Literature Review of Open Shop Scheduling Problems

Anand, Ellur; Panneerselvam, Ramasamy

doi:10.4236/iim.2015.71004

Cited by 37 publications

(23 citation statements)

References 84 publications

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“…Anand and Panneerselvam [5] carried out a comprehensive review of literature on open shop scheduling problem. Senthilkumar and Shahbudeen [6] proposed a heuristic based on Genetic Algorithm (GA) to minimize the makespan of the open shop scheduling problem and they compared it to an earlier work of Panneerselvam [3].…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

Anand¹,

Panneerselvam²

2016

AJIBM

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Open Shop Scheduling Problem (OSSP) is a combinatorial optimization problem for more than two machines and n jobs. Open Shop Scheduling Problem is another kind of scheduling problem along with flow shop and job shop scheduling problems. The open shop scheduling problem involves scheduling of jobs, where the sequence of the operations of each job can be arbitrarily chosen and need not be same. This means that the operations of the jobs can be performed in any sequence. In the absence of sequences for the jobs, for a given set of jobs, finding different parameters like maximum completion time Cmax becomes highly difficult and complex. One can use complete enumeration method or branch and bound method to solve this problem optimally for small and medium size problems. The large size problems of open shop problem with more than two machines and with n jobs can be solved by either a heuristic or meta-heuristics such as genetic algorithm, simulated annealing algorithm, etc. to obtain very near optimal solution. The performance of the genetic algorithm is affected by crossover operator performed between two parent chromosomes. Hence, this paper explores various crossover operators used, while using evolutionary based genetic algorithm to solve open shop scheduling problems. It further attempts to propose a new crossover operator using three chromosomes.

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Section: Literature Reviewmentioning

confidence: 99%

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

Anand¹,

Panneerselvam²

2016

AJIBM

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“…They also proposed a memetic algorithm with simulated algorithm (SA) to obtain efficient algorithm in a reasonable time. According to the survey work of Ellur and Ramasamy [11], a variety of the open shop problem with different objective function has been considered in the literature, however, less interest to develop a particle swarm optimization (PSO) algorithm to solve MOSP problem is apparent in the literature when compared to other scheduling problems. Therefore, in this paper we formulate the LED sorting machine scheduling problem as a mixed integer programming model to specify the problem and to obtain optimal solutions as benchmarks, furthermore, we propose a PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization to Minimize Makespan for a Four-Stage Multiprocessor Open Shop with Dynamic Job Release Time

Wang¹,

Chou²

2015

WJET

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This paper considers the scheduling problem observed in chip sorting operation of LED manufacturing, where each lot (job) with release time have four operations to be processed on a set of processing stages without pre-determined necessary route. Each stage has one and more identical sorting machines. The sorting machines scheduling problem can be treated as a four-stage multiprocessor open shop problem with dynamic job release, and the objective is minimizing the makespan in the paper. This problem is formulated into a mixed integer programming (MIP) model and empirically shows its computational intractability. Due to the computational intractability, a particle swarm optimization (PSO) algorithm is proposed. A series of computational experiments are conducted to evaluate the performance of the proposed PSO in comparison with exact solution on various small-size problem instances. The results show that the PSO algorithm could finds most optimal or better solutions in one second.

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“…Third, at the capacity-to-patient level, we only consider appointment planning systems in which interrelated appointments can be planned separately. An example of a multi-appointment planning system that is not included is Condotta 13 2 Chapter 2. Multi-disciplinary planning in healthcare: a review and Shakhlevich [46], who plan multiple chemotherapy appointments which need to follow a fixed cyclic pattern.…”

Section: Focus Of the Reviewmentioning

confidence: 99%

“…Furthermore, in many situations, patients with various characteristics use the open-shop system, which makes the flexible open-shop system most prevalent both in practice and in the literature. For general information and applications of open-shop systems, refer to [13].…”

Section: Open-shopmentioning

confidence: 99%

Organizing timely treatment in multi-disciplinary care

Bikker

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Literature Review of Open Shop Scheduling Problems

Cited by 37 publications

References 84 publications

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

A Particle Swarm Optimization to Minimize Makespan for a Four-Stage Multiprocessor Open Shop with Dynamic Job Release Time

Organizing timely treatment in multi-disciplinary care

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