Title: An aggregate production planning mathematical model, under a peak-demand electrical control policy

Authors: Javier F. Urrutia; Lorena Pradenas

Addresses: Department of Industrial Engineering, University of Concepción, 4070043, Edmundo Larenas 215, Concepción, Chile ' Department of Industrial Engineering, University of Concepción, 4070043, Edmundo Larenas 215, Concepción, Chile

Abstract: The impact of energy costs on the production planning decisions of most manufacturing companies is certainly crucial. For electrical consumption, there is a link between production activities and the regions energy policies, for example where electricity price variations are a tactical problem to be solved via equipment sequencing to address the known electricity costs. The goal of this study is to apply an aggregate production planning mathematical model, under a peak-demand electrical control policy, for an energy intensive manufacturer of grinding media, in Chile. The objective function is to maximise the profit of a company in a horizon time of T periods, where a penalty is incurred when production lines are used at peak hours (per electric market regulations/contracts). Furthermore, the model determines the optimum period for a major preventive maintenance for each of its process lines. The case studied is a plant which produces steel balls that are used for mineral grinding; the plant manufactures ten types steel balls from round steel bars (different diameters of balls/bar), a process with high energy demand (induction bar heating). The proposed model was implemented in the Lingo software, allowing for a consistent aggregate production planning to maximising the company's profits.

Keywords: steel-ball manufacturing; electrical demand control policies; mathematical programming; energy demand manufacturing; mixed integer programming; production planning and scheduling.

DOI: 10.1504/IJPS.2019.103034

International Journal of Planning and Scheduling, 2019 Vol.3 No.1, pp.68 - 83

Received: 03 May 2018
Accepted: 14 Apr 2019

Published online: 11 Oct 2019 *

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