Simulation-based Genetic Algorithm Research Articles

A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

The objective of this paper is to introduce a multi-year pavement maintenance programming methodology that can explicitly account for uncertainty in pavement deterioration. This is accomplished with the development of a simulation-based genetic algorithm (GA) approach that is capable of planning the maintenance activities over a multi-year planning period. A stochastic simulation is used to simulate the uncertainty of future pavement conditions based on the calibrated deterioration model while GA is used to handle the combinatorial nature of the network-level pavement maintenance programming. The effects of the uncertainty of pavement deterioration on the maintenance program are investigated using a case study. The results show that programming the maintenance activities using only the expected pavement conditions is likely to underestimate the required maintenance budget and overestimate the performance of pavement network.

Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters

In an uncertain economic environment, experts’ knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts’ knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Credibility-based chance-constrained integer programming models for capital budgeting with fuzzy parameters

In this paper, we discuss a problem of capital budgeting in a fuzzy environment. Two types of models are proposed using credibility to measure confidence level. Since the proposed optimization problems are difficult to solve by traditional methods, a fuzzy simulation-based genetic algorithm is applied. Two numerical experiments demonstrate the effectiveness of the proposed algorithm.

Path finding under uncertainty

Path finding problems have many real-world applications in various fields, such as operations research, computer science, telecommunication, transportation, etc. In this paper, we examine three definitions of optimality for finding the optimal path under an uncertain environment. These three stochastic path finding models are formulated as the expected value model, dependent-chance model, and chance-constrained model using different criteria to hedge against the travel time uncertainty. A simulation-based genetic algorithm procedure is developed to solve these path finding models under uncertainties. Numerical results are also presented to demonstrate the features of these stochastic path finding models.

A simulation-based genetic algorithm for inventory optimization in a serial supply chain

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.

Fuzzy quadratic minimum spanning tree problem

In this paper, a fuzzy quadratic minimum spanning tree problem is formulated as expected value model, chance-constrained programming and dependent-chance programming according to different decision criteria. Then the crisp equivalents are derived when the fuzzy costs are characterized by trapezoidal fuzzy numbers. Furthermore, a simulation-based genetic algorithm using Prüfer number representation is designed for solving the proposed fuzzy programming models as well as their crisp equivalents. Finally, a numerical example is provided for illustrating the effectiveness of the genetic algorithm.

Stochastic Transportation Network Design Problem with Spatial Equity Constraint

Equity issues and demand uncertainty are two important issues in the network design problem (NDP). Spatial equity in NDP is concerned with the benefit distribution among network users. By considering demand uncertainty, a more realistic evaluation of the network performance given a network improvement plan can be obtained. Two stochastic models that consider both spatial equity and demand uncertainty are formulated: an expected-value model and a chance-constrained model. Both models are solved by a simulation-based genetic algorithm procedure. The genetic algorithm is used to solve NDP, and stochastic simulation is used to simulate the demand uncertainty. The results of numerical experiments are provided to demonstrate the significance of the equity issue and demand uncertainty in NDP.

An Evolutionary Computing Approach to Hierarchical Team Decision Problems

This work addresses the numerical optimization problem in team theory and proposes a new approach to hierarchical team models based on genetic algorithms. Team theory enables one to decide analytically the optimal decision rules only in a few cases. This drawback and the need for a computational distributed algorithm lead us to approximate the functional optimal team decision problem to a parametric one. In this paper, we propose evolutionary team models. We formulate hierarchical team programming models and introduce stochastic simulation-based genetic algorithms. Then, we present an approach to the induction of solutions to hierarchical team decision problems using fuzzy genetic algorithms. This contribution will be an effective tool to investigate the behavior of hierarchical multi-agent systems.

Fuzzy programming with fuzzy decisions and fuzzy simulation-based genetic algorithm

This paper provides a spectrum of chance-constrained programming as well as chance-constrained multiobjective programming and chance-constrained goal programming with fuzzy rather than crisp decisions, which will seek a fuzzy set from the given reference collection as an optimal solution. The technique of fuzzy simulation is also presented to check fuzzy chance constraints and to handle fuzzy objective and goal constraints. Finally, a fuzzy simulation-based genetic algorithm for solving these models will be designed and illustrated by some numerical examples.

Uncertain programming: a unifying optimization theory in various uncertain environments

By uncertain programming we mean the optimization theory in generally uncertain (random, fuzzy, fuzzy random, grey, etc.) environments. Three broad classes of uncertain programming are expected value models and chance-constrained programming as well as dependent-chance programming. In order to solve general uncertain programming models, a simulation-based genetic algorithm is also documented. Finally, some applications and further research problems appearing in this area are posed.

Topological optimization models for communication network with multiple reliability goals

Network reliability models for determining optimal network topology have been presented and solved by many researchers. This paper presents some new types of topological optimization model for communication network with multiple reliability goals. A stochastic simulation-based genetic algorithm is also designed for solving the proposed models. Some numerical examples are finally presented to illustrate the effectiveness of the algorithm.

Dependent-chance programming with fuzzy decisions

Dependent-chance programming (DCP) is a new type of stochastic programming and has been extended to the area of fuzzy programming. This paper provides a spectrum of DCP and dependent-chance multiobjective programming (DCMOP) as well as dependent-chance goal programming (DCGP) models with fuzzy rather than crisp decisions. The terms of uncertain environment, event, chance function, and induced constraints are discussed in the case of fuzzy decisions. A technique of fuzzy simulation is also designed for computing chance functions. Finally, we present a fuzzy simulation-based genetic algorithm for solving these models and illustrate its effectiveness by some numerical examples.