Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)

Gupta, Arun; Kumar, Shailendra

doi:10.4995/ijpme.2016.4102

Cited by 6 publications

(8 citation statements)

References 48 publications

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“…The asset at the Baking Company selected for study is a high-volume, flow shop production line (Gupta & Kumar, 2016). The production line is scheduled as a single unit since all machines connect via a conveyor system with low work in process inventory between machines.…”

Section: Discussionmentioning

confidence: 99%

“…A yeast bread-making process typically consists of the following stages: dough making, dividing, proving, baking, cooling, slicing and packaging (Zhou & Therdthai, 2006). Characteristic production systems for baking and other food processes are flow shops, a configured set of dedicated machines which process all jobs in a fixed order (Dewa et al, 2013;Gupta & Kumar, 2016).…”

Section: Baked Goods Manufacturingmentioning

confidence: 99%

See 1 more Smart Citation

Product Wheels for Scheduling in the Baking Industry: A Case Study

Trattner

Hansen

Hvam

2018

Int. J. Prod. Manag. Eng.

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This paper illustrates current challenges and suggests solutions within the area of scheduling in the baking industry. The analysis applies the product wheel heuristic approach of King (2009) and tests the production cycles generated using actual sales and production data from a manufacturer of frozen baked goods. The product wheel method showed to be a suitable method for application at the baked goods manufacturer and generated a 23% reduction in setup and inventory cost at the case company. Despite the benefits, the product wheel method proved difficult to apply in a high variety setting, where an operations research model may have achieved more significant results.

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Section: Discussionmentioning

confidence: 99%

Section: Baked Goods Manufacturingmentioning

confidence: 99%

Product Wheels for Scheduling in the Baking Industry: A Case Study

Trattner

Hansen

Hvam

2018

Int. J. Prod. Manag. Eng.

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“…Gupta and Kumar [50] tried to solve the problem solely with TOPSIS. In their study, machines represent criteria and jobs stand for alternatives.…”

Section: Solving Fssp With Mcdmmentioning

confidence: 99%

“…This part includes a small-sized illustrative example with 5 jobs and 5 machines. For benchmark purposes, the considered problem is taken from the paper of Gupta and Kumar [50]. The processing times of jobs on machines are shown in Tab.…”

Section: Illustrative Examplementioning

confidence: 99%

An Integrated Solution Approach for Flow Shop Scheduling

Karacan

Şenvar

et al. 2021

Teh. vjesn.

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This study seeks to integrate Random Key Genetic Algorithm (RKGA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to compute makespan and solve the Flow Shop Scheduling Problem (FSSP). FSSP is considered as a Multi Criteria Decision Making Problem (MCDM) by setting machines as criteria and jobs as alternatives. RKGA is employed to determine the best weights for the criteria that directly affect the robustness of the solution. The proposed methodology is presented with illustrative example and applied to benchmark problems. The solutions are compared to well-known construction heuristics. The proposed methodology provides the best or reasonable solutions in acceptable computational times.

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“…There are many heuristic methods to reduce the completion time, the first of which is the method developed by Palmer [7]. These methods are constructive methods.…”

Section: Flow Shop Scheduling Problemsmentioning

confidence: 99%

A Data Mining Based Solution Method for Flow Shop Scheduling Problems

2020

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The scheduling theme which determines where and when the manufacturing actions will be performed is important to perform the activities in time, efficiently and cost-effectively. In this paper, an algorithm is proposed for the solution of a flow shop scheduling problem which holds an important place in the scheduling literature. The path relinking algorithm and data mining are used together for the solution of the flow shop scheduling problem studied here. While data mining is used for globally searching the solution space, path relinking is used for local search. Data Mining is a method which is used to extract the embedded information in a cluster that includes implicit information. Path relinking is an algorithm which advances by making binary displacements in order to convert the initial solution to the guiding solution and repeats by assigning the best obtained solution within this process as to the starting point. The efficiency of the model was tested on Taillard's flow shop scheduling problems. Consequently, it has been possible to solve the large-size problem without the considerable mathematical background. The obtained results show that the proposed method is in the competitiveness level with the other metaheuristic methods.

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Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)

Cited by 6 publications

References 48 publications

Product Wheels for Scheduling in the Baking Industry: A Case Study

Product Wheels for Scheduling in the Baking Industry: A Case Study

An Integrated Solution Approach for Flow Shop Scheduling

A Data Mining Based Solution Method for Flow Shop Scheduling Problems

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