Conference Agenda

This paper presents a framework for solving assembly line design problems by considering ergonomics aspects. Although ergonomics factors have been ignored in conventional optimization problems in this area, in the long term, ergonomics risks and work-related injuries can impose considerable expenses on production systems. Moreover, in the design stage, different types of uncertainty in operational and ergonomics aspects can affect the optimization model. Therefore, the optimization framework in this study includes the results of ergonomics assessment tools and employs fuzzy logic to tackle imprecise factors. In the context of our problem, the sources of imprecision are twofold: environmental uncertainty and system uncertainty. Environmental uncertainty is related to demand uncertainty derived from market variations and customers’ behavior. System uncertainty includes the uncertainties within the production process that partially relate to human aspects, such as uncertainty in task execution time and the physical capacity of the operators.

The use of mathematical programming models for production planning has been proposed since the 1950s, being a widely applied tool, since it can provide optimal solutions for production planning problems. For manufacturing companies, it is a great challenge to plan in uncertain environments when there are large variations in planning parameters. Thus, the greatest difficulty in dealing with Mathematical Programming models in production planning is that, in general, with the intention of simulating reality through these models, it is necessary to estimate values for the planning parameters, which may not always be possible accurately, and consequently, the model's optimal solution may not represent the best solution to the problem. In this context, the classic approach to deal with a dynamic economic scenario is the use of robust optimization models, which propose a suboptimal solution in relation to the deterministic model. Therefore, the objective of this paper is to apply a robust optimization model in the operations management process of an electronic components manufacturing company. First, a content analysis was performed, then company data was collected, the model was proposed, and the results were analyzed. Results suggested more than 80% of the production should be done in anticipation. The optimal solution, at the lowest cost, was obtained from the minimal scenario. The worst and robust solution, bringing the highest cost, came from the intermediate scenario, proving that the production plan could be performed even with adversities on sight.

In electronic devices, the number of transistors and components per unit of area is increasing over years. The attention paid to the quality of the substrate on which such devices are built is consequently increasing, causing the number of quality controls that a silicon wafer undergoes during its production process to become stricter and more pervasive. This issue is extremely important for producers to be addressed to reduce costs in quality controls moving towards zero-defect manufacturing. The purpose of this work is to reduce time and costs spent in calibration procedures of instruments measuring mechanical parameters of silicon wafers, by revising and standardizing the already adopted procedures. Therefore, to address this goal, the extant literature, patents, and standard about procedures employed for measuring the mechanical parameters of silicon wafers is studied. The results are elaborated and applied to an industrial case study, the Italian branch of a Taiwanese manufacturing company. In particular, the focus of the case is on the Bow/Warp machine’s calibration which needs to be performed periodically to guarantee a correct measurement accuracy. Such calibration has strong implications for production efficiency and flow. The results are reported and discussed to highlight the key practical and theoretical implications.