An ANN-based optimization model for facility layout problem using simulation Technique

Abstract

Abstract. A real manufacturing system faces lots of real-world situations, such as stochastic behaviors; the lack of attention to this issue is noticeable in the previous research. The aim of this paper is to nd the optimum layout and the most appropriate handling transporters for the problem by a novel solving algorithm. The new model contains two objective functions including the Material Handling Costs (MHC) and the complication time of jobs (makespan). Real-world situations such as stochastic processing times, random breakdowns, and cross tra cs among transporters are considered in this paper. Several experiment designs have been produced using DOE technique in simulation software and an Arti cial Neural Network (ANN) as a meta-model is used to estimate the objective functions in the metaheuristic algorithms. A hybrid non-dominated sorting genetic algorithm (H-NSGA-II) is applied for the optimization task. The proposed methodology is evaluated through a real case study. First, simulation model is validated by comparing it with a real data set. Then, the prediction performance of ANN is investigated. Finally, the ability of H-NSGA-II in searching the solution space is compared with the traditional NSGA-II. The results show that the proposed approach, combing simulation, ANN, and H-NSGA-II, provides promising solutions for practical applications.

Bibliometric Study on the Use of Machine Learning as Resolution Technique for Facility Layout Problems

“…We found that for these approaches, the key functionality of the ANN used is the capability to represent non-linear relationships to compute input parameters for the construction or optimization algorithms in use. Tam and Tong [67] and Azimi and Soofi [68] both present solutions that make use of Genetic Algorithms and Neural Networks. In [67], a simple back-propagation network (five input nodes, one fully connected hidden layer, one output node) predicts hoisting times (supply and return) of tower cranes on construction sites.…”

“…Azimi and Soofi [68] make use of ANNs to estimate the make-span of production jobs in different layout scenarios: a number of different layouts is generated and the makespan is determined using discrete-event simulation. These input-output pairs, with the input being a vector of machine locations and transporter allocations, are used to train an ANN to approximate the objective function.…”

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

“…Tweet texts are usually lacking a formal writing standard and because of that each text is purified by implementing the steps in Table 1 to create a sounder model [ 30 , 31 ]. Purpose of the data preprocessing is to achieve more sensible results by decreasing the size of feature [ 32 – 34 ].…”

A Simulation Based Approach for Efficient Yard Planning in a Container Port

“…Discharge yard planning based on arrival dates of vessels as in blocks is considered to be an appropriate approach for both optimal yard use and reducing handling operations. Simulation is effectively utilized in order to evaluate different scenarios for layout problems as shown in the study of Azimi and Soofi [23]. In this study, simulation has been utilized as a decision support system in order to determine suitable yard layout in a container port.…”