Solving constrained portfolio optimization model using stochastic fractal search approach

Purpose-Optimum utilization of investments has always been considered one of the most crucial aspects of capital markets.Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk.In this series,a population-based evolutionary approach,stochastic fractal search(SFS),is derived from the natural growth phenomenon.This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.Design/methodology/approach-This paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints.SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory.Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm,particle swarm optimization,simulated annealing and differential evolution.The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225,DAX 100,FTSE 100,Hang Seng31 and S&P 100 have been taken in the study.Findings-The study confirms the better performance of the SFS model among its peers.Also,statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.Originality/value-In the recent past,researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach.However,this is the first attempt to apply the SFS optimization approach to the problem.

Multi-objective vendor managed inventory system with interval type-2 fuzzy demand and order quantities

Purpose-The proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer,multi-item and a consolidated vendor store.Regarding demand and order quantities with the deterministic and type-1 fuzzy numbers,we have also formulated the classic/crisp MOVMI model and type-1 fuzzy MOVMI(T1FMOVMI)model.The suggested solution technique can solve both crisp MOVMIand T1FMOVMIproblems.By finding the optimal ordered quantities and backorder levels,the Paretofronts are constructed to form the solution sets for the three models.Design/methodology/approach-A multi-objective vendor managed inventory(MOVMI)is the most recognized marketing and delivery technique for the service provider and the retail in the supply chain in Industry 4.0.Due to the evolving market conditions,the characteristics of the individual product,the delivery period and the manufacturing costs,the demand rate and order quantity of the MOVMI device are highly unpredictable.In such a scenario,a MOVMI system with a deterministic demand rate and order quantity cannot be designed to estimate the highly unforeseen cost of the problem.This paper introduces a novel interval type-2 fuzzy multi-objective vendor managed inventory(IT2FMOVMI)system,which uses interval type-2 fuzzy numbers(IT2FNs)to represent demand rate and order quantities.As the model is an NP-hard,the well-known meta-heuristic algorithm named NSGA-II(Non-dominated sorted genetic algorithm-II)with EKM(Enhanced Karnink-Mendel)algorithm based solution method has been established.Findings-The experimental simulations for the five test problems that demonstrated distinct conditions are considered from the real-datasets of SAPCO company.Experimental study concludes that T1FMOVMI and crisp MOVMI schemes are outclassed by IT2FMOVMI model,offering more accurate Pareto-Fronts and efficiency measurement values.Originality/value-Using fuzzy sets theory,a significant amount of work has been already done in past decades from various points of views to model the MOVMI.However,this is the very first attempt to introduce type-2 fuzzy modelling for the problem to address the realistic implementation of the imprecise parameters.