A genetic algorithm for optimization problems with fuzzy relation constraints using max-product composition

We consider nonlinear optimization problems constrained by a system of fuzzy relation equations. The solution set of the fuzzy relation equations being nonconvex, in general, conventional nonlinear programming methods are not practical. Here, we propose a genetic algorithm with max-product composition to obtain a near optimal solution for convex or nonconvex solution set. Test problems are constructed to evaluate the performance of the proposed algorithm showing alternative solutions obtained by our proposed model.     

Multi-facility location problems in the presence of a probabilistic line barrier: a mixed integer quadratic programming model

We consider a multi-facility location problem in the presence of a line barrier with the starting point of the barrier uniformly distributed. The objective is to locate n new facilities among m existing facilities minimising the summation of the weighted expected rectilinear barrier distances of the locations of new facilities and new and existing facilities. The proposed problem is designed as a mixed-integer nonlinear programming model, conveniently transformed into a mixed-integer quadratic programming model. The computational results show that the LINGO 9.0 software package is effective in solving problems with small sizes. For large problems, we propose two meta-heuristic algorithms, namely the genetic algorithm and the imperialist competitive algorithm for optimisation. The numerical investigations illustrate the effectiveness of the proposed algorithms.     

Monitoring and control of polarization-related impairments in optical fiber systems

Polarization-related impairments have become a critical issue for high-data-rate optical systems, particularly when considering polarization-mode dispersion (PMD) and polarization-dependent loss (PDL). In general, polarization effects are stochastic processes and can occur on short or long time scales. Therefore, control and monitoring of these effects may be crucial in any systems-level mitigation. This tutorial will highlight the following key issues: PMD and PDL, monitoring schemes, emulation of proper statistics, interaction, and applications of polarization scrambling.     

First-order PMD monitoring for NRZ data using RF clock regeneration techniques

We propose and demonstrate two novel techniques for 10 Gb/s polarization-mode-dispersion (PMD) monitoring for NRZ signals that use a regenerated RF clock tone as a monitoring signal. Our techniques regenerate the RF clock tone that is usually absent after square-law detection in the electrical NRZ data spectrum (in the absence of dispersion). Our first technique uses a dispersive element in the monitoring tap-line to put the beat terms between the optical clock sidebands and the carrier in phase and thus regenerates the RF clock tone after detection. Our second technique involves the use of an optical filter that is centered at the bit rate frequency on either the upper or lower sideband of the optical spectrum, removing one of the sidebands and thus preventing the beating that normally cancels the RF clock tone. We show (theoretically, via simulation, and experimentally) the effect that PMD has on these regenerated RF clock tones. We also demonstrate PMD compensation at 10 Gb/s using these techniques for monitoring and show a 6-dB improvement in the 1% power penalty tail. Our techniques are simple, do not require modification at the transmitter, and can be applied to WDM systems via the use of a multichannel dispersive element or a tunable filter swept across all channels.     

Modeling and solving a capacitated stochastic location-allocation problem using sub-sources

We study a capacitated multi-facility location-allocation problem in which the customers have stochastic demands based on Bernoulli distribution function. We consider the capacitated sub-sources of facilities to satisfy demands of customers. In the discrete stochastic problem, the goal is to find optimal locations of facilities among candidate locations and optimal allocations of existing customers to operating facilities so that the total sum of fixed costs of operating facilities, allocation cost of the customers, expected values of servicing and outsourcing costs is minimized. The model is formulated as a mixed-integer nonlinear programming problem. Since finding an optimal solution may require an excessive amount of time depending on nonlinear constraints, we transform the nonlinear constraints of the problem to linear ones to arrive at a simple formulation of the model. Numerical results show that the LINGO 9.0 software is capable of solving small size problems. For medium and large-size problems, we propose two meta-heuristic algorithms, namely a genetic algorithm and a discrete version of colonial competitive algorithm. Computational results show that the proposed algorithms efficiently obtain effective solutions.     

A bi-objective possibilistic programming model for open shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates

We are concerned with an open shop scheduling problem having sequence-dependent setup times. A novel bi-objective possibilistic mixed-integer linear programming model is presented. Sequence-dependent setup times, fuzzy processing times and fuzzy due dates with triangular possibility distributions are the main constraints of this model. An open shop scheduling problem with these considerations is close to the real production scheduling conditions. The objective functions are to minimize total weighted tardiness and total weighted completion times. To solve small-sized instances for Pareto-optimal solutions, an interactive fuzzy multi-objective decision making (FMODM) approach, called TH method proposed by Torabi and Hassini, is applied. Using this method, an equivalent auxiliary single-objective crisp model is obtained and solved optimally by the Lingo software. For medium to large size examples, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed. This algorithm consists of a decoding procedure using a permutation list to reduce the search area in the solution space. Also, a local search algorithm is applied to generate good initial particle positions. Finally, to evaluate the effectiveness of the MOPSO algorithm, the results are compared with the ones obtained by the well-known SPEA-II, using design of experiments (DOE) based on some performance metrics.     

Large-scale building damage assessment using a novel hierarchical transformer architecture on satellite images

This paper presents damage assessment using a hierarchical transformer architecture (DAHiTrA), a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale postdisaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the feature domain after applying a transformer encoder to the spatial features. The proposed network achieves state-of-the-art performance when tested on a large-scale disaster damage data set (xBD) for building localization and damage classification, as well as on LEVIR-CD data set for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery data set, Ida-BD (related to 2021 Hurricane Ida in Louisiana) for domain adaptation. Further, it demonstrates an approach of using this data set by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.     

A dynamic programming approach for finding shortest chains in a fuzzy network

Graph theory has numerous applications to problems in systems analysis, operations research, transportation, and economics. In many cases, however, some aspects of a graph-theoretic problem may be uncertain. For example, the vehicle travel time or vehicle capacity on a road network may not be known exactly. In such cases, it is natural to make use of fuzzy set theory to deal with the uncertainty. Here, we are concerned with finding shortest chains in a graph with fuzzy distance for every edge. We propose a dynamic programming approach to solve the fuzzy shortest chain problem using a suitable ranking method. By using MATLAB, two illustrative examples are worked out to demonstrate the proposed algorithm.     

Enhancing the dynamic range and DGD monitoring windows in DOP-based DGD monitors using symmetric and asymmetric partial optical filtering

We propose and demonstrate a new type of degree-of-polarization (DOP)-based differential-group-delay (DGD) monitor using an optical filter such that the DGD monitoring range and DOP dynamic range are dramatically increased. We apply this technique to varying pulsewidth return-to-zero (RZ), carrier-suppressed RZ (CSRZ), and alternate-chirped RZ (ACRZ) signals and show that by optimally setting the position, bandwidth, and shape of a filter, we can double the DGD monitoring range compared to traditional DOP-based DGD monitors. Using our technique, the DGD monitoring ranges for 10, 20, and 40 Gb/s /spl sim/12.5-ps pulsewidth RZ signals are increased by 32, 33, and 12 ps, respectively. We also show that a narrow-band optical filter, offset from the center of the optical spectrum by the bit-rate frequency, can double the dynamic range of DOP-based DGD monitors for non-RZ (NRZ) signals.