Exact algorithms to minimize makespan on single and parallel batch processing machines

Muter, İbrahim

doi:10.1016/j.ejor.2020.01.065

Cited by 60 publications

(30 citation statements)

References 25 publications

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“…The algorithms' simulation proved that they outperformed standard algorithms in terms of makespan and cloud utilization. Minimizing makespan was the objective of the research conducted by Muter [35] which tackled single and parallel batch processing machine scheduling. The author presented a reformulation for parallel batch processing machines and proposed an exact algorithm to solve this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

2021

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The financial data supply chain is vital to the economy, especially for banks. It affects their customer service level, therefore, it is crucial to manage the scheduling of the financial data supply chain to elevate the efficiency of banking sectors’ performance. The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. This work investigates the problem of scheduling the processing of tasks with non-identical sizes and different priorities on a set of parallel processors. An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. The objective is to minimize different cost types while satisfying constraints such as resources availability, customer service level, and tasks dependency relation. The algorithm proved its effectiveness by allocating tasks with higher priority and weight while taking into consideration customers’ Service Level Agreement, time, and different types of costs, which led to a lower total cost of the batching process. The developed algorithm proved effective by testing it on an illustrative network. Also, a sensitivity analysis is conducted by varying the model parameters for networks with different sizes and complexities to study their impact on the total cost and the problem under study.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

2021

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“…When studying batch scheduling problem of flexible flow shop, Zeng Z et al (2018) took the maximum makespan, power consumption and material loss model as the optimization target, established multi-objective optimization model, adopted hybrid non-dominated genetic algorithm II (NSGA -II) to solve it, and took the actual paper mill scheduling problem as an example to verify the effectiveness of this method by comparing manual scheduling [14]. Muter B (2020) studied scheduling problem of single and parallel batch processing machines, proposed a reconstruction method based on Dantzig-Wolfe two-layer decomposition parallel batch processing machine, and gave precise algorithm, so that strict upper and lower limits of parallel problem were available by using the single processing machine, providing insights for the solutions of follow-up researches [15]. Through regarding the waiting time of the workpiece as the scheduling target, considering the dynamic arrival time, and ensuring the delivery time, Shen Fengping et al (2020) figured out this problem with the heuristic algorithm for the batch process in combination with the particle swarm algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Non-Dominated Genetic Algorithm Based on Decoding Rule of Heat Treatment Equipment Volume and Job Delivery Date

Liang

Zhang

2021

Algorithms

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This paper investigated the flexible job-shop scheduling problem with the heat treatment process. To solve this problem, we built an unified mathematical model of the heat treatment process and machining process. Up to now, this problem has not been investigated much. Based on the features of this problem, we are intended to minimize Cmax, maximize the space utilization rate of heat treatment equipment, and minimize the total delay penalty to optimize the scheduling. By taking the dynamic process arrival under consideration, this paper proposed a set of decoding rules based on the heat treatment equipment volume and job delivery date to achieve a hybrid dynamic scheduling solution during one scheduling procedure. When the utilization rate of heat treatment equipment volume is maximized, and the job delivery date is taken under consideration, it is preferred to minimize the number of workpiece batches in the same job, and reduce the waiting time of the pending job. In combination with the improved adaptive non-dominated genetic algorithm, we worked out the solution. Furthermore, we verified the effectiveness of the proposed decoding rules and improved algorithm through algorithm comparison and calculation results. Finally, a software system for algorithm verification and algorithm comparison was developed to verify the validity of our proposed algorithm.

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“…To test the performance of the proposed BMBO, the objective value of makespan and average relative percentage deviation (RPD) is utilized to evaluate the obtained objective values with expression (18). Here, som e F is the average value achieved by a given algorithm among 10 run times, and best F is the best function value obtained by all algorithms for the same instance.…”

Section: Algorithmic Performancementioning

confidence: 99%

“…To tackle the job shop scheduling problem, four types of procedures are proposed: exact, heuristic and meta-heuristic algorithms. Among them, exact algorithms can solve the problems to optimality but they are limited to the smallscaled [16][17][18]. Heuristics are simple to implement and easy to explain but their strongly problem-specific attribute may bring about huge fluctuations following tiny change in production [19,20].…”

Section: Introductionmentioning

confidence: 99%

A Bottleneck Degree-Based Migrating Birds Optimization Algorithm for the PCB Production Scheduling

et al. 2020

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To address the printed circuit board shop scheduling problem, a dynamic based on multiindicator bottleneck degree is proposed to formulate this complex flexible job shop scheduling problem (FJSP). Then, a bottleneck degree-based migrating birds optimization algorithm (MBO) is proposed to tackle this NP-hard problem. Specifically, a multi-indicator assessment model is first developed to obtain the bottleneck degree, and then a two-layer code-decoding structure based on the variable domain structure and the addition of the improved migratory bird algorithm with competitive mechanisms are employed to improve the effectiveness. Finally, the computational experiments demonstrate that the proposed dynamic scheduling approach based on bottleneck degrees and improved migrating bird algorithms can obtain promising results and it outperforms several other algorithms.

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Exact algorithms to minimize makespan on single and parallel batch processing machines

Cited by 60 publications

References 25 publications

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

A Non-Dominated Genetic Algorithm Based on Decoding Rule of Heat Treatment Equipment Volume and Job Delivery Date

A Bottleneck Degree-Based Migrating Birds Optimization Algorithm for the PCB Production Scheduling

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