Modeling and Solving Production Planning Problem under Uncertainty: A Case Study (original) (raw)
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Models for production planning under uncertainty: A review
International Journal of Production Economics, 2006
The consideration of uncertainty in manufacturing systems supposes a great advance. Models for production planning which do not recognize the uncertainty can be expected to generate inferior planning decisions as compared to models that explicitly account for the uncertainty. This paper reviews some of the existing literature of production planning under uncertainty. The research objective is to provide the reader with a starting point about uncertainty modelling in production planning problems aimed at production management researchers. The literature review that we compiled consists of 87 citations from 1983 to 2004. A classification scheme for models for production planning under uncertainty is defined. r (J. Mula). Analytical models [3] 3. Material requirement planning Conceptual models [9] Analytical models [6] Artificial intelligence models [4] Simulation models [10] 4. Capacity planning Analytical models [4] Simulation models [1] 5. Manufacturing resource planning Analytical models [7] Artificial intelligence models [5] Simulation models [2] 6. Inventory management Analytical models [10] Artificial intelligence models [5] 7. Supply chain planning Conceptual models [1] Analytical models [5] Artificial intelligence models [5] J. Mula et al. / Int. J. Production Economics 103 (2006) 271-285 272
Hybrid Uncertainties Modeling for Production Planning Problems
Communications in Mathematics and Applications, 2017
The formulated mathematical model needs pre-determined and precise model parametersto find a solution. However, the model parameters such as coefficient value are usually not precisely known. Coefficient plays a pivotal role sincethe coefficientcouldprovide important information in relationship between algebraic and linguistic expression. Existing method which is commonly used to generate the precise parametric valuesis unable to handle the coexistence of fuzzy information. Moreover, selecting real numbers for coefficients in random process increases the complexity inprogramming process. Hence, we proposed a fuzzy random regression method in this paper to estimate the precise coefficient values which contains fuzzy random information. An illustrative numerical example is provided to deduce coefficient values from different data representation which included the fuzziness and randomness.The coefficients were treated based on the property of fuzzy random regression. The approach resul...
Applying fuzzy stochastic programming for multi- product multi-time period production planning
This paper presents an integration of fuzzily imprecise and probabilistically uncertain data in multi-time period production planning problem. We consider fluctuation of demands and resources by a fuzzy stochastic approach due to incomplete and/or unavailable information. A mathematical programming model that incorpo- rates these aspects of uncertainty with grading products based on different qualities is developed to maximize total profit, considering total costs includes cost of production, outsourcing, labor, and holding, with subject to constraints associated with customer satisfaction, demand, and holding inventory. We also extend a new approach of defuzzifying and derandomizing methods by measuring the superiority and inferiority of the fuzzy stochastic variables when the model has fuzzy stochastic parameters both in the constraints and in the objec- tive function. To illustrate the behavior of the proposed model and verify the performance of the developed fuzzy stochastic-based approach, we introduce a number of numerical examples to explain the use of the fore- going approach. Consequently, the results obtained are reported and discussed.
Stability of Production Planning Problem with Fuzzy Parameters
Open Journal of Applied Sciences, 2012
The traditional production planning model based upon the famous linear programming formulation has been well known in the literature. The consideration of uncertainty in manufacturing systems supposes a great advance. Models for production planning which do not recognize the uncertainty can be expected to generate inferior planning decisions as compared to models that explicitly account the uncertainty. This paper deals with production planning problem with fuzzy parameters in both of the objective function and constraints. We have a planning problem to maximize revenues net of the production inventory and lost sales cost. The existing results concerning the qualitative and quantitative analysis of basic notions in parametric production planning problem with fuzzy parameters. These notions are the set of feasible parameters, the solvability set and the stability set of the first kind.
Dynamic Programming Approach in Aggregate Production Planning Model under Uncertainty
International Journal of Advanced Computer Science and Applications
In order to achieve a competitive edge in the market, one of the most essential components of effective operations management is aggregate production planning, abbreviated as APP. The sources of uncertainty discussed in the APP model include uncertainty in demand, uncertainty of production costs, and uncertainty of storage costs. The problem of APP usually involves many imprecise, conflicting and incommensurable objective functions. The application of APP in real conditions is often inaccurate, because some information is incomplete or cannot be obtained. The aim of this study is to develop APP model under uncertainty with a dynamic programming (DP) approach to meet consumer demand and minimize total costs during the planning period. The APP model includes several parameters including market demand, production costs, inventory costs, production levels and production capacity. After describing the problem, the optimal APP model is formulated using artificial neural network (ANN) techniques in the demand forecasting process and fuzzy logic (FL) in the DP framework. The ANN technique is used to forecast the input demand for APP and minimize the total cost during the planning period using the FL technique in the DP framework to accommodate uncertainties. The model input is historical data obtained through interviews. A case study was conducted on the the need for aluminum plates for the automotive industry. The results show that the ANN technique proposed for demand projection has a low error value in forecasting demand and FL in the DP framework is able to find minimal production costs in the APP model.
Currently, with regard to the increasing complexities in the industrial and organizational environments, the mathematical programming methods of the creation type used in the past do not meet the demands of the decision-makers of technical and managerial fields. As a result, making use of a combination of mathematical programming models and fuzzy set theory has led to creating further flexible methods and producing more reliable results for optimization problems. Thus, the main objective of applying the methods is to use the limited uncertainties in the decision-making model through the use of fuzzy logic. In the present article, a practical managerial case has been chosen to investigate how to obtain the optimum value for nonlinear programming problems using fuzzy techniques in models with uncertain resource constraints in the optimization of manufacturing and production dimensions. The modelling for this problem has led to creating a fuzzy nonlinear programming model and convertin...
A stochastic approach for evaluating production planning efficiency under uncertainty
International Journal of Electrical and Computer Engineering (IJECE), 2023
Planning production is an essential component of the decision-making process, which has a direct bearing on the effectiveness of production systems. This study’s objective is to investigate the efficiency performance of decision-making units (DMU) in relation to production planning issues. However, the production system in a manufacturing environment is frequently subject to uncertain situations, such as demand and labor, and this can have an effect not only on production but also on profit. The robust stochastic data envelopment analysis model was proposed in this study with maximizing the number of outputs as the objective function thus means of handling uncertainty in input and output in production planning problems. This model, which is based on stochastic data envelopment analysis and a method of robust optimization, was proposed with the intention of providing an efficient plan of production for each DMU of stage production. The model is applied to small and medium-sized businesses (SMEs), with inputs consisting of the cost of labor, the number of customers, and the quantity of raw materials, and the output consisting of profit and revenue. It has been demonstrated through implementation that the proposed model is both efficient and effective.
Long-term planning in manufacturing production systems under uncertain conditions
International Journal of Automotive Technology and Management, 2003
Nowadays, the frequency of decisions related to the configuration and capacity evaluation of manufacturing production systems is increasing in more and more industrial sectors, especially in the automotive field. This is due to a variety of factors, such as the reduction of the life cycle of the product, increasing competition, etc. In such a context, decision makers have to take their actions in shorter times than they ever did in the past: as an example, they typically need to take quick decisions about different production system alternatives. This specific problem has increased in complexity because of the necessity to take into account all the sources of variability and each related level of uncertainty in the available data definition. Two main aspects lead to such difficulties: the lack of a proper decision support system and the need to contextually model the uncertain data. This paper presents the first step in this direction. In particular, a decision support system (DSS) has been developed to help decision makers take productive capacity planning decisions according to the uncertain characterisation of the market evolution. First, a strategy evaluation tool allows the decision maker to specify several productive capacity expansion policies and, then, uses a fuzzy discrete event simulation paradigm (Fuzzy-DEVS) to compare them, providing the possibility of choosing between the different alternatives according to performance indicators. A strategy design tool helps the decision maker by inferring the best expansion policy on the basis of the system analysis conducted in the first step. Finally, our approach has been validated by means of an industrial test case in the automotive sector.
isara solutions, 2023
Organizations seeking to improve their production and inventory decisions face significant obstacles posed by the growing complexities and uncertainties of the production process. This paper addresses the need for an efficient data analysis model that can improve decision-making under uncertain conditions in light of these challenges.The proposed model combines Fixed Pricing, Dynamic Pricing, Quantity Discounts, and Seasonal Pricing by employing the innovative Dual Ant Lion Optimizations (ALO) process. The initial ALO enables the selection of the most appropriate model, whereas the dual ALO enables the determination of the optimal internal hyperparameters within the selected process.This model's applications are extensive and advantageous for businesses in various industries& scenarios. By effectively analyzing production and inventory data, businesses are able to make informed decisions regarding pricing strategies, order quantities, and production schedules, thereby enhancing their overall operational efficiency and financial performance levels.The incorporation of internal components is justified by their complementary strengths. Fixed Pricing ensures pricing stability and predictability, whereas Dynamic Pricing permits adjustments in real time based on market conditions. Quantity Discounts incentivize larger order quantities, resulting in economies of scale, while Seasonal Pricing accommodates seasonal fluctuations in demands. The combination of these pricing strategies within the Dual ALO framework provides a comprehensive solution for addressing production process uncertainties.The proposed model has been subjected to extensive experimentation and evaluation, and the results are remarkable. The model achieves a significant reduction of 2.9% in costs, an increase of 3.5% in profitability, an improvement of 1.9% in fill rate, an increase of 8.5% in inventory turnover, and a reduction of 2.5% in CASIRJ Volume 14 Issue 8 [Year-2023]