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

Abstract

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.

A data model for collaborative manufacturing environments

“…C2NET Optimiser (OPT) provides advanced optimization algorithms for single and collaborative computations of production, replenishment and delivery plans in order to optimize using the supply network's manufacturing and logistics assets from a holistic point of view. It provides decision makers with a set of tools to easily manage decision rules collaboratively and to recalculate alternative plans in real time by increasing the efficiency of using the supply network's manufacturing and logistics assets from a holistic point of view by the global optimization of operations plans and schedules (Andres et al 2018;Cunha et al 2018;Katasonov et al 2018;Andres, Sanchis, Poler, Mula, et al 2017;Diaz-Madronero et al 2018;Sanchis et al 2018;Andres et al 2019).…”

“…The following algorithm, developed as part of the C2NET project to respond to a real company's optimization collaborative planning need, was selected as an illustrative example of using CMData in a real case of a production plan, or a Make (M) plan according to the SCOR model. This mixed integer linear programming (MILP) explained in Andres, Sanchis, Poler, Mula, et al (2017) is related to a multi-machine injection molding sequencing plan, and has four indices for modeling: products (i), molds (j), machines (k), periods (t). The objective function of this MILP minimizes the sum of the cost of setting up the mold in a machine, and the product's inventory and backorder costs.…”

“…The third column corresponds to the table in CMData where this field is defined following the name of that item in CMData. Moreover for each field, the type and units are defined following the nomenclature of the mixed integer linear programming model for multimachine injection molding sequencing (Andres, Sanchis, Poler, Mula, et al 2017). The last column is the field description.…”

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

“…The case studies of manufacturing design are generally focusing on traditional manufacturing environment, and only a few of them are discussing the cyber-physical systems. The most important fields of case studies are from the automotive industry [58,59], but valuable case studies were published in the field of aircraft final assembly [60], in-mold decoration manufacturing [61], timber industry [62], semiconductor manufacturing [63,64], food manufacturing [65], and injection molding [66].…”