Search

In this article, we are presenting a model for optimizing the selection of cost drivers in activity-based costing such that the optimal set of cost drivers will minimize the sum of the absolute product unit price deviations and will keep the budget under the intended amount utilizing a quasi-Knapsack structure. Comparative analysis from two benchmark problems showed the effectiveness of the proposed model. Moreover, the comparative analysis showed that categorizing the cost drivers into four operational expenses levels as suggested by cooper and Kaplan (1991) did not always lead to a better solution. Furthermore, the study showed that the budget is not the only determinant that determines the best set of cost drivers, the way that the expenses of the removed cost drivers are handled is also a determinant for the effectiveness of the model.

The question of how to calculate the effective credit hour costs for different departments in Higher Learning Institutions was approached in this paper using Joint Costing and Activity-Based Costing techniques. The cost of the effective credit hour in the higher learning institutions was treated as joint cost problem. The main advantage of joint cost analysis is its ability to handle multiple faculties who are using common resources up to achieve split off so that each faculty has its own separable cost. The departments within the faculty were also treated as joint cost problem as these departments use common resources up to their split off point as well. The Activity-Based Costing system (ABC) then was used because of its ability to allocate the joint costs to the corresponding faculties and departments. Furthermore, the separable costs pertaining the different departments were added to calculate the departments’ costs. We suggest that the annual effective departmental credit-hour cost to be calculated by dividing the annual total cost of the department by the annual effective number of credit hours taught in that department. The Knapsack model was applied at each cost level to determine the optimal cost driver set for the Activity-Based costing analysis such that a tradeoff between the precision and the cost of the information obtained from the analysis was reached. The proposed model was explained using a hypothetical example of a university containing 9 faculties such that the costs incurred for the university were decomposed into four levels: Facility level and it included all the costs that were not directly related to any of the faculties or departments, Product level and it included all the costs that were related to a certain faculty and not related to a specific department within that faculty, Batch level and it included all the costs that were directly related to a specific department, and finally, the Unit level and it included the annual effective number of hours registered in a department. Originally 12 cost drivers were considered for this hypothetical problem, and then a binary programming model utilizing the Knapsack setup was used to select an optimal set of 9 cost drivers such that those who are not selected were combined with the ones that were selected. The results showed that the proposed method offered precise information about the annual departmental credit-hour cost for higher learning institutions.

The mathematical model to find the optimal shopping policy from many available manufacturers for 1-out-of-n active redundancy series systems under budget constraint was formulated and tested using GA. The study showed that the number of possible combinations for this problem can be very high and the majority of those combinations are infeasible. This renders the enumeration technique ineffective or even impossible in practice, the matter that calls for a solution through GA.The results showed that the proposed genetic algorithm has high degree of robustness. Moreover, the results showed that the proposed algorithm is superior to the enumeration technique in terms of both computational time and quality of solution. Furthermore, the results showed that the convergence of the algorithm to the optimal solution is high.

In this paper a model for selecting Weibull-life and Lognormal-repair components for a series system using Total Cost of Ownership (TCO) approach is proposed. The model has been used for selecting the suppliers of the different components constituting the system. The TCO of the system is calculated for each possible combination of components available in the market and then the combination that gives the minimum TCO is considered for purchasing. The results have showed that the model is able to compromise between the different cost categories such that the optimal cost elements values are between the minimum and maximum values of the cost categories.

In this paper a three-step parameters tuning model for time-constrained Genetic Algorithms (GAs) was presented. The first step involved modeling the objective function using multiple regression model where the fitness value was the response variable and the GA parameters were the regressors. The second step involved constraint modeling using the objective function found in the first step and using the upper and lower limits of the GA parameters along with an upper limit on the execution time as constraints. The third step involved optimizing the constraint model found in the second step using a suitable deterministic optimization method to determine the optimal GA parameters taking into consideration four aspects that affect the GA performance. These aspects were: the problem under consideration, the GA parameters used, the execution time, and the power of the computer used.The validation of this model was demonstrated using two capacitated lot sizing problems. The model was able to predict the fitness values and the optimal parameters of the GA for these problems to a high degree of precision. Moreover, the results showed that tuning the GA parameters using multiple regression along with a suitable deterministic optimization method was an effective and robust method that enhanced the performance of the GA. The statistical analysis showed that in order to do a proper tuning for a certain GA, the designer of the GA must take into consideration not only the type of problem but also the size of the problem, the allowable execution time, and the hardware used in executing the GA. Furthermore, the results agreed with the "No Free Lunch" theorem.

Genetic algorithm (GA) is based on Darwin’s natural selection theory and is used extensively in combinatorial problems as these problems are demanding in terms of computational time. GA shows very good results in terms of both computational time and quality of solution for combinatorial problems as GAs have some traits that make them one of the best evolutionary algorithms (EAs). The use of both mutation and crossover operators make them, relative to other EAs, highly immune to be trapped in a local optima and thus less vulnerable to premature convergence problem. Traditionally, the solution for premature convergence problem is to maintain a certain degree of diversity of the GA’s population without affecting the convergence process itself. In this paper, this concept has been practiced where Frequency Crossover strategy (FC) along with nine different mutation strategies have been proposed and applied to travel salesman problem (TSP) to reduce the effect of premature convergence problem. Three sets of benchmark data have been used to test the effectiveness of this GA. The results showed that both the nine mutation types and the FC are essential for the proposed GA to perform well. While this GA has been applied on TSP in this paper, it is also believed that it is applicable on any problem that has an Order-Based chromosome representation.

In this paper a hybrid two-stage algorithm is proposed to find the optimal solution for transportation problem (TP). The proposed algorithm consists of two stages: the first stage uses genetic algorithm (GA) to find an improved nonartificial feasible solution for the problem and the second stage utilizes this solution as a starting point in the RSM algorithm to find the optimal solution for the problem. The algorithm utilizes big M method to handle ? constraints and northwest corner method, minimum cost method, and Vogel's method are also used to generate the initial population for the GA. Performance of the algorithm is tested under different simulated scenarios and compared to both GA and revised simplex method (RSM). The results showed that the new hybrid algorithm performs competitively against GA and RSM. The proposed algorithm can be easily extended to cover different kinds of linear programming (LP) problems with minor changes such as inventory control, employment scheduling, personnel assignment and transshipment problems.

This paper discusses the design of the optimal SSALT plan using Bayesian approach and progressive Type-I right censoring for an exponential life distribution under large sample size and small censoring proportion. The cumulative exposure model and the exponential life distribution in both steps are assumed. The progressive Type-I right censoring can reduce the cost of the test. This reduction, unfortunately, comes on the expense of reducing the precision of the test. The optimal test parameters, the stress changing time and the first step stress, are obtained by minimizing the expected variance of the life for the pth percentile using Bayesian approach. A comparison between conventional Type-I and progressive Type-I right censoring is also provided. The results showed that progressive Type-I right censoring is recommended when strong prior information for the model parameters is used as the test precision becomes less sensitive to the censoring proportion.

This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.