2022
DOI: 10.1109/access.2022.3183281
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A Multiobjective Multiperiod Mixed-Integer Programming Optimization Model for Integrated Scheduling of Supply Chain Under Demand Uncertainty

Abstract: The problem of integrated scheduling of supply chain has a huge impact on operational efficiency and cost effectiveness. The increasing number of nodes, different time window constraints for customers, and a variety of uncertain scenarios make supply chain scheduling complicated. This research develops a multi-objective multi-period mixed-integer programming optimization model. We consider comprehensively the effect of demand uncertainty, time window constraints, the constraints of node capability, multi-perio… Show more

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Cited by 7 publications
(12 citation statements)
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References 31 publications
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“…The link flow in this paper contains period attribute and sub-period attribute [18]. Each cycle time within the supply chain planning period is defined as the period attribute, whose unit is one week.…”
Section: ) Two Attributes Of Time Of Link Flowmentioning
confidence: 99%
See 1 more Smart Citation
“…The link flow in this paper contains period attribute and sub-period attribute [18]. Each cycle time within the supply chain planning period is defined as the period attribute, whose unit is one week.…”
Section: ) Two Attributes Of Time Of Link Flowmentioning
confidence: 99%
“…Our definition of the research problem in this paper is as follows: 1) Supply chain operating mechanism Retailers generate uncertain demand at the beginning of each period, and suppliers are selected to allocate orders. The material flow is supplied from suppliers to manufacturers [18], where the products are produced. Then they are transported to distributors [23], where one part of products are kept in stock, and the other part are delivered to the retailers in bulk [24].…”
Section: ) Two Attributes Of Time Of Link Flowmentioning
confidence: 99%
“…In production and manufacture, some study need optimization, such as in flow shop scheduling in multi factory environment [8], nozzle assignment in printed circuit board assembly [9], and job assignment in manufacture where there are several parallel machines [10], and so on. Several objectives in the manufacturing optimization problems are minimizing the completion time [8], total assembly time [9], make-span [11], maximizing service level [12], and so on. Meanwhile, the common objectives in the optimization work in the power system is higher energy harvesting [13], minimizing power loss [14], maximizing power point [15], and so on.…”
Section: Introductionmentioning
confidence: 99%
“…In [3,4], scheduling problems were effectively reduced to the associated problems of integer linear programming. In [5], integer linear programming model was proposed to solve some industrial planning problems. An extended overview of applications of integer linear programming, as well as modern methods of solving them, are provided in [6][7][8][9].…”
Section: Introductionmentioning
confidence: 99%
“…According to Theorem 1, the complexity of Alg(Z, M, δ) is estimated at O(nm∆), where ∆ is defined as (5). It is clear that the complexity of the algorithm is directly proportional to the parameter δ.…”
mentioning
confidence: 99%