A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.     

Synthesis of Half‐Sphere/Half‐Funnel‐Shaped Silica Structures by Reagent Localization and the Role of Water in Shape Control

Shape control of silica structures is demonstrated by localization of the reagents. A uniform dispersion of reagents provided straight silica rods, whereas localization of the reagents in the emulsion droplet periphery provided a new type of half‐sphere/half‐funnel structure. The effect of water concentration appeared to be related to the ease of diffusion of the silica precursor inside the emulsion droplet (i.e., the higher the water concentration, the lower the silica precursor diffusion).     

The reduction of computation times of upper and lower tolerances for selected combinatorial optimization problems

Due to the growing interest in approximation for multiobjective optimization problems (MOPs), a theoretical framework for defining and classifying sets representing or approximating solution sets for MOPs is developed. The concept of tolerance function is proposed as a tool for modeling representation quality. This notion leads to the extension of the traditional dominance relation to \(t\hbox {-}\)dominance. Two types of sets representing the solution sets are defined: covers and approximations. Their properties are examined in a broader context of multiple solution sets, multiple cones, and multiple quality measures. Applications to complex MOPs are included.     

Simulated Annealing and Genetic Algorithms for the Facility Layout Problem: A Survey

The facility layout problem (FLP) has many practical applications and is known to be NP-hard. During recent decades exact and heuristic approaches have been proposed in the literature to solve FLPs. In this paper we review the most recent developments regardingsimulated annealing and genetic algorithms for solvingfacility layout problems approximately.     

A Hypergraph Framework for Optimal Model-Based Decomposition of Design Problems

Decomposition of large engineering system models is desirable sinceincreased model size reduces reliability and speed of numericalsolution algorithms. The article presents a methodology for optimalmodel-based decomposition (OMBD) of design problems, whether or notinitially cast as optimization problems. The overall model isrepresented by a hypergraph and is optimally partitioned into weaklyconnected subgraphs that satisfy decomposition constraints. Spectralgraph-partitioning methods together with iterative improvementtechniques are proposed for hypergraph partitioning. A known spectralK-partitioning formulation, which accounts for partition sizes andedge weights, is extended to graphs with also vertex weights. TheOMBD formulation is robust enough to account for computationaldemands and resources and strength of interdependencies between thecomputational modules contained in the model.     

Asymptotic behavior of the expected optimal value of the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) is a higher-dimensional version of the Linear Assignment Problem that arises in the areas of data association, target tracking, resource allocation, etc. This paper elucidates the question of asymptotical behavior of the expected optimal value of the large-scale MAP whose assignment costs are independent identically distributed random variables with a prescribed probability distribution. We demonstrate that for a broad class of continuous distributions the limiting value of the expected optimal cost of the MAP is determined by the location of the left endpoint of the support set of the distribution, and construct asymptotical bounds for the expected optimal cost.     

Subsystem fault tolerance with the Bacon-Shor code

We discuss how the presence of gauge subsystems in the Bacon-Shor code [D. Bacon, Phys. Rev. A 73, 012340 (2006)10.1103/PhysRevA.73.012340 (2006)] leads to remarkably simple and efficient methods for fault-tolerant error correction (FTEC). Most notably, FTEC does not require entangled ancillary states, and it can be implemented with nearest-neighbor two-qubit measurements. By using these methods, we prove a lower bound on the quantum accuracy threshold, 1.94 x 10(-4) for adversarial stochastic noise, that improves previous lower bounds by nearly an order of magnitude.     

Evaluation of high-field asymmetric waveform ion mobility spectrometry coupled to nanoelectrospray ionization for bioanalysis in drug discovery

The potential of high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to nanoelectrospray ionization (nanoESI) as a method to improve sample throughput for bioanalysis in a discovery pharmaceutical setting was explored in this work. The ability of FAIMS to separate gas-phase ions in the millisecond timescale was exploited to eliminate the need for liquid chromatography. Samples were introduced into the FAIMS electrodes/mass spectrometer using offline nanoESI at 20 nL/min and 1.5 kV. Signals were averaged for 30 s after which the next sample could be analyzed. The separation of simple mixtures, e.g., the removal of metabolite and endogenous interferences from parent drug, was demonstrated. Moreover, the application of nanoESI attenuated the ion suppression effects that normally plague conventional electrospray. On average, approximately two-thirds of the neat sample signal intensity was preserved in extracted plasma samples. Standard curves were prepared for several compounds and linearity was obtained over approximately two to three orders of magnitude. This methodology was further tested with the analysis of plasma samples from a mouse pharmacokinetic study. Concentration values determined using nanoESI-FAIMS were comparable to those determined using conventional LC/MS as demonstrated by percent differences of less than 30%. This work demonstrated the proof of concept that the combination of FAIMS and nanospray ionization can be a potentially useful tool to improve the throughput of discovery bioanalysis.