Pareto-based algorithm for adaptive aggregate production and distribution planning in shrimp agroindustry supply chain
L. Herlina,
Machfud,
E. Anggraeni and
Sukardi
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L. Herlina: Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia
Machfud: Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia
E. Anggraeni: Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia
Sukardi: Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia
Journal of Applied and Physical Sciences, 2019, vol. 5, issue 1, 21-29
Abstract:
In the global supply chain, the integration of production and distribution is one of the important activities that must be carried out. This also applies to the shrimp agroindustry supply chain. The shrimp agroindustry is one of the agro-food industries that deals with processing raw shrimp into various frozen shrimp products. The demand for frozen shrimp products is very diverse, while the supply of raw shrimp consists of various sizes and has perishable properties. To fulfill consumer demand, aggregate production planning must be made adaptively. Adaptive means being able to improve aggregate planning due to changes in demand. Integration of adaptive aggregate production and distribution planning will result in better planning. Based on this, we developed an adaptive aggregate production and distribution model for the shrimp agroindustry supply chain. Non-dominated Sorting Genetic Algorithm II (NSGA-II) which is a pareto-based algorithm is used to solve the problem. The aim is to minimize total costs and maximize service levels. The sample problem from the shrimp agroindustry in East Java is used to show the efficiency of the proposed algorithm.
Keywords: Shrimp agroindustry; Supply chain; Adaptive aggregate; Production and distribution Planning; NSGA-II (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:apb:japsss:2019:p:21-29
DOI: 10.20474/japs-5.1.3
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