Mathematical formulations for scheduling in manufacturing cells with limited capacity buffers

Fahmy, Sherif A.; ElMekkawy, Tarek Y.; Balakrishnan, S.

doi:10.1504/ijor.2010.032422

Cited by 12 publications

(12 citation statements)

References 31 publications

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“…For details of the deadlock control, please refer to previous studies. [12][13][14][15][16][17][18][19][20] Multi-objective deadlock-free genetic scheduling algorithm for AMSs…”

Section: Deadlock Controllers For Pnsmentioning

confidence: 99%

“…The finiteness of all these resources makes the system not only have the blockage problem but also encounter the so-called deadlock. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] The deadlock situation occurs when a set of jobs are in ''circular waiting,'' that is, each job in the set waits for a resource held by another job in the same set. 9 For such an AMS, the required resource allocation or scheduling strategy can not only prevent the system from falling into deadlock but also optimize the system performance.…”

Section: Introductionmentioning

confidence: 99%

“…9 For such an AMS, the required resource allocation or scheduling strategy can not only prevent the system from falling into deadlock but also optimize the system performance. 7,12,13,[22][23][24][25][26] Therefore, it is more important to develop such a scheduling strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Based on PNs, various deadlock control strategies or controllers for AMSs have been proposed, mainly including two kinds: deadlock avoidance and deadlock prevention. Of these, the former are online control policies that use feedback information on the current resource allocation status and future process resource requirements to keep the system away from deadlock states, 13,14 while latter are offline and guarantee that necessary conditions for circular waiting or deadlock cannot be satisfied at any time of the AMS dynamic. [20][21][22][23][24] The joint consideration of scheduling and deadlock control of AMSs leads to more complex and difficult combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

Xin

Guo³

et al. 2020

Advances in Mechanical Engineering

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This work focuses on multi-objective scheduling problems of automated manufacturing systems. Such an automated manufacturing system has limited resources and flexibility of processing routes of jobs, and hence is prone to deadlock. Its scheduling problem includes both deadlock avoidance and performance optimization. A new Pareto-based genetic algorithm is proposed to solve multi-objective scheduling problems of automated manufacturing systems. In automated manufacturing systems, scheduling not only sets up a routing for each job but also provides a feasible sequence of job operations. Possible solutions are expressed as individuals containing information of processing routes and the operation sequence of all jobs. The feasibility of individuals is checked by the Petri net model of an automated manufacturing system and its deadlock controller, and infeasible individuals are amended into feasible ones. The proposed algorithm has been tested with different instances and compared to the modified non-dominated sorting genetic algorithm II. The experiment results show the feasibility and effectiveness of the proposed algorithm.

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“…For details of the deadlock control, please refer to previous studies. [12][13][14][15][16][17][18][19][20] Multi-objective deadlock-free genetic scheduling algorithm for AMSs…”

Section: Deadlock Controllers For Pnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

Xin

Guo³

et al. 2020

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…For example, Oliveira et al (2005) presented a combinatorial algorithm based on network optimisation concepts for scheduling messages on a controller area network. Fahmy et al (2010) proposed a mixedinteger programming formulations for the deadlock-free scheduling problem of flexible manufacturing cells.…”

Section: Literature Reviewmentioning

confidence: 99%

A unique hybrid particle swarm optimisation algorithm for simulation and improvement of crew scheduling problem

Azadeh

Asadipour

Eivazy

et al. 2012

IJOR

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The crew scheduling problem is a set covering or set partitioning problem. It schedules the crew members so that all flights are covered, while the cost is minimised. The crew scheduling is an non-deterministic polynomialtime hard constrained combinatorial optimisation problem, so it cannot be exactly solved in a reasonable computation time. This paper presents a particle swarm optimisation (PSO) algorithm for simulating and solving the crew scheduling problem. The proposed algorithm is extended from the discrete version of PSO. By applying PSO to the crew scheduling problem, the cost is A unique hybrid PSO algorithm 407 improved when compared with other well-known algorithms. This is the first study that introduces PSO for simulation and optimisation of the crew scheduling problem.

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An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

Zhang

2015

J. Cent. South Univ.

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Mathematical formulations for scheduling in manufacturing cells with limited capacity buffers

Cited by 12 publications

References 31 publications

A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

A Pareto-based genetic algorithm for multi-objective scheduling of automated manufacturing systems

A unique hybrid particle swarm optimisation algorithm for simulation and improvement of crew scheduling problem

An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

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