A STATISTICAL MODEL FOR EXPECTED CYCLE TIME OF SP-AS/RS: AN APPLICATION OF MONTE CARLO SIMULATION

The elimination of international trade barriers, lower tariffs, and shifting centers of global manufacturing and consumption leads to new dynamics in intermodal shipping. Advanced technologies, and in particular, automated storage/retrieval systems (AS/RSs) for designing high-capacity automated container terminals, have been recently proposed as possible candidates for improving the terminal's efficiency and meeting the challenges of the future in marine transportation. In this article, the authors present an analytical statistical model for computing expected cycle time of split-platform AS/RS (SP-AS/RS), in order to reduce average handling time of this system. The accuracy of the proposed statistical model is validated by Monte Carlo simulation. The results show that the analytical model is reliable for the design and analysis of the SP-AS/RS.     

Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R ² = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model.     

Comparisons of several variants of continuous quantum-inspired evolutionary algorithms

Abstract

In this study, an extensive numerical analysis is carried out to investigate the effects of different quantum-based operators on the performance of continuous quantum-inspired evolutionary algorithms (QEAs). In this context, different variants of quantum-inspired evolutionary operators are adopted for numerical simulations. Furthermore, some novel chaos-enhanced QEAs are proposed and their performances are evaluated through the numerical comparative study. Based on evaluating the accuracy, robustness, convergence, scalability and sensitivity to initialisation of the rival methods, it is indicated that the algorithmic structure of QEAs is prone to being combined with chaotic maps. The results demonstrate that chaotically implemented QEAs can effectively explore/exploit the solution spaces of different landscapes and dimensionality, and finally, converge to acceptable regions within the solution domain.     

Teleoperation in the presence of varying time delays and sandwich linearity in actuators

In this paper, a novel control scheme is proposed to guarantee global asymptotic stability of bilateral teleoperation systems that are subjected to time-varying time delays in their communication channel and sandwich linearity in their actuators. This extends prior art concerning control of nonlinear bilateral teleoperation systems under time-varying time delays to the case where the local and the remote robots’ control signals pass through saturation or similar nonlinearities that belong to a class of systems we name sandwich linear systems. Our proposed controller is similar to the proportional plus damping (P+D) controller with the difference that it takes into account the actuator saturation at the outset of control design and alters the proportional term by passing it through a nonlinear function; thus, we call the proposed method as nonlinear proportional plus damping (nP+D). The asymptotic stability of the closed-loop system is established using a Lyapunov–Krasovskii functional under conditions on the controller parameters, the actuator saturation characteristics, and the maximum values of the time-varying time delays. To show the effectiveness of the proposed method, it is simulated on a variable-delay teleoperation system comprising a pair of planar 2-DOF robots subjected to actuator saturation. Furthermore, the controller is experimentally validated on a pair of 3-DOF PHANToM Premium 1.5A robots, which have limited actuation capacity, that form a teleoperation system with a varying-delay communication channel.     

A new approach on auxiliary vehicle assignment in capacitated location routing problem

The location routing problem (LRP) considers locating depots and vehicle routing decisions simultaneously. In classic LRP the number of customers in each route depends on the capacity of the vehicle. In this paper a capacitated LRP model with auxiliary vehicle assignment is presented in which the length of each route is not restricted by main vehicle capacity. Two kinds of vehicles are considered: main vehicles with higher capacity and fixed cost and auxiliary vehicles with lower capacity and fixed cost. The auxiliary vehicles can be added to the transportation system as an alternative strategy to cover the capacity limitations and they are just used to transfer goods from depots to vehicles and cannot serve the customers by themselves. To show the applicability of the proposed model, some numerical examples derived from the well-known instances are used. Moreover the model has been solved by some meta-heuristics for large sized instances. The results show the efficiency of the proposed model and the solution approach, considering the classic model and the exact solution approach, respectively.     

A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization

Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the Bayesian optimization algorithm (BOA), the set of promising solutions forms a Bayesian network and the new solutions are sampled from the built Bayesian network. This paper proposes a novel real-coded stochastic BOA for continuous global optimization by utilizing a stochastic Bayesian network. In the proposed algorithm, the new Bayesian network takes advantage of using a stochastic structure (that there is a probability distribution function for each edge in the network) and the new generation is sampled from the stochastic structure. In order to generate a new solution, some new structure, and therefore a new Bayesian network is sampled from the current stochastic structure and the new solution will be produced from the sampled Bayesian network. Due to the stochastic structure used in the sampling phase, each sample can be generated based on a different structure. Therefore the different dependency structures can be preserved. Before the new generation is generated, the stochastic network’s probability distributions are updated according to the fitness evaluation of the current generation. The proposed method is able to take advantage of using different dependency structures through the sampling phase just by using one stochastic structure. The experimental results reported in this paper show that the proposed algorithm increases the quality of the solutions on the general optimization benchmark problems.     

A quantum particle swarm optimization for the 0–1 generalized knapsack sharing problem

This study proposes a new hybrid heuristic approach that combines the quantum particle swarm optimization (QPSO) technique with a local search phase to solve the binary generalized knapsack sharing problem (GKSP). The approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problems. This study is the first to report on the application of the QPSO method to the GKSP. The efficiency of our proposed approach was tested on a large set of instances, and the results were compared to those produced by the commercial mixed integer programming solver CPLEX 12.5 of IBM-ILOG. The Experimental results demonstrated the good performance of the QPSO in solving the GKSP.     

Optimum K-coverage in Wireless Sensor Network with no Redundant Node by Cellular Learning Automata

Wireless Personal Communications - Wireless Sensor Networks have been widely considered as one of the most important technologies for the twenty-first century. Thus, the coverage and energy...     

Efficient resource-aware control on SIP servers in 802.11n wireless edge networks

World Wide Web - Voice transmission over wireless edge networks is a conventional, cost-effective technology to transmit voice calls over 802.11 edge networks. In this type of service, Session...