An effective VNSSA algorithm for the blocking flowshop scheduling problem with makespan minimization

The well-known particle swarm optimization (PSO) proposed by Kennedy and Eberhart has been widely applied to the continuous optimal problems. However, it is still intractable to apply PSO to discrete optimization problems, such as permutation flow shop scheduling problems (PFSSP). In this paper, a new high performing metaheuristic algorithm hybridizing PSO with variable neighborhood search (VNS) is proposed to solve PFSSP with the objective of minimizing makespan. NEH heuristic has been adopted in the first step to generate good solutions in the initial population, and then PSO and VNS are hybridized to search for optimal or near-optimal solutions of the PFSSP. Two effective neighborhood structures concerned with characteristics of PFSSP have been adopted to enhance VNS's performance. Computational experiments have been conducted on benchmarks and comparison results with other existing algorithms show the efficiency of the proposed algorithm.

A hybrid variable neighborhood search and simulated annealing algorithm to estimate the three parameters of the Weibull distribution

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

Optimizing blocking flow shop scheduling problem with total completion time criterion

Main transformer ULTC and capacitors scheduling by simulated annealing approach

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.

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.

The blocking flow shop scheduling problem (BFSP) prevails widely in the process manufacturing industry. In this paper, a multi-group collaborative sparrow search algorithm (MGCSSA) is proposed for minimizing the makespan for BFSP. Firstly, a collaborative initialization strategy is designed to generate initialized populations with certain quality. Second, an elite solution set and a multi-group collaboration mechanism are introduced to improve the adequacy of the global search, and the producer and scrounger position update formulas are improved to increase the population diversity. Then, three neighborhood heuristics are used to locally strengthen individuals in the elite solution set that enhance the ability of the algorithm to jump out of the local optimum. Finally, the proposed algorithm is evaluated experimentally in test cases. The experimental data and comparative results show that the proposed MGCSSA has superior performance and is highly competitive in solving BFSP.

Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities

Robust face alignment and tracking by combining local search and global fitting

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 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.

1. Introduction 3721.1 Residual dipolar couplings as a route to structure and dynamics 3721.2 A brief history of oriented phase high resolution NMR 3742. Theoretical treatment of dipolar interactions 3762.1 Anisotropic interactions as probes of macromolecular structure and dynamics 3762.1.1 The dipolar interaction 3762.1.2 Averaging in the solution state 3772.2 Ordering of a rigid body 3772.2.1 The Saupe order tensor 3782.2.2 Orientational probability distribution function 3802.2.3 The generalized degree of order 3802.3 Molecular structure and internal dynamics 3813. Inducing molecular order in high resolution NMR 3833.1 Tensorial interactions between the magnetic field and anisotropic magnetic susceptibilities 3833.2 Dilute liquid crystal media: a tunable source of order 3843.2.1 Bicelles : from membrane mimics to aligning media 3853.2.2 Filamentous phage 3873.2.3 Transfer of alignment from ordered media to macromolecules 3883.3 Magnetic field alignment 3893.3.1 Paramagnetic assisted alignment 3893.3.2 Advantages of using magnetic alignment 3894. The measurement of residual dipolar couplings 3914.1 Introduction 3914.2 Frequency based methods 3924.2.1 Coupling enhanced pulse schemes 3924.2.2 In phase anti-phase methods (IPAP): 1DNH couplings in proteins 3934.2.3 Exclusive correlated spectroscopy (E-COSY): 1DNH, 1DNC′ and 2DHNC′ 3954.2.4 Extraction of splitting values from the frequency domain 3964.3 Intensity based experiments 3974.3.1 J-Modulated experiments: the measurement of 1DCαHα in proteins 3974.3.2 Phase modulated methods 3994.3.3 Constant time COSY – the measurement of DHH couplings 3994.3.4 Systematic errors in intensity based experiments 4005. Interpretation of residual dipolar coupling data 4015.1 Structure determination protocols utilizing orientational constraints 4015.1.1 The simulated annealing approach 4015.1.2 Order matrix analysis of dipolar couplings 4025.1.3 A discussion of the two approaches 4025.2 Reducing orientational degeneracy 4035.2.1 Multiple alignment media in the simulated annealing approach 4045.2.2 Multiple alignment media in the order matrix approach 4055.3 Simplifying effects arising due to molecular symmetry 4065.4 Database approaches for determining protein structure 4076. Applications to the characterization of macromolecular systems 4086.1 Protein structure refinement 4086.2 Protein domain orientation 4096.3 Oligosaccharides 4136.4 Biomolecular complexes 4156.5 Exchanging systems 4167. Acknowledgements 4188. References 419Within its relatively short history, nuclear magnetic resonance (NMR) spectroscopy has managed to play an important role in the characterization of biomolecular structure. However, the methods on which most of this characterization has been based, Nuclear Overhauser Effect (NOE) measurements for short-range distance constraints and scalar couplings measurements for torsional constraints, have limitations (Wüthrich, 1986). For extended structures, such as DNA helices, for example, propagation of errors in the short distance constraints derived from NOEs leaves the relative orientation of remote parts of the structures poorly defined. Also, the low density of observable protons in contact regions of molecules held together by factors other than hydrophobic packing, leads to poorly defined structures. This is especially true in carbohydrate containing complexes where hydrogen bonds often mediate contacts, and in multi-domain proteins where the area involved in domain–domain contact can also be small. Moreover, most NMR based structural applications are concerned with the characterization of a single, rigid conformer for the final structure. This can leave out important mechanistic information that depends on dynamic aspects and, when motion is present, this can lead to incorrect structural representations. This review focuses on one approach to alleviating some of the existing limitations in NMR based structure determination: the use of constraints derived from the measurement of residual dipolar couplings (D).

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.

Scheduling of jobs has been a challenging task in manufacturing and the most real life scheduling problems, which involves multi-objectives and multi-machine environments. This paper presents tabu search and simulated annealing approaches for scheduling jobs on identical parallel machines. The identical parallel machine scheduling problem has been considered to minimize the mean tardiness for the jobs. Initially, an initial solution has been obtained using EDD dispatching rule then, simulated annealing and tabu search have been applied to reach a near optimal solution. Computational experiments are performed on problems with up to 10 machines and 150 jobs. The computational results indicate that the two proposed approaches are capable of obtaining better solutions for the given scheduling problem. Moreover, the tabu search approach provides better solution then simulated annealing approach.