Determination of flexibility of workers working time through Taguchi method approach

Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.

Optimization of process parameters involved in laser bending operation using taguchi experimental design method

A laser-bending process has many advantages such as no mechanical spring-back effect, precise incremental adjustment, high level of process flexibility, and the capability of production of complex shapes due to which it has shown a great promise and so has lately been the subject of considerable interest. This paper reports the variation of bending angle with change of different process parameters. Experiments are conducted following a well planned experimental schedule based on Taguchi’s design of experiments (DOE) method and the optimal values of process parameters for maximum bending angle is thus determined. Process parameters include laser power, pulse diameter, pulse duration and scan speed. Significant control factors predominantly influencing the bending angle are also identified. Specimen used for experiments is Aluminium metal sheet and Nd-YAG laser is used as laser source

Decision-based genetic algorithms for solving multi-period project scheduling with dynamically experienced workforce

The importance of the flexibility of resources increased rapidly with the turbulent changes in the industrial context, to meet the customers’ requirements. Among all resources, the most important and considered as the hardest to manage are human resources, in reasons of availability and/or conventions. In this article, we present an approach to solve project scheduling with multi-period human resources allocation taking into account two flexibility levers. The first is the annual hours and working time regulation, and the second is the actors’ multi-skills. The productivity of each operator was considered as dynamic, developing or degrading depending on the prior allocation decisions. The solving approach mainly uses decision-based genetic algorithms, in which, chromosomes don’t represent directly the problem solution; they simply present three decisions: tasks’ priorities for execution, actors’ priorities for carrying out these tasks, and finally the priority of working time strategy that can be considered during the specified working period. Also the principle of critical skill was taken into account. Based on these decisions and during a serial scheduling generating scheme, one can in a sequential manner introduce the project scheduling and the corresponding workforce allocations

What drives export performance of firms in Eastern and Western Poland?

We use a unique firm-level survey dataset that draws from the EFIGE (European Firms In Global Economy) questionnaire, to unveil differences in factors driving export performance in structurally most diverse areas of Poland. While conventional results about the role of size, foreign ownership and innovation activity are confirmed at the aggregate level, the picture breaks down when Western and Eastern macroregions are extracted. Our results suggest that the common perception of a more developed West (Poland “A”) and a backward East (Poland “B”) might be outdated. Rather, firms in both regions seem to follow distinct strategies and have dissimilar success factors for competing internationally. Interestingly, export performance in the East is found to benefit from family ties in business, but also product innovation and non-price competitiveness. In the West, it is in turn associated mostly with size and foreign ownership. Overall, our results on the one hand add support to the ‘New’ new trade theory and ‘New’ new economic geography’s premises related to the importance of microeconomic factors and, on the other, shed a new light on the pattern of regional development in Poland. We also discuss some implications for policy makers and managers and suggest directions of further research.National Science Centre, grant no. DEC-2011/03/D/HS4/0195

Improving project management planning and control in service operations environment.

Projects have evidently become the core activity in most companies and organisations where they are investing significant amount of resources in different types of projects as building new services, process improvement, etc. This research has focused on service sector in attempt to improve project management planning and control activities. The research is concerned with improving the planning and control of software development projects. Existing software development models are analysed and their best practices identified and these have been used to build the proposed model in this research. The research extended the existing planning and control approaches by considering uncertainty in customer requirements, resource flexibility and risks level variability. In considering these issues, the research has adopted lean principles for planning and control software development projects. A novel approach introduced within this research through the integration of simulation modelling techniques with Taguchi analysis to investigate ‗what if‘ project scenarios. Such scenarios reflect the different combinations of the factors affecting project completion time and deliverables. In addition, the research has adopted the concept of Quality Function Deployment (QFD) to develop an automated Operations Project Management Deployment (OPMD) model. The model acts as an iterative manner uses ‗what if‘ scenario performance outputs to identify constraints that may affect the completion of a certain task or phase. Any changes made during the project phases will then automatically update the performance metrics for each software development phases. In addition, optimisation routines have been developed that can be used to provide management response and to react to the different levels of uncertainty. Therefore, this research has looked at providing a comprehensive and visual overview of important project tasks i.e. progress, scheduled work, different resources, deliverables and completion that will make it easier for project members to communicate with each other to reach consensus on goals, status and required changes. Risk is important aspect that has been included in the model as well to avoid failure. The research emphasised on customer involvement, top management involvement as well as team members to be among the operational factors that escalate variability levels 3 and effect project completion time and deliverables. Therefore, commitment from everyone can improve chances of success. Although the role of different project management techniques to implement projects successfully has been widely established in areas such as the planning and control of time, cost and quality; still, the distinction between the project and project management is less than precise and a little was done in investigating different levels of uncertainty and risk levels that may occur during different project phase.United Arab Emirates Governmen

A Structured Approach to Modelling Lean Batch Production

A problem relating to the manufacture of automotive body panels concerns the appropriate choice of production size or batch quantity of a body panel production run that ensures a minimum inventory profile is maintained while not compromising production efficiency. Due to underlying variation within the body panel production process it is difficult to determine a relationship between the batch quantity and production efficiency.This thesis determines the appropriate production batch size through the creation of an iterative modelling methodology that initially examines the nature of the variation within the panel production process. Further iterations of the methodology apply appropriate analytical modelling methods until a satisfactory solution is achieved. The modelling construction is designed so that it is potentially applicable to a wider range of manufacturing problems. As there is variation inherent within the system, regression analysis, experimental design (traditional and Taguchi) are considered. Since an objective of creating the modelling methodology is the potential of apply the methodology to a wider variety of manufacturing problems, additional modelling methods are assessed. These include the operational research methods of mathematical programming (linear and non-linear and dynamic programming) and queuing systems. To model discrete and continuous behaviour of a manufacturing system, the application of hybrid automata is considered. Thus a suite of methodologies are assessed that assess variation, optimisation and networks of manufacturing systems. Through the iterative stages of the modelling approach, these analytical methods can be applied as appropriate to converge on to the appropriate solution for the problem under investigation. The appropriate methods identified to quantify a relationship between the batch production quantity and production efficiency include regression modelling and traditional experimental design. The conclusion drawn from the application of both methods is that relative to the inherent variation present in the production system, lower batch quantities can be chosen for production runs without affecting the production performance. Consequently, a minimum inventory profile can be maintained satisfying the objective of a lean system

Statistical experimental design screening strategies for free-monomeric isocyanates determination by UPLC in materials used in cork stoppers manufacturing

A statistical experimental design was used to screen variables of the analytical procedure to quantify free monomeric isocyanates presented in polyurethane based pre-polymers in trace amounts. For this purpose, diphenylmethane-4,4’-diisocyanate (4,4'-MDI), 2,4-toluene diisocyanate (2,4-TDI) and 2,6-toluene diisocyanate (2,6-TDI) were analysed by Ultra Performance Liquid Chromatography with a Photo Diode Array detector (UPLC-PDA). A preliminary study was performed with three derivatization agents, being 1-(2-piridyl) piperazine (1,2-PP) the most suitable one. Column temperature, flow and percentage of ammonium acetate (% NH4Ac.) were the factors studied at two levels each. A sequence of experiments was planned according to a 23 full factorial design with three replicates and two repetitions. Analysis of variance (ANOVA) was applied for the identification of significant factors and interactions. Higher responses were achieved when the column temperature was 30 °C, a flow of 0.3 mL min-1 and a solvent with a percentage of ammonium acetate of 0.1 %. Figures of merit were assessed within-laboratory as a preliminary step for method validation. Similar values were obtained for TDI and MDI. Recoveries are approximately 100 %. In addition, the values of detection limits (LODs) for MDI and TDI were 0.08 and 0.11 μg mL−1, respectively, and quantification limits (LOQs) were 0.25 and 0.33 μg mL−1 for MDI and TDI, respectively. The working range was between 0.01 and 10.00 μg mL−1 for MDI and 0.01 – 4.95 μg mL−1 for TDI. These figures of merit seemed adequate to detect low amounts of free monomeric isocyanates presented in agglomerates and foams for agglomerated cork stoppers production. This data is suitable to address the optimization of an analytical method by a response surface methodology.Catarina André and Inês Delgado acknowledge the financial support from the Portuguese Innovation Agency (ADI) in the frame of project QREN 5012-LIRACork. This work was supported by the Portuguese Innovation Agency (ADI) in the frame of project QREN 5012-LIRACork.info:eu-repo/semantics/publishedVersio

Evolutionary optimization of neural networks with heterogeneous computation: study and implementation

In the optimization of artificial neural networks (ANNs) via evolutionary algorithms and the implementation of the necessary training for the objective function, there is often a trade-off between efficiency and flexibility. Pure software solutions on general-purpose processors tend to be slow because they do not take advantage of the inherent parallelism, whereas hardware realizations usually rely on optimizations that reduce the range of applicable network topologies, or they attempt to increase processing efficiency by means of low-precision data representation. This paper presents, first of all, a study that shows the need of heterogeneous platform (CPU–GPU–FPGA) to accelerate the optimization of ANNs using genetic algorithms and, secondly, an implementation of a platform based on embedded systems with hardware accelerators implemented in Field Pro-grammable Gate Array (FPGA). The implementation of the individuals on a remote low-cost Altera FPGA allowed us to obtain a 3x–4x acceleration compared with a 2.83 GHz Intel Xeon Quad-Core and 6x–7x compared with a 2.2 GHz AMD Opteron Quad-Core 2354.The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.Fe, JD.; Aliaga Varea, RJ.; Gadea Gironés, R. (2015). Evolutionary optimization of neural networks with heterogeneous computation: study and implementation. The Journal of Supercomputing. 71(8):2944-2962. doi:10.1007/s11227-015-1419-7S29442962718Farmahini-Farahani A, Vakili S, Fakhraie SM, Safari S, Lucas C (2010) Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization. Eng Appl Artif Intell 23(2):177–187Curteanu S, Cartwright H (2011) Neural networks applied in chemistry. i. Determination of the optimal topology of multilayer perceptron neural networks. 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Designing multi-period supply chain network considering risk and emission: a multi-objective approach

This research formulates a multi-objective problem (MOP) for supply chain network (SCN) design by incorporating the issues of social relationship, carbon emissions, and supply chain risks such as disruption and opportunism. The proposed MOP includes three conflicting objectives: maximization of total profit, minimization of supply disruption and opportunism risks, and minimization of carbon emission considering a number of supply chain constraints. Furthermore, this research analyses the effect of social relationship levels between different tiers of SCN on the profitability, risk, and emission over the time. In this regard, we focus on responding to the following questions. (1) How does the evolving social relationship affect the objectives of the supply chain (SC)? (2) How do the upstream firms’ relationships affect the relationships of downstream firms, and how these relationships influence the objectives of the SC? (3) How does the supply disruption risk interact with the opportunism risk through supply chain relationships, and how these risks affect the objectives of the SC? (4) How do these three conflicting objectives trade-off? A Pareto-based multi-objective evolutionary algorithm–non-dominated sorting genetic algorithm-II (NSGA-II) has been employed to solve the presented problem. In order to improve the quality of solutions, tuning parameters of the NSGA-II are modulated using Taguchi approach. An illustrative example is presented to manifest the capability of the model and the algorithm. The results obtained evince the robust performance of the proposed MOP