An optimized evacuation model with compatibility constraints in the context of disability: an ancient-inspired Giza Pyramids Construction metaheuristic approach

Applied Intelligence - In recent years, the number and severity of natural disasters have increased. These disasters always carry the risks of mortality and injury. But its mortality risks are much...     

Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times

In this paper, we consider a three-stage assembly flowshop scheduling problem with bi-objectives, namely the mean flow time and maximum tardiness. This problem can be considered as a production system model consisting of three stages: (1) different production operations are done in parallel, concurrently and independently, (2) the manufactured parts are collected and transferred to the next stage, and (3) these parts are assembled into final products. In this paper, sequence-dependent setup times and transfer times are also considered as two important presumptions in order to make the problem more realistic. We present a novel mathematical model for a production system with a new lower bound for the given problem. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. Thus, we propose two meta-heuristics, namely simulated annealing and tabu search, to solve a number of test problems generated at random. Finally, the computational results are illustrated and compared in order to show the efficiency of the foregoing meta-heuristics.     

Signed distance ranking based approach for solving bounded interval-valued fuzzy numbers linear programming problems

Fuzzy linear programming (FLP) problems with a wide varietyof applications in sciencesand engineering allow working with imprecise data and constraints, leading to more realistic models. The main contribution of this study is to deal with the formulation of a kind of FLP problems, known as bounded interval-valued fuzzy numbers linear programming (BIVFNLP) problems, with coefficients of decision variables in the objective function, resource vector, and coefficients of the technological matrix represented as interval-valued fuzzy numbers (IVFNs), and crisp decision variables limited to lower and upper bounds. Here, based on signed distance ranking to order IVFNs, the bounded simplex method is extended to obtain an interval-valued fuzzy optimal value for the BIVFNLP problem under consideration. Finally, one illustrative example is given to show the superiority of the proposed algorithm over the existing ones.     

Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

One of the fundamental challenges of the robotics field is robot's movement. That is, why route planning is an eminent issue of robotics research and it is used to enhance autonomy of moving robots in complex environments. The objective of route planning problem is to find the shortest route without collide from initiation point to destination point so that the amount of energy consumption by robot would not exceed a predefined amount. Because neither the amount of energy consumption nor the robot's passed distance index cannot be measured precisely due to environmental conditions, and fuzzy data is used for modeling the problem and the problem would be called “Robot Fuzzy Constrained shortest Route” problem. The main contributions of this study are fivefold: (i) The mathematical model of fuzzy constrained shortest route problem (FCSRP) is formulated; (ii) An elite artificial bees' colony (EABC) algorithm is used to solve the robot's FSCRP; (iii) The proposed EABC algorithm is simulated with two fuzzy networks; (iv) The performance of the proposed approach is compared with the performance of genetic algorithm and particle swarm optimization algorithm; and (v) The results show the convergence speed of the EABC algorithm is higher than the existing algorithms.     

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

Many studies have recently been conducted on the evaluation of system performance with a two‐stage network structure in data envelopment analysis (DEA) literature. One of the topics of interest to researchers has been the mitigation of undesirable products or nondiscretionary factors into their corresponding possible production set (PPS) and their impact on overall efficiency calculations. Determination of decision‐making units (DMUs) with Pareto–Koopmans efficiency status is decisive in identifying benchmark units. The calculated overall efficiency status is compromised when both undesirable products and nondiscretionary factors are present. This work utilizes an axiomatic approach. A novel PPS for a two‐stage network in presence of undesirable intermediate products and nondiscretionary exogenous inputs is introduced. Based on this PPS and by focusing on the principle of mathematical dominance, new models for evaluating overall and divisional efficiencies are presented. In addition, by proposing a two‐step network DEA approach, a necessary and sufficient condition for detection of DMUs with Pareto–Koopmans efficiency status is provided. And by introducing a two‐step algorithm, a novel technique for determining overall efficiency conditions is produced. Finally, the proposed technology is applied to a practical example, and outcomes are discussed.     

Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model

In recent years, BOT approach has provided an increasingly popular project financing to move toward infrastructure development in Asian countries such as Iran. There are many complexities in projects because of the variety of factors in project’s trend and also the dependence of project on mainly national factors. Due to these complexities and their long-term operating, the projects meet with uncertainty and numerous risks. In this paper, an attempt is made at identifying common risks in BOT projects. A novel hierarchical structure of risks is presented on the basis of the project-oriented point of view; next, some effective criteria for risk ranking in BOT projects are introduced.Then, the problem is defined in Fuzzy Multi Attribute Decision Making (FMADM) field. Therefore, Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods are presented in order to rank high risks in BOT projects. In this study, these two methods are compared in four respects: separation among alternatives, fuzzy error in criteria’s weights, risk response planning, and numerousness of alternatives in proportion to criteria. The proposed model is used for identifying and assessing risks in Iran BOT power plant project. Finally, the rankings of high risks are determined according to their results at the end of this paper.     

A fuzzy bi-objective mathematical model for optimum portfolio selection by considering inflation rate effects

Selecting and scheduling of project portfolios from a given set of investment proposals is one of the most important problems for which managers take several aspects into consideration. Inflation rate as one of these aspects is inherently uncertain, and because of this, various inflation rates can cause risky environments. Since risk can be decision driver for a proper selection, the purpose of this paper is fixed to minimize the risk and maximize the projects rate of return as two conflict objectives by considering inflation rate effects. A bi-objective mathematical programming model with fuzzy parameters is developed and well-known NSGA-II is applied to solve the developed model. Pareto plot of two conflict objectives helps decision maker (DM) to choose the best solution based on DM’s acceptable risk level. Moreover, the proposed model makes the project portfolio selection process more effective with developing a novel mathematical programming by sharing limited available budgets and resources. Finally, numerical results indicate the effectiveness of the proposed model with consideration the inflation rate effects on selecting project portfolios.     

Note on “A fuzzy approach to transport optimization problem”

In the article (Sudhagar and Ganesan in Optim Eng  10.1007/s11081-012-9202-6) Sudhagar and Ganesan presented an algorithm to solve a fuzzy transportation problem without converting it into a crisp transportation problem based upon score method of ranking fuzzy numbers. Using an example we show that their method will not always lead to a fuzzy optimal solution. Specially, we give a non negative fuzzy solution for the numerical example solved by them which has less total fuzzy transportation cost.     

An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

The using of an autonomous wheeled mobile robot (AWMR) that perform diverse processes in a numerous number of applications without human’s interposition in an unknown environment is thriving, nowadays. An AWMR can search the environment, create an adequate map, and localizing itself into this map, by interpreting the environment, autonomously. The FastSLAM is a structure for simultaneous localization and mapping (SLAM) for an AWMR. The correctness and efficiency of the estimation of the FastSLAM often depend on the accurate a previous knowledge of the control and measurement noise covariance matrices. Also, inaccurate previous knowledge may seriously degrade their efficiency. One of the major causes of losing particle manifold is sample impoverishment in the FastSLAM. These cases of the most main problems. This paper presents a robust new method to solve these problems as called Hybrid filter SLAM. In this method, for learning the measurement and control noise covariance matrices for increasing correctness and consistency are utilized Intuitionistic Fuzzy Logic System (IFLS). In order to optimize efficiency of sampling from Cuckoo Search (CS). The results of the simulation and experimental shown that the Hybrid filter SLAM is efficient than the FastSLAM that has less number of computations and good performance for the larger environment.

     

Parallel multi-objective artificial bee colony algorithm for software requirement optimization

Requirements Engineering - In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development....