One year ago, the coronavirus (COVID-19) virus began spreading around the world. Vaccines have now been developed by a variety companies from different countries.Vaccines must be stored in good condition within cold boxes designed to keep vaccines within acceptable temperature ranges. This paper proposes a hybrid algorithm based on a chemical reaction called Hybrid Artificial Chemical Reaction Optimization Algorithm (HACROA) which has been used to design a vaccine cold chain network in northern Thailand. The scope of this research is to focus on the Office of Disease Prevention and Control Area 1 (Chiang Mai, Thailand). The objective of this research is to rearrange routes to minimize total travel distances. Experiments were conducted to compare the efficiency of the hybrid algorithm with other algorithms in term of the distance. The optimal parameters of the algorithm employed a statistical experiment theory namely full factorial design. Experiment design and analysis were adopted to investigate the factors that affect the performance of this algorithm. HACROA was able to rearrange routes and found a better solution than the other algorithms.

Abstract:Purpose: The goal of this paper is to develop a pragmatic system of a production throughput forecasting system for an automated test operation in a hard drive manufacturing plant. The accurate forecasting result is necessary for the management team to response to any changes in the production processes and the resources allocations.Design/methodology/approach: In this study, we design a production throughput forecasting system in an automated test operation in hard drive manufacturing plant. The proposed system consists of three main stages. In the first stage, a mutual information method was adopted for selecting the relevant inputs into the forecasting model. In the second stage, a generalized regression neural network (GRNN) was implemented in the forecasting model development phase. Finally, forecasting accuracy was improved by searching the optimal smoothing parameter which selected from comparisons result among three optimization algorithms: particle swarm optimization (PSO), unrestricted search optimization (USO) and interval halving optimization (IHO).Findings: The experimental result shows that (1) the developed production throughput forecasting system using GRNN is able to provide forecasted results close to actual values, and to projected the future trends of production throughput in an automated hard disk drive test -330-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1464 operation; (2) IHO algorithm performed as appropriate optimization method better than the other two algorithms. (3) Compared with current forecasting system in manufacturing, the results show that the proposed system's performance is superior to the current system in prediction accuracy and suitable for real-world application. Originality/value:The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. The production throughput forecasting result is useful information for management team to respond to any changes in production processes and resources allocation. However, a practical forecasting system for production throughput has not been described in detail yet. The experiments were conducted on a real data set from the final testing operation of hard disk drive manufacturing factory by using Visual Basic Application on Microsoft Excel© to develop preliminary forecasting system for testing and verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system.

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