Solving Extended Hybrid-Flow-Shop Problems Using Active Schedule Generation and Genetic Algorithms

Kreutz, Martin; Hanke, Detlef; Gehlen, Stefan

doi:10.1007/3-540-45356-3_29

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Kreutz et al [24] have stated that active schedule contains no excel idle time and no operation can be completed earlier without delaying other operations. The schedule for make-and-pack problem is also active because (1) each gene or order will be processed by assigning resource unit in premix, main mix, storage, and packing line, (2) each resource is connected, and (3) there is transfer time between resource units.…”

Section: Forward Assignment Strategymentioning

confidence: 99%

Genetic algorithm for short-term scheduling of make-and-pack batch production process

Wongthatsanekorn

Phruksaphanrat

2015

Chinese Journal of Chemical Engineering

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Section: Forward Assignment Strategymentioning

confidence: 99%

Genetic algorithm for short-term scheduling of make-and-pack batch production process

Wongthatsanekorn

Phruksaphanrat

2015

Chinese Journal of Chemical Engineering

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“…A non-delay schedule means that no machine is kept idle when it could start processing some operation. An active schedule contains no excess idle times and has no operations that can be completed earlier without delaying other operations (Kreutz et al, 2000). For the minimization of the makespan and the total flow time of a job shop schedule, it is well known that at least one of the optimal schedules is an active one.…”

Section: Active Scheduling Techniquementioning

confidence: 99%

“…6 (b). Kreutz et al (2000) proposed an active scheduler for hybrid FSSP. As previously stated, MMSP is a hybrid FSSP.…”

Section: Active Scheduling Techniquementioning

confidence: 99%

Genetic algorithm for large-size multi-stage batch plant scheduling

Hui

2007

Chemical Engineering Science

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This paper presents a heuristic approach based on genetic algorithm (GA) for solving large-size multi-stage multi-product scheduling problem (MMSP) in batch plant. The proposed approach is suitable for different scheduling objectives, such as total process time, total flow time, etc. In the algorithm, solutions to the problem are represented by chromosomes that will be evolved by GA. A chromosome consists of order sequences corresponding to the processing stages. These order sequences are then assigned to processing units according to assignment strategies such as forward or backward assignment, active scheduling technique or similar measures, and some heuristic rules. All these measures greatly reduce unnecessary search space and increase the search speed. In addition, a penalty method for handling the constraints in the problem, e.g., the forbidden changeovers, is adopted, which avoids the infeasibility during the GA search and further greatly increases the search speed.

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“…Therefore, exact algorithms such as branch-and-bound [3,4] and mixed-integer linear programming can optimally solve the small-size problems. For decades, much more effort has been devoted to searching high-quality solutions in a reasonable computational time by heuristics [5][6][7][8] and metaheuristics [9][10][11]. The purpose of this work is to improve the performance of an NEH-based heuristic [7], and solve the HFS problems in a way we approached using local search (LS).…”

Section: Introductionmentioning

confidence: 99%

An Effective Local Search for Hybrid Flow Shop Scheduling Problems

Su¹,

Yi²

2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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Abstract. To solve the hybrid flow shop scheduling problems with minimum makespan objective, a local search based on the active scheduling technique was proposed. First, a good initial solution was generated by the NEH-based heuristic. Next, a problem-specific local search was developed to improve the initial solution. Last, the experimental results of benchmark instances indicate the effectiveness of the proposed algorithm, which can find the optima for more instances with a small overall average deviation of 3.445% (decreased by 2.359% compared with NEH-based heuristic).

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Solving Extended Hybrid-Flow-Shop Problems Using Active Schedule Generation and Genetic Algorithms

Cited by 7 publications

References 5 publications

Genetic algorithm for short-term scheduling of make-and-pack batch production process

Genetic algorithm for short-term scheduling of make-and-pack batch production process

Genetic algorithm for large-size multi-stage batch plant scheduling

An Effective Local Search for Hybrid Flow Shop Scheduling Problems

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