One of the most important solutions for dimensionality reduction in data preprocessing, and improving classification performance is gene selection in microarray data since they usually have several thousand genes with very few samples. Because of these characteristics, the complexity of classification models increases and their efficiency decreases. The gene selection problem inherently pursues two goals: reducing the number of genes and increasing the classification efficiency. Therefore, this paper presents a novel hybrid filter-wrapper solution based on the Fisher-score method and Multi-Objective Forest Optimization Algorithm (MOFOA). In the proposed method, as a preprocessing step, the Fisher-score method selects 500 discriminative genes by removing redundant/irrelevant genes. Then, MOFOA searches to find the subset of optimal genes using concepts such as repository, crowding-distance, and binary tournament selection. Moreover, the proposed method solves the gene selection problem and, at the same time, optimizes the kernel parameters in the SVM classification model. Six microarray datasets were used to evaluate the performance of the proposed method. Afterward, a comparison was made between its results and those of the four multi-objective hybrid methods presented in the literature in terms of classification performance, the number of selected genes, running time, and hypervolume criteria. According to the results, in addition to selecting fewer genes, the proposed solution has achieved greater classification accuracy in most cases and has been able to obtain a performance similar to or better than that of other multi-objective gene selection approaches.

Hub networks play a crucial role in optimizing transportation costs in air and road systems. Their main objective is to strategically locate hubs and allocate non-hub nodes within the network. The modular hub location problem is a specific area of hub network design that focuses on accurately calculating transportation costs, considering factors like trip numbers and capacity constraints in network routes. This study proposes a mixed-integer programming model to address the modular hub location problem with multiple allocations. It considers dependent and independent costs associated with vehicles per trip between hub network routes, considering specific vehicle capacities. Two datasets are utilized for validation: the CAB dataset representing 25 nodes of US airports and the TR dataset representing the Turkish transportation system with 81 nodes. To tackle the NP-hard nature of hub location models and the computational complexity of the proposed model, two solutions are developed. Firstly, a novel LP relaxation-based method using GAMS software provides near-optimal solutions for medium-sized instances. Additionally, a Genetic Algorithm (GA) implemented in MATLAB handles larger instances. The GA's efficiency is enhanced by tuning its parameters using the Taguchi method. Results analysis shows that both proposed algorithms yield high-quality solutions within significantly reduced timeframes compared to the CPLEX solver in GAMS software. The LP relaxation-based method performs well for medium-sized instances, while the GA approach is efficient for larger instances after parameter tuning with the Taguchi method.

With the increase of construction activities in Ghana, there is an increasing demand in building materials leading to the shortage of the conventional materials. The informal sector is gradually seeing the introduction of quarry dust as a substitute of sand in block production. This paper focuses on a comparative analysis of the quality of sandcrete blocks and quarry dust cement blocks. Block samples were gathered from various suppliers around the Prampram and Dawhenya areas and through various laboratory tests were tested for their dimension tolerance, water absorption and compressive strengths. Aggregate samples were also taken from suppliers for sieve analyses. The study revealed that the quarry dust cement blocks contained relatively higher percentages of coarse grade particles compared to the sandcrete blocks. The total average water absorption of sandcrete blocks was found to be 3.90% while quarry dust showed an improved value of 3.28%. Sandcrete blocks were averagely found to be of a higher compressive strength of 4.31N/mm2, with quarry dust at 3.0N/mm2. The study suggested the likelihood of a lesser use of cement in the production of quarry dust cement blocks due to the similarities in colour between the quarry dust and cement, hence, negatively affecting its compressive strength.

In recent years, many studies on the multi-agent scheduling problems in which agents compete for using the shared resources, have been performed. However, relatively few studies have been undertaken in the field of the multi-agent scheduling in a flowshop environment. To bridge the gap, this paper aims at addressing the two-agent scheduling problem in a two-machine flowshop. Because of the importance of delay penalties and efficient resource utilization in many manufacturing environments, the objective is to find an optimal schedule which has the minimum total tardiness for the first agent’s jobs, under the makespan limitation for the second agent. Since this problem is strongly NP-hard, several theorems and properties of the problem are proposed to apply in exact and meta-heuristic methods. Also, for some instances of the problem for which exact methods cannot achieve optimal solutions in a reasonable amount of time, a tabu search algorithm is developed to achieve near-optimal solutions. Computational results of the tabu search algorithm show that the average absolute error value is lower than 0.18 percent for instances with 20 to 60 jobs in size.

Multi-purpose forest fire fighting vehicles should carry a set of firefighting equipment such as high-pressure water pumps, tree cutters to create a fire isolation corridor, vacuum, and blowing machine high-speed wind sandblasting devices etc. The bumpy road and velocity of moving vehicles can affect the vibration response of these vehicles. Hence, in this research the vibration analysis of a Multi-purpose forest fire fighting vehicle mounted on a URAL4320 active three-wheel drive vehicle is simulated and analyzed. The effect of road bumping and speed of vehicle on the vibration parameters are studied via building mathematical models with 19 degree of freedom for simulating the suspension system of the investigated vehicle.

This paper considered a case of supplier selection problem in a glove manufacturer located at Yogyakarta, Indonesia that uses genuine sheep leather as the raw material. The problem is solved using both Analytical Hierarchy Process (AHP) and Fuzzy AHP, in which three versions of Fuzzy AHP are applied i.e. Extent Analysis proposed by Chang (1996) [Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.], Extent Analysis proposed by Wang (2008) [Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research,186(2), 735-747.], and the modified Fuzzy LLSM proposed by Wang (2006) [Wang, Y. M., Elhag, T. M., & Hua, Z. (2006). A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets and Systems, 157(23), 3055-3071.]. Moreover, the research is conducted by incorporated four expert respondents, who have more than 12 years of experience in the problem. It is found that the top four priorities obtained from AHP are similar with those from Fuzzy AHP with Extent Analysis proposed by Chang (1996) and Fuzzy AHP with the modified Fuzzy LLSM proposed by Wang (2006). This priority list of supplier can be used by the manufacturer to select the raw material supplier.

The basic aim of this study was to find a relationship between heat input and mechanical properties of high strength low alloy steel (HSLA) welded joints and also elaborate its effect on microstructure. The combined effect of welding current, voltage and speed i.e. Heat Input on mechanical properties of High Strength Low Alloy Steel (ASTM A242 type-II) weldments have been studied in the present work. HSLA steel work pieces were welded by Gas metal arc welding (GMAW) process under varying welding current, arc voltage, and welding speed. Total nine samples were prepared at different heat input level i.e. 1.872 kJ/mm, 1.9333 kJ/mm, 2.0114 kJ/mm, 2.1 kJ/mm, 2.1956 kJ/mm, 2.296 kJ/mm, 2.4 kJ/mm, 2.5067 kJ/mm and 2.6154 kJ/mm It was observed that as heat input increases the ultimate tensile strength and microhardness of weldment decreased while impact strength increased and it was also observed that on increasing the heat input grain size of microstructure tends to coarsening it is only due to decreasing in cooling rate.

In this article, we propose Simulated Annealing (SA) heuristic to solve Unequal Area Dynamic Facility Layout Problem (FBS) with Flexible Bay Structure (UA-DFLPs with FBS). The UA-DFLP with FBS is the problem of determining the facilities dimension and their location coordinates with flexible bays formation in the layout for various periods of the planning horizon. The UA-DFLP with FBS is more constrained than general UA-DFLP and it is an NP-complete problem. The proposed SA is tested with the available UA-DFLPs instances in the literature. The proposed SA heuristic has given new best solution or the same solution for FBS based problems as compared with the best-known reported in the UA-DFLPs with FBS literature. The proposed SA heuristic is also tested on standard UA-DFLPs used in non-FBS approaches. The SA heuristic solution is not significantly different from the best solution reported in the literature for non-FBS approaches. Equal area DFLP instances are also solved with the proposed SA and the results obtained are promising with the solutions reported in the literature. Hence the results obtained indicate that the proposed SA for UA-DFLP with FBS is effective and versatile for both equal and unequal area dynamic facility layout problems. The computational efficiency of the proposed SA heuristic is very much competitive as compared to other meta-heuristics computational timings reported in the literature.

In the search for the solution to energy storage problems, this study investigates the interfacial energy interaction and temperature stability of the composites made of polypyrrole-graphene-borophene (PPy-Gr-Bon) by using molecular dynamics simulations. From the calculated thermodynamics and interfacial energies of the system, comparisons between the ternary and the binary-binary systems were made. The materials in the entity show a good degree of temperature stability to a dynamic process at 300, 350, 400, and 450 K. Moreso, at 300 K, the interaction energy of PPy-Gr, PPy-Bon, and PPy-Gr-Bon are: -5.621e3 kcal/mol, -26.094e3 kcal/mol, and -28.206e3 kcal/mol respectively. The temperature stability of the systems is in the order of: PPy-Gr-Bon > PPy-Bon > PPy-Gr. The effect of temperature on the interaction energy of the systems was also investigated. The ternary system showed higher stability as the temperature increased. In addition, the radial distribution function computed for the three systems revealed that there is a strong, but non-chemical bonding interaction between PPy-Gr-Bon, Bon-PPy, and Gr-PPy. By considering the excellent mechanical properties of PPy-Gr-Bon and the already established high electrical conductivity and chemical stability of Gr, Bon and PPy, their composite is therefore suggested to be considered for the manufacturing of electrochemical electrodes.

The main goal of this study is to investigate the classification capability of several machine learning (ML) techniques, including decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for predicting purchase decisions. The application case is related to consumer purchase decisions of domestic goods in the context of Vietnam. Firstly, factors influencing Vietnamese consumers’ purchase decision of domestic products were identified. Then, data from 240 consumers in Vietnam were collected. Different classifying models based on ML techniques were developed to analyse the sampling data after the performances of the models were evaluated and compared using confusion matrix, accuracy rate and several error indexes. The results indicate that the DT(J48) obtained the highest performance with the corrected prediction percentage of 91.6667%. The findings also show that machine-learning techniques can be used to explicitly in forecasting Vietnamese consumers’ purchase behaviour.