Explore the vast range of titles available.

The flexible job shop scheduling problem with parallel batch processing operation (FJSP_PBPO) in this study is motivated by real-world scenarios observed in electronic product testing workshops. This research aims to tackle the deficiency of effective methods, particularly global scheduling metaheuristics, for FJSP_PBPO. We establish an optimization model utilizing mixed-integer programming to minimize makespan and introduce an enhanced walrus optimization algorithm (WaOA) for efficiently solving the FJSP_PBPO. Key innovations of our approach include novel encoding, conversion, inverse conversion, and decoding schemes tailored to the constraints of FJSP_PBPO, a random optimal matching initialization (ROMI) strategy for generating diverse and high-quality initial solutions, as well as modifications to the original feeding, migration, and fleeing strategies of WaOA, along with the introduction of a novel gathering strategy. Our approach significantly improves solution quality and optimization efficiency for FJSP_PBPO, as demonstrated through comparative analysis with four enhanced WaOA variants, eleven state-of-the-art algorithms, and validation across 30 test instances and a real-world engineering case.

As a complicated optimization problem, parallel batch processing machines scheduling problem (PBPMSP) exists in many real-life manufacturing industries such as textiles and semiconductors. Machine eligibility means that at least one machine is not eligible for at least one job. PBPMSP and scheduling problems with machine eligibility are frequently considered; however, PBPMSP with machine eligibility is seldom explored. This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition (CSFLA) to minimize makespan. In CSFLA, the initial population is produced in a heuristic and random way, and the competitive search of memeplexes comprises two phases. Competition between any two memeplexes is done in the first phase, then iteration times are adjusted based on competition, and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase. An adaptive population shuffling is given. Computational experiments are conducted on 100 instances. The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.

The Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative batch processing time across all ovens. This objective distinguishes the OSP from other batch processing problems which typically minimize objectives related to makespan, tardiness or lateness. We propose to solve this NP-hard scheduling problem using exact techniques and present two different modelling approaches, one based on batch positions and another on representative jobs for batches. These models are formulated as constraint programming (CP) and integer linear programming (ILP) models and implemented both in the solver-independent modeling language MiniZinc and using interval variables in CP Optimizer. An extensive experimental evaluation of our solution methods is performed on a diverse set of problem instances. We evaluate the performance of several state-of-the-art solvers on the different models and on three variants of the objective function that reflect different real-life scenarios. We show that our models can find feasible solutions for instances of realistic size, many of those being provably optimal or nearly optimal solutions.

Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines (BPM). In this study, the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered, and an adaptive cooperated shuffled frog-leaping algorithm (ACSFLA) is proposed to minimize makespan and total tardiness simultaneously. ACSFLA determines the search times for each memeplex based on its quality, with more searches in high-quality memeplexes. An adaptive cooperated and diversified search mechanism is applied, dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality. During the cooperated search, ACSFLA uses a segmented and dynamic targeted search approach, while in non-cooperated scenarios, the search focuses on local search around superior solutions to improve efficiency. Furthermore, ACSFLA employs adaptive population division and partial population shuffling strategies. Through these strategies, memeplexes with low evolutionary potential are selected for reconstruction in the next generation, while those with high evolutionary potential are retained to continue their evolution. To evaluate the performance of ACSFLA, comparative experiments were conducted using ACSFLA, SFLA, ASFLA, MOABC, and NSGA-CC in 90 instances. The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases, highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.

The flexible job-shop scheduling problem (FJSP) with parallel batch processing machine (PBM) is one of those long-standing issues that needs cutting-edge approaches. It is a recent extension of standard flexible job shop scheduling problems. Despite their wide application and prevalence in practical production, it seems that current research on these types of combinatorial optimization problems remains limited and uninvestigated. More specifically, existing research mainly concentrates on the flow shop scenarios in parallel batch machines for job shop scheduling but few literature emphasis on the flexible job shop integration in these contexts. To directly address the above mentioned problems, this paper establishes an optimization model considering parallel batch processing machines, aiming to minimize the maximum completion time in operating and production environments. The proposed solution merges variable neighborhood search with multi-population genetic algorithms, conducting a neighborhood search on the elite population to reduce the likelihood of falling into local optima. Subsequently, its applicability was evaluated in computational experiments using real production scenarios from a partnering enterprise and extended datasets. The findings from the analyses indicate that the enhanced algorithm can decrease the objective value by as much as 15% compared to other standard algorithms. Importantly, the proposed approach effectively resolves flexible job shop scheduling problems involving parallel batch processing machines. The contribution of the research is providing substantial theoretical support for enterprise production scheduling.

Globally, up to 10% of the total annual rice production is wasted due to excessively high moisture content during storage. Mechanized drying of rice is an important measure to reduce this loss. However, traditional hot-air rice dryers have issues such as low drying rates and high energy consumption. This paper introduces a new type of gas-catalytic infrared rice dryer, including its working principle and components. By using drying rate and fissuring rate as evaluation indicators before and after drying, the performance of this dryer is compared with that of traditional hot-air dryers. Further, a three-factor, three-level orthogonal experimental method was employed. Batch processing capacity, conveyor belt speed, and tempering time were selected as variables to calculate the optimal operating conditions of the gas-catalytic infrared rice dryer using the comprehensive balance method. Experimental results show that under nine different operating conditions, the gas-catalytic infrared rice dryer outperforms the traditional hot-air dryer. The drying rate of the gas-catalytic infrared dryer increased by 215.15% compared to the traditional hot-air dryer, and the fissuring rate decreased by 86%. The orthogonal experimental results indicate that, based on a comprehensive evaluation of moisture content difference and fissuring rate, the gas-catalytic infrared dryer achieves the best drying effect when the conveyor belt speed is 1.92 m/min and the tempering time is 40 minutes. These research findings provide important references for further optimization of rice drying technology.

This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires are maintained throughout the search process. Two types of assimilations are achieved: The strongest and weakest empires cooperate in their assimilation, while the remaining two empires, having a close normalization total cost, combine in their assimilation. A new form of imperialist competition is proposed to prevent insufficient competition, and the unique features of the problem are effectively utilized. Computational experiments are conducted across several instances, and a significant amount of experimental results show that the new strategies of CICA are effective, indicating promising advantages for the considered BPM scheduling problems.

Batch processing machines (BPMs) are extensively present in high energy-consuming manufacturing processes such as casting, and they show some symmetries on adjacent batches and jobs within each batch. Preventive maintenance (PM) is very important for the stable running and energy saving of BPMs; however, PM in a parallel BPM shop is seldom studied. In this study, the energy-efficient parallel BPM scheduling problem with PM is considered and an imperialist competitive algorithm with three empires (TEICA) is presented to minimize makespan and total energy consumption. To obtain high-quality solutions, the number of empires is not used as a parameter and fixed at 3, a new way is applied to construct three initial empires, each of which has a new structure like two imperialists, a new assimilation is given, and an adaptive imperialist competition is implemented based on historical competition data. A number of computational experiments are conducted on 108 instances. The computational results show that the new strategies of TEICA are effective; TEICA can provide better results than all comparative methods on more than 90% instances of the considered BPM scheduling problem, and TEICA may be an effective way to solve other BPM scheduling problem.

Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption.

This paper addresses the scheduling problem for a multi-stage hybrid flow shop (HFS) with single processing machines and batch processing machines. Each stage consists of nonidentical machines in parallel, and only one of the stages is composed of batch processing machines. Such a variant of the HFS problem is derived from the actual manufacturing of complex products in the equipment manufacturing industry. Aiming at minimizing the maximum completion time and minimizing the total weighted tardiness, respectively, a heuristic-search genetic algorithm (HSGA) is developed in this paper, which selects assignment rules for parts, sequencing rules for machines (including single processing machines and batch processing machines), and batch formation rules for batch processing machines, simultaneously. Then parts and machines are scheduled using the obtained combinatorial heuristic rules. Since the search space composed of the heuristic rules is much smaller than that composed of the schedules, the HSGA results in lower complexity and higher computational efficiency. Computational results indicate that as compared with meta-heuristics that search for scheduling solutions directly, the HSGA has a significant advantage with respect to the computational efficiency. As compared with combinatorial heuristic rules, other heuristic-search approaches, and the CPLEX, the HSGA provides better optimizational performance and is especially suitable to solve large dimension scheduling problems.