Algorithms for Scheduling Multi-Level Just-In-Time Production Systems

Miltenburg, John; Sinnamon, Gordon

doi:10.1080/07408179208964209

Cited by 45 publications

(16 citation statements)

References 10 publications

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“…Miltenburg and Sinnamon (1995) expressed the importance of using the weights in the formulation. Miltenburg and Sinnamon (1992) extended their earlier work and proposed techniques for determining good schedules. The procedures given by them can solve very large problems.…”

Section: Literature Reviewmentioning

confidence: 96%

Genetic algorithms for sequencing problems in mixed model assembly lines

Ponnambalam

Aravindan²,

Rao

2003

Computers & Industrial Engineering

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Section: Literature Reviewmentioning

confidence: 96%

Genetic algorithms for sequencing problems in mixed model assembly lines

Ponnambalam

Aravindan²,

Rao

2003

Computers & Industrial Engineering

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“…In the sequencing problem of mixed-model assembly line, one of the two major goals is to level the workload at each workstation on the line against the different assembly time per product (4) . Another one is to keep every usage rate of parts at the assembly line constant (5) .…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

Multi-Objective Analysis Applied to Mixed-Model Assembly Line Sequencing Problem through Elite Induced Evolutionary Method

Shimizu

Sakaguchi

Pralomkarn

2012

JAMDSM

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To meet higher customer satisfaction and shorter production lead time, assembly lines are shifting to mixed-model assembly lines. Accordingly, sequencing is becoming an increasingly important operation scheduling that directly affects on efficiency of the entire process. In this study, such sequencing problem at the mixed-model assembly line has been formulated as a bi-objective integer programming problem so that decision making through trade-off analysis can bring about significant production improvements. Then we have developed a multi-objective analysis method by hybridizing conventional and recent meta-heuristic methods. After showing its generic idea, the car mixed-model assembly line sequencing problem is concerned as a case study. Certain measures are also introduced to quantitatively evaluate the performances of the method through comparison.

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“…. ., D T , n is the number of unique products to be produced, and d i is the demand for product i. U gives the matching www.scienceasia.org between the actual product intermixing and demand for a production sequence as a metric to quantify this goal 2,26 . Feasible sequences for MMAL sequencing having n products can be computed using…”

Section: Multi-objective Mixed-model Sequencing Problemmentioning

confidence: 99%

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Chutima¹,

Pinkoompee²

2009

ScienceAsia

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This paper investigates the performance of local searches embedded in memetic algorithms for solving multi-objective mixed-model assembly line sequencing problems that are common in a just-in-time production system. Two inversely related objectives, namely, setup times and production rate variation, are simultaneously considered. We use memetic algorithms which are a type of evolutionary algorithm using a local search algorithm to exercise exploitation. Simulation results demonstrate that memetic algorithms employed in conjunction with an appropriate local search outperform highly meta-heuristic algorithms such as Strength Pareto Evolutionary Algorithm 2 and Non-dominated Sorting Genetic Algorithm II in terms of ability to find Pareto-optimal solutions.

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Algorithms for Scheduling Multi-Level Just-In-Time Production Systems

Cited by 45 publications

References 10 publications

Genetic algorithms for sequencing problems in mixed model assembly lines

Genetic algorithms for sequencing problems in mixed model assembly lines

Multi-Objective Analysis Applied to Mixed-Model Assembly Line Sequencing Problem through Elite Induced Evolutionary Method

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