Unrelated parallel machine scheduling with eligibility constraints and delivery times to minimize total weighted tardiness

Maecker, Söhnke; Shen, Liji; Mönch, Lars

doi:10.1016/j.cor.2022.105999

Cited by 24 publications

(4 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In injection molding industry it is necessary to plan in advance the production scheduling, to maximize the system productivity and resource efficiency [5]. In some environments there are several machines that can be used in parallel to process jobs [7], and usually the objective is to optimize a certain time-related metric [9].…”

Section: Scheduling Problemsmentioning

confidence: 99%

Optimization of Sole Production Scheduling

Moura,

Lopes,

Matias

et al. 2024

Preprint

View full text Add to dashboard Cite

This report addresses a challenge proposed at the 174th European Study Group in Industry to develop an algorithm to optimize processes on rubber soles manufacturing. The objective is to create an order distribution plan that maximizes efficiency, reduces setup times synchronizing molds changing, ensuring that delivery dates are met. This problem can be classified as a parallel machines scheduling problem with sequence dependent setup times. This work presents an heuristic that minimizes the makespan, accounting for the sequence dependent setup times, divided into two steps: (i) a client’s order will be partitioned into sets corresponding to the assigned slots; (ii) reorder the molds within each position of the press, so that setup times are simultaneous. We find solutions for real data, also for cases with double and triple molds for a given size. Results show that synchronization of setup times due to heating and mold changes decrease significantly the makespan.

show abstract

Section: Scheduling Problemsmentioning

confidence: 99%

Optimization of Sole Production Scheduling

Moura,

Lopes,

Matias

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Pulansari & M. (2021) did research regarding UPMSP with the concern of setup duration that is dependent on one another and solved it using Ant Colony Optimization in an effort to minimize makespan and tardiness which in turn lowers the company's waste of resources. The previous study addressed UPMSP which involved the delivery times of a job from a machine that is dependent on one another and also eligibility concerns to minimize the total weighted tardiness (Maecker et al, 2023). Lei & Yang (2022) did a UPMSP study with the collaboration of preventive maintenance and sequence-dependent setup duration times which arrangement is dependent on each one of them.…”

Section: Literature Review Unrelated Parallel Machine Scheduling Prob...mentioning

confidence: 99%

Unrelated Parallel Machine Considering Multi-task Simultaneous Supervision Dual Resources Constraints to Minimize Makespan

Rugova,

Sukoyo,

Akbar

2024

JRIEM

View full text Add to dashboard Cite

To maximize their production, many companies invest their funds into a semi-automatic machine to help them reach their fullest potential. However, many forgot to also take into account the adjustments that have to be done to the preexisting schedule because of the idle time that operators have now that the new machine has arrived. Multi-task simultaneous supervision dual resource constraints (MTSSDRC) have been the go-to method to solve that problem. Nonetheless, there is still an absence in an MTSSDRC field that involves an Unrelated Parallel Machine Scheduling Problem (UPMSP). This research proposed a model of MTSSDRC that is in line with UPMSP where the machine is a complex system and variations of machining speed can happen from a certain machine to certain jobs. This research also brought MTSSDRC closer to its real-life counterpart as a shop floor that exists in many manufacturing industries.

show abstract

“…Several priority rule-based algorithms have been studied to minimize total tardiness for parallel machine scheduling problems [4,[8][9][10]. Recently, a variable neighborhood search algorithm was applied to search for a schedule to minimize total weighted tardiness in a parallel machine scheduling problem with eligibility constraints, and it is better than a mixed integer linear programming and existing models [11]. However, the scheduling performances of these algorithms are not guaranteed in a real manufacturing environment since they simply consider the preference of jobs and machines in a myopic manner.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Scheduler on Parallel Dedicated Machine Scheduling Problem towards Minimizing Total Tardiness

et al. 2023

View full text Add to dashboard Cite

This study considers a parallel dedicated machine scheduling problem towards minimizing the total tardiness of allocated jobs on machines. In addition, this problem comes under the category of NP-hard. Unlike classical parallel machine scheduling, a job is processed by only one of the dedicated machines according to its job type defined in advance, and a machine is able to process at most one job at a time. To obtain a high-quality schedule in terms of total tardiness for the considered scheduling problem, we suggest a machine scheduler based on double deep Q-learning. In the training phase, the considered scheduling problem is redesigned to fit into the reinforcement learning framework and suggest the concepts of state, action, and reward to understand the occurrences of setup, tardiness, and the statuses of allocated job types. The proposed scheduler, repeatedly finds better Q-values towards minimizing tardiness of allocated jobs by updating the weights in a neural network. Then, the scheduling performances of the proposed scheduler are evaluated by comparing it with the conventional ones. The results show that the proposed scheduler outperforms the conventional ones. In particular, for two datasets presenting extra-large scheduling problems, our model performs better compared to existing genetic algorithm by 12.32% and 29.69%.

show abstract

Unrelated parallel machine scheduling with eligibility constraints and delivery times to minimize total weighted tardiness

Cited by 24 publications

References 79 publications

Optimization of Sole Production Scheduling

Optimization of Sole Production Scheduling

Unrelated Parallel Machine Considering Multi-task Simultaneous Supervision Dual Resources Constraints to Minimize Makespan

Deep Reinforcement Learning-Based Scheduler on Parallel Dedicated Machine Scheduling Problem towards Minimizing Total Tardiness

Contact Info

Product

Resources

About