A Combined Statistical Selection Procedure Measured by the Expected Opportunity Cost

• Published in: Arabian journal for science and engineering (2011) Vol. 43; no. 6; pp. 3163 - 3171
• Main Authors: Almomani, Mohammad H., Alrefaei, Mahmoud H., Al Mansour, Shahd
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018; Springer Nature B.V
• Subjects: Algorithms; Budgeting; Budgets; Communication networks; Computation; Computer simulation; Engineering; Humanities and Social Sciences; multidisciplinary; Opportunity costs; Research Article - Systems Engineering; Science; Statistical analysis; Supply chains; Ordinal optimization; Ranking and selection; Opportunity cost; Budget allocation
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-017-2865-8

Selecting one of the best systems among finite but huge stochastic alternative systems is very important area of research since it has many applications in real life such as manufacturing systems, supply chain management and communication networks. Simulation is the main approach to estimate the performance values of these alternative systems. We propose a selection procedure which consists of two phases. In phase one, the ordinal optimization ( OO ) technique is used to select a set K of k systems randomly. The probability that this set contains the actual best system can be measured using order statistics. In the second step of this phase, the optimal computing budget allocation ( OCBA m ) is used to select a smaller set of m systems from the set K . It is assumed that the computational budget is limited, the aim of OCBA m is to allocate these computational budget on the different systems to maximize the probability of correct selection P ( CS ). It gives the alternatives that have more influence on the solution, more computational budget; these two procedures are performed repeatedly until the available budget is consumed. In the second phase, the ranking and selection ( R & S ) approach is applied on the smaller subset in order to select the best system. The algorithm is implemented on a generic example, and the expected opportunity cost is utilized to measure the potentially of incorrect selection. The numerical results show that the algorithm indeed selects a good enough system.