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In recent years, both distributed scheduling and energy-efficient scheduling have attracted great attention in production systems. This paper studies an energy-efficient distributed blocking flowshop scheduling problem where several heterogeneous factories cooperate to process jobs. A knowledge-based iterated Pareto greedy algorithm (KBIPG) is proposed to minimize simultaneously the makespan and total energy consumption. Based on a speed scaling framework that allows machines to process different jobs at different speed levels or remain in the standby mode, the KBIPG has two stages, where the difference lies in whether to adjust the processing speed. First, two multi-objective insertion procedures are proposed to form construction procedures. Second, we presented an efficient destruction procedure for each stage separately. Third, two local intensification methods are designed based on adjusting machine speeds, including the energy-saving procedure that optimizes the total energy consumption and the speedup-based local search procedure that optimizes the makespan. The KBIPG algorithm starts with generating solutions under various initial machine speed matrixes with different levels and then goes through a two-stage loop based on the proposed procedures. Computational experiments and comparisons with five algorithms demonstrate the effectiveness of the proposed KBIPG algorithm. [ABSTRACT FROM AUTHOR]

In the realm of hybrid flow shop environments, existing literature predominantly overlooks a practical scenario that jobs with different constraints often need to be simultaneously processed. This article addresses a constrained hybrid flow shop scheduling problem (CHFSP) in which certain jobs are bound by a common deadline while other jobs are not. We first propose constructive heuristics to generate initial solutions, completely based on the characteristics of the problem. Additionally, we present a variable-representation discrete artificial bee colony algorithm (VRDABC). The algorithm introduces a novel variable solution representation along with a tailored decoding method, allowing an extensive exploration of the solution space while maintaining a remarkable search speed. By leveraging the distinctive solution representation, we design an insertion operation, a multi-phase evolutionary process, and an efficient phase-transition mechanism, thereby enabling the VRDABC to successfully address this problem. Experimental validations, including algorithmic components analysis and algorithmic comparison, confirm the effectiveness and potential of our approach in producing high-quality solutions. Through the theoretical explanation and experimental analysis, the VRDABC presents an influential contribution to the constrained hybrid flow shop scheduling problem. [ABSTRACT FROM AUTHOR]

This article addresses the printed circuit board (PCB) grouping problem (PGP) in the electronic assembly industry. First, a mathematical model of PGP is presented. Then, four heuristics based on an iterative scheme are proposed. Two of them are constructive heuristics and the other two are composite heuristics. Based on the problem-specific characteristics, a new solution representation that consists of PCB assignment and component assignment is proposed. For the constructive heuristics, one is based on a single PCB and the other is based on a pair of PCBs. The composite heuristics consist of the constructive heuristics and a local search method formed by three new operators. To test the effectiveness of the presented heuristics, 100 problem instances are designed according to the real-world production process. The experiments and comparisons show that the presented heuristics perform much better than those in the literature. [ABSTRACT FROM AUTHOR]

A new scheduling problem, the distributed permutation flowshop scheduling problem with uncertain processing times and carryover sequence-dependent setup time (DPUC), is addressed. The DPUC is an important application problem in modern electronics manufacturing. A robust model is established for the DPUC with makespan criterion. A counter-intuitive paradox is found, that is, adding a new job to one of the production lines can reduce the completion time of the production line. Two acceleration methods are provided to save computational efforts. An iterated greedy algorithm called IG_FS is proposed to solve the DPUC. A heuristic based on the well-known NEH is proposed to generate the initial solution for the IG_FS. In the destruction phase of the IG_FS, dynamic sizes based on both adaptability and randomness are provided to improve the exploration capability. During the local search phase of the IG_FS, a hybrid local search method consisting of shift and swap operators is presented to exploit more diverse search areas. Extensive experiments show that the proposed IG_FS performs significantly better than the six competing algorithms adapted from the closely related scheduling literature. [ABSTRACT FROM AUTHOR]

Although the distributed permutation flowshop scheduling problem (DPFSP) has recently received extensive research attention, most studies assume that either all jobs have due date constraints or none of them do. Nevertheless, in practice, it is very common to schedule jobs with due dates alongside jobs without a due date. This paper addresses a DPFSP with part of jobs subject to a common deadline (DPFSP-PJCD). The objective is to minimize the total completion time. We establish a mathematical model and propose a Self-adaptive Population-based Iterated Greedy (SPIG) algorithm that is specifically tailored to the characteristics of the problem. We design a hybrid constructive heuristic to generate a population of potentially high-quality solutions. We introduce an insertion-based acceleration method that combines three distinct accelerations to improve operational efficiency. We propose some effective operators to carry out the selection, destruction, and construction of solutions, as well as a local search mechanism, to balance the exploitation and exploration of the algorithm. Additionally, we employ a self-adaptive method to determine a key algorithmic parameter depending on the search phase and search space. We also utilize a self-adjustment insertion procedure to handle infeasible solutions. Through comprehensive experimental evaluations, we demonstrate that the proposed SPIG outperforms five state-of-the-art metaheuristics from the closely related literature, providing effective solutions for the DPFSP-PJCD considered. [ABSTRACT FROM AUTHOR]

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance. [ABSTRACT FROM AUTHOR]

A self-adaptive fruit fly optimization (SFFO) algorithm is presented for solving high-dimensional global optimization problems. Unlike the conventional self-adaptive swarm intelligence algorithms that try to modify the values of control parameters during the run by taking the actual search process into account, the proposed SFFO algorithm self-adaptively adjusts its search along an appropriate decision variable from its previous experience in generating promising solutions. The presented self-adaptive method significantly improves the intensive search capability of the fruit fly optimization algorithm around promising areas that are problem and search process dependent. Extensive computational simulations and comparisons are performed based on a set of 40 benchmark functions from the literature. The computational results show that the proposed SFFO is a new state-of-the-art algorithm for global optimization. [ABSTRACT FROM AUTHOR]

• We study a new hot rolling scheduling problem from compact strip production. • We divide the problem into two sub-problems of minimizing virtual sheet strips and optimizing the sequence of sheet strips. • An improved heuristic is developed to minimize virtual sheet strips. • A multi-objective evolutionary algorithm is proposed to optimize the sequence of sheet strips. • The effectiveness of the presented algorithms is demonstrated by example instances from practice. This paper addresses a hot-rolling scheduling problem from compact strip production processes. At first, a mathematical model that consists of two coupled sub-problems is presented. The first sub-problem is the sheet-strip assignment problem that is about how to assign sheet-strips to rolling-turns with the objective of minimizing virtual sheet-strips. The second is the sheet-strip sequencing problem that is about how to sort the sheet-strips in each rolling-turn with the objective of minimizing the maximal changes in thickness between adjacent sheet-strips and the change times of the thickness so as to ensure high quality sheet-strips to be produced. And then, an improved hot-rolling scheduling heuristic is proposed to solve the sheet-strip assignment problem. A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem. Besides, the problem-specific knowledge is explored. The key operators including crossover operator, mutation operator and repair operator are designed for the multi-objective evolutionary algorithm. At last, extensive experiments based on real-world instances from a compact strip production process are carried out. The results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective hybrid flowshop rescheduling problem with respect to the production efficiency and instability is addressed in a dynamic shop environment, where two types of real-time events are simultaneously considered, i.e., machine breakdown and job cancelation. To solve the problem, a multi-objective migrating birds optimization (MMBO) is proposed. In the proposed algorithm, each solution is evaluated based on the Pareto dominance relationship, and an improvement procedure is further designed to help improve the solutions quality. The fast non-dominated sorting technique is introduced to sequence the solutions in the V-shaped population. For the leader evolution, it is conducted by a Pareto-based local search method, and within the process two neighbors sets are produced to, respectively, participate in the followers evolution in the two lines. For the followers evolution, the reproduction process is introduced and the benefit mechanism is implemented by combing the genetic operators. And in the two lines, different genetic operators are employed to achieve their collaboration. For the leader change, only the promising solutions can be forwarded to the leader position. A shuffling process is proposed to help share evolutionary information between the two lines and promote their joint efforts. The performance of the MMBO is evaluated by comparing with several state-of-the-art evolutionary multi-objective optimizers, and the computational results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Total flowtime minimization for the distributed permutation flowshop is addressed. • Three novel heuristics are presented to obtain good results in a few minutes. • Four effective metaheuristics are proposed to further improve the quality of solutions. • Problem-specific knowledge and accelerations to evaluate neighboring solutions are explored. • Experimental results show that the presented methods significantly outperform the existing algorithms in the literature. Abstract Distributed permutation flowshop scheduling problem (DPFSP) has become a very active research area in recent years. However, minimizing total flowtime in DPFSP, a very relevant and meaningful objective for today's dynamic manufacturing environment, has not captured much attention so far. In this paper, we address the DPFSP with total flowtime criterion. To suit the needs of different CPU time demands and solution quality, we present three constructive heuristics and four metaheuristics. The constructive heuristics are based on the well-known LR and NEH heuristics. The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems. We explore the problem-specific knowledge and accelerations to evaluate neighboring solutions for the considered problem. We introduce advanced and effective technologies like a referenced local search, a strategy to escape from local optima, and an enhanced intensive search method for the presented metaheuristics. A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out. The results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion. [ABSTRACT FROM AUTHOR]

The distributed permutation flowshop problem (DPFSP) has been extensively studied in recent years. However, most of the research has overlooked the disturbance factors in the processing environment, such as the arrival of new jobs. To address this issue, a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning has been developed. First, an iterated greedy algorithm (IG) is proposed to generate an initial solution for the first stage. Then, the NSGA-II algorithm is designed to optimize dual-objective problems in the second stage, and the Q-learning algorithm is used to adjust the algorithm parameters. Next, two local search strategies based on key factories are adopted, including critical factory-based insert and swap operations. Finally, a comprehensive experiment of the proposed algorithm against other advanced multiobjective algorithms is conducted. The results confirm that the proposed algorithm can solve the distributed permutation flowshop rescheduling problem with high efficiency. [ABSTRACT FROM AUTHOR]

This paper studies the distributed permutation flowshop group scheduling problem (DPFGSP) with the consideration of minimizing total flowtime (T F), which has important applications in the modern manufacturing process. Based on the characteristics of the problem, a cooperative population-based iterated greedy (CPIG) algorithm is proposed by combining the advantages of the divide-and-rule policy, population-based evolution and iterated greedy algorithm. The CPIG divides the DPFGSP into two coupled sub-problems of group scheduling sub-problem and job scheduling sub-problem, and starts with a single population for simplicity. Unlike in the traditional cooperative co-evolutionary algorithms, the two-coupled sub-problems are addressed with a certain probability that can be determined in favor of solving the whole scheduling problem. Some advanced technologies are used, including the constructive heuristics based initialization, the critical factories based destruction and construction, the new best solution based population updating mechanism. The comprehensive experimental evaluation of 810 instances shows that the CPIG algorithm performs much better than the five state-of-the-art metaheuristics in the literature which are closely related to the considered scheduling problem. • The distributed permutation flowshop group scheduling problem (DPFGSP) with total flowtime criterion is addressed at the first time. • A cooperative population-based iterated greedy (CPIG) algorithm is proposed. • A probability strategy is used to balance the search process of the sub-problems. • A composite NEH heuristic is presented to generate an initial solution. • The computational experiments prove the effectiveness of presented algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses a new hot rolling scheduling problem from the compact strip production process, which is the mainstream production technology that is used worldwide for sheet strips. The problem is modeled as a combination of two coupled sub-problems. One sub-problem is a sheet strip assignment problem that assigns sheet strips to rolling turns with the constraints of safe values of different gauge levels, and the other is a sheet strip sequencing problem that decides the rolling sequence for all of the sheet strips in a rolling turn to form a particular parabolic shape in thickness. To solve this hot rolling scheduling problem, we present a novel approach that consists of a sheet strip assignment heuristic and a sheet strip sequencing heuristic. The sheet strip assignment heuristic minimizes the number of virtual sheet strips by generating rolling turns according to the ordered sheet strips with maximum gauge level and their safe values. The sheet strip sequencing heuristic minimizes the average change of the thickness of adjacent sheet strips by arranging a certain number of duplicate sheet strips to the increasing stage of a rolling turn. Extensive experiments based on both synthetic and real-world instances from a compact strip production process show the effectiveness of the proposed two-stage heuristic in solving the hot rolling scheduling problem. [ABSTRACT FROM AUTHOR]

This paper proposes a total of nine algorithms to minimize the makespan for the hybrid flowshop scheduling problem with sequence-dependent setup times. The first six algorithms are trajectory-based metaheuristics, including three variants of iterated local search and three variants of iterated greedy. The remaining three algorithms are population-based metaheuristics, namely, the improved fruit fly optimization, the improved migrating birds optimization, and the discrete artificial bee colony optimization. We present some advanced and effective technologies, including three mixed neighborhood structures, an enhanced perturbation method, and an enhanced destruction and construction procedure for the trajectory-based metaheuristics. We propose a path-relinking-based cooperative search, a diversity control scheme, and a diversified initialization approach for the improved fruit fly optimization. We calibrate the parameters and operators for the proposed algorithms by means of a design of experiments approach. To evaluate the proposed algorithms, we present several adaptations of other recent well-known meta-heuristics for the problem and conduct a comprehensive set of computational and statistical experiments to demonstrate the effectiveness of the presented algorithms. Among them, the discrete artificial bee colony optimization is the best-performing algorithm and it is able to improve 126 out of the 240 best known solutions for the benchmarks in the literature. [ABSTRACT FROM AUTHOR]

• A multi-AGVs scheduling problem with charging and maintenance is studied. • A mixed-integer linear programming model with the total cost is built. • A self-adaptive iterated greedy algorithm is proposed to solve the problem. • Experimental results show the effectiveness of the proposed algorithm. The automatic guided vehicle (AGV) scheduling problem in matrix manufacturing workshop has been a research hotspot in recent years because of its wide industrial applications. However, the research on multi-AGVs scheduling problem with charging and maintenance (MAGVS CM) is rarely reported. In the paper, the MAGVS CM is investigated to reduce the total cost composed of travel cost, penalty cost and vehicle cost. To this end, a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm are proposed. Based on the problem characteristics, a new solution presentation and the solution accelerated evaluation method are presented and applied in the SAIG. In the SAIG, several nearest-neighbor-based improved heuristics are proposed and used to generate an initial solution. A destruction procedure with self-adaptive strategy is developed to enhance the exploration capability of the algorithm. A merging local search method is used to reduce the number of AGVs as much as possible without lowering the total cost. An acceptance criterion with restart strategy is designed to determine the current solution in the next iteration. A large number of experimental results show that the proposed algorithm significantly outperforms the existing algorithms in solving the problem under consideration. [ABSTRACT FROM AUTHOR]

In this study, we propose a hybrid fruit fly optimization algorithm (HFOA) to solve the hybrid flowshop rescheduling problem with flexible processing time in steelmaking casting systems. First, machine breakdown and processing variation disruptions are considered simultaneously in the rescheduling problem. Second, each solution is represented by a fruit fly with a well-designed solution representation. Third, two novel decoding heuristics considering the problem characteristics, which can significantly improve the solution quality, are developed. Several routing and scheduling neighborhood structures are proposed to balance the exploration and exploitation abilities. Finally, we propose an effective HFOA with well-designed smell and vision search procedures. In addition, an iterated greedy (IG) local search is embedded in the proposed algorithm to further enhance its exploitation ability. The proposed algorithm is tested on sets of instances generated from industrial data. Through comprehensive computational comparisons and statistical analyses, the performance of the proposed HFOA algorithm is favorably compared against several algorithms in terms of both solution quality and efficiency. [ABSTRACT FROM AUTHOR]

The steelmaking-continuous-casting (SCC) process, which includes steelmaking, refining and continuous casting, is one of the major bottlenecks of iron and steel production. Efficient and effective scheduling of this process is essential to improve the productivity and reduce the production costs of the entire production system. We present a time-index formulation for this scheduling problem and a Lagrangian relaxation (LR) approach based on the relaxation of the machine capacity constraints. The relaxed problem is solved using an efficient polynomial dynamic programming algorithm. The corresponding Lagrangian dual (LD) problem is solved using a deflected conditional subgradient level method. Unlike the conventional subgradient algorithms for the LD problem, our method guarantees convergence using the Brannlund's level control strategy to replace the strict convergence condition that the optimum of the dual problem is known a priori. Furthermore, our method enhances the efficiency by introducing a deflected conditional subgradient to weaken the zigzagging phenomena that slows the convergence of conventional subgradient algorithms. The computational results demonstrate that the approaches can quickly obtain high-quality solutions and are notably promising for the SCC scheduling. [ABSTRACT FROM PUBLISHER]

Grade, due date, priority, and demand are attributes of magnetic material products. Planners are required to seek the optimal combination of production work orders to minimize cost and improve efficiency based on these attributes. The magnetic material group furnace optimization problem is a generalization of the 1-D bin-packing problem wherein bins of varying sizes are used. Bin sizes are determined by the grade and demand of the grouped work orders. A mathematical model is established to solve the magnetic material group furnace optimization problem by using a specialized genetic algorithm (SGA). In SGA, an initial population generation method is designed by following the sort criteria of the earliest completion date. The furnace charging weight is set according to several rules derived from work order attributes. An elite strategy and an improved greedy three-crossover operator are introduced to enhance convergence speed and precision. In addition, a reverse operator is applied to exploit the proposed algorithm. Simulation results based on practical production data show that the established model is suitable and that the presented algorithm is effective. [ABSTRACT FROM PUBLISHER]

In this study, we proposed a discrete teaching-learning-based optimisation (DTLBO) for solving the flowshop rescheduling problem. Five types of disruption events, namely machine breakdown, new job arrival, cancellation of jobs, job processing variation and job release variation, are considered simultaneously. The proposed algorithm aims to minimise two objectives, i.e., the maximal completion time and the instability performance. Four discretisation operators are developed for the teaching phase and learning phase to enable the TLBO algorithm to solve rescheduling problems. In addition, a modified iterated greedy (IG)-based local search is embedded to enhance the searching ability of the proposed algorithm. Furthermore, four types of DTLBO algorithms are developed to make detailed comparisons with different parameters. Experimental comparisons on 90 realistic flowshop rescheduling instances with other efficient algorithms indicate that the proposed algorithm is competitive in terms of its searching quality, robustness, and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an effective fruit fly optimisation algorithm (FOA) to solve the steelmaking casting problem. First, we model the realistic problem as a hybrid flow shop (HFS) scheduling problem with batching in the last stage. Next, the proposed FOA algorithm is applied to solve the realistic HFS problems. In the proposed algorithm, each solution is represented by a fruit fly. Each fruit fly first improves its status through a well-designed smell search procedure. During the vision-based search procedure, the worst fruit fly in the population will be induced by the best fruit fly found thus far to improve the exploitation ability of the entire fruit fly population further. To enhance the exploration ability of the proposed algorithm, in each generation, each fruit fly that has not updated its status during the last several iterations will be replaced by a newly-generated fruit fly. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Moreover, the influence of the parameter setting is also investigated using the Taguchi method of the design-of-experiment (DOE) to determine the suitable values for the key parameters. The results indicate that the proposed FOA is more effective than the four presented algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. [Copyright &y& Elsevier]

Distributed scheduling problems have been an active research topic due to their close connection with the multifactory production environment in today's decentralized economy. In this paper, the distributed permutation flowshop scheduling problem with preventive maintenance is studied with the goal of minimizing the total flowtime. A mathematical model and a hash map-based algorithm are presented to tackle the problem. The Nawaz–Enscore–Ham heuristic is improved and incorporated with the distributed Liu–Reeves heuristic to provide a promising initial solution in a finite number of steps. The proposed algorithm employs the data structure of the hash map to store all the candidate solutions in the form of key–value pairs. The selection, crossover, and mutation operators are also modified to expand the scope of exploration in the discrete domain. The local search hybridizes the job insertion operator and the job swap operator to further improve the obtained offspring solutions and utilizes the characteristic of the population hash map to reduce the efforts of solution evaluations. The candidate solution obtained by the local search is hashed by a rotating hash method and then placed at the node with the corresponding hash code. A series of experiments were conducted to verify the effectiveness of the hybrid local search operators and hash map strategy. Computational results indicate that the multiple strategies not only help to escape from the local optimization but also improve the computational efficiency. When compared with 10 state-of-the-art algorithms, the proposed algorithm generates an average relative percentage deviation of 0.134%, which is significant improvement. • We study a distributed flowshop with preventive maintenance to minimize total flowtime. • We present a mathematical model for this problem. • A memetic algorithm is improved with a hash map based population. • A hybrid local search is well designed. • Experimental results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, green manufacturing has attracted wide attention from researchers. However, the energy efficiency problem in matrix manufacturing workshops is still a blank area. This paper considers a novel automatic guided vehicle (AGV) energy-efficient scheduling problem with release time (AGVEESR) to optimize the three objectives of energy consumption, number of AGVs used and customer satisfaction simultaneously. Considering the development of the AGVEESR, we extract problem-specific knowledge, establish a multiobjective mathematical model, and design a hybrid constructive heuristic. Due to the complexity of the problem, we propose an efficient multiobjective greedy algorithm (MOGA) with effective strategies such as new population initialization, greedy operation, and self-adaptive multiple neighbourhood local search. Meanwhile, an ideal-point-based construction operator in the greedy operation phase is presented to lower the computational complexity. Simulation results show that the proposed MOGA has a tremendously superior performance to the five state-of-the-art algorithms in solving the problem considered. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed assembly mixed no-idle permutation flowshop scheduling problem (DAMNIPFSP) with total tardiness objective. We first formulate the problem. Second, based on the characteristics of the DAMNIPFSP, an improved Iterated Greedy algorithm, named RIG (Referenced Iterated Greedy), with two novel destruction methods, four new reconstruction methods and six new local search methods is presented. Among them, two of the reconstruction methods and four of the local search methods are based on a reference, which proves key to performance. Finally, RIG is compared with the related algorithms through experiments. The results show that the new RIG algorithm is a new state-of-the-art procedure for the DAMNIPFSP with the total tardiness criterion. • We study a distributed assembly mixed no-idle permutation flowshop scheduling. • A mixed-integer linear programming model is presented. • A Referenced Iterated greedy (RIG) algorithm is proposed. • Experimental results show that the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz–Enscore–Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293–301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all, a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly, some novel methods of calculating the total tardiness from makespan are introduced for the no-idle permutation flowshop scheduling problem. Finally, the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately, we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard’s benchmark suit. [Copyright &y& Elsevier]

This paper aims to provide a solution method for the real-world hybrid flowshop scheduling problem resulting from a steelmaking process, which has important applications in modern iron and steel industry. We first present a mixed integer mathematic model based on a comprehensive investigation. Then, we develop a heuristic method and two improvement procedures for a given schedule based on the problem-specific characteristics. Finally, we propose an effective artificial bee colony (ABC) algorithm with the job-permutation-based representation for solving the scheduling problem. The proposed ABC algorithm incorporates the heuristic and improvement procedures as well as new characteristics including a neighboring solution generation method and two enhanced strategies. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics to the problem and conduct a serial of experiments with the instances generated according to real-world production process. The results show that the proposed ABC algorithm is more effective than all other adaptations after comprehensive computational comparisons and statistical analysis. [ABSTRACT FROM PUBLISHER]

Abstract: Harmony search (HS) is a population-based meta-heuristic imitating the music improvisation process, which has been successfully applied to optimization problems in recent years. This paper presents an effective harmony search algorithm for solving global continuous optimization problems. The proposed method presents a novel improvisation process which is different from the classical HS in two aspects. Firstly, the candidate harmony is chosen from the harmony memory by a tournament selection rule, so that the harmonies with better fitness will have more opportunities to be used in generating new harmonies. Secondly, two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are adjusted dynamically with respect to the evolution of the search process and the different search spaces of the optimization problems. Numerical results demonstrate that the proposed algorithm performs much better than the existing HS variants in terms of the solution quality and the stability. [Copyright &y& Elsevier]

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion. [ABSTRACT FROM AUTHOR]

In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time. [ABSTRACT FROM PUBLISHER]

In this paper, a discrete harmony search algorithm (DHS) is developed for solving the no-wait flow shop scheduling problem with the objective to minimize total flowtime. Firstly, a harmony is represented as a discrete job permutation, and a new heuristic based on the well-known NEH method is proposed to initialize the harmony memory. Secondly, a novel pitch adjustment rule is employed in the improvisation to produce a new harmony. Thirdly, a local search is presented and embedded to enhance the algorithm's local exploitation ability. Extensive computational experiments are carried out based on a set of well-known benchmark instances. Computational results and comparison show the effectiveness of the presented DHS algorithm in solving the no-wait flow shop scheduling with total flowtime criterion. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic multi-swarm particle swarm optimizer (DMS-PSO) for solving the blocking flow shop scheduling problem with the objective to minimize makespan. To maintain good global search ability, small swarms and a regrouping schedule were used in the presented DMS-PSO. Each small swarm performed searching according to its own historical information, whereas the regrouping schedule was employed to exchange information among them. A specially designed local search phase was added into the algorithm to improve its local search ability. The experiments based on the well-known benchmarks were conducted. The computational results and comparisons indicated that the proposed DMS-PSO had a better performance on the blocking flow shop scheduling problems than some other compared algorithms in the literature. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM), namely DLHS algorithm, is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all, to make the HS algorithm suitable for solving the problem considered, a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences, and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly, an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly, during the evolution process, the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly, a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile, a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition, a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion. [ABSTRACT FROM AUTHOR]

novel hybrid tabu search algorithm with a fast public critical block neighborhood structure (TSPCB) is proposed in this paper to solve the flexible job shop scheduling problem with the criterion to minimize the maximum completion time (makespan). First, a mix of four machine assignment rules and four operation scheduling rules is developed to improve the quality of initial solutions to empower the hybrid algorithm with good exploration capability. Second, an effective neighborhood structure to conduct local search in the machine assignment module is proposed, which integrates three adaptive approaches. Third, a speedup local search method with three kinds of insert and swap neighborhood structures based on public critical block theory is presented. With the fast neighborhood structure, the TSPCB algorithm can enhance its exploitation capability. Simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed TSPCB algorithm is superior to several recently published algorithms in terms of solution quality, convergence ability, and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an effective shuffled frog-leaping algorithm (SFLA) for solving a lot-streaming flow shop scheduling problem with equal-size sublots, where a criterion is to minimize maximum completion time (i.e., makespan) under both an idling and no-idling production cases. Unlike the original SFLA, the proposed SFLA represents an individual or frog as a job permutation and utilizes a position-based crossover operator to generate new candidate solutions. An efficient initialization scheme based on the Nawaz-Enscore-Ham heuristic is proposed to construct an initial population with a certain level of quality and diversity. A simple but effective local search approach is embedded in SFLA to enhance the local intensification capability. In addition, a speed-up method to evaluate insert neighborhood is presented to improve the algorithm's efficiency. Extensive computational experiments and comparisons are provided, which demonstrate the effectiveness of the proposed SFLA against the best performing algorithms from the literature. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, three hybrid harmony search (HS) algorithms, namely, hybrid harmony search (hHS) algorithm, hybrid globalbest harmony search (hgHS) algorithm and hybrid modified globalbest harmony search (hmgHS) algorithm, are developed for solving the flow shop scheduling with blocking to minimize the total flow time. Firstly, a largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Secondly, an initialization scheme based on a variant of the NEH heuristic is presented to construct the initial harmony memory with certain quality and diversity. Thirdly, HS is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. In addition, some new pitch adjustment rules are developed to well inherit good structures from the globalbest harmony vector. Based on a set of well-known benchmark instances, extensive computational experiments are carried out. Computational results show the effectiveness of the hybrid harmony search algorithms, especially the (hmgHS) algorithm, in solving the blocking flow shop scheduling with total flow time criterion. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants. [Copyright &y& Elsevier]

Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots, aiming to simultaneously optimize two conflicting objectives: the makespan and total number of sublots. Considering the setup and transportation operations, a multi-objective mixed integer programming model is developed and the trade-off between the two objectives is evaluated. Because of the NP-hard property of the addressed problem, metaheuristics are suggested. It is well known that the performance of metaheuristics is highly dependent on the setting of algorithmic parameters, referred to as numerical and categorical parameters. However, the traditional design process might be biased by previous experience. To eliminate these issues, an automated algorithm design (AAD) methodology is introduced to conceive a multi-objective evolutionary algorithm (MOEA) in a promising framework. The AAD enables designing the algorithm by automatically determining parameters and their combinations with minimal user intervention. With regard to the problem-specific characteristics and the employed algorithm framework, for the categorical parameters, including decomposition, solution encoding and decoding, solution initialization and neighborhood structures, several operators are designed specifically. Along with the numerical parameters, these categorical parameters are determined and combined using the designed iterated racing procedure. Comprehensive computational results demonstrate that the automated MOEA outperforms other state-of-the-art MOEAs for the addressed problem. [ABSTRACT FROM AUTHOR]

An improved iterated greedy algorithm (IIGA) is proposed in this paper to solve the no-wait flow shop scheduling problem with the objective to minimize the makespan. In the proposed IIGA, firstly, a speed-up method for the insert neighborhood is developed to evaluate the whole insert neighborhood of a single solution with ( n − 1)2 neighbors in time O( n 2), where n is the number of jobs; secondly, an improved Nawaz-Enscore-Ham (NEH) heuristic is presented for constructing solutions in the initial stage and searching process; thirdly, a simple local search algorithm based on the speed-up method is incorporated into the iterated greedy algorithm to perform exploitation. The computational results based on some well-known benchmarks show that the proposed IIGA can obtain results better than those from some existing approaches in the literature. [ABSTRACT FROM AUTHOR]

We consider the distributed blocking flowshop scheduling problem (DBFSP) which is a meaningful generalization of the blocking flowshop scheduling problem in the distributed production environment. The objective of minimizing the total flowtime is relevant and important in the current dynamic manufacturing environment, but, as far as we know, it has not been investigated in the DBFSP previously. In this paper, a population-based iterated greedy (PBIG) algorithm is proposed to solve the DBFSP with the total flowtime criterion, which takes the advantage of both the population-based search approach and the iterated greedy algorithm. First, an effective constructive heuristic is proposed by integrating two existing constructive approaches to initialize the population with a high level of quality and diversity. Second, three different procedures to generate the offspring solutions are tested for the effective exploration capability, each of which rationally combines the destruction, reconstruction and selection operator. Third, the insertion neighborhood and swap neighborhood are investigated to enhance the local exploitation ability and a hybrid local search procedure that utilizes simultaneously both the two neighborhoods are proposed. The comprehensive experimental evaluation based on a total of 720 well-known instances shows that the proposed algorithms outperform the existing effective algorithms at a significant margin. • We study the distributed blocking flowshop scheduling problem for total flowtime. • We propose a novel population-based iterated greedy algorithm. • We propose a constructive heuristic to obtain good initial solutions. • We propose three local search methods and three selection operators. • We compare the proposed algorithms with effective algorithms in the literature. [ABSTRACT FROM AUTHOR]

• We study a distributed flowshop problem with preventive maintenance. • We present a novel multi-start iterated greedy algorithm. • An improved NEH with dropout is proposed to generate searching starting-point. • A destruction with the tournament selection is well designed. • The effective of the iterated greedy is proved by extensive experiments. In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration. [ABSTRACT FROM AUTHOR]

This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. [ABSTRACT FROM AUTHOR]