Multi-objective Simulation Optimization Problem Research Articles

Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimization

Motivated by the vector evaluation genetic algorithm (VEGA), this research develops simulation budget allocation rules for the VEGA in solving simulation optimization problems. We formulate the selection problem of the VEGA using the optimal computing budget allocation approach, and derive the asymptotically optimal allocation rule and an easily implementable approximated allocation rule. The efficiency of the propose simulation budget allocation rules is demonstrated via comparing with some existing allocation rules. Furthermore, the proposed allocation rule is integrated with the VEGA to solve the multi-objective simulation optimization problems. The numerical experiments on the benchmarking test problems indicate that the proposed allocation rule can improve the search efficiency of the VEGA in stochastic environment by reducing the average distance towards the true Pareto front and improving the purity of the estimated Pareto front.

Productivity Improvement Through Multi-Objective Simulation Optimization—A Case Study

To maximize profitability and meet customers’ expectations, assembly plants strive to enhance operations and minimize costs. Using discrete-event system simulation and optimization, this paper investigates different scenarios to enhance operations efficiency and presents a decision support tool to aid operational decision making in an air conditioner assembly plant. Specifically, simulation is used to evaluate the system-wide impact of an optimal sequencing rule at a bottleneck station; in addition, a multi-objective simulation optimization problem is solved to enable manufacturers to evaluate their time/cost tradeoffs. Using actual data from a previous month, the simulation study illustrates possible savings throughout the evaluated alternatives. To sustain the study outcomes, we combine mathematical programming and simulation into an integrated decision support tool. Overall, this study illustrates the benefits of the solution approach to a real-life system.

SCORE Allocations for Bi-objective Ranking and Selection

The bi-objective ranking and selection (R8S) problem is a special case of the multi-objective simulation optimization problem in which two conflicting objectives are known only through dependent Monte Carlo estimators, the decision space or number of systems is finite, and each system can be sampled to some extent. The solution to the bi-objective R8S problem is a set of systems with non-dominated objective vectors, called the set of Pareto systems. We exploit the special structure of the bi-objective problem to characterize the asymptotically optimal simulation budget allocation, which accounts for dependence between the objectives and balances the probabilities associated with two types of misclassification error. Like much of the R8S literature, our focus is on the case in which the simulation observations are bivariate normal. Assuming normality, we then use a certain asymptotic limit to derive an easily-implementable Sampling Criteria for Optimization using Rate Estimators (SCORE) sampling framework that approximates the optimal allocation and accounts for correlation between the objectives. Perhaps surprisingly, the limiting SCORE allocation exclusively controls for misclassification-by-inclusion events, in which non-Pareto systems are falsely estimated as Pareto. We also provide an iterative algorithm for implementation. Our numerical experience with the resulting SCORE framework indicates that it is fast and accurate for problems having up to ten thousand systems.

A simulated annealing technique for multi-objective simulation optimization

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing budget allocation (MOCBA) method for the multi-objective simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.

Multi-objective ordinal optimization for simulation optimization problems

Ordinal optimization (OO) has been successfully applied to accelerate the simulation optimization process with single objective by quickly narrowing down the search space. In this paper, we extend the OO techniques to address multi-objective simulation optimization problems by using the concept of Pareto optimality. We call this technique the multi-objective OO (MOO). To define the good enough set and the selected set, we introduce two performance indices based on the non-dominance relationship among the designs. Then we derive several lower bounds for the alignment probability under various scenarios by using a Bayesian approach. Numerical experiments show that the lower bounds of the alignment probability are valid when they are used to estimate the size of the selected set as well as the expected alignment level. Though the lower bounds are conservative, they have great practical value in terms of narrowing down the search space.

A methodology for solvng multi-objective simulation-optimization problems

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Due to the difficulties involved in the analytical expression, simulation may be the most effective means of studying these complex systems. Furthermore, many of these problems are characterized by the presence of multiple and conflicting objectives. The goal of this paper is to introduce a new methodology through an interactive algorithm for solving this multi-objective simulation optimization problem.