Fuzzy Simulation Approach Research Articles

Coordination of Cyclic crossover and Bat Algorithm for the Travelling Salesman Problems in Different Environments: A Simulation Approach

In this study, the features of cyclic crossover process and K-opt are incorporated in the bat algorithm (BA) to solve the Travelling Salesman Problems (TSP) in different environments. Swap operation and swap sequence are applied for the modification of the different operations of the BA to solve the TSPs. The cyclic crossover operation is applied in a regular interval of iterations on the best found solution and each solution of the final population of BA for the enhancement of the exploration as well as exploitation of the search process. K-Opt operation is applied on the population in each iteration of the BA with some probability for the exploitation. The algorithm is tested with a set of benchmark test instances of the TSPLIB. The algorithm produces exact results for a set of significantly large size problems. For the TSPs in fuzzy environment, a fuzzy simulation approach is proposed to deal with the fuzzy data having linear as well as non-linear membership functions. Also, a rough simulation process is proposed to deal with the TSPs in the rough environment where rough estimation can be done following any type of rough measure. The performance of the algorithm is compared with the state-of-the-art algorithms for the TSPs with crisp cost matrices using different statistical tools.

A new fuzzy simulation approach for project evaluation based on concepts of risk, strategy, and group decision making with interval-valued intuitionistic fuzzy sets

A new fuzzy simulation approach for project evaluation based on concepts of risk, strategy, and group decision making with interval-valued intuitionistic fuzzy sets

Influence of briefing clarity on construction projects: a fuzzy hybrid simulation approach

Predicting brief clarity and assessing its impact on project performance at an early stage of the project delivery can aid in mitigating possible cost overruns and delays. However, this is extremely challenging because of two main issues: the complexity of multiple interrelationships between factors influenced by the briefing process, and the lack of data at the early stage of the project delivery. This study proposes a hybrid simulation approach that addresses these issues and models the influence of briefing clarity on project cost and time performance. This was achieved by developing a system dynamics (SD) model of the briefing process. The initial brief clarity is predicted using a fuzzy control system. The fuzzy outcomes were compiled into the SD model, and the obtained hybrid model was validated. Finally, the model was applied to refurbishment projects to evaluate its efficiency. The implication for the refurbishment projects shows that by clearly defining the brief, it significantly reduces the detailed design and construction cost and duration. The results confirm that the proposed hybrid method can be effective in evaluating the impact of briefing clarity on project performance, particularly at an early stage where there is a lack of project data.

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.

A hybrid fuzzy-GA algorithm for the integrated machine allocation problem with fuzzy demands

The integrated machine allocation and facility layout problem (IMALP) is a branch of the general facility layout problem in which, besides selecting machine locations, the processing route of each product is determined. Most research in this area suppose that the flow of material is certain and exact, which is an unrealistic assumption in today's dynamic and uncertain business environment. Therefore, in this paper the demand volume has been assumed as fuzzy numbers with different membership functions. To solve this problem, the deterministic model is first integrated with a fuzzy implication via the expected value model, and thereafter an intelligent hybrid algorithm, including a genetic algorithm and a fuzzy simulation approach has been applied. Finally, the efficiency of the proposed algorithm is evaluated with a set of numerical examples. The results show the effectiveness of the hybrid algorithm in finding the IMALP solutions.

Fuzzy simulation approach to multi-task scheduling in uncertain distributed server systems

The efficient scheduling of tasks in distributed server systems is of great importance to minimise the response time and thus improve the performance of such systems. This paper presents an integrated fuzzy simulation approach to improve the performance of distributed server systems. Performance is defined in terms of time-varying distributions of job arrival, job parallelism and task service demand with fuzzy parameters. In order to achieve the most efficient policies for job scheduling in this fuzzy system, an integrated fuzzy simulation approach is used. This is the first study that uses fuzzy simulation for performance improvement of uncertain distributed server systems.

Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method

In reality, decision-makers are always in front of imprecise and vague operational conditions. We propose a practical multiobjective joint replenishment and delivery scheduling (JRD) model with deterministic demand and fuzzy cost. This model minimizes the total cost defuzzified by the signed distance method and maximizes the credibility that the total cost does not exceed the budget level. Then, an inverse weight fuzzy nonlinear programming (IWFNLP) method is adopted to formulate the proposed model. This method embeds the idea of inverse weights into the Max-Min fuzzy model. Thirdly, the fuzzy simulation approach and differential evolution algorithm (DE) are utilized to solve this problem. Results show that solutions derived from the IWFNLP method satisfy the decision-maker’s desirable achievement level of the cost objective and credibility objective. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component. Our study also shows that the improved DE outperforms DE with a faster convergence speed.

Performance optimisation of mammography clinics with non-crisp inputs by an integrated fuzzy simulation approach

This paper presents an integrated fuzzy simulation model for performance optimisation of mammography clinics. It estimates patients flow, length of stay and utilisation of personnel and equipments. The integrated model considers the arrival rates of patients with two modes of probabilistic and fuzzy parameters. The results of each mode is shown and compared which show that fuzzy simulation is better than probabilistic mode especially when we do not have exact input data. In this paper, we consider two models for two clinics that are different in the number of personnel and equipments. Visual SLAM is used to develop both fuzzy and conventional simulation. Furthermore, fuzzy simulation provides better solution for the mammography clinic with respect to desired outputs. Moreover, the integrated fuzzy simulation optimises the performance of the stated clinics. This is the first study that uses an integrated fuzzy simulation approach for performance optimisation of mammography clinics.

Solving Water Quality Management Problem through Combined Genetic Algorithm and Fuzzy Simulation

A combined genetic algorithm and fuzzy simulation approach (GAFSA) was developed through integrating fuzzy chance-constrained programming (FCCP) and genetic algorithm (GA) into a general optimization framework. The major advantage of GAFSA is that it could tackle generally-shaped fuzzy membership functions on both sides of the model constraints, rather than handle single special forms like triangular or trapezoidal. An agricultural water quality management problem that has been investigated by a number of previous studies was used to demonstrate the applicability of the proposed method. The results showed that GAFSA allowed violation of system constraints at specified possibilistic confidence levels, leading to model solutions with higher system benefits. A conservative planning scheme could bring a more reliable system, but would be less economically attractive. Conversely, planning towards a higher system benefit would lead to a higher risk of system failure. The proposed model could help agricultural water managers analyse the trade-off between the overall system benefit and the failure risk of environmental compliance. A comparison of GAFSA to FCCP was given, and the potential limitations of the proposed method were also discussed.

Replenish-up-to multi-chance-constraint inventory control system under fuzzy random lost-sale and backordered quantities

In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of inventory for each product such that the total profit under budget and service level constraints is maximized. In order to solve the model, a hybrid method of fuzzy simulation, stochastic simulation, and particle swarm optimization approach (Hybrid FS–SS–PSO) is used. At the end, several numerical illustrations are given to demonstrate the applicability of the proposed methodology and to compare its performances with the ones of another hybrid algorithm as a combination of fuzzy simulation, stochastic simulation, and genetic algorithm (FS–SS–GA). The results of the numerical illustrations show that FS–SS–PSO performs better than FS–SS–GA in terms of both objective functions and CPU time.

An integrated fuzzy DEA–Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS

This paper presents an integrated fuzzy data envelopment analysis (FDEA) and fuzzy computer simulation approach for optimization of operator allocation in multi product cellular manufacturing systems (CMS) with learning effects. Operator allocation with learning effects is a challenging issue in flexible manufacturing systems in general and in CMS in particular. The main contribution of this work is taking into consideration various operators layouts and learning effects using fuzzy simulation and fuzzy DEA. FDEA is utilized to assess simulation alternatives in various levels of uncertainty. Previous studies consider only one type of product with crisp inputs, whereas this study considers multi-products and fuzzy set up times and processing times for CMS modeling. In addition, this study considers and integrates learning effects for optimum operators’ allocation. Moreover, more robust CMS assessment indicators are used in the proposed model. A case study illustrates the practicability, effectiveness and superiority of the proposed methodology.

Fuzzy modelling and simulation of an emergency department for improvement of nursing schedules with noisy and uncertain inputs

This study presents an integrated fuzzy simulation for improvement of quality of care in the emergency department of a large general hospital. The simulation model is developed to cover the complete flow of patients in the emergency department. Moreover, fuzzy simulation is applied to find the best nursing schedule based on the minimum patients’ queue time. The data for this research was collected from an actual emergency department in a major hospital. A-cut method shows the patient’s waiting time in a queue in the emergency department is shortened, thereby raising the quality of patient-care and patient-satisfaction. The fuzzy simulation approach of this study is ideal for all emergency departments because of extensive uncertainties they face in dealing with patients’ care and treatments. This is the first study that integrates fuzzy logic with simulation for improvement of patient flow in emergency departments with uncertainty and ambiguity.

Evaluating Flight Delays Rating Using Fuzzy Simulation Approach

This paper offers an assessment of flight delays rating, which deals with general concept of risk and safety. With the use of linguistic terms, subjective assessments of qualitative delays rating indexes are represented with fuzzy variable. The relative weights of rating attributes are assessed using a pairwise comparison process. Based on the concept of optimality, Fuzzy simulation approach is employed to dispose of the evaluation model and compute the expected values. Finally, an empirical study about a major domestic airline is conducted to demonstrate the effectiveness of the approach.

A unique fuzzy simulation approach for concurrent improvement of customer satisfaction in integrated information and production processes with ambiguity

In a real business process, the parameters for probability distributions are unknown or ambiguous. Therefore, conventional modeling of these systems using deterministic parameters is questionable. Fuzzy simulation enables modeling uncertainties in such systems. The objective of this study is to model and improve concurrently the performance of integrated information, business, and production process of a manufacturing system using fuzzy simulation. Here, performance is defined as customer satisfaction. The superiority of the fuzzy simulation approach over conventional models used in prior studies is discussed. The integrated fuzzy approach in this study enables evaluating customer lead times in six dimensions with uncertainties considerations. Major effects of business process reengineering and information technology are evaluated before the actual implementation. The comparison results indicate that the fuzzy model is closer to the actual system. This is the first study that presents an integrated fuzzy model for improvement of customer satisfaction in integrated information, business, and production processes with ambiguity.

Model and algorithm of fuzzy joint replenishment problem under credibility measure on fuzzy goal

The joint replenishment problem (JRP) has received considerable attention and all of the work on JRP is under explicit environment. In fact, the decision makers often have to face vague operational conditions. In this paper, a novel JRP model with fuzzy minor replenishment cost and fuzzy inventory holding cost is developed. More concisely, this model is a fuzzy dependent-chance programming (DCP) model. Subsequently, the technique of the traditional fuzzy simulation (FS) approach and differential evolution algorithm (DE) are integrated to design a hybrid intelligent algorithm named FSDE-I to solve this practical fuzzy JRP. Thirdly, another intelligent algorithm named FSDE-II using an improved FS approach is proposed to estimate the credibility more precisely. Finally, FSDE-I and FSDE-II are illustrated with numerical examples and the results show the effectiveness of FSDE-II.

Optimisation of train scheduling in complex railways with imprecise and ambiguous input data by an improved integrated model

Modelling and scheduling of cargo and passenger trains with time limitations, queue priority and limited station lines is a cumbersome task. Furthermore, when distribution of traverse times are unknown or could not be estimated, conventional simulation approach fails and the need for a more intelligent approach becomes inevitable. This paper presents an integrated fuzzy modelling and simulation approach for such ambiguous cases. The case of this study is based on a specific and actual train route (800 kilometres). To show superiority of the approach the system under study was modelled through both conventional and fuzzy simulation approach. For the purpose of verification and validation, the fuzzy interval data of fuzzy simulation model was compared with the interval data of actual system through fuzzy analysis of variance (ANOVA). Then, random data from fuzzy simulation, conventional simulation models and actual system were compared with design of experiment (DOE). Hence, ANOVA and least significant difference (LSD) method were used to show fuzzy simulation approach is an ideal and superior modelling approach for uncertain and ambiguous scheduling railroad systems such as the actual case of this study.

A Hybrid Approach towards the Assessment of Groundwater Quality for Potability: A Fuzzy Logic and GIS Based Case Study of Tiruchirappalli City, India

The present study aims to develop a new hybrid Fuzzy Simulink model to assess the groundwater quality levels in Tiruchirappalli city, South India. Water quality management is an important issue in the modern times. The data collected for Tiruchirappalli city have been utilized to develop the approach. This is illustrated with seventy nine groundwater samples collected from Tiruchirappalli city Corporation, South India. The characteristics of the groundwater for this plain were monitored during the years 2006 and 2008. The quality of groundwater at several established stations within the plain were assessed using Fuzzy Logic (FL) and GIS maps. The results of the calculated FL and GIS maps with the monitoring study have yielded good agreement. Groundwater quality for potability indicated high to moderate water pollution levels at Srirangam, Ariyamangalam, Golden Rock and K. Abisekapurm zones of the study area, depending on factors such as depth to groundwater, constituents of groundwater and vulnerability of groundwater to pollution. Fuzzy logic simulation approach has shown to be a practical, simple and useful tool to assess groundwater quality assessment for potability. This approach is capable of showing and updating the water quality assessment for drinking.

A CLASS OF INTEGRATED LOGISTICS NETWORK MODEL UNDER RANDOM FUZZY ENVIRONMENT AND ITS APPLICATION TO CHINESE BEER COMPANY

In this paper, we integrated the forward logistics and the reuse reverse logistics to set up a closed-loop integrated logistics system under a random fuzzy environment. A random fuzzy multi-objective programming model was established which can describe the integrated logistics as a cycle of production, distribution, consumption, collection, transportation, recycling, disposal, reuse and redistribution. We then used the expected value operator and the chance-constrained operator to handle the random fuzzy objective functions and the random fuzzy constraints. The solution scheme was pursued by a genetic algorithm based on random fuzzy simulation and compromise approach. And an application to a Chinese beer company was given as an illustration.

Parameter optimization of tandem queue systems with finite intermediate buffers via fuzzy simulation

In this study, the issue of parameter optimization of tandem queue systems (TQS) is discussed. Since the data of such systems are regularly imprecise, a fuzzy logic modeling approach seems to be very necessary and more practical than conventional simulation or mathematical modeling. This issue has been ignored in the previous studies. Therefore, a fuzzy simulation based method is proposed and its performance is compared with the conventional simulation through a numerical example. In order to compare the results of these simulation methods, an efficiency index is proposed. It is seen that the proposed fuzzy simulation based method performs more efficient with regard to the efficiency index. Aside from this advantage, by using the fuzzy simulation approach of this study, we can have more realistic solutions when imprecision occurs in TQS. This is the first study that uses fuzzy simulation approach for TQS with imprecise and uncertain data.

Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishment and Discount under Fuzzy Purchasing Price and Holding Costs

While in multi-periodic inventory control problems the usual assumption are that the orders are placed at the beginning of each period (periodic review) or depending on the inventory level they can happen at any time (continuous review), in this research, we relax these assumptions and assume that the periods between two replenishments of the products are independent and identically distributed random variables. Furthermore, assuming the purchasing price are triangular fuzzy variables, the quantities of the orders are of integer-type and that there are space, budget and service level constraints, incremental discount is considered to purchase products and a combination of back-order and lost-sales are taken into account for the shortages. We show that the model of this problem is a fuzzy mixed-integer nonlinear programming type and in order to solve it, a hybrid method of fuzzy simulation and genetic algorithm approach is used. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology in real world inventory control problems.