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.

The forming steps by permanent deformation controlled by the tools generate a distribution of stresses inside the material which directly depends on the work hardening properties of the latter. The change in boundary conditions following the removal of the tools imposes the material to redistribute the stresses in the sections in a manner compatible with the new boundary conditions. This new distribution necessarily operates by local elastic deformations that result globally in a general change of shape called springback. This geometrical deviation can be minimized by the meticulous focus of the tools, but it cannot generally be completely annihilated due to the influence of several parameters. For this reason, the study of the influence of the different technological factors and physico-metallurgical parameters on the springback for the different metals is very important to design and properly realize forming tools. The main objective of this work is to find solutions to problems encountered in sheet metal forming such as the problem of springback. Our work has two essential purposes: the first is summarized in an experimental study based on theoretical analyses. To this end, much effort is made to add a new design of parts for a U-type stretch-bending device and adapt it to a tensile testing machine. This design has the advantage of modifying and assembling all parameters affecting springback at the same time and also of carrying out several forming processes on the same device. The second goal is the experimental and numerical prediction of springback, and the study of the effect of various stretch-bending process parameters such as punch velocity, the orientation of the sheet (anisotropy), hold time and punch-die clearance on springback behavior under heat treatment of aluminum alloy sheets with three different rolling directions (0°,45°,90°). A finite element (FE) model of stretch-bending has been established by utilizing ABAQUS/CAE software. From this analysis, it can be concluded that the springback is affected by the anisotropy of the sheet and the heat treatment in the stretch-bending process. The obtained experimental results were compared with the numerical simulations found in good agreement.

Nowadays, special attention has been paid to the environmental and social issues in both developed and developing countries. Therefore, research on the sustainable supply chain is greatly expanded dramatically over the years. In some industries like food industry, these issues are more significantly emphasized because of the particular characteristics the food products. In this paper, we considered a multi-objective model for designing the sustainable supply chain in the processed food industry with fixed shelf-life products. Model objectives include economic with profit maximization as an economic index, environmental with the index of carbon dioxide emissions and wastewater treatment in manufacturing sector and social objectives with maximizing the amount of jobs created as a social index. For the proposed model, in addition to determining the optimal location of the facility and the flow rate between facility, the type of delivery of products is determined either directly from plant or indirectly from distribution centers with mechanized transportation system. Finally, the model is implemented in a processed food industry as a case study in Iran and is solved via the augmented ε-constraint method.

A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch|Σ Cj ) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated.

This work is devoted to development of optimum recipes of high-strength concretes based on filled binders with fine-milled anthropogenic mineral filler intended for earthquake resistant high-rise monolithic construction. The optimum recipes of concretes in this work have been developed on the basis of computations and experimental designing of cast concrete mixes with chemical additives and anthropogenic mineral fillers, as well as destructive inspection methods as the most precise for analysis of physicomechanical and deformation properties of concrete. The following raw materials have been used for production of high-strength concretes: natural quartz sands with the fineness modulus F.M. = 1.7-1.8; crushed limestone with the particles sizes of 5-20 mm; water reducing chemical additives and hardening retarder to control specifications of concrete mixes; plain Portland cement, grade PTs 500 D0; anthropogenic mineral additives (fillers) in the form of crushed concrete and ceramic bricks. Optimum recipes of monolithic concretes have been designed using anthropogenic raw materials including normal concrete grades with compressive strength of M30-M40 and high-strength concrete grades of M50-M80, characterized by high homogeneity of cement stone with significantly finer pores and lower shrinkage. Herewith, it has been established that fine-milled anthropogenic mineral filler in the form of crushed concrete and ceramic bricks at the ratio of 70:30, respectively, efficiently influences specifications of concrete mixes on their basis significantly increasing resistance of the mix against sedimentation and water gain. It has been established that the developed high-strength concretes based on filled binders with fine-milled anthropogenic mineral filler are characterized by high freeze–thaw resistance (from F400 to F600) and water tightness (W14 and higher), which is a solid base providing high lifecycle of such concretes.

In the existing market, companies confront a fierce competition, so the need for new and efficient process for supply chain has become necessarily important. To this end, supply chain management among multi agent system is proposed for addressing the selection and evaluation process related to the inbound logistics. However, most of recent systems deal solely with the negotiation including the selection of one or multiple suppliers, without supporting the transportation provider selection simultaneously and consider it as decision criterion that affect the final choice of cooperative suppliers. As part of win-win negotiation, active supplier involvement can enhance efficiency and effectiveness of supply chain. Then again, transport cost constitutes the most important factor in the third of the total operational costs of a supply chain. To face this challenge, a new form of supplier selection including transportation provider selection is proposed. For this purpose, we present a multi-issue decision protocol based on ontology to support the negotiation between upstream nodes of supply chain in the proposed multi agent system. Furthermore, the automated multi-criteria analysis model based on combined analytical hierarchy process (AHP) and TOPSIS is judged helpful for decision-makers to make quick decision with less human interactions.