Zhongshi Shao

A hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism for no-wait flow shop scheduling

Inspired by the phenomenon of teaching and learning introduced by the teaching-learning based optimization (TLBO) algorithm, this paper presents a hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism (HDTPL) to solve the no-wait flow shop scheduling (NWFSSP) with minimization of makespan. The HDTPL consists of four components, i.e. discrete teaching phase, discrete probabilistic learning phase, population reconstruction, neighborhood search. In the discrete teaching phase, Forward-insert and Backward-insert are adopted to imitate the teaching process. In the discrete probabilistic learning phase, an effective probabilistic model is established with consideration of both job orders in the sequence and similar job blocks of selected superior learners, and then each learner interacts with the probabilistic model by using the crossover operator to learn knowledge. The population reconstruction re-initializes the population every several generations to escape from a local optimum. Furthermore, three types of neighborhood search structures based on the speed-up methods, i.e. Referenced-insert-search, Insert-search and Swap-search, are designed to improve the quality of the current learner and the global best learner. Moreover, the main parameters of HDTPL are investigated by the Taguchi method to find appropriate values. The effectiveness of HDTPL components is analyzed by numerical comparisons, and the comparisons with some efficient algorithms demonstrate the effectiveness and robustness of the proposed HDTPL in solving the NWFSSP.

Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion

Display Omitted A hybrid node and edge histogram matrix (NEHM) is constructed.A random sample crossover with NEHM is proposed.An improved general variable neighborhood search with the simulated annealing acceptance is presented.89 new best solutions for the benchmark of Ruiz are provided. This paper presents a memetic algorithm with hybrid node and edge histogram (MANEH) to solve no-idle permutation flow shop scheduling problem (NIPFSP) with the criterion to minimize the maximum completion time (the makespan criterion). The MANEH mainly composes of two components: population-based global search and local refinements for individuals. At the initialization stage, a modified speed-up NEH method and the random initialization are utilized to generate more promising solutions with a reasonable running time. At the population-based global search stage, a random sample crossover is first proposed to construct a hybrid node and edge histogram matrix (NEHM) with superior solutions in the population, and then a new sequence is generated by sampling the NEHM or selecting jobs from a template sequence. At the local refinements stage, an improved general variable neighborhood search with the simulated annealing acceptance (GVNS-SA) is developed to improve the current best individual. The GVNS-SA adopts a random referenced local search in the inner loop and the probability of SA to decide whether accept the incumbent solution for the next iteration. Moreover, the influence of key parameters in the MANEH is investigated based on the approach of a design of experiments (DOE). Finally, numerical simulation based on the benchmark of Ruiz and thorough statistical analysis are provided. The comparisons between MANEH and some existing algorithms as well as MA-based algorithms demonstrate the effectiveness and superiority of the proposed MANEH in solving the NIPFSP. Furthermore, the MANEH improves 89 out of the 250 current best solutions reported in the literature.

Self-adaptive discrete invasive weed optimization for the blocking flow-shop scheduling problem to minimize total tardiness

Abstract This paper proposes a self-adaptive discrete invasive weed optimization (SaDIWO) to solve the blocking flow-shop scheduling problem (BFSP) with the objective of minimizing total tardiness which has important applications in a variety of industrial systems. In the proposed SaDIWO, an improved NEH-based heuristic is firstly presented to generate an initial solution with high quality. Then, to guide the global exploration and local exploitation, a self-adaptive insertion-based spatial dispersal is presented. A distance-based competitive exclusion is developed to strike a compromise between the quality and diversity of offspring population. A variable neighborhood search with a speed-up mechanism is embedded to further enhance exploitation in the promising region around the individuals. Afterward, the parameters setting and the effectiveness of each component of the proposed algorithm are investigated through numerical experiments. The performance of the proposed algorithm is evaluated by comparisons with the existing state-of-the-art algorithms in the literature. Experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms. Furthermore, the proposed SaDIWO also improves the best known solutions for 132 out of 480 problem instances.

An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem

Abstract The no-wait flow shop scheduling problem (NWFSSP) performs an important function in the manufacturing industry. Inspired by the overall process of teaching-learning, an extended framework of meta -heuristic based on the teaching-learning process is proposed, which consists of four parts, i.e. previewing before class, teaching phase, learning phase, reviewing after class. This paper implements a hybrid meta -heuristic based on probabilistic teaching-learning mechanism (mPTLM) to solve the NWFSSP with the makespan criterion. In previewing before class, an initial method that combines a modified Nawaz-Enscore-Ham (NEH) heuristic and the opposition-based learning (OBL) is introduced. In teaching phase, the Gaussian distribution is employed as the teacher to guide learners to search more promising areas. In learning phase, this paper presents a new means of communication with crossover. In reviewing after class, an improved speed-up random insert local search based on simulated annealing (SA) is developed to enhance the local searching ability. The computational results and comparisons based on Reeves, Taillard and VRF’s benchmarks demonstrate the effectiveness of mPTLM for solving the NWFSSP.

A novel discrete water wave optimization algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times

Abstract This paper considers n-job m-machines blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDST), which has important ramifications in the modern industry. To solve this problem, two efficient heuristics are firstly presented according to the property of the problem. Then, a novel discrete water wave optimization (DWWO) algorithm is proposed. In the proposed DWWO, an initial population with high quality and diversity is constructed based on the presented heuristic and a perturbation procedure. A two-stage propagation is designed to direct the algorithm towards the good solutions. The path relinking technique is employed in refraction phase to help individuals escape from local optima. A variable neighborhood search is developed and embedded in breaking phase to enhance local exploitation capability. A new population updating scheme is applied to accelerate the convergence speed. Moreover, a speedup method is presented to reduce the computational efforts needed for evaluating insertion neighborhood. Finally, extensive numerical tests are carried out, and the results compared to some state-of-the-art metaheuristics demonstrate the effectiveness of the proposed DWWO in solving BFSP with SDST.

Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms

Abstract The distributed production lines widely exist in modern supply chains and manufacturing systems. This paper aims to address the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion by using proposed iterated greedy (IG) algorithms. Firstly, several speed-up methods based on the problem properties of DNWFSP are investigated to reduce the evaluation time of neighborhood with O(1) complexity. Secondly, an improved NEH heuristic is proposed to generate a promising initial solution, where the iteration step of the insertion step of NEH is applied to the factory after inserting a new job. Thirdly, four neighborhood structures (i.e. Critical_swap_single, Critical_insert_single, Critical_swap_multi, Critical_insert_multi) based on factory assignment and job sequence adjustment are employed to escape from local optima. Fourthly, four local search methods based on neighborhood moves are proposed to enhance local searching ability, which contains LS_insert_critical_factory1, LS_insert_critical_factory2, LS_swap, and LS_insert. Finally, to organize neighborhood moves and local search methods efficiently, we incorporate them into the framework of variable neighborhood search (VNS), variable neighborhood descent (VND) and random neighborhood structure (RNS). Furthermore, three variants of IG algorithms are presented based on the designed VNS, VND and RNS. The parameters of the proposed IG algorithms are tuned through a design of experiments on randomly generated benchmark instances. The effectiveness of the initialize phase and local search methods is shown by numerical comparison, and the comparisons with the recently published algorithms demonstrate the high effectiveness and searching ability of the proposed IG algorithms for solving the DNWFSP. Ultimately, the best solutions of 720 instances from the well-known benchmark set of Naderi and Ruiz for the DNWFSP are proposed.

An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

ABSTRACT This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

A multi-objective discrete invasive weed optimization for multi-objective blocking flow-shop scheduling problem

Abstract The flow-shop scheduling problem with blocking constraints has received an increasing concern recently. However, multiple scheduling criteria are rarely considered simultaneously in most research. Therefore, in this paper, a multi-objective blocking flow-shop scheduling problem (MOBFSP) that minimizes the makespan and total tardiness simultaneously is investigated. To address this problem, a multi-objective discrete invasive weed optimization (MODIWO) algorithm is proposed. In the proposed MODIWO, a high-quality and diversified initial population is firstly constructed via two heuristics and varying weighed values. Then, a reference line-based reproduction and a sliding insertion-based spatial dispersal are developed to guide the global exploration and local exploitation of algorithm. Meanwhile, to enhance intensification search in local region, a self-adaption phase is introduced, which is implemented by a Pareto-based two stage local search with speedup mechanism. Furthermore, a new competitive exclusion strategy is also embedded to construct a superior population for the next generation. Finally, extensive computational experiments and comparisons with several recent state-of-the-art algorithms are carried out based on the well-known benchmark instances. Experimental results demonstrate the efficiency and effectiveness of the proposed MODIWO in solving the considered MOBFSP.

A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion

Abstract This paper proposes a hybrid discrete teaching-learning based meta-heuristic (HDTLM) to solve the no-idle flow shop scheduling problem (NIFSP) with the total tardiness criterion. To imitate the teaching-learning phenomenon in the real world, the HDTLM is composed of three phases, i.e. discrete teaching phase based on probabilistic model, discrete learning phase based on hierarchical structure, and reinforcement learning. In the discrete teaching phase, a probabilistic model based on the elite learners and the best learner is used to generate a series of position sequences, and the concept of consensus permutation is employed to replace the mean individual in the teaching-learning based optimization (TLBO) algorithm. Each job of the consensus permutation is inserted into a new sequence according to the position sequence. In the discrete learning phase, according to different levels of learners, all learners are divided into three layers, i.e. top layer, middle layer, bottom layer, and then the proposed learning phase adopts the order of top-down to spread the knowledge. The reinforcement learning phase is applied to the best learner to further improve the knowledge level of teacher. The parameters of the HDTLM are calibrated by a design of experiments (DOE) on randomly generated testing instances. The computational results on Taillard and Ruizs benchmark sets and statistical analyses show that the HDTLM is an efficient and effective method for solving the NIFSP.

A hybrid iterated greedy algorithm for the distributed no-wait flow shop scheduling problem

This paper proposes a hybrid iterated greedy (HIG) algorithm to solve the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion. The HIG mainly consists of four components, i.e. initialization phase, construction and destruction, local search, acceptance criterion. In the initialization phase, a modified NEH (Nawaz-Enscore-Ham) is proposed to generate a promising initial solution. In the local search phase, four local searching methods based on problem properties (i.e. insert move within factory, insert move between factories, swap move between factories) are proposed to enhance searching ability. The effectiveness of the initialization phase and local search method is shown by numerical comparison, and the comparisons with the recently published iterated greedy algorithms demonstrate the high effectiveness and searching ability of the proposed HIG for solving the DNWFSP.