USING LINEAR PROGRAMMING TECHNIQUE TO DETERME THE OPTIMAL COMODITY COMBINATION IN THE GENERAL COMPANY FOR FOOD PRODUCTS - VEGITABLE OIL FACTORIES (CASE STUDY)

The objectives of the research are to determine the optimal allocation of resources available to the general company for food products using a linear programming(LP) technique to obtain high levels of production and high levels of profits by maximizing the value of the objective function and increasing the profitability of the company, and conducting a sensitivity analysis to see the extent of changes in the optimum commodity composition in terms of quality and quantity with the level of profits in a manner commensurate with the requirements of growth in the company , the study based on the use of (LP)technique to determine the optimum production plan with the highest net income for the products under study for vegetable oil factories as well as using sensitivity analysis by using the statistical program (Win QSB) .The research showed a number of results, perhaps the most important: proof of the hypothesis of achieving high levels of production and profits as well as the optimal allocation of available resources, as the quantity of production increased from 472 tons in the actual production plan to 974 tons in the derived plan , achieving a jump estimated at 106. 4%, and the results of the optimal solution using the (LP)technique showed that the value of net income at current prices has reached 348739500 dinars, an increase of 184.6% over the net income actually achieved at current prices of 122528565 dinars for the year 2018. The research had reached a number of conclusions, perhaps the most important is the matching of the research results to the research hypothesis as well as the optimal solution using the LP technique had showed the difference in the optimum commodity composition and its quantities from the actual commodity composition of the products , the research provides a number of recommendations, the most important of which is the application of a linear programming technique to know the extent of investing available resources in a way efficiency, which helps to increase production in order to achieve economic efficiency and the need to generalize it to production units with similar conditions in order to determine the optimal use of the various production resources.

Nonprofit Benchmarking With Data Envelopment Analysis

Benchmarking nonprofit performance can be challenging, constraining the ways nonprofits can use operational data to learn from each other and highlight organizational progress. Valid output or outcome data are scarce, and there is much to learn about measuring performance even when these data are available. Data envelopment analysis (DEA) is a mathematical linear programming technique that can be used to measure performance in a way that not only produces valid efficiency scores but also allows for benchmarking nonprofits with similar service missions. Using financial and production data from the nonprofit transportation sector, we walk through an example to explore DEA as a tool to measure and benchmark nonprofits. We conclude with suggestions for practice, emphasizing that DEA is useful for stakeholders looking to benchmark nonprofits by underscoring production and performance.

General anesthesia regulation using propofol infusion for adult patient administered by a positive system during induction phase

The control of depth of anesthesia presents a very challenging and realistic control problem that calls for optimum, fast, and adaptable control algorithms. The anesthesia system is a positive system. The proposed controller possesses all qualities of a reliable control scheme, namely bounded control, impose positivity, optimum feedback gain finding, and no offset. The controller is designed based on known patients’ models using linear programming technique. From simulation, the controller has shown the capability to drive the patient from a wake patient to general anesthesia by regulating propofol into the venous system for controlling compartment site concentration that give BIS=50. It is observed that the controller provides smooth control signals and has no constraint violation. It is also noticed that a nominal controller is designed for this study. Noisy signals in state are introduced and controlled using the proposed nominal controller. The application of the nominal controller in simulation study shows reasonable good results.

Mathematical modeling to optimize pine lumber yield

In Brazil, only 4% of the 7.84 million hectares of planted forests is devoted to the production of lumber wood, being in Santa Catarina state most of the wood used for this purpose is of the Pinus genus. This work aims to estimate the maximum utilization of logs due to the production of wood boards in the city of Canoinhas, Santa Catarina. For that, a sawmill of the region were consulted and the dimensions of the pieces produced were verified. The dimensions and classes of logs commonly traded in the region were also raised. As a result, 82 models were created in Maxitora software in diagram format. With the cutting models the sawmill had its performance optimized with the use of techniques of operational research in a cutting problem. The whole linear programming technique was used for an estimated demand for the quantity of each of the pieces produced. The results showed that only five models are required to meet such demand, so the yield is 43.18%.

Multi-item a supply chain production inventory model of time dependent production rate and demand rate under space constraint in fuzzy environment

In this paper, we have developed an integrated production inventory model for two echelon supply chain consisting of one vendor and one retailer. Production rate and demand rate of retailer and customer are time dependent. Idle time cost of the vendor has been considered. Multi-item inventory has been considered. In integrated inventory model average cost has been calculated under limitation on stroge space. Two echelon supply chain fuzzy inventory model has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is illustrated to test the model. Finally to make the model more realistic, sensitivity analysis has been shown.