Production Planning Problems In Manufacturing

This chapter presents an overview of the literature review of production planning problems in manufacturing. There has been a renewed interest in the application of mathematical programming to address aggregate production planning decisions. The interest is mainly due to recent advances in information technology as it allows production managers to acquire and process production data on a real-time basis. As a result, managers are actively seeking decisions support systems to improve their decision-making. The subjected work related to the problem and the recent development that has been applied in industry will be reviewed.

Primarily, the aggregate production planning divided two types of approaches, the first approach is the integrated approach,

Lookett [10] presented the problem is a finite stage stochastic decision problem with zero-one variables. Li [9] Studies the effect of market competition and customer behavior based on high quality, price and expected delivery lead-time on the strategy MTO/MTS production decision in a single product situation. Here, the discussion is about what happens when one of the factors changes, relying on the work the competition can increase a demand for MTS, just as other economic phenomenon such as economies of measured, uncertainty and that delivery time competition decreases producer’s good. Donk et al [13] presented a decision aid for the MTO or MTS, such decisions are generally taken once every three or six months or every year. They used a deterministic approach with stable demand and infinite capacity assumptions as in independent lot size formula. Risa and DeCroix [16] studied the classical optimality MTO policy versus MTS one for a firm producing multiple various products at a shared manufacturing facility. As it's well known, the manufacturing times are general random variables; i.e. various products may have different manufacturing-time distributions. They model demand of production as independent Poisson processes with change arrival rates and optimality conditions for MTO versus MTS policies as

As example, Aliev et al. [11] Considered uncertain market demand and capacities and imprecise process times in a production environment, presented a fuzzy integrated for multi-period and multi-product production to deal with these types of uncertainty. Karabuk and Wu [18] developed a strategic capacity planning model, where demand uncertainties are incorporated via a scenario structure. Gfrerer and Zapfel [7] present a multi-period hierarchical production planning model by uncertain demand. Ning et al [19] proposed multiproduct aggregate production planning problems, in which the demand, product sales revenue, product capacity, all characterized as fuzzy random variables. The presented algorithm combines fuzzy random simulation, and neural network (NN). The simultaneous disturbance stochastic approximation (SPSA) algorithm and genetic algorithm (GA) was proposed to solve the model. Rota et al [20] presented a model to the Aggregate Production and Distribution Planning (APDP) to help managers to evaluate the need and the relevance of integrating production and distribution decisions with the mixed Integer Linear Programming. The model considered two-stage production and has been applied in a supply chain included of multi-site, multiple demand point and a make-to-stock strategy. The aim is to reduce the operational total cost, including production, transportation costs,