A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP.

Air traffic prediction with fuzzy constraints

In view of the deficiencies of poor prediction accuracy, time-consuming and low efficiency of traditional traffic prediction models, fuzzy constraints are introduced into air traffic traffic system to represent some uncertain information in the field of artificial intelligence, and construct a fuzzy constraint-based air traffic flow prediction The fuzzy constraint-based air traffic prediction model is constructed. By analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set, the prediction model is proposed. The air traffic flow prediction model is built by analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set that affect the fuzzy constraint, and proposing the construction process of the prediction model. The experimental results show that the air traffic flow prediction model can be used to predict the air traffic flow. The experimental results show that the improved prediction model is better than the traditional prediction model in predicting air traffic flow. The results show that the improved prediction model has better prediction results, shorter time consumption and higher accuracy than the traditional prediction model.

A chance-constraint programming model with interval-valued pythagorean fuzzy constraints

Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets are more proficient in handling uncertain and imprecise information than intuitionistic fuzzy sets and fuzzy sets. In this article, we put forward a chance-constraint programming method to solve linear programming network problems with interval-valued Pythagorean fuzzy constraints. This practice is developed using score function and upper and lower membership functions of interval-valued Pythagorean fuzzy numbers. The feasibility of the anticipated approach is illustrated by solving an airway network application and shown to be used to solve different types of network problems with objective function having interval-valued Pythagorean fuzzy numbers by employing it on shortest path problem and minimum spanning tree problem. Furthermore, a comparative examination was performed to validate the effectiveness and usefulness of the projected methodology.

Supply Interruption Supply Chain Network Model with Uncertain Demand: An Application of Chance-Constrained Programming with Fuzzy Parameters

The downstream supply interruption of manufacturers is a disaster for the company when the demand is uncertain in the market; a fuzzy programming with fuzzy parameters model of supply interruption supply chain network is established by simulating market operation rules. The aim of the current study is to build a fuzzy chance-constrained programming method which is developed for supporting the uncertainty of demand. This method ensured that the fuzzy constraints can be satisfied at specified confidence levels, leading to cost-effective solutions under acceptable risk magnitudes. Finally, through the case of the electronic product manufacturing enterprise, the feasibility and effectiveness of the proposed model are verified by adopting a sensitivity analysis of capacity loss level and minimizing objective function. Numerical simulation shows that selecting two manufacturing centers can effectively reduce the supply chain cost and maintain business continuity.

Employing fuzzy logic for debris management after earthquakes. Case study: Khorramabad city

Debris treatment after earthquake is very important matter especially after large-scale disasters. Efficient debris treatment is leading to faster reconstruction. Particularly, this article examines fuzziness in debris treatment planning. The problem reduces to a standard transport problem by fundamental formulation. A fuzzy debris transport problem for supply and demand formulated fuzzy constraints. A problem-solving algorithm by referring numerical example demonstrates an irrelevant approach. Realistic debris transport planning can be defined by using the recommended method.

An Improved Fuzzy Inventory Model Under Two Warehouses

The objective of this work is to present an improved inventory system with fuzzy constraints dealing with two warehouses system-own and rented. In the present model, we analyze the system under the consideration of two warehouses and without shortages with the assumptions of the linear demand function (increasing function of time). Generally, in today’s business scenario for sessional products, some constraints like storage cost, deteriorating cost, and ordering cost change with their original values. Therefore, these constraints cannot be assumed to be constant in that situation. Depending on these facts that we handle these costs as a triangular fuzzy number and hence apply the signed distance technique to solve the corresponding problem. The key objective of this work is to determine the optimal inventory level, and inventory time schedule to a minimum of the whole inventory cost. The proposed model is demonstrated with two numerical examples to observe the behavior of constraints with system cost and compare their performance with and without fuzzy environment.