Blocking Flow Shop Scheduling Based on a Multi-Group Collaborative Sparrow Search Algorithm

A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.

An accelerated discrete artificial bee colony algorithm under the makespan constraint: Solving the distributed blocking flow shop scheduling problem with balanced energy consumption costs

This paper formulates a bi-criteria fuzzy blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date. An improved multi-objective grey wolf optimization (MOGWO) algorithm is proposed to solve this combinational optimization problem. The proposed MOGWO utilizes the ranked-order-value (ROV) rule for solution representation, employs a dynamic maintenance strategy to maintain archive, and develops a thorough mechanism for leader selection. In addition, to improve the performance of the neighborhood search, a VNS structure with three randomly ranked neighborhood search operators is introduced and implemented on the members of archive that may become the selected leaders. The proposed MOGWO is tested on a fuzzy blocking flow shop scheduling problem of panel block construction, and is compared with general MOGWO and multi-objective particle swarm optimization (MOPSO). Computational results suggest that the proposed MOGWO is superior to the compared algorithms in terms of the convergence, spread and coverage of the optimal solutions. This demonstrates the feasibility and effectiveness of the proposed MOGWO.

Optimizing blocking flow shop scheduling problem with total completion time criterion

This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.

The blocking flow shop scheduling problem: A comprehensive and conceptual review

This paper propose a harmony search (HS) algorithm to minimize makespan for the blocking flow shop scheduling problem. In the proposed algorithm, the HS-based search is employed to evolve harmony vectors to perform exploration, whereas a local search procedure based on the insert neighborhood is used to enhance the local exploitation ability. Extensive computational experiments and comparisons are carried out. Computational results show that the proposed HS algorithm is effective in finding optimal and near-optimal solutions.

This paper presents an innovative hybrid estimation of distribution algorithm, named HEDA, for blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDSTs) to minimize the makespan criterion, which has been proved to be typically NP-hard combinatorial optimization problem with strong engineering background. Firstly, several efficient heuristics are proposed according to the property of BFSP with SDSTs. Secondly, the genetic information of both the order of jobs and the promising blocks of jobs are concerned to generate the guided probabilistic model. Thirdly, after the HEDA-based global exploration, a reference sequence-based local search with path relinking technique is developed and incorporated into local exploitation to escape from local optima and improve the convergence property. Due to the reasonable balance between EDA-based global exploration and sequence dependent local exploitation as well as comprehensive utilization of the speedup evaluation method, the BFSP with SDSTs can be solved effectively and efficiently. Finally, computational results and comparisons with the existing state-of-the-art algorithms are carried out, which demonstrate the superiority of the proposed HEDA in terms of searching quality, robustness, and efficiency.

This paper addresses the minimization of makespan for the permutation flow shop scheduling problem with blocking and sequence and machine dependent setup times, a problem not yet studied in previous studies. The 14 best known heuristics for the permutation flow shop problem with blocking and no setup times are pre-sented and then adapted to the problem in two different ways; resulting in 28 differ-ent heuristics. The heuristics are then compared using the Taillard database. As there is no other work that addresses the problem with blocking and sequence and ma-chine dependent setup times, a database for the setup times was created. The setup time value was uniformly distributed between 1% and 10%, 50%, 100% and 125% of the processing time value. Computational tests are then presented for each of the 28 heuristics, comparing the mean relative deviation of the makespan, the computa-tional time and the percentage of successes of each method. Results show that the heuristics were capable of providing interesting results.

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem. Highlights The blocking flowshop scheduling problem is very attractive problem in engineering. A particle swarm optimization approach is proposed. Extensive experiments are conducted to choose the initialization way. Improvement procedure is introduced based on iterated local search algorithm. A real-world case is solved by the proposed algorithm.

Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

This paper proposes a hybrid algorithm based on the variable neighborhood search (VNS) and the simulated annealing approach (SA) for minimizing the makespan in a blocking flowshop scheduling problem. The proposed hybrid algorithm (VNSSA) adopts simulated annealing approach (SA) as the local search method in the third stage of variable neighborhood search (VNS), and uses a perturbation mechanism consisting of various neighborhood operators to diversify the search. The destruction and construction phases of iterated greedy algorithm are used to generate a trial solution during the perturbation process of SA to further promote the search capability. To validate the performance of the proposed VNSSA algorithm, computational experiments and comparisons were conducted on the well-known benchmark problems of Taillard. The computational results of the proposed algorithm when evaluated against the state-of-art algorithms from the literature show good performance, some new best solutions for Taillard's instances are reported for this problem.

Turbomachinery aerodynamic and aeroacoustic modelling is recognized to be one of the main areas of interest in the frame of industrial engineering, due to the large number of applications concerning the rotating fluid machines. Numerical modelling is today widely integrated in the design and development process of industrial components, as well as Computational Fluid Dynamics (CFD) is a tool well integrated into the industrial development and production life-cycles. This has been made because of two main factors: the increase in the performance of relatively cheap computing systems and the progress made by development of integrated CAE virtual environment even more oriented to design and optimization. Furthermore well established and validated CFD methodologies able to deal with the high complexity of geometries and multiphysics approaches are available and practicable within the industrial environment. Nevertheless since for many of applications such as aerodynamics, heat exchange, etc., CFD is largely used, there are still some areas where it represents a challenge, in particular for its industrial applicability. One of these areas is the Computational AeroAcoustics (CAA), whose interest is rapidly growing due to the increasing demand of quick and efficient numerical tools for the design-to-noise approach to airframe components. The major difficulty of CAA is that noise is a secondary effect of other physical phenomena, and noise prediction has to deal with highly complex topics such as turbulence modelling and transition to turbulence prediction. Moreover the sound pressure compared with the hydrodynamic pressure is several orders of magnitude smaller making its numerical prediction very difficult. The goal of the present work is to perform investigations on two rotating fluid machines: the vertical axis wind turbines (VAWT) and the high speed transonic cruise propellers. The investigations performed were focused on the aerodynamic and aeroacoustic theoretical and numerical modelling in order to achieve the numerical results that actually miss in the literature improving the know-how for two classes of problems which play a central role in the modern industry: the renewable energies and the advanced transportation systems. VAWT modelling was approached from a theoretical point of view, by the implementation of a modern unsteady aerodynamic model for non-stationary airfoil aerodynamic coefficients prediction (the Beddoes Leishman model) into a classical theory, the Double Streamtube Paraschivoiu model. The acoustic modelling of WT was approached on a general basis with the modelling of the airfoil self-generated problem. It was chosen as it is one of the most prominent noise sources for wind turbines and it represents a big challenge for CFD. The preliminary literature review pointed out how there are different airfoil self-generated noise typologies, according to the laminar/turbulent characteristics of the flow field, airfoil aerodynamic parameters and far-field flow conditions. Different CFD numerical approaches were used for the different noise generation mechanisms, including transient RANS/LES simulations with a particular focus for novel turbulence modelling methodologies, involving transitional models and Embedded LES. The acoustic pressure flow computations were performed with the Ffowcs Williams Hawkings (FW-H) acoustic analogy and pressure time signals post-processed with an in-house built acoustic tool. Deep explorations of acoustic signal noise processing, Fast Fourier Transformation techniques and noise signal corrections have been necessary in order to achieve good agreement between experimental and computational data. The reference test-case was the NACA 0012 airfoil, chosen for the wide amount of experimental data available in literature in a significant range of velocities and angles of attack. The problem of propeller aerodynamic and aeroacoustic modelling was approached with the CFD and CAA simulation of a subsonic propeller, the NACA 4-(3)(08)-03, chosen for the experimental results available in literature for the 2-bladed and 7-bladed configurations. This test-case allowed us to set up properly the numerical simulation, the CAD model and the computational grid requirements for such kind of geometries. In a second stage the attention was focused on the NASA SR2 and SR3 geometries, both in the 8-bladed configuration. Experimental data in transonic conditions for wind tunnel and flight test were available for these two geometries, and numerical simulations were performed for 5 different data sets, for asymptotic Mach numbers between 0.6 and 0.9 and rotational speeds between 6000 and 9000 rpm. Results of the simulations are compared with experiments, showing the ability of this approach to predict noise with a discrepancy within a few dB for the different simulated conditions and microphones locations. Particular attention is given to the set of corrections to be applied to acoustic experimental data in order to be consistently compared with free field CFD results. The CFD simulation strategy has been refined to perform complete aerodynamic and aeroacoustic calculations with highly competitive computational cost.

Pulse voltammetry (PV) encompasses a range of electroanalytical techniques utilizing a series of voltage pulses to study electrochemical reactions. These techniques provide numerous advantages over other electroanalytical techniques such as cyclic voltammetry (CV) and impedance spectroscopy by providing high sensitivity and discrimination against charging currents. Critically, this enables a more quantitative analysis of the voltammogram than is typical. In conjunction with mathematical models, PV can provide valuable quantitative information on the reaction kinetics, thermodynamics, and consistency with a hypothetical mechanism. Regression of experimental PV data against simulations, as opposed to comparing a limited set of features such as peak heights or full width at half-height, also enables the researcher to evaluate statistical confidence in the model and estimated parameters. The objective of this work is to develop a pulse voltammetry analysis toolkit (PVAT) to automate kinetic parameter estimation, determine confidence, and enable mechanism discrimination using PV experimental data. The desired toolkit will address multiple PV techniques including square wave voltammetry (SWV), one of the most popular pulse techniques, and other common waveforms including normal pulse (NPV) and staircase (SC). As different pulse waveforms are best suited for elucidating different physical phenomena, the ability to analyze several techniques simultaneously provides significant value. Additional objectives include providing statistical analysis and sensitivity to mechanism parameters. Evaluating sensitivity to the PV parameters (e.g. pulse width, pulse height) is also desired, so that the analyst can improve the experimental design to further reduce uncertainty in kinetic, transport, and thermodynamic parameters. A long-term goal is to extend and apply these techniques to porous electrodes, to enable quantitative evaluation of emerging electroactive materials in batteries and fuel cells. This presentation will include examples demonstrating the application of the developed PV toolkit to quantitative analysis of several PV studies using both experimental and simulated data. Support for multiple techniques has been successfully enabled through a modular toolkit design. Applications of the toolkit in simulation mode, which simply outputs the predicted voltammogram for a user specified pulse profile and mechanism, will be presented to verify the toolkit against prior work. The focus will be on the toolkit application in parameter estimation mode, which utilizes experimental data to determine the kinetic and thermodynamic parameters (e.g. rate constants, half-wave potential) for one or more mechanisms which best fit the observed data. Examples of statistical evaluation of multiple mechanisms against voltammogram data will be shown, demonstrating quantitative comparison to determine the most applicable mechanism for a given system. Currently, PVAT can simulate a sequence of any number of sequential single step electron transfer reactions on a planar electrode, with or without homogeneous reactions affecting any of the intermediates. Analysis of multi-step mechanisms including surface adsorption/desorption coupled with surface-confined electron transfer reaction will also be presented. An example application will show PVAT analysis of the oxidation of acetaminophen under acidic, neutral, and basic pH of cyclic using square wave experimental data. Acetaminophen oxidation was selected as a test case because the pH dependence of the mechanism provides a simple means to generate data with several reaction pathways. The estimated parameters were consistent with available literature data and statistical analysis was able to discriminate among the candidate mechanisms and the modeled voltammograms agreed well with experimental data, as shown in Figure 1. The presentation will conclude with preliminary results for analysis of electrochemical reactions in porous electrodes. Development of a computationally efficient software to utilize PV experiments on porous electrodes will enable rapid characterization of novel candidate electrode materials in battery and fuel cell operations more efficiently and confidently than traditional cycling at various rates. The resulting kinetic, thermodynamic, and transport properties will provide critical input for models to accelerate the insertion of promising electrodes into prototype hardware. Ideally, the data obtained from PV experiments on porous electrodes will allow for prediction of battery and fuel cell behavior operating under a range of use conditions. Acknowledgements This material is based upon work supported by the Army Small Business Innovation Research (SBIR) Program Office under Contract Number: W911NF-17-C-0026. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Army Small Business Innovation Research Program Office or the Army Research Office. Figure 1: CSWV Net Voltammograms Comparing Experimental (Markers) and Model (Lines) Using CE-Mechanism of the Oxidation of Acetaminophen at pH1. The Square Wave Frequencies Ranged from 2.5 – 50 Hz. Figure 1