Aggregate production planning by focusing on energy saving: A robust optimization approach. (1st October 2016)

• Record Type: Journal Article
• Authors: Modarres, Mohammad; Izadpanahi, Ehsan
• Is Part Of: Journal of cleaner production. Volume 133(2016:Oct.)
• Page Start: 1074
• Page End: 1085
• Publication Date: 2016-10-01
• Subjects: Energy planning -- Aggregate planning -- Robust optimization -- Production planning -- Uncertainty; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.05.133
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: One of the major challenges of today's world is the energy crisis, which has significant impact on other issues, such as world economy or the environment. Due to its vital role in manufacturing, energy saving should be considered as an important element of production planning, especially for energy intensive manufacturing plants. In this paper, an aggregate planning model is developed to consider energy planning, demand and production capacity simultaneously. The model happens to be a linear program with three objective functions, which minimize the operational cost, the energy cost and carbon emission. The proposed model is solved as a single-objective model applying a goal attainment technique. To overcome the effects of uncertain input data, we apply the robust optimization approach to this model. The practicality of the proposed approach is illustrated by applying it to a smelting manufacturer, which produces a wide range of products such as aluminum, copper and lead. Considering uncertainty in objective function parameters, we provide some insights into the relationship between the budgets of uncertainty and optimal values. Moreover, regarding uncertainty in product demands, we generate the robust solutions which have a better performance than the deterministic solutions; also we test " Aggregate constraints approach " to the maximum production capacity parameters successfully. The sensitivity of different uncertain parameters is also analyzed.