Solving bus terminal location problems using evolutionary algorithms

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential objective function (POF) for the nodes and design a new mutation operator based on POF. To make the memetic algorithm faster, we estimate the variation of the objective function based on POF in the local search as part of an operator in memetic algorithms. Finally, we explore numerically the performance of nine proposed algorithms on over a thousand randomly generated problems and select the best two algorithms for further testing. The comparative studies show that our new hybrid algorithm composing the evolutionary algorithm with the GLS outperforms the multistart simulated annealing algorithm.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

Producer’s behavior analysis in an uncertain bicriteria AGV-based flexible jobshop manufacturing system with expert system

Here, an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost is proposed. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, time is considered to be a triangular fuzzy number and apply an expert system to infer the cost. The expert system based on fuzzy rule backpropagation network to configure the rules for estimating the cost under uncertainty is proposed. A multiple linear regression model is applied to analyze the rules and find the effective rules for cost estimation. The objective is to find a path minimizing an aggregate weighted unscaled time and cost criteria. A fuzzy dynamic programming approach is presented for computing a shortest path in the network. Then, a comprehensive economic and reliability analysis is worked out on the obtained paths to find the optimal producer’s behavior. Finally, an application of the model is illustrated by a numerical example. The results show the effectiveness of our approach for finding an optimal path in a manufacturing system under uncertainty.     

A two-phase linear programming methodology for fuzzy multi-objective mixed-model assembly line problem

We develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective mixed-model assembly line problem. In practice, vagueness and imprecision of the goals in this problem make the fuzzy decision-making complicated. The proposed model considers minimizing total utility work, total production rate variation, and total setup cost, using a two-phase linear programming approach. In the first phase, the problem is solved using a max–min approach. The max–min solution not being efficient, in general, we propose a new model in the second phase to maximize a composite satisfaction degree at least as good as the degrees obtained by phase one. To show the effectiveness of the proposed approach, a numerical example is solved and the results are compared with the ones obtained by the fuzzy mixed integer goal programming and weighted additive methods. The computational results show that the proposed FMOLP model achieves lower objective functions as well as higher satisfaction degrees.     

Design of a neuro-fuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty.     

Degree-of-polarization-based PMD monitoring for subcarrier-multiplexed signals via equalized carrier/sideband filtering

We demonstrate a method for differential-group-delay (DGD) and polarization-mode-dispersion (PMD) monitoring using the degree-of-polarization (DOP) in subcarrier-multiplexed (SCM) systems. Traditional SCM signal show very little DOP sensitivity to DGD/PMD due to the low modulation depth used for generating SCM signals. We use a narrow-band optical filter to equalize the power of the carrier and one of the sidebands by offsetting the filter from the carrier, enabling PMD and DGD monitoring and more than tripling the DOP sensitivity to DGD/PMD. Our technique is simple, uses only a single optical filter, and can be applied to both single- and double-sideband (SSB and DSB) SCM signals as well as single and multisubcarrier systems. Additionally, we show that our monitoring technique is robust to the chromatic dispersion-induced radio-frequency (RF) power fading effect seen in DSB SCM signals. Using this technique to enhance the DOP sensitivity to DGD/PMD and generate a feedback signal to a PMD compensator (PMDC), we obtain an 11-dB improvement in the 5% RF power tail.     

Performance optimization of RZ data format in WDM systems using tunable pulse-width management at the transmitter

Tunable pulse-width management is one of the efficient methods to enhance the robustness of return-to-zero (RZ) data formats for long-haul transmission systems. We demonstrate both single channel and 4 /spl times/ 10-Gb/s wavelength-division-multiplexed performance optimization using tunable pulse-width management. Pure RZ single with tunable pulse-width is generated by changing the driving voltages on a phase modulator and the dispersion values of a tunable dispersion element simultaneously according to our simulation results. Varying the pulse width from 50 to 10 ps at the transmitter can almost double the transmission distance with 4% variation in the residual link dispersion.     

TAMER: an adaptive task allocation method for aging reduction in multi-core embedded real-time systems

The Journal of Supercomputing - Technology scaling has exacerbated the aging impact on the performance and reliability of integrated circuits. By entering into nanotechnology era in recent years,...     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.     

Statistical optimization of process conditions for photocatalytic degradation of phenol with immobilization of nano TiO<Subscript>2</Subscript> on perlite granules

Response surface methodology (RSM) using D-optimal design was applied to optimization of photocatalytic degradation of phenol by new composite nano-catalyst (TiO₂/Perlite). Effects of seven factors (initial pH, initial phenol concentration, reaction temperature, UV irradiation time, UV light intensity, catalyst calcination temperature, and dosage of TiO₂/perlite) on phenol conversion efficiency were studied and optimized by using the statistical software MODDE 8.02. On statistical analysis of the results from the experimental studies, the optimum process conditions were as follows: initial pH, 10.7; initial phenol concentration, 0.5 mM; reaction temperature, 27 °C; UV irradiation time, 6.5 h; UV light intensity, 250 W; catalyst calcination temperature, 600 °C; and TiO₂/perlite dosage, 6 g/L. Analysis of variance (ANOVA) showed a high coefficient of determination (R²) of 91.8%.