Bin Qian

Hybrid differential evolution optimization for no-wait flow-shop scheduling with sequence-dependent setup times and release dates

In this paper, a hybrid algorithm based on differential evolution (DE), namely HDE, is proposed to minimize the total completion time criterion of the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving flow-shop scheduling problem, a largest-order-value (LOV) rule is used to convert the continuous values of individuals in DE to job permutations. Secondly, a speed-up evaluation method is developed according to the property of the NFSSP with SDSTs and RDs. Thirdly, after the DE-based exploration, a problem-dependent local search is developed to emphasize exploitation. Due to the reasonable balance between DE-based global search and problem-dependent local search as well as the utilization of the speed-up evaluation, the NFSSP with SDSTs and RDs can be solved effectively and efficiently. Simulation results and comparisons demonstrate the superiority of HDE in terms of searching quality, robustness, and efficiency.

A hybrid differential evolution algorithm for the multi-objective reentrant job-shop scheduling problem

This paper proposes a hybrid differential evolution algorithm (HDE) for solving the multi-objective reentrant job-shop scheduling problem (MRJSSP) with total machine idleness and maximum tardiness criteria. Firstly, a so-called reentrant-smallest-order-value (RSOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Secondly, after the global search based on DE, a problem-dependent local search with different neighborhoods is presented to emphasize local search. Since both global and local search are well balanced, HDE has the ability to obtain good results. Simulation results and comparisons show the effectiveness of the proposed algorithm.

A copula-based hybrid estimation of distribution algorithm for m -machine reentrant permutation flow-shop scheduling problem

Abstract Aiming at the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP), a copula-based hybrid estimation of distribution algorithm (CHEDA) is presented to minimize the makespan criterion. Firstly, we establish both the operation-based model and the graph model for MRPFSSP, and then several inherent properties about critical path and blocks are proposed and analyzed. Secondly, the copula theory is utilized to build CHEDA’s probability model (i.e., the joint distribution function, JDF) to efficiently extract the useful information from the excellent individuals. Thirdly, the global search based on the JDF model and a new population sampling method is designed to find the promising sub-regions in the total solution space. Fourthly, a problem-dependent local search based on the critical path and blocks is embedded into CHEDA to enhance the local exploitation ability. Finally, simulation experiments and comparisons demonstrate the effectiveness of the proposed CHEDA.

A self-adaptive hybrid population-based incremental learning algorithm for M -machine reentrant permutation flow-shop scheduling

This paper proposes a self-adaptive hybrid population-based incremental learning algorithm (SHPBIL) for the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP) with makespan criterion. At the initial phase of SHPBIL, the information entropy (IE) of the initial population and an Interchange-based search are utilized to guarantee a good distribution of the initial population in the solution space, and a training strategy is designed to help the probability matrix to accumulate information from the initial population. In SHPBILs global exploration, the IE of the probability matrix at each generation is used to evaluate the evolutionary degree, and then the learning rate is adaptively adjusted according to the current value of IE, which is helpful in guiding the search to more promising regions. Moreover, a mutation mechanism for the probability model is developed to drive the search to quite different regions. In addition, to enhance the local exploitation ability of SHPBIL, a local search based on critical path is presented to execute the search in some narrow and promising search regions. Simulation experiments and comparisons demonstrate the effectiveness of the proposed SHPBIL.

A differential evolution approach for NTJ-NFSSP with SDSTs and RDs

In this paper, an efficient differential evolution approach, namely DE_NTJ, is presented to minimize the number of tardy jobs (NTJ) for the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), which is a complex problem and can be abbreviated as NTJ-NFSSP with SDSTs and RDs. To balance the exploration and exploitation abilities of our DE_NTJ, DE-based global search is utilized to obtain the promising regions or solutions over the solution space, and a local search based on the interchange-based neighborhood and problems properties is developed to exploit the neighborhoods from these regions. Simulation results based on a set of random instances show the superiority of DE_NTJ in terms of searching quality, efficiency, and robustness.

An Improved Quantum-Inspired Evolution Algorithm for No-Wait Flow Shop Scheduling Problem to Minimize Makespan

In this paper, an improved quantum-inspired evolution algorithm (IQEA_M) with a special designed local search is proposed to deal with the no-wait flow shop scheduling problem (NFSSP) with sequence-independent setup times (SISTs) and release dates (RDs), which has been proved to be strongly NP-hard. The criterion is to minimize makespan. The method was tested with other literature methods. Experimental results show that IQEA_M presented the best performance regarding other algorithm.

An Enhanced Estimation of Distribution Algorithm for No-Wait Job Shop Scheduling Problem with Makespan Criterion

In this paper, an enhanced estimation of distribution algorithm (EEDA) is proposed for the no-wait job shop scheduling problem (NWJSSP) with the makespan criterion, which has been proved to be strongly NP-hard. The NWJSSP can be decomposed into the sequencing and the timetabling problems. The proposed EEDA and a shift timetabling method are used to address the sequencing problem and the timetabling problem, respectively. In EEDA, the EDA-based search is applied to guiding the search to some promising sequences or regions, and an Interchange-based local search is presented to perform the search from these promising regions. Moreover, each individual or sequence of EEDA is decoded by applying a shift timetabling method to solving the corresponding timetabling problem. The experimental results show that the combination of the EEDA and the shift timetabling method can accelerate the convergence speed and is helpful in achieving more competitive results.

Inverse Solution of Planar Redundant Manipulator Based on Cuckoo Search Algorithm with Dynamic Step Size Regulating

Aiming at the complex problem of solving the planar redundant manipulator inverse solution, the method of optimizing the inverse solution based on the cuckoo search algorithm (CS) is studied. To improve the poor adaptability of the step size update method of the traditional CS, a cuckoo search algorithm with dynamic step size regulating (DRCS) is presented, in which the updating step size is adjusted dynamically according to the difference between the previous generation optimal solution and the current updated position and that between the previous generation and the current optimal solutions. The stability and accuracy of the proposed theory are verified through the test function simulation. In the application of the inverse solution of the manipulator, the smaller rotation angle and less kinetic energy are obtained. Experimental results show that the rotation angle of manipulator can be reduced by 5.1 degrees averagely in once location shift and kinetic energy can be saved about 3.73% by using DRCS strategy.

Hybrid Discrete Teaching-Learning-Based Optimization Algorithm for Solving Parallel Machine Scheduling Problem with Multiple Constraints.

This paper proposes a hybrid discrete teaching-learning-based optimization algorithm (HDTLBO) for solving the parallel machine scheduling problem with arrival time, multiple operations and process restraints (PMSP_AMP), which widely exists in the various manufacturing process. The criterion is to minimize the maximum completion time (i.e., makespan). Firstly, a discretization method is designed to remold the standard teaching-learning-based optimization algorithm, which enhances its global exploration ability and makes it can execute the global search directly in the discrete solution space. Then, the swap-based and the insert-based neighborhood are utilized to construct the local search for performance improvement. Simulation results and comparisons based on s set of random instances demonstrate the effectiveness and searching quality of the presented HDTLBO.

Hybrid Estimation of Distribution Algorithm for Blocking Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times

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.