Sequence-dependent Setup Times Research Articles

Accelerated evaluation of blocking flowshop scheduling with total flow time criteria using a generalized critical machine-based approach

Despite the considerable advances in the research of the blocking flowshop scheduling problem (BFSP), several unresolved challenges persist. Algorithmic complexity presents hurdles. Although the insertion-based method is considered to generate superior solutions, its high computational demand diminishes the efficiency of algorithms, especially within large-scale sequences. The existing accelerated evaluation methods cannot utilize the existing information to quickly calculate the total flow time or the total tardiness time of the changed sequence after the job insertion, but recalculates it from scratch. This does not significantly reduce computational effort and needs to be further improved.In this paper, we delve into the intrinsic features of these challenges, proposing a generalized accelerated critical machine-based evaluation tailored for the total flow time and tardiness criteria of the BFSP with and without sequence-dependent setup times. First, we propose three theorems, one corollary, and their proofs based on the critical machine. Second, we propose the accelerated evaluation procedure based on these theorems to calculate the objectives related to the total flow time. Third, we also extend the proposed accelerated evaluation method to the BFSP with sequence-dependent setup times, aiming to significantly reduce the time complexity. Finally, we conduct four experiments on five well-known benchmarks (a total of 3540 test instances). Through statistical analysis, it becomes evident that our computational efforts have significantly decreased in computing both the total flow time and the total tardiness time. This performance enhancement is superior to the effectiveness of existing acceleration techniques.

A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times

This paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of the assembly stage, which inherits the merits of genetic programming (GP) and neighbourhood search operators. In HGP, a hybrid problem-specific initialization heuristic is developed to make populations more diverse. Multiple neighbourhood search operators are employed as the leaf nodes, which are vital for the success of GP. A product shift strategy is proposed to strengthen its exploitability. In addition, a simulated annealing criterion is adopted to make the HGP explore more thoroughly. Finally, statistical and computational experiments are carried out on the benchmark instances. The results exhaustively identify the notable competitiveness of the HGP algorithm in coping with the DAPFSP-TSDST.

A cooperative grey wolf optimizer for the joint flowshop scheduling problem with sequence-dependent set-up time

With the complexity involved in manufacturing products, many companies use multiple processes to complete product processing. Most studies have been concerned with single production but have neglected the widespread joint flowshop scheduling problem (JFSP). In this article, a cooperative grey wolf optimizer (CGWO) is developed to solve the JFSP. First, according to the features of the JFSP, a corresponding mathematical model is constructed, and three collaborative strategies and random generation are proposed to initialize the population. In the process of searching for prey, a discretized search for prey update mechanism is proposed, which is conducive to balancing exploration and exploitation. An energy-saving strategy is proposed to decrease energy consumption. Moreover, four local search mechanisms are developed for different optimization objectives to enhance the performance of the method in attacking the prey. The results show that the CGWO is effective in solving the JFSP.

An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no-wait flowshop group scheduling problem with sequence-dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm·(EEDA) with problem-specific knowledge. First, a mixed integer linear programming(MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem-specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two-stage heuristics are presented to construct a high-quality population with diversity. Fourth, a probability model with problem-specific knowledge and a family-based updating mechanism are developed to accumulate valuable pattern information from high-quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-the-art algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

An ALNS to optimize makespan subject to total completion time for no-wait flow shops with sequence-dependent setup times

An ALNS to optimize makespan subject to total completion time for no-wait flow shops with sequence-dependent setup times

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

A new dispatching mechanism for parallel-machine scheduling with different efficiencies and sequence-dependent setup times

The Apparent Tardiness Cost (ATC) dispatching rule was initially developed to minimize tardiness in single-machine scheduling problems. ATC extensions have been frequently applied in other production settings, relying heavily on blocking idle machine capacity with a single-machine outlook; this approach may not result in the best outcomes, considering that machines have different efficiencies. This study develops a new dispatching rule for parallel-machine scheduling, considering different machine efficiencies, ready times, and sequence-dependent setup times to minimize the total weighted tardiness. The proposed method reduces the time interference factor of the denominator item in the dispatching rule and uses more effective methods for selecting the best processing machine for the jobs. The grid approach is used to evaluate the method against the state-of-the-art. The experimental results confirm that the developed method is superior regardless of the type of parallel machines, the problem scale, and other operational parameters. It is also shown that other ATC dispatching rules can be improved by applying the proposed approach. The proposed method could be incorporated into soft computing techniques for more effective and efficient scheduling.

Evolving many-objective dispatching rule pairs for unrelated parallel machine scheduling with sequence-dependent setup times

The real-world problem of unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSPSST) is investigated and focuses on two key aspects: fast solution and many-objective optimization. While dispatching rules (DRs) are effective in meeting demand, the manual design of many-objective DRs (MaODRs) is challenging. An alternative approach, based on the automatic evolution of MaODRs using hyper-heuristics, shows promise. However, limited literature exists on the automatic evolution of MaODRs specifically for UPMSPSST. In this study, a many-objective genetic programming (MaOGP) algorithm is formed by combining many-objective evolutionary algorithms (MaOEAs) with genetic programming (GP) algorithms. This enables the automatic evolution of MaODRs, leveraging the respective advantages of each approach. The exploration results demonstrate that the evolved MaODRs outperform manually designed DRs reported in prior literature. Furthermore, when considering combinations of objectives with different correlations, the MaODRs exhibit outstanding performance across multiple objectives simultaneously, surpassing even the performance of DRs trained separately for individual objectives. These findings highlight the potential of the proposed MaOGP approach in efficiently and effectively solving the UPMSPSST problem, opening avenues for further research and practical applications in related industries.

A novel predictive–reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time

In practical printed circuit boards (PCB) drilling production systems, multiple spindle processing capabilities and uncertainties are commonplace. This paper addresses predictive–reactive scheduling, considering parallel batch processor lot-sizing and scheduling with sequence-dependent setup times in a dynamic environment. We propose a predictive scheduling algorithm named S-AGAIG to balance machine spindle utilization and order tardiness in steps. Multiple splitting solutions are first formed by using a split heuristic algorithm to size the sub-lots of orders. Then the adaptive genetic algorithm with iterated greedy search is applied to select the solutions and scheduling. Furthermore, we present a schedule repair strategy based on sub-lot co-processing considering the impact of critical sub-lots, and construct a scheduling stability metric for rescheduling. In 36 sets of case experiments encompassing diverse load and machine type configurations, S-AGAIG showcases a 21.27% enhancement in average tardiness performance when compared to its closest rival. Across 28 cases involving varying disturbances, the framework demonstrates exceptional robustness.

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects

In the real-world production environment, the employee skills affect production efficiency. Especially, the learning and forgetting effects largely influence the processing time. This paper investigates a hybrid flow-shop scheduling problem with learning and forgetting effects (HFSP-LF). Two learning and forgetting effects models are constructed. The sequence-dependent setup time (SDST) is also considered. The objective is to minimize the makespan of all jobs. To solve such problem, a mixed integer linear programming (MILP) model is built to formulate HFSP-LF. Then, a meta-reinforcement learning-based metaheuristic (MRLM) is proposed. In MRLM, a constructive heuristic is employed to generate the initial solution. Several problem-specific search operators are developed to explore and exploit the solution space. The search framework of MRLM comprises a meta-training phase and a Q-learning-driven search phase. In the meta-training phase, the search operators are trained to obtain prior knowledge of their selection and an initial learning model is constructed. In the Q-learning-driven search phase, Q-learning is employed to implement automatic selection of search operators through continuously perfecting the learning model and absorbing the feedback information of searching. Finally, we conduct a comprehensive experiment. The experimental results demonstrate that the designs of MRLM are effective, and MRLM significantly outperforms several well-performing methods on solving HFSP-LF.

Exact algorithm and machine learning-based heuristic for the stochastic lot streaming and scheduling problem

This article presents a probabilistic variant of the classic Lot Streaming and Scheduling Problem (LSSP), in which the arrival times of products are stochastic. The LSSP involves a multi-product lot streaming problem and a sublot scheduling problem with a flow shop model and sequence-dependent setup times. Although the deterministic LSSP has been studied in the literature, the problem with stochastic arrival times of products has not been explored. In this article, we first derive some properties of the LSSP solution and propose closed-form expressions to compute the objective function of a given solution under three commonly used stochastic distributions. Based on these expressions, we develop a new exact Dynamic Programming (DP) algorithm and propose an efficient DP-based heuristic algorithm. Additionally, we build a machine learning model to predict whether a DP transition needs to be considered in the heuristic to improve its efficiency. Our computational study of test instances with various arrival time distributions shows that our algorithms can achieve promising results. Furthermore, we find that the machine learning model can simultaneously reduce the computational complexity and improve the algorithm’s accuracy.

Modeling and optimization of the hybrid flow shop scheduling problem with sequence-dependent setup times

The hybrid flow shop scheduling problem (HFSP) is an extension of the classic flow shop scheduling problem and widely exists in real industrial production systems. In real production, sequence-dependent setup times (SDST) are very important and cannot be neglected. Therefore, this study focuses HFSP with SDST (HFSP-SDST) to minimize the makespan. To solve this problem, a mixed-integer linear programming (MILP) model to obtain the optimal solutions for small-scale instances is proposed. Given the NP-hard characteristics of HFSP-SDST, an improved artificial bee colony (IABC) algorithm is developed to efficiently solve large-sized instances. In IABC, permutation encoding is used and a hybrid representation that combines forward decoding and backward decoding methods is designed. To search for the solution space that is not included in the encoding and decoding, a problem-specific local search strategy is developed to enlarge the solution space. Experiments are conducted to evaluate the effectiveness of the MILP model and IABC. The results indicate that the proposed MILP model can find the optimal solutions for small-scale instances. The proposed IABC performs much better than the existing algorithms and improves 61 current best solutions of benchmark instances.

Scheduling parallel extrusion lines

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

A bi-objective MILP model for an open-shop scheduling problem with reverse flows and sequence-dependent setup times

A bi-objective MILP model for an open-shop scheduling problem with reverse flows and sequence-dependent setup times

A bi-objective MILP model for an open-shop scheduling problem with reverse flows and sequence-dependent setup times

A bi-objective MILP model for an open-shop scheduling problem with reverse flows and sequence-dependent setup times

An exact branch-and-bound algorithm for <i>seru</i> scheduling problem with sequence-dependent setup time and release date

An exact branch-and-bound algorithm for <i>seru</i> scheduling problem with sequence-dependent setup time and release date

Mathematical programming formulations for the reclaimer scheduling problem with sequence-dependent setup times and availability constraints

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case.

Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A Comparative Analysis of Flow-based and Set Partitioning Formulations

This article addresses the Hybrid Flow Shop Green Scheduling Problem with Sequence and Machine Dependent Setup Times (HFSGSP-SMDST) in the context of Industry 4.0. The objective is to minimize carbon emission while optimizing scheduling. Two mathematical formulations, a flow-based positional formulation (FB) and a set partitioning positional formulation (HCG), are proposed to provide tighter lower bounds. The FB formulation is solved using a commercial solver, while the HCG algorithm incorporates an Iterated Local Search (ILS) to generate initial solutions and an upper bound for the problem. Computational experiments demonstrate that HCG achieves better lower bounds, although it requires more time compared to FB. The proposed algorithms offer valuable solutions for carbon emission optimization and scheduling in Industry 4.0, with a trade-off between lower bounds and convergence speed.

A study on simulation-based optimization of a stochastic flexible job shop scheduling undergoing preventive maintenance with sequence-dependent setup time

A study on simulation-based optimization of a stochastic flexible job shop scheduling undergoing preventive maintenance with sequence-dependent setup time