Abstract: This paper presents the comparative study of coded
data methods for finding the benefit of concealing the natural data
which is the mercantile secret. Influential parameters of the number
of replicates (rep), treatment effects (τ) and standard deviation (σ)
against the efficiency of each transformation method are investigated.
The experimental data are generated via computer simulations under
the specified condition of the process with the completely
randomized design (CRD). Three ways of data transformation consist
of Box-Cox, arcsine and logit methods. The difference values of F
statistic between coded data and natural data (Fc-Fn) and hypothesis
testing results were determined. The experimental results indicate
that the Box-Cox results are significantly different from natural data
in cases of smaller levels of replicates and seem to be improper when
the parameter of minus lambda has been assigned. On the other hand,
arcsine and logit transformations are more robust and obviously,
provide more precise numerical results. In addition, the alternate
ways to select the lambda in the power transformation are also
offered to achieve much more appropriate outcomes.
Abstract: Various intelligences and inspirations have been
adopted into the iterative searching process called as meta-heuristics.
They intelligently perform the exploration and exploitation in the
solution domain space aiming to efficiently seek near optimal
solutions. In this work, the bee algorithm, inspired by the natural
foraging behaviour of honey bees, was adapted to find the near
optimal solutions of the transportation management system, dynamic
multi-zone dispatching. This problem prepares for an uncertainty and
changing customers- demand. In striving to remain competitive,
transportation system should therefore be flexible in order to cope
with the changes of customers- demand in terms of in-bound and outbound
goods and technological innovations. To remain higher service
level but lower cost management via the minimal imbalance scenario,
the rearrangement penalty of the area, in each zone, including time
periods are also included. However, the performance of the algorithm
depends on the appropriate parameters- setting and need to be
determined and analysed before its implementation. BEE parameters
are determined through the linear constrained response surface
optimisation or LCRSOM and weighted centroid modified simplex
methods or WCMSM. Experimental results were analysed in terms
of best solutions found so far, mean and standard deviation on the
imbalance values including the convergence of the solutions
obtained. It was found that the results obtained from the LCRSOM
were better than those using the WCMSM. However, the average
execution time of experimental run using the LCRSOM was longer
than those using the WCMSM. Finally a recommendation of proper
level settings of BEE parameters for some selected problem sizes is
given as a guideline for future applications.
Abstract: This study proposes a multi-response surface
optimization problem (MRSOP) for determining the proper choices
of a process parameter design (PPD) decision problem in a noisy
environment of a grease position process in an electronic industry.
The proposed models attempts to maximize dual process responses
on the mean of parts between failure on left and right processes. The
conventional modified simplex method and its hybridization of the
stochastic operator from the hunting search algorithm are applied to
determine the proper levels of controllable design parameters
affecting the quality performances. A numerical example
demonstrates the feasibility of applying the proposed model to the
PPD problem via two iterative methods. Its advantages are also
discussed. Numerical results demonstrate that the hybridization is
superior to the use of the conventional method. In this study, the
mean of parts between failure on left and right lines improve by
39.51%, approximately. All experimental data presented in this
research have been normalized to disguise actual performance
measures as raw data are considered to be confidential.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: This paper presents a comparative study of statistical methods for the multi-response surface optimization of a cryogenic freezing process. Taguchi design and analysis and steepest ascent methods based on the desirability function were conducted to ascertain the influential factors of a cryogenic freezing process and their optimal levels. The more preferable levels of the set point, exhaust fan speed, retention time and flow direction are set at -90oC, 20 Hz, 18 minutes and Counter Current, respectively. The overall desirability level is 0.7044.