Master thesis
Industrial Engineering Department
Winter 92
Abstract
Production planning is actually allocating and determining the order of priorities for doing things optimally. It is clear that minimizing cost and increasing productivity is very important for a production unit, so it is necessary to adjust activities in the program in order to minimize cost and increase productivity. Considering the demand as a random variable brings the planning results closer to reality. In case the amount of demand is a random variable, the problem becomes very complicated. In this research, by presenting a model for determining the accumulated size with limited capacity [1] (CLSP) for possible environments, the optimal accumulated size is obtained to optimize the total costs. The desired product is produced during a finite time horizon and during several periods, and at the end of each period, some of it must be produced and offered to customers and the market. Due to the real world conditions and the inability to accurately determine the amount of demand, the amount of demand is investigated probabilistically, and in each period of demand, independent of other periods, they follow a probability distribution. To solve the model for determining the optimal stock size with limited capacity, the combination of two pdla solving algorithms and the shortest path have been used, and the obtained results show the efficiency of this combination for solving the CLSP problem.
Key words: production planning, possible demand, stock size problem with limited capacity
Chapter One:
Introduction and general research
1-1- Introduction
Production planning is actually scheduling and determining the order of priorities for doing work in an optimal way. It is clear that for a production unit, minimizing cost and increasing productivity is very important, therefore scheduling in the program is necessary in order to minimize cost and increase productivity. Due to the use of production scheduling with possible demand in production systems according to customer orders and assembly systems according to order, the importance of this type of planning has increased. Considering the demand as a random variable brings the planning results closer to reality. In case the amount of demand is a random variable, the problem becomes very complicated. Having a suitable production schedule has a great impact on increasing efficiency and reaching the organization's goals. The production scheduling model in each of the production organizations is different according to the goals and priorities of access to each of them. Therefore, in order to determine the appropriate scheduling model in the organization, the goals, priorities and resource limitations must first be examined.
Given that we are facing dynamic and uncertain issues in the real environment. Therefore, it is necessary in An integrated mathematical model and using possible concepts to explain the problem of production timing. This research is to achieve a mathematical model of production timing and provide a solution algorithm and try to improve the final solution. 1-2 Assumptions: 1-2 Assumptions: The inputs of the model include inputs with definite values ??and inputs with probable values. In this model, it will be assumed that the demand is expressed in a probabilistic manner and other parameters of the model are considered deterministic. In this problem, the planning horizon is formed in the time period H, and the demand in each period is non-negative and independent of other periods and possibly with a specific density function. May Maintenance cost (ht) and shortage cost (πt) are calculated at the end of each period. In this problem, we consider the ratio of shortage to maintenance cost equal to P. The start-up cost (At) is calculated in each production period, and the start-up cost is calculated in the period in which the accumulation is entered. There will be no discount in the scheduling period, and no accumulation will be produced before the first period, and the first production accumulation will be entered into the system in the first period. It can be done.
1-3- Purpose of implementation:
The goal is to schedule the allocation of limited resources over time to perform a group of activities. This research has been started with the aim of obtaining a mathematical model for the planning of the desired product, and then using the algorithms PDLA and the shortest route and by adapting the model to the real world, we will solve it. The necessity of planning is not hidden from anyone, and in particular, the issue of planning in the production process has such advantages that if it does not exist, it diverts production organizations from the healthy path of growth and survival in a competitive environment. In case of successful implementation of a comprehensive production management system, companies can enjoy the following benefits. The complete and successful implementation of common production and materials management systems such as material requirements planning, theory of constraints, just-in-time production in the real world due to the inclusion of its implementation requirements in most sub-sectors of the production system, which has led to their practical complexity, will be a time-consuming and long-term task in most developing countries that have semi-traditional production systems. We have had management in this field.
And also with Paying attention to the shortcomings of the PDLA solution algorithm, when the model has limitations, we tried to cover this deficiency. 5-1- Research method and implementation methods: The research method is based on the experimental method in the sense that the real information of the conditions with the parameters of the model is made available and the developed model is evaluated with the real information so that after collecting the information, its mathematical model is presented under conditions of uncertainty and then this model is used to solve a problem in the real world. 1-6- The general arrangement of the research: Next, in the second chapter, concepts such as long-term, short-term, and medium-term planning and thematic literature related to this project will be presented, and we will pass on the research done in this field, and in the third chapter, we will describe the problem and the proposed model, and we will examine some of the features of the model, and to solve it, we will try to find a solution by combining two solution algorithms. in the fourth chapter, after explaining how the model is made, by presenting examples and its calculations, we will explain the solution algorithm and check the results, and Finally, in the fifth chapter, we conclude our research by presenting a series of suggestions for future research.
Chapter two:
Literature and research background
2-1- Introduction
Today, production planning [2] has a very prominent role in the survival and progress of a factory. As businesses move rapidly towards globalization, manufacturing industries are also competing for a greater share of global markets. In such a dense environment, the conditions of companies are affected by internal and external variables. This makes decision making on various issues very complicated. The ever-increasing complexity of problems faced by businesses, on the one hand, and the inability of humans as decision-makers to solve these problems mentally, on the other hand, has caused more attention to optimization models. Production planning, as one of the most important production problems, has always had a special place among researchers. The purpose of production planning is to use limited resources in production processes in such a way that customer demand[3] is met within the planning horizon[4]. In other words, production planning problems are part of a group of production problems that satisfy the market demand with the minimum cost or satisfy the market demand in such a way that the profit is maximized.
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