Zhongshi Shao

Bio: Zhongshi Shao is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Flow shop scheduling & Job shop scheduling. The author has an hindex of 14, co-authored 16 publications receiving 417 citations. Previous affiliations of Zhongshi Shao include Shaanxi Normal University.

A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time

Abstract: Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST. Note to Practitioners —This paper is motivated by the process cycles in multiproduction factories (or lines) of baker production, surgery scheduling, and parallel computer systems. In these process cycles, jobs are assigned to multiproduction factories (or lines), and no interruption exists between consecutive operations. This paper models this process as a multiobjective distributed no-wait flow-shop scheduling with SDST. Scheduling becomes more challenging when facing distributed factories. This paper provides an estimation of distributed algorithm with Pareto dominate concept which uses a probabilistic model to generate offspring. Experiment results suggest that the proposed algorithm can find superior solutions of large-scale instances. This scheduling model can be extended to practical problems by considering other constraints, such as assembly process, mixed no-wait, and transporting times. Besides, the proposed algorithm can be applied to solve other distributed scheduling problems and industrial cases, once their constraints are known, i.e., the processing time of operations, the setup time of machines.

Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment

Abstract: Scheduling in distributed production environments is becoming widespread in recent years due to the increasing advantages of multi-factory manufacture. This paper investigates the distributed blocking flow-shop scheduling problem (DBFSP) with the objective of minimizing the makespan. To solve this problem, a hybrid enhanced discrete fruit fly optimization algorithm (HEDFOA) is proposed. In the proposed algorithm, an effective constructive heuristic is developed based on a new assignment rule of jobs and an insertion-based improvement procedure to initialize the common central location of all fruit fly swarms. In the smell-based foraging, an effective insertion-based neighborhood operator is designed for exploration in global scope. In the vision-based foraging, a local search is embedded to intensify the exploitation ability of algorithm in local region. Meanwhile, a simulated annealing-like acceptance criterion is employed to help algorithm escape from the local optimum. Finally, an extensive computational experiment is conducted. Experimental results show that the proposed HEDFOA is more effective than the existing state-of-the-art methods. Furthermore, 516 best known solutions out of 720 benchmark instances are also updated.

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.

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.

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

Abstract: 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.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

An Ensemble Discrete Differential Evolution for the Distributed Blocking Flowshop Scheduling with Minimizing Makespan Criterion

Abstract: The distributed blocking flowshop scheduling problem (DBFSP) plays an essential role in the manufacturing industry and has been proven to be as a strongly NP-hard problem In this paper, an ensemble discrete differential evolution (EDE) algorithm is proposed to solve the blocking flowshop scheduling problem with the minimization of the makespan in the distributed manufacturing environment In the EDE algorithm, the candidates are represented as discrete job permutations Two heuristics method and one random strategy are integrated to provide a set of desirable initial solution for the distributed environment The front delay, blocking time and idle time are considered in these heuristics methods The mutation, crossover and selection operators are redesigned to assist the EDE algorithm to execute in the discrete domain Meanwhile, an elitist retain strategy is introduced into the framework of EDE algorithm to balance the exploitation and exploration ability of the EDE algorithm The parameters of the EDE algorithm are calibrated by the design of experiments (DOE) method The computational results and comparisons demonstrated the efficiency and effectiveness of the EDE algorithm for the distributed blocking flowshop scheduling problem

A memetic differential evolution algorithm for energy-efficient parallel machine scheduling

Abstract: This paper considers an energy-efficient bi-objective unrelated parallel machine scheduling problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a memetic differential evolution (MDE) algorithm. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To accelerate the convergence of the algorithm, only the speed vector of each individual evolves and a list scheduling heuristic is applied to derive its job-machine assignment vector based on its speed vector. To further enhance the algorithm, we propose efficient speed adjusting and job-machine swap heuristics and integrate them into the algorithm as a local search approach by an adaptive meta-Lamarckian learning strategy. Computational results reveal that the incorporation of list scheduling heuristic and local search greatly strengthens the algorithm. Computational experiments also show that the proposed MDE algorithm outperforms SPEA-II and NSGA-II significantly.