Re-entrant Hybrid Flow Shop Scheduling Research Articles

Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed; and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms.

Integration of deep reinforcement learning and multi-agent system for dynamic scheduling of re-entrant hybrid flow shop considering worker fatigue and skill levels

In real-life manufacturing systems, production management is often affected by urgent demands and unexpected interruptions, such as new job insertions, machine breakdowns and operator unavailability. In this context, agent-based techniques are useful and able to respond quickly to dynamic disturbances. The ability of agents to recognize their environment and make decisions can be further enhanced by deep reinforcement learning (DRL). This paper investigates a novel dynamic re-entrant hybrid flow shop scheduling problem (DRHFSP) considering worker fatigue and skill levels to minimize the total tardiness of all production tasks. An integrated architecture of DRL and MAS (DRL-MAS) is proposed for real-time scheduling in dynamic environments. Two DRL models are proposed for different sub-decisions, where a reward-shaping technique combining long-term and short-term returns is proposed for the job sequence and machine selection sub-decisions, and an attention-based network is proposed for the worker assignment sub-decision for efficient feature extraction and decision making. Numerical experiments and case studies demonstrate the superior performance of the proposed DRL models compared with existing scheduling strategies.

A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

This paper deals with a reentrant hybrid flow shop problem with sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model with the minimization of total weighted completion time is constructed for this problem. Considering the complexity of the problem at hand, an effective cooperative adaptive genetic algorithm (CAGA) is proposed. In the algorithm, a dual chain coding scheme and a staged-hierarchical decoding approach are, respectively, designed to encode and decode each solution. Six dispatch heuristics and a dynamic adjustment method are introduced to define initial population. To balance the exploration and exploitation abilities, three effective operations are implemented: (1) two new crossover and mutation operators with collaborative mechanism are imposed on genetic algorithm; (2) an adaptive adjustment strategy is introduced to re-optimize better solutions after mutation operations, where ant colony search algorithm and modified greedy heuristic are intelligently switched; (3) a reset strategy with dynamic variable step strategy is embedded to re-generate some non-improved solutions. A Taguchi method of design of experiment is adopted to calibrate the parameter values in the presented algorithm. Comparison experiments are executed on test instances with different scale problems. Computational results show that the proposed CAGA is more effective and efficient than several well-known algorithms in solving the studied problem.

Improved MOEA/D with local search for solving multi-stage distributed reentrant hybrid flow shop scheduling problem

Steel tubes are known as “the vessels of industry” and widely used in oil exploration, drilling, aerospace and other fields. This study focuses on cold-drawn seamless steel tubes, aiming to reduce the high logistics cost and improve the low efficiency in production. This study models three stages of heat treatment, cold drawing, and finishing as a multi-stage distributed reentrant hybrid flow shop scheduling problem (MSDRHFSP). First, a multi-objective optimization model is established to minimize makespan and transferring cost. Second, an improved multi-objective evolutionary algorithm based on decomposition with local search (IMOEA/D-LS) is proposed. The balanced decoding method according to the similarity degree of the jobs (BDMASDJ) is developed to trade off the two objectives. Crossover and mutation operators suitable for MSDRHFSP are designed, and a local search operator is performed. Finally, comprehensive experiments are conducted and the results show that IMOEA/D-LS can solve the MSDRHFSP effectively.

Multi-objective re-entrant hybrid flow shop scheduling problem considering fuzzy processing time and delivery time

Although re-entrant hybrid flow shop scheduling is widely used in industry, its processing and delivery times are typically determined using precise values that frequently ignore the influence of machine failure, human factors, the surrounding environment, and other uncertain factors, resulting in a significant gap between theoretical research and practical application. For fuzzy re-entrant hybrid flow shop scheduling problem (FRHFSP), an integrated scheduling model is established to minimize the maximum completion time and maximize the average agreement index. According to the characteristics of the problem, a hybrid NSGA-II (HNSGA-II) algorithm is designed. Firstly, a two-layer encoding strategy based on operation and machine is designed; Then, a hybrid population initialization method is designed to improve the quality of the initial population; At the same time, crossover and mutation operators and five neighborhood search operators are designed to enhance the global and local search ability of the algorithm; Finally, a large number of simulation experiments verify the effectiveness and superiority of the algorithm.

Energy-efficient distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times considering factory eligibility constraints

In the face of energy crisis, manufacturers pay more and more attention to energy-saving scheduling. In the paper, we consider the distributed heterogeneous re-entrant hybrid flow shop scheduling problem (DHRHFSP) with sequence dependent setup times (DHRHFSP-SDST) considering factory eligibility constraints under time of use (TOU) price, which means that each job can only be assigned to its available set of factories and all factories have different number of machines and processing capacity, and so on. To deal with DHRHFSP-SDST, a multi-objective Artificial Bee Colony Algorithm (MOABC) is proposed to optimize both the makespan and total energy consumption. For the MOABC, firstly, a hybrid initialization method is presented to initialize the population; then, due to the electricity price shows significant differences vary from periods under TOU price, the energy saving operator based on right-shift strategy is proposed to avoid processing jobs with the high electricity price without affecting the productivity; thirdly, based on the full consideration of distributed heterogeneous and factory eligibility, crossover and mutation operators, three neighborhood search operators and new food sources generation strategy are designed; lastly, extensive experiments demonstrate the effectiveness of the proposed algorithm on solving the DHRHFSP-SDST.

An improved multi-objective evolutionary algorithm based on decomposition for solving re-entrant hybrid flow shop scheduling problem with batch processing machines

Cold-drawn seamless steel pipes play a pivotal role in special industries. However, scheduling plan, as the core of the production, is manually made, which leads to the issue of low production efficiency. The change in processing time or job families when the jobs reenter and the appropriate buffer size of batch processing machines are not considered in the current research. The energy consumption is not considered as an important objective as well. These gaps motivate this study. The production process of cold-drawn seamless steel pipes can be defined as a re-entrant hybrid flow shop scheduling problem with batch processing machines. The model is formulated to minimize the makespan and the energy consumption of the batch processing machines. An improved multi-objective evolutionary algorithm based on decomposition is proposed to address this problem. To make an energy-efficient production scheduling, the buffer size of batch processing machines is discussed and the batching method is developed. Besides, a greedy selection strategy is proposed to choose a machine for cold-drawn operation considering the influence of the long processing time. The results of experiments show that the appropriate buffer size is determined to be the same as the capacity of batch processing machines. The proposed greedy selection strategy can select suitable machines for jobs. The results prove the effectiveness of the batching constraint and the balanced decoding algorithm, and the proposed algorithm can effectively solve the re-entrant hybrid flow shop scheduling problem with batch processing machines.

A memetic algorithm for energy-efficient distributed re-entrant hybrid flow shop scheduling problem

Facing the worsening environmental problems, green manufacturing and sustainable development have attracted much attention. Aiming at the energy-efficient distributed re-entrant hybrid flow shop scheduling problem considering the customer order constraints (EDORHFSP) under Time-of-Use (TOU) electricity price, a mathematical model is established to minimize the maximum completion time and total consumption energy cost. In the study, some customer orders require production in multiple factories and jobs belonging to the same customer order must be processed in one factory. Firstly, a memetic algorithm (MA) was proposed to solve the problem. To improve the performance of the algorithm, encoding and decoding methods, energy cost saving procedure, three heuristic rules about the population initialization and some neighborhood search methods are designed. Then, Taguchi method is adopted to research the influence of parameters setting. Lastly, numerical experiments demonstrate the effectiveness and superiority of MA for the EDORHFSP.

Surgery scheduling in outpatient procedure centre with re-entrant patient flow and fuzzy service times

To meet the increase in demand for outpatient surgical services, surgery scheduling for outpatient procedure centres (OPCs) has recently attracted considerable attention in both the healthcare industry and the academic community. This paper considers a novel OPC daily surgery scheduling problem (ODSSP) to minimise the average recovery completion time of all patients. To satisfy the OPC surgical practice, patient intake and recovery are applied in the same area for more resource flexibility, and uncertain service times for intake, surgical procedures and recovery are considered. Owing to the similarities shared between healthcare delivery systems and production systems, ODSSP is formulated as a two-stage no-wait re-entrant hybrid flow shop scheduling problem with fuzzy service times. Considering the NP-hardness of such scheduling problem, a new hybrid meta-heuristic (GA-BAVNS) is employed to obtain detailed daily OPC surgery schedules. To achieve greater balance between exploration and exploitation in the search space, GA-BAVNS hybridises a genetic algorithm (GA) and a variable neighbourhood search (VNS) to schedule outpatients for surgical services. To improve the local search performance of the VNS, six novel block-based neighbourhood structures are employed to generate neighbourhood solutions. Moreover, an adaptive neighbourhood change procedure (ANCP) is employed to systematically change the search order of neighbourhood structures for better solutions. Computational results on a set of test problems indicate the superiority of the proposed GA-BAVNS.

Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs

Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

Reentrant hybrid flow shop scheduling problem with non-anticipatory sequence-dependent setup time

This paper studies a hybrid flow shop scheduling problem originated from a steam turbine rotor production. Reentrance and non-anticipatory sequence-dependent setup time are considered. The objectives are to minimise makespan and total tardiness. A mixed integer linear programming model is formulated. A bi-objective simulated annealing algorithm is developed to solve problems with real size. A modified version of the Nawaz-Enscore-Ham algorithm is applied to find a good initial solution and a Pareto search mechanism is introduced to find the Pareto set. The proposed bi-objective simulated annealing algorithm is compared with a single-objective algorithm. Experimental results demonstrate the superiority of the bi-objective algorithm. Sensitivity analysis provides valuable managerial insights for real production.

Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity

Diversification of products has increased the involvement of reentrant manufacturing processes, in which a job returns multiple times to a machine at the preceding workflow stage to continue the manufacturing process. Reentrant flow shop manufacturing can substantially improve manufacturing efficiency when scheduled properly. In practice, advanced manufacturing companies (e.g., semiconductor foundries) have introduced automated material handling system (AMHS), including stockers that serve as centralized inventory buffer space for temporarily storing the inventories owing to limited buffer capacity of each machine. However, no previous studies on reentrant flow shop scheduling have considered the impact of limited buffer capacity or stockers on scheduling efficiency. Consequently, this study investigated the application of stockers in solving the reentrant hybrid flow shop scheduling problem with limited buffer capacity. With the objective of optimizing the makespan and mean flowtime of a schedule, this problem is NP-hard because it generalizes the flow shop problem. Therefore, this study developed a hybrid harmony search and genetic algorithm (HHSGA) for the problem, in which limited buffer capacity and stockers cause solution decoding to be non-trivial. Experimental comparison on scheduling problems with different numbers of jobs showed that the HHSGA performed better than conventional algorithms. Moreover, among three manufacturing conditions (i.e., with buffers and stockers, with buffers only, and with stockers only), the results indicated that the condition using inventory buffers and stockers was more beneficial.

Reentrant hybrid flow shop scheduling problem with non-anticipatory sequence-dependent setup time

This paper studies a hybrid flow shop scheduling problem originated from a steam turbine rotor production. Reentrance and non-anticipatory sequence-dependent setup time are considered. The objectives are to minimise makespan and total tardiness. A mixed integer linear programming model is formulated. A bi-objective simulated annealing algorithm is developed to solve problems with real size. A modified version of the Nawaz-Enscore-Ham algorithm is applied to find a good initial solution and a Pareto search mechanism is introduced to find the Pareto set. The proposed bi-objective simulated annealing algorithm is compared with a single-objective algorithm. Experimental results demonstrate the superiority of the bi-objective algorithm. Sensitivity analysis provides valuable managerial insights for real production.

A Novel SFLA for Re-entrant Hybrid Flow Shop Scheduling

In order to optimize makespan and total tardiness for re-entrant hybrid flow shop scheduling problems simultaneously, this paper proposes a novel Shuffled Frog Leaping Algorithm. The algorithm relies on the lexicographic method to process total tardiness and makespan. A suitable crossover operator is introduced. During the searching process of memeplex, instead of worst solution, multiple best solutions are used to generate new solutions. Further with that, the process of generating random solution is replaced with the neighborhood search. Then as indicated by the simulation based on a group of instances, the novel SFLA is superior in addressing hybrid flow shop scheduling problem.

Multi-Objective Reentrant Hybrid Flowshop Scheduling with Machines Turning on and off Control Strategy Using Improved Multi-Verse Optimizer Algorithm

This paper focuses on the multi-objective optimization of the reentrant hybrid flowshop scheduling problem (RHFSP) with machines turning on and off control strategy. RHFSP exhibits significance in many industrial applications, but scheduling with both energy consumption consideration and reentrant concept is relatively unexplored at present. In this study, an improved Multi-Objective Multi-Verse Optimizer (IMOMVO) algorithm is proposed to optimize the RHFSP with objectives of makespan, maximum tardiness, and idle energy consumption. To solve the proposed model more effectively, a series of improved operations are carried out, including population initialization based on Latin hypercube sampling (LHS), individual position updating based on Lévy flight, and chaotic local search based on logical self-mapping. In addition, a right-shift procedure is used to adjust the start time of operations aiming to minimize the idle energy consumption without changing the makespan. Then, Taguchi method is utilized to study the influence of different parameter settings on the scheduling results of the IMOMVO algorithm. Finally, the performance of the proposed IMOMVO algorithm is evaluated by comparing it with MOMVO, MOPSO, MOALO, and NSGA-II on the same benchmark set. The results show that IMOMVO algorithm can solve the RHFSP with machines turning on and off control strategy effectively, and in terms of convergence and diversity of non-dominated solutions, IMOMVO is obviously superior to other algorithms. However, the distribution level of the five algorithms has little difference. Meanwhile, by turning on and off the machine properly, the useless energy consumption in the production process can be reduced effectively.

Swarm intelligent algorithm for re-entrant hybrid flow shop scheduling problems

In order to solve Re-entrant Hybrid Flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses Wolf Pack Algorithm (WPA) as global optimization. For local assignment, it takes minimum remaining time rule. Scouting behaviors of wolf are changed in former optimization by means of levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hanming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality.A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focus on resolving this problem with LDWPA (Dynamic wolf pack algorithm based on Levy Flight). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively.

Swarm intelligent algorithm for re-entrant hybrid flow shop scheduling problems

In order to solve Re-entrant Hybrid Flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses Wolf Pack Algorithm (WPA) as global optimization. For local assignment, it takes minimum remaining time rule. Scouting behaviors of wolf are changed in former optimization by means of levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hanming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality.A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focus on resolving this problem with LDWPA (Dynamic wolf pack algorithm based on Levy Flight). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively.

A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints

A production scheduling problem originating from a real rotor workshop is addressed in the paper. Given its specific characteristics, the problem is formulated as a re-entrant hybrid flow shop scheduling problem with machine eligibility constraints. A mixed integer linear programming model of the problem is provided and solved by the Cplex solver. In order to solve larger sized problems, a discrete differential evolution (DDE) algorithm with a modified crossover operator is proposed. More importantly, a new decoder addressing the machine eligibility constraints is developed and embedded to the algorithm. To validate the performance of the proposed DDE algorithm, various test problems are examined. The efficiency of the proposed algorithm is compared with two other algorithms modified from the existing ones in the literatures. A one-way ANOVA analysis and a sensitivity analysis are applied to intensify the superiority of the new decoder. Tightness of due dates and different levels of scarcity of machines subject to machine eligibility restrictions are discussed in the sensitivity analysis. The results indicate the pre-eminence of the new decoder and the proposed DDE algorithm.

A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem

This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE–eEDA, is proposed to solve the problem. DE–eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.

An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times

This paper deals with a bi-objective hybrid flow shop scheduling problem minimizing the maximum completion time (makespan) and total tardiness, in which we consider re-entrant lines, setup times and position-dependent learning effects. The solution method based on genetic algorithm is proposed to solve the problem approximately, which belongs to non-deterministic polynomial-time (NP)-hard class. The solution procedure is categorized through methods where various solutions are found and then, the decision-makers select the most adequate (a posteriori approach). Taguchi method is applied to set the parameters of proposed algorithm. To demonstrate the validation of proposed algorithm, the full enumeration algorithm is used to find the Pareto-optimal front for special small problems. To show the efficiency and effectiveness of the proposed algorithm in comparison with other efficient algorithm in the literature (namely MLPGA) on our problem, the experiments were conducted on three dimensions of problems: small, medium and large. Computational results are expressed in terms of standard multi-objective metrics. The results show that the proposed algorithm is able to obtain more diversified and competitive Pareto sets than the MLPGA.