A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints

Costa, Antonio; Cappadonna, Fulvio Antonio; Fichera, Sergio

doi:10.1007/s00170-014-6195-7

Cited by 23 publications

(9 citation statements)

References 34 publications

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“…More recently, several research groups have focused on the scheduling problems on parallel batch machines with different capacities and applications in many fields [30][31][32][33][34][35][36][37][38][39][40][41]. The special case of P s j , p − batch, K i C max , where all s j ≤ K 1 (i.e., all jobs can be assigned to any machine), is denoted as P s j ≤ K 1 , p − batch, K i C max .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The special case of P s j , p − batch, K i C max , where all s j ≤ K 1 (i.e., all jobs can be assigned to any machine), is denoted as P s j ≤ K 1 , p − batch, K i C max . Costa et al [30] studied P s j ≤ K 1 , p − batch, K i C max and developed a genetic algorithm for it. Wang and Chou [31] proposed a metaheuristic for P r j , s j ≤ K 1 , p − batch, K i C max (the problem where jobs have different release times).…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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“…To solve BPM many methods based on the First Fit (FF) heuristic have been proposed and applied in different kind of production lines to minimize distinct objectives functions [9,10]. Uzsoy [11], Ghazvini et al [12], suggested heuristics for creating batches, their method was based on the bin packing problem for a single batch processing machine where the jobs had different sizes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Heuristic to Minimize the Bottlenecks Presence in Batch Generation. Case of Study.

Eg²,

Ds³

et al. 2021

Preprint

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This paper presents a new heuristic method capable of minimizing the presence of bottlenecks generated when production batches have a distinct makespan. The proposed heuristic groups the jobs into items, where the one with the longest processing time in the batch determines the makespan. To test the heuristic, information was collected from a real paint process with two stations: one with a single cabin and the other with two parallel cabins. The capacity of processing jobs is limited by the cabin dimensions where jobs have different sizes and processing times. A makespan comparison between the heuristic proposed versus the First in First out (FIFO) dispatching rule that the case of study uses. Additionally, ten random instances based on data taken from the real process were created with the purpose to compare the new heuristic method versus Genetic Algorithm (GA) and Simulated Annealing (SA). The result of the comparison to FIFO, GA and SA showed that the proposed heuristic minimizes the bottleneck in a and creating batches almost with the same makespan. Results indicated a bottleneck time reduction of 96% when new heuristic method were compared to FIFO rule, while compared to Generic Algorithm and Simulated Annealing the bottleneck reduction were around 89% in both cases.

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“…Costa el al. (2013 and2014) proposed a mixed integer linear programming model combined with dual encoding and smart-decoding based genetic algorithms framework to address the makespan minimization problem for a hybrid flow shop with a parallel batching system. Aboutaleb et al (2017) used simulation modeling and data mining approach to develop the standalone closed-loop formula for a throughput rate of normally distributed asynchronous human-dependent serial flow lines.…”

Section: Introductionmentioning

confidence: 99%

“…(2015) develop a heuristic search genetic algorithm (GA) for job sequencing aiming at minimizing the makespan and total weighted tardiness. Costa et al . (2013, 2014) proposed a mixed integer linear programming model combined with dual encoding and smart-decoding-based GAs framework to address the makespan minimization problem for a hybrid flow shop with a parallel batching system.…”

Section: Introductionmentioning

confidence: 99%

Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation

Kang

Bhatti

2018

BPMJ

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Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

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A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints

Cited by 23 publications

References 34 publications

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

A Novel Heuristic to Minimize the Bottlenecks Presence in Batch Generation. Case of Study.

Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation

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