Abstract:The cutting processes of rebar stock in housing project cause a large amount of waste because of high variety between required rebar lengths and standard rebar lengths, this problem is called one dimensional cutting stock problem. This research aims to utilize the rebar stock and minimize the losses using integer linear programming approach-aided by Advanced Interactive Mathematical Modeling System (AIMMS) software for solving the problems. These one-dimensional cutting rebar stock problems are divided into 19-problem according to the variety in the rebar diameter (25 mm, 16 mm, 12 mm, and 10 mm), and the variety in the design of activities in that housing project. The losses that are produced for each activity are utilized in the next activity when the length of item losses is larger than the required small item lengths in next activity for the same rebar diameters. That makes the utilization is to be maximized, where the Utilization Stock Ratio is reached to (98.53%).

Reverse Engineering (RE) is the process by which the geometry of a physical part is recreated digitally by digitizing and data modification. RE main difficulty is surface reconstruction. This difficulty increases by increasing part complexity. In this research, a developed reverse engineering approach has been adopted to create a 3D CAD model for power stern. The approach begins from the existing and goes through three main steps (scanning, data pre-processing, and part segmentation and surface reconstruction) and finalize to a readable CAD model. A mathematical representation for the NURB curve has been created to formalize the edges of segments then reconstructing plan and tabulated surfaces according to the segment geometry. Algorithms and computer programs using MATLAB programs have been built to implement the proposed approach and save the data.

The assessment and selection of small projects are usually carried out using commercial, official, environmental and marketing data before the investment decision is made. Small projects are among the most important sources of job opportunities and revenue in many nations. The contribution of small enterprises the provision of work has been a controversial issue around the world, it is therefore important to clarify how best to choose between projects. This paper shows an example from Karbala governorate, which demonstrates project prioritization through two project selection methods. A questionnaire was completed and scores were computed for prioritizing potential projects. The first category used an analytical hierarchy process (AHP) model for investment selection when comparing a number of options (alternatives) with respect to specific criteria, then investment with the highest weight was used to select small projects in this field for second stage transference. The second category used a weighted sum model (WSM) for top small project selection, which operated by obtaining all small project scores, and declaring that with the highest project to score to be the best small project. After combining the data and research procedure, we found that the best option from among small projects in Karbala was an industrial dairy factory.

In this work, an approach to solve mixed-model assembly line balancing Problem has been suggested which consists of six stages. In the fourth stage of this approach, a heuristic method is suggested for solving the combined model. This method is programmed using C# language. This program is called "Assembly line balancing-Method of Merging Shortest and Longest Operation" symbol by (MMSLO), which is based on merging two heuristic methods "Shortest Operation time" (SOT) and "Longest Operation Time" (LOT). This approach has been applied in the actual industrial environment at Electronic Industries Company (EIC) in assembly plant for assemble three models of automatic changeover (ACH). The results of the suggested method in (MMSLO) program compared with the results of the basic method (SOT) and (LOT) that existed in "production and operations management, quantitative methods" software that symbol by (POM-QM). It has been proved the ability of the suggested method to give a better solution than the traditional methods, in which the efficiency increased from 92.75% for (LOT) method and 86.98% for (SOT) method to 93.53% for the suggested method.

Production Line Balancing (PLB) is the technique of assigning the operations to workstations in such a way that the assignment minimizes the idle time between workstations. PLB aims to equator the workload in each workstation to assure maximum production flow. By adding machine in specific configurations is one treatment which leads to this leveling in workload. This research studies the different efficiencies of the added machine and the effect of these efficiencies on line balancing to select the machine with suitable efficiency. This will be led to reduce the idle time between workstations and increasing production flow. The work time considered as the efficiency criterion for this case study. The study has been implemented on a dumb truck production line and resulted in increasing the line efficiency to 81.7%.