The present study is an attempt to develop an inventory model for deteriorating items with negative exponential demand. Shortages are allowed with partial back logging. This model is different from the existing models where deterioration is a function of time. Accordingly, three different types of probabilistic deterioration functions have been considered to find the associated decision variables and also to make comparisons among them. The optimality is illustrated with numerical values of system parameters and the graphical representations are given to depict the trend. The necessary observations in obtaining optimal values of decision variables are analyzed in the light of the practical aspect of the developed model. Finally, considering the numerical values of system parameters, sensitivity analyses are carried out to study the effect of changes in most important system parameters.
In this paper, we have modeled a decision making problem of a tea industry as a multi-objective optimization problem in interval environment. The goal of this problem is to maximize the overall profit as well as to minimize the total production cost subject to the given resource constraints depending on budget, storage space and allotted processing times in different machines. For this purpose, the problem has been formulated as a multi-objective integer linear programming problem with interval objectives. To solve the problem, we have proposed extended elitist non-dominated sorting genetic algorithm (ENSGA-II) for integer variables with interval fitness, crowded tournament selection, intermediate crossover, one neighborhood mutation and elitism. To develop this algorithm, we have proposed modified non-dominated sorting and crowding distance based on interval mathematics and interval order relations. Finally, to test the performance of the proposed algorithm, a numerical example has been solved.