Simulation-based Genetic Algorithm Research Articles

A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios.

Optimisation of hybrid composite shields for hypervelocity Micro-Meteoroid and Orbital Debris (MMOD) impact

Advancements in space exploration over the last few decades have led to a sharp increase in Micro-Meteoroid and Orbital Debris (MMOD), with the associated increased risk of hypervelocity impact on satellites and space structures. The Whipple shield can mitigate the effects of such impacts and current research is exploring further developments towards effective lightweight passive shielding technology to counter the damaging effects of MMOD. With the increase in MMOD and the prospect of increased space travel in the coming years and decades, it is vital that more research is conducted and improvements are made in the development and design of efficient, lightweight shielding technology to protect both unmanned and manned spacecraft against hypervelocity impacts (HVI). The optimisation of shield design for HVI is a high dimensionality problem, suited to advanced computational approaches. Key variables in HVI shield design that should inform the focus on optimisation include: impact velocity, rear wall thickness, projectile diameter and bumper thickness. A hybrid shield configuration and numerical model are proposed and validated, with alternating layers of aluminium (AL2024) and carbon fibre composites (CFRP, T300 woven-fabric) to form a 5 mm thick target plate consisting of 5 plies. The adaptive coupled FEM-SPH method is used to model the target plates. The proposed hypervelocity shield design optimisation methodology is based on Direct Simulation-based Genetic Algorithm (DSGA) optimisation and is implemented to optimise a multi-variable shield design space. Objective weightings are used to analyse and discuss results, referring to the ratio of the kinetic energy to the shield areal density objectives. A clear transition in the impact behaviour of the optimised MMOD shields is observed in the transition region from high-velocity to hypervelocity impact, where significant levels of kinetic energy dissipation are observed below the transition region, and lower energy dissipation at hypervelocity. The shield design optimisation results show that with a weighted kinetic energy to mass objective of 90:10, the kinetic energy of the back shield plate decreases significantly (62.7%) and the areal density can be reduced by more than 18%. Alternative configurations displayed sub-optimal results based on a trade-off between objective functions.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.

An extensive operational law for monotone functions of LR fuzzy intervals with applications to fuzzy optimization

The operational law proposed by Zhou et al. (J Intell Fuzzy Syst 30(1): 71–87, 2016) contributes to developing fuzzy arithmetic, while its applicable conditions are confined to strictly monotone functions and regular LR fuzzy numbers, which are hindering their operational law from dealing with more general cases, such as problems formulated as monotone functions and problems with fuzzy variables represented as fuzzy intervals (e.g., trapezoidal fuzzy numbers). In order to handle such cases we generalize the operational law of Zhou et al. in both the monotonicity of function and fuzzy variables in this paper and then apply the extensive operational law to the cases with monotone (but not necessarily strictly monotone) functions with regard to regular LR fuzzy intervals (LR-FIs) (of which regular LR fuzzy numbers are special cases). Specifically, we derive the computational formulae for expected values (EVs) of LR-FIs and monotone functions with regard to regular LR-FIs, respectively. On the other hand, we develop a solution scheme to dispose of fuzzy optimization problems with regular LR-FIs, in which a fuzzy programming is converted to a deterministic equivalent one and a newly devised solution algorithm is utilized to get the deterministic programming solved. The numerical experiments are conducted using our proposed solution scheme and the traditional fuzzy simulation-based genetic algorithm in the context of a purchasing planning problem. Computational results show that our method is much more efficient, yielding high-quality solutions.

A machine learning-enhanced design optimizer for urban cooling

Urban cooling becomes a priority in urban planning and design practices. Limited by the slow running speed and prescriptive nature, existing computational tools such as simulation and optimization are yet to be fully integrated in the design decision-making process. This paper describes the Machine Learning-Enhanced Design Optimizer (MLEDO), a novel workflow in search of optimal design option for urban cooling. A physics-based simulation model was developed to assess the cooling performances of a large database of urban design variations. The database was used to train an Artificial Neural Network model, which was then linked with a Genetic Algorithm to rapidly identify optimal design options. The MLEDO workflow was evaluated using a new development urban site against a traditional Simulation-based Genetic Algorithm Design Optimizer (SGADO) as well as human designers. MLEDO outperformed the latter two in terms of efficiency and the performance of the optimal design options. It can also quantify the importance of design parameters in their contribution to cooling performances, which can be used to enhance the understanding of human designers and inform design revisions. MLEDO has the potential to be further developed into a software tool in support of early-stage urban design.

Application of multi-objective probabilistic fractional programming problem in production planning

This paper presents the application of multi-objective probabilistic fractional programming problem in production planning. The production planning model for a manufacturing company that produce multiple products with a specified period is formulated by considering some of the parameters in the right hand sides of the constraints as random variables following continuous distribution namely gamma distribution. The formulated mathematical model is a multi-objective probabilistic fractional programming problem. In the solution procedure, the deterministic equivalent of the probabilistic programming problem has not been obtained. The analytical method for multi-objective fractional programming problem has also not been applied to solve the proposed model. A stochastic simulation-based genetic algorithm is applied to solve the proposed model directly. A set of Pareto optimal solutions is obtained for the formulated production planning problem.

A simulation-based multi-objective genetic optimization framework for efficient building design in early stages: application for Vietnam's hot and humid climates

PurposeIn many countries, innovation in building design for improving energy performance, reducing CO2 emissions and minimizing life cycle cost has received much attention for sustainable development. This paper investigates the importance of optimization tools for enhancing the design performance in the early stages of Vietnam's cooling-dominated buildings in hot and humid climates using an integrated building design approach.Design/methodology/approachThe methodology of this study exploits the non-dominated sorting genetic algorithm (NSGA-II) optimization algorithm coupled with building simulation to research a trade-off between the optimization of investment cost and energy consumption. Our approach focuses on the whole optimization problem of thermal envelope, glazing and energy systems from preliminary design phases. The methodology is then tested for a case study of a non-residential building located in Hanoi.FindingsThe results show a considerable improvement in design performance by our method compared to current building design. The optimal solutions present the trade-off between energy consumption and capital cost in the form of a Pareto front. This helps architects, engineers and investors make important decisions in the early design stages with a large view of impacts of all factors on energy performance and cost.Originality/valueThis is one of the original research to study integrated building design applying the simulation-based genetic optimization algorithm for cooling-dominated buildings in Vietnam. The case study in this article is for a non-residential building in the north of Vietnam but the methodology can also be applied to residential buildings and other regions.

A novel two-phase approach for the bi-objective simultaneous delivery and pickup problem with fuzzy pickup demands

This paper is dedicated to providing a solution framework for the bi-objective vehicle routing problem with simultaneous delivery and pickup which aims to minimize the comprehensive cost as well as maximize the recycling revenue in each round of dispatching. Considering that the real weights of goods to be recycled from the customers are fixed but cannot be precisely given while making one-time routing scheme in the daily operations, the pickup demands are considered as fuzzy numbers, and accordingly a fuzzy chance-constraint programming model for obtaining a prior route solution from the perspective of risk is presented. Afterwards, by demonstrating the continuity and monotonicity of the objective functions, a two-phase approach based on the operational law of the inverse credibility distribution is introduced to solve the model, including translating it into an equivalent nonlinear programming model and resorting to the existing algorithms for obtaining optimal solutions afterwards. Subsequently, in order to validate the performance of the proposed approach and in consideration of the inherent complexity of the vehicle routing problem, a two-phase-based genetic algorithm and the conventional fuzzy simulation-based genetic algorithm are designed and compared by a clothes delivery and pickup problem. The computational results demonstrate that the proposed solution framework is competitive in effectiveness and efficiency, and the parameter analyses provide some suggestions for guidance.

Application of Multi-Objective Probabilistic Fractional Programming Problem in Production Planning

This paper presents the application of multi-objective probabilistic fractional programming problem in production planning. The production planning model for a manufacturing company that produce multiple products with a specified period is formulated by considering some of the parameters in the right hand sides of the constraints as random variables following continuous distribution namely gamma distribution. The formulated mathematical model is a multi-objective probabilistic fractional programming problem. In the solution procedure, the deterministic equivalent of the probabilistic programming problem has not been obtained. The analytical method for multi-objective fractional programming problem has also not been applied to solve the proposed model. A stochastic simulation-based genetic algorithm is applied to solve the proposed model directly. A set of Pareto optimal solutions is obtained for the formulated production planning problem.

Solving Multi-Objective Linear Fractional Stochastic Transportation Problems Involving Normal Distribution using Simulation-Based Genetic Algorithm

In real-life situations, we human beings faced with multi-objective problems that are conflicting and non-commensurable with each other. Especially, when goods are transported from source to locations with a goal to keep exact relationships between a few parameters, those parameters of such problems might also arise in the form of fractions which are linear in nature such as; actual transportation fee/total transportation cost, delivery fee/desired path, total return/total investment, etc. Due to the uncertainty of nature, such a relationship is not deterministic. Mathematically such kinds of mathematical problems are characterized as a multi-objective linear fractional stochastic transportation problem. However, it is difficult to handle such types of mathematical problems. It can't be solved directly using mathematical programming approaches. In this paper, a solution procedure is proposed for the above problem using a stochastic Genetic Algorithm based simulation. The parameters in the constraint of the above problem follow a normal distribution. The probabilistic constraints are handled by stochastic simulation-based GA for the solution procedure of the proposed problem. The feasibility of probability constraints is checked by the stochastic programming through the Genetic Algorithm approach, without finding the equivalent deterministic model. The feasibility is maintained all-over the problem. The stochastic simulation-based Genetic Algorithm is considered to generate non-dominated solutions for the given problem. Then, a numerical case study is provided to illustrate the method.

Solving multi-objective probabilistic factional programming problem

This paper presents the solution methodology of a multi-objective probabilistic fractional programming problem. In the proposed model the parameters in the constraints coefficient and the right-hand sides of the constraints follow continuous random variables having known distribution. Since the programming problem consists of random variables, multi-objective function and fractional objective function, it is lengthy, time-consuming and clumsy to solve the proposed programming problem using analytical methods. Stochastic simulation-based genetic algorithm approach is directly applied to solve multi-objective probabilistic non-linear fractional programming problem involving beta distribution and chi-square distribution. In the proposed method, it is not necessary to find the deterministic equivalent of a probabilistic programming problem and applying any traditional methods of fractional programming problem. The stochastic simulation-based genetic algorithm is coded by Code block C++ 16.01 compiler. A set of Pareto optimal solutions are generated for a multi objective probabilistic non-linear fractional programming problem. A numerical example and case study on inventory problem are presented to validate the proposed method.

Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

We study transportation network design with stochastic demands and emergency vehicle (EV) lanes. Different from previous studies, this paper considers two groups of users, auto and EV travelers, whose road access rights are differentiated in the network, and addresses the value of incorporating inverse-direction lanes in network design. We formulate the problem as a bilevel optimization model, where the upper-level model aims to determine the optimal design of EV lanes and the lower-level model uses the user equilibrium principle to forecast the route choice of road users. A simulation-based genetic algorithm is proposed to solve the model. With numerical experiments, we demonstrate the value of deploying inverse-direction EV lanes and the computational efficiency of the proposed algorithm. We reach an intriguing finding that both regular and EV lane users can benefit from building EV lanes.

An integrated strategy for fleet maintenance planning

PurposeConventionally, fleet maintenance decisions are made based on the level of repair (LOR) analysis. A general assumption made during LOR analysis is the consideration of the lifetime distribution with constant failure rate (CFR). However, industries do use preventive maintenance (PM) to extend the life of such components, which in turn may affect the LOR decisions such as repair/move/discard. The CFR assumption does not allow the consideration of effect of PM in LOR analysis. The purpose of this paper is to develop a more practical LOR analysis approach, considering the time-dependent failure rate (TDFR) of components and the effect of PM.Design/methodology/approachIn the proposed methodology, first, a detailed life cycle model considering the effect of various parameters related to LOR and PM is developed. A simulation-based genetic algorithm approach is then used to obtain an integrated solution for LOR and PM schedule decisions. The model is also evaluated for the various cases of quality of maintenance measured in terms of degree of restoration.FindingsThe results, from the illustrative example for a multi-indenture and multi-echelon fleet maintenance network, show that the proposed integrated strategy leads to better LCC performance compare to the conventional approach. Additionally, it is identified that the degree of restoration also affects the PM schedule as well as LOR decisions of the fleet system. Therefore, consideration of TDFR is important to truly optimize the LOR decisions. The proposed approach can be applied to fleet of any equipment.Research limitations/implicationsThe approach is illustrated using a hypothetical example of an industrial system. A more complex system structure in terms of number of machines, types of machines (identical vs non-identical), number of echelons, possible repair actions at various echelons, etc. may be present for a particular industrial case. However, the approach presented is generic and can be extended to any system. Moreover, the aim of the paper is to highlight the importance of the considering PM and quality of maintenance in LOR decision making.Originality/valueTo the best of the authors’ knowledge, this is the first work which considers the effect of PM and quality of maintenance on LOR analysis. Consideration of TDFR and imperfect maintenance while optimizing LOR decisions is a complex problem. Thus, the work is of high significance from the research point of view. Also, most of the real life fleet systems use PM to extend the life of the equipment. Thus, present paper is a more practical approach for LOR analysis of such systems.

A new simulation-based genetic algorithm to efficiency measure in IDEA with weight restrictions

To efficiency measure in real-life applications of data envelopment analysis (DEA) model, the inputs and outputs data are sometimes imprecise, and we need to use the weight restrictions in order to incorporate management’s views. For this purpose, the present paper investigates the problems of existing approaches in this area, and proposes a comprehensive DEA model which enables us to estimate the relative efficiency scores of real-life systems. The proposed model contains different types of imprecise data and general form of weight restrictions. A new simulation-based genetic algorithm (GA) is developed to estimate the expected values of relative efficiencies with the comprehensive DEA model. It is shown that the proposed model and the solution approach solves the drawbacks of existing models, and gives more informative and reliable results. Some numerical examples are provided to illustrate the theoretical content of the paper and to show the effectiveness of the new approach.

A multistate-based travel time schedule model for fixed transit route

ABSTRACTIn urban public transportation system, operators tend to set a series of time points and scheduled travel time (STT) to monitor bus operation. Meanwhile, bus drivers are generally supposed to adjust speed to keep on schedule, among which STT is of rather importance. This paper aims to develop a multistate-based model to design travel time schedule for fixed transit route. First, multistate model is used to identify service states and model travel time distribution. Then, an optimization model was proposed, followed by a Monte Carlo simulation-based genetic algorithm procedure to obtain the optimal slack time. A numerical example from a fixed transit route in the city of Shenzhen, China was used to demonstrate the model applicability. The results indicate that the multistate model fits real-world travel time better than lognormal distribution model. For the optimal solutions, sensitivity analysis with respect to random samples and deployed buses was also conducted. Findings of this study may be used for transit travel time modeling and timetable design.

Genetic algorithm approach for solving multi-objective fuzzy stochastic programming problem

This paper is concerned with the solution procedure of a multi-objective fuzzy stochastic optimisation problem by simulation-based genetic algorithm. In this article, a multi-objective fuzzy chance constrained programming problem is considered with continuous fuzzy random variables. The uncertain parameters are considered as fuzzy normal and fuzzy log-normal random variables. The feasibilities of the fuzzy chance constraints are checked by the fuzzy stochastic programming with the genetic process without deriving the deterministic equivalents. The proposed procedure is illustrated by a numerical example.

Mean-Semi-Entropy Models of Fuzzy Portfolio Selection

In this paper, a concept of fuzzy semientropy is proposed to quantify the downside uncertainty. Several properties of fuzzy semientropy are identified and interpreted. By quantifying the downside risk with the use of semientropy, two mean-semi-entropy portfolio selection models are formulated, and a fuzzy simulation-based genetic algorithm is designed to solve the models to optimality. We carry out comparative analyses among the fuzzy mean-entropy models and the fuzzy mean-semi-entropy models and demonstrate that the mean-semi-entropy models can significantly improve the dispersion of investment. Several illustrative examples using stock dataset from the real-world financial market (China Shanghai Stock Exchange) also show the effectiveness of the models.

A Decision Making System for Construction Temporary Facilities Layout Planning in Large-Scale Construction Projects

The construction temporary facilities layout planning (CTFLP) requires an identification of necessary construction temporary facilities (CTFs), candidate locations and a layout of CTFs at candidate locations. This study proposes a decision making system to decide on an appropriate CTFLP in large-scale construction projects to improve the operation safety and efficiency. The system is composed of the input, CTF and candidate location identification, layout optimization, evaluation and selection, as well as output stages. The fuzzy logic is employed to address uncertain factors in real-world situations. In the input stage, the knowledge bases for identifying CTFs and candidate locations are determined. Then, CTFs and candidate locations are identified in the following two stages. Furthermore, a multiobjective mathematical optimization model with fuzzy parameters is established and fuzzy simulation-based Genetic Algorithm is proposed to obtain alternative CTFLPs. The intuitionistic fuzzy TOPSIS method is used to evaluate and select the most satisfactory CTFLP in the last stage. Finally, a large-scale hydropower dam project is used as a practical application to demonstrate the effectiveness and efficacy of the proposed system.

Multi-item EPQ model with learning effect on imperfect production over fuzzy-random planning horizon

Uncertainty is certain in the world of uncertainty. This study revisits an economic production quantity (EPQ) model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a random planning horizon under the joint effect of inflation and time value of money, where the expected time length is imprecise in nature. Transmission of learning effect has been incorporated to reduce the defective production. The total expected profit over the random planning horizon is maximized subject to the imprecise space constraint. The possibility, necessity and credibility measures have been introduced to defuzzify the model. The simulation-based genetic algorithm is used to make decision for the above EPQ model in different measures of uncertainty. The model is illustrated through an example. Sensitivity analysis shows the impacts of different parameters on the objective function in the model.

Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty

Absolute deviation is a commonly used risk measure, which has attracted more attentions in portfolio optimization. The existing mean-absolute deviation models are devoted to either stochastic portfolio optimization or fuzzy one. However, practical investment decision problems often involve the mixture of randomness and fuzziness such as stochastic returns with fuzzy information. Thus it is necessary to model portfolio selection problem in such a hybrid uncertain environment. In this paper, we employ random fuzzy variable to describe the stochastic return on individual security with ambiguous information. We first define the absolute deviation of random fuzzy variable and then employ it as risk measure to formulate mean-absolute deviation portfolio optimization models. To find the optimal portfolio, we design random fuzzy simulation and simulation-based genetic algorithm to solve the proposed models. Finally, a numerical example for synthetic data is presented to illustrate the validity of the method.