Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

Vanchipura, Rajesh; Sridharan, R.; Babu, A. Subash

doi:10.1016/j.jmsy.2013.07.003

Cited by 28 publications

(9 citation statements)

References 51 publications

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“…A tabu search heuristic with makespan and weighted tardiness criteria for SDST flow shop was expanded by Fred Choobineh et al [29]. They demonstrated that recommended heuristic gives optimal or near to optimal solutions in a realistic time.Rajesh Vanchipura et al [30] offered the application of variable neighborhood descent (VND) approach for solving SDST flow shop scheduling problem. They incorporated the VND approach with two constructive heuristics, NEHRB and FJSRA.…”

Section: E Improvement Heuristics For Sdst Flow Shopmentioning

confidence: 99%

Estimation of Heuristics for adaptation Flow Shops and Flow Shops with sequence Dependent Setup Times (SDST)

Shukla¹

2018

IJRASET

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Section: E Improvement Heuristics For Sdst Flow Shopmentioning

confidence: 99%

Estimation of Heuristics for adaptation Flow Shops and Flow Shops with sequence Dependent Setup Times (SDST)

Shukla¹

2018

IJRASET

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“…Ebrahimi et al (2014) proposed two algorithms based on GAs for the hybrid flow shop scheduling problem with sequence dependent setup time, and used the group technology for reducing setup times. Vanchipura et al (2014) developed two neighbourhood search-based heuristics for scheduling a flow shop with sequence dependent setup times.…”

Section: Ffsp With Sequence Dependent Setup Timesmentioning

confidence: 99%

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

Han

Zhu

et al. 2016

IJMIC

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This paper studies the flexible flow shop scheduling problem with component altering times (FFSP-CAT), which is a specific form of the flexible flow shop scheduling problem with sequence dependent setup times in the practical scenario. Dealing with FFSP-CAT includes the jobs' machine assignment determination and the globe optimisation. We develop six rules for jobs' machine assignment, and since these rules will easily conflict with one another if they are used with the same priorities, we construct a repeated cooperative model and provide a game theoretical analysis, then derive a Nash equilibrium machine assignment scheme (NEMAS) to effectively manage these rules for jobs' machines assignment at each stage. Furthermore, to achieve the global optimum, we design an advanced compact genetic algorithm (ACGA). By combining NEMAS and ACGA, an approach named advanced compact genetic algorithm with Nash equilibrium machine assignment scheme (ACGA-NEMAS) is used for minimising the makespan of FFSP-CAT. Through extensive comparison experiments with different scales of instances, we show that the algorithm with NEMAS acquires 56.85% improvement over the algorithm without NEMAS, and ACGA-NEMAS performs 80.28% better than genetic algorithm.

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“…Gao, Sun, and Gen (2008) use VND as the search operators of a genetic algorithm to minimize the makespan, maximal workload and the total workload of a flexible job shop. Vanchipura, Sridharan, and Babu (2013) apply VND to solutions obtained by two well-known constructive heuristics for the minimal makespan flow shop with sequence dependent setup time. Czogalla and Fink (2008) hybridize a particle swarm optimization with a VND which is applied as a local search for the minimal flow time flow shop problem where jobs cannot wait between their consecutive stages.…”

Section: Variable Neighborhood Descentmentioning

confidence: 99%

An iterated local search variable neighborhood descent hybrid heuristic for the total earliness tardiness permutation flow shop

M’Hallah

2014

International Journal of Production Research

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This paper considers the minimal earliness tardiness m-machine permutation flow shop scheduling problem with distinct due dates and no inserted idle time when a job is waiting. It models the problem as a mixed integer program, and approximately solves it using V, a fast variable neighborhood based hybrid heuristic. V is an iterated local search that applies a variable neighborhood descent as its search engine. The numerical testing provides computational proof of the good performance of V, which matches existing upper bounds of 20.02% benchmark instances and tightens 70.54% ones with an average percent deviation from the best upper bound of 0.07%.

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Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

Cited by 28 publications

References 51 publications

Estimation of Heuristics for adaptation Flow Shops and Flow Shops with sequence Dependent Setup Times (SDST)

Estimation of Heuristics for adaptation Flow Shops and Flow Shops with sequence Dependent Setup Times (SDST)

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

An iterated local search variable neighborhood descent hybrid heuristic for the total earliness tardiness permutation flow shop

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