In this paper fishing trawler scheduling and production planning for a quota-based integrated commercial fishery is modelled mathematically. The catch capacity of fishing trawlers and the capacity of processing firms are two major factors which influence the scheduling of fishing trawlers. Production planning in fish processing firms depends on steady supply of fresh fish from the fishing trawlers to the processing firms. We develop a mixed integer linear programming (MILP) model to co-ordinate trawler scheduling, fishing, processing, and labour allocation of quota based integrated fisheries. We demonstrate the workability of our model with a numerical example and sensitivity analysis based on data obtained from one of the major fisheries in New Zealand.

An unconstrained problem with nonlinear objective function has many applications. This is often viewed as a discipline in and of itself. In this paper, we develop a computer technique for solving nonlinear unconstrained problems in a single framework incorporating with Golden section, Gradient Search method. For this, we first combine this algorithm and then develop a generalized computer technique using the programming language MATHEMATICA. We demonstrate our computer technique with a number of numerical examples.

In this paper, a new method is proposed for finding an optimal solution to a Quasi-Concave Quadratic Programming Problem with Bounded Variables in which the objective function involves the product of two indefinite factorized linear functions and constraints functions are in the form of linear inequalities. The proposed method is mainly based upon the primal dual simplex method. The Linear Programming with Bounded Variables (LPBV) algorithm is extended to solve quasi-concave Quadratic Programming with Bounded Variables (QPBV). For developing this method, we use programming language MATHEMATICA. We also illustrate numerical examples to demonstrate our method.

Business organizations are always facing uncertainties in demand, supply and inventories. For this, it is important for them to make the strategic plans to cope up with the uncertainty accordingly. To sustain, the business organizations must plan the future in such a way that the inventory cost, labor cost will be minimized and the utilization of time, financial resources and profit will be maximized. The optimum planning of resources also help the organizations to avoid wastage. A good forecasting technique can help the manager of a company to deal with the uncertainties. In this paper, we will work on such a planning fora fertilizer company in Bangladesh. To minimize the inventory cost, we will apply a new approach known as Artificial Neural Network (ANN), which is recently used for the problem of prediction and analyze the main characteristics of a system through an iterative training process. For this, we will first forecast the demand of fertilizer by using existing forecasting methods. We will then apply ANN for forecasting the demand of fertilizer of the company. We will also identify Economic Order Quantity (EOQ) to minimize total cost including inventory costs of the company. We use programming language MATLAB for analyzing different forecasting methods including ANN. Finally, we will use these results to find out the right forecasting technique for the fertilizer company with optimal inventory cost. Dhaka Univ. J. Sci. 69(3): 133-142, 2022 (June)

In this paper, a new method is proposed for solving the problem in which the objective function is a linear fractional Bounded Variable (LFBV) function, where the constraints functions are in the form of linear inequalities and the variables are bounded. The proposed method mainly based upon the primal dual simplex algorithm. The Linear Programming Bounded Variables (LPBV) algorithm is extended to solve Linear Fractional Bounded Variables (LFBV).The advantages of LFBV algorithm are simplicity of implementation and less computational effort. We also compare our result with programming language MATHEMATICA.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11522 Dhaka Univ. J. Sci. 60(2): 223-230, 2012 (July)