An Overview of Optimization Models for Integrated Replenishment and Production Planning Decisions

Orbegozo, Arturo; Andrés, Beatriz; Lauras, Matthieu; Monteiro, Carlos; Malheiro, Miguel

doi:10.1007/978-3-319-58409-6_27

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…The research work carried out in these papers (Andres et al, 2016a(Andres et al, , 2016b(Andres et al, , 2017Orbegozo et al, 2018) identified concrete solutions proposed in the literature to support the (i) individual production, replenishment and delivery planning; and (ii) collaborative production and replenishment plans, collaborative production and delivery plans, collaborative replenishment and delivery plans and collaborative replenishment, production and delivery plans. The literature plans were compared with the real requirements collected from the industry.…”

Section: Problem Descriptionmentioning

confidence: 99%

A MILP for multi-machine injection moulding sequencing in the scope of C2NET Project

Andrés¹,

Sanchís²,

Mula³

et al. 2018

Int. J. Prod. Manag. Eng.

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The goal of C2NET European H2020 Funded Project is the creation of cloud-enabled tools for supporting the SMEs supply network optimization of manufacturing and logistic assets based on collaborative demand, production and delivery plans. In the scope of C2NET Project, and particularly in the Optimisation module (C2NET OPT), this paper proposes a novel holistic mixed integer linear programing (MILP) model to optimise the injection sequencing in a multi-machine case. The results of the MILP will support the production planner decision-making process in the calculation of (i) moulds setup in certain machines, and (ii) the amount of products to produce in order to minimise the setup, inventory, and backorders costs. The designed MILP takes part of the algorithms repository created in C2NET European Funded Project to solve realistic industry planning problems. The MILP is verified in realistic data considering three data sets with different sizes, in order to test its the computation efficiency.

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Section: Problem Descriptionmentioning

confidence: 99%