In this study, a multi-product, multi-period and non-linear programming model is developed for production planning problem where demand is under uncertainty. The proposed study is designed for a real-world case study of chemicals production factory with 1 production line and 2 manual and automatic technologies. In manual technology, workers are working with 3 amateur, typical and professional skills in 2 typical and overtime working. Automatic technology in this system has n machines in which the repairing and maintenance of the machineries are also included. This system has n products and the products are life-limited and with diversity. The primary goal is to propose a model for improvement of the production planning and minimization of the production system costs. The products in high volume and various types are produced and they are stored in bottles as the final products. For different production periods, the human forces capacities are considered and the level of employment or forces dismissal are considered. The production process is forwarding and backward process is not acceptable; that is, it is not allowable to rework in this system. Delivering final product from stockpiles to the retailers is conducted using vehicles with limited capacity. To solve the model in larger space and because of the complexity of the model, meta-heuristic algorithm is used. Finally, it is concluded that due to covering most of the assumptions in perishable products production, the proposed model is closer to the real-world circumstances and reduces costs in production systems.
One of the most important issues in production planning is to reduce waste materials as much as possible. This helps reduce all necessary expenses and increase productivity and efficiency. This paper presents an empirical investigation to find important factors for reaching lean production planning. The proposed study determines nine different factors including information technology, organization and leadership, human resources development, inventory management, total quality management, process management, equipment management, maintenance engineering. In order to find out the effects of these factors, the study designs a questionnaire and distributes it among some managers in producer of radiator in Iran. Cronbach alpha is equal to 0.86, which validates the questionnaire. Using some statistical observations, the study has confirmed that all mentioned factors influences production planning, positively.
In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.
With globalization, sweeping technological development, and increasing competition, customers are placing greater demands on manufacturers to increase quality, flexibility, on time delivery of product and less cost. Therefore, manufacturers must develop and maintain a high degree of coherence among competitive priorities, order winning criteria and improvement activities. Thus, the production managers are making an attempt to transform their organization by adopting familiar and beneficial management philosophies like cellular manufacturing (CM), lean manufacturing (LM), green manufacturing (GM), total quality management (TQM), agile manufacturing (AM), and just in time manufacturing (JIT). The main objective of this paper is to propose an optimal assembly method for an engine manufacturer’s assembly line in India. Currently, the Indian manufacturer is following traditional assembly method where the raw materials for assembly are kept along the sideways of conveyor line. It consumes more floor space, more work in process inventory, more operator's walking time and more operator's walking distance per day. In order to reduce the above mentioned wastes, lean kitting assembly is suggested by some managers. Another group of managers suggest JIT assembly as it consumes very less inventory cost compared to other types of assembly processes. Hence, a Multi-attribute decision making model namely analytical hierarchy process (AHP) is applied to analyse the alternative assembly methods based on various important factors.
When a production facility is designed, there are various parameters affecting the number machines such as production capacity and reliability. It is often a tedious task to optimize different objectives, simultaneously. The other issue is the uncertainty in many design parameters which makes it difficult to reach a desirable solution. In this paper, we present a new mathematical model with two objectives. The primary objective function is considered to be the production capacity and the secondary objective function is total reliability. The proposed model is formulated on different units of production which are connected together in serial form and for each unit, we may have various machines. The resulted model is formulated using recent advances of robust optimization and solution procedure is analyzed with some numerical examples.