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This paper reviews monitoring and error prediction of PLC-program using Neural Network. In the PLC-device controlled manufacturing line, PLC-program holds place of underlying component. It becomes controlling mechanism. The level of automation in the production line relies on control mechanism practiced. In the modern manufacturing, PLC devices can handle whole production line given that structured and smart PLC-program is executed. In other words, PLC-program can manage whole process structure consisting set of procedures. We present a method to monitor PLC-program and PLC error prediction it using neural network. The neural network method being predictive in nature, it rigorously can monitor process signals from sensors, sensed during operation of PLC devices or execution of PLC-program. Subsequently, a neural network algorithm practiced for the analysis of signals. In this way, thorough monitoring of PLC-program can find possible errors from temporal parameters (e.g. Voltage, bias etc). In addition, possible alterations in program and irregularities can be minimized. That can result, easily to use in fault detection, maintenance, and decision support in manufacturing organization. Similarly, it can lessen down-time of machines and prevent possible risks.

This paper is to develop heuristic algorithm for ship-quay assignment problem. The work is based on specialist's experience in shipbuilding industry, specifically for outfitting. At this point of time, the shipbuilding industry is in centre of attention, because of huge involvement of capital and its role in economy of countries. As in other complex areas, constraints in ship building industry are delivery date, standard working time, number of employees, resources allocation, equipment assignment, working floor management etc. In addition, inner wall and docking problem is problematic in shipbuilding industry, which canpsilat be widened as it requires. Hence, suitable ship quay assignment system is required to reduce time spending for processing, and to overcome limitations of outfitting process, which only is possible to do at inner wall. For this kind of problem, mathematical modeling cannot be suitable to solve it because itpsilas analytical nature, it can be better solved using optimization techniques. In this work, we develop an algorithm which is based on experts idea in similar domain. For the applicability of the algorithm, some work has been carried out in the latter part of paper.