ICIT)

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This paper presents an interval algebra that can be used to create an analytic description of a smart factory. Such a description (recently termed a 'digital twin' of the factory) is used to evaluate alternative manufacturing configurations as part of a search-based optimisation process. Several extensions are proposed to the interval algebra for specifying smart factory production line details. A number of real-life manufacturing scenarios are described, related to Wire-cut Electrical Discharge Machining. The experimental results show the applicability and scalability of the proposed method.

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A survey on simulation optimization for the manufacturing system operation

Cited by 43 publications

References 98 publications

A complex garment assembly line balancing using simulation‐based optimization

A complex garment assembly line balancing using simulation‐based optimization

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Implementing Digital Twins of Smart Factories with Interval Algebra

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