Üretim planlamada lineer programlama modelleri ve bir işletme uygulaması
Linear programming models in production planning and a firm application
- Tez No: 22047
- Danışmanlar: PROF. DR. GÖNÜL YENERSOY
- Tez Türü: Yüksek Lisans
- Konular: İşletme, Business Administration
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1992
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 221
Özet
Üretim planlama, planlama dönemine ait üretim hedeflerinin belirlenmesi ile ilgili bir kavramdır. Bu nedenle, gelecekte ihtiyaç duyulacak olan imalât işlemlerine mevcut kaynaklar özerinde karar verme süreci ola rak tanımlanabilir. üretim planlama, üretim ihtiyaçlarının karşılanması için gereken kaynakların optimal düzeyde kullanımına da olanak tanır. üretim planlamada geniş bir uygulama alam bulan lineer programlama modelleri günümüzde çok sık kullanılan bir karar verme mekanizması haline gelmiştir. Sözü edilen bu modeller, negatif olmayan karar değişkenleri ile yapısal kısıtlar çerçevesinde şekillenen amaç fonksiyonunun optimizasyonu ile ilgilidir. Bu nedenle standart bir lineer programlama modeli amaç fonksiyonu ile kısıt denklemlerinden oluşmaktadır. Amaç fonksiyonu minimizasyon veya maksimizasyon seklinde olabilmektedir. üretim planlamada kullanılan lineer programlama modelleri statik ve dinamik modeller olmak üzere iki kısıma ayrılır. Statik modellerde talebin zaman içinde değişmediği, dinamik modellerde ise talebin zaman içinde farklılaştığı kabul edilir. Statik modeller arasında en yaygın olarak uygu lama alanı bulanları, ürün karışımı, süreç seçimi ve malzeme karışımı modelleridir. Söz konusu bu modeller tek kademeli ve yahut da çok kademeli bir yapıya sahip olabilmektedir. Talebin zaman içinde derişiklik gösterdiği dinamik modeller ise, statik modellere ek olarak, üretim ve envanter maliyetlerini içeren modelleri, üre tim oranını değiştirme ile karşılanamayan talebin mali yeti olan modelleri ve işgücü seviyesi ve fazla mesai kararları ile ilgili modelleri içermektedir. Statik modellerden dinamik modellere geçilirken dikkat edilmesi gereken en önemli husus, dinamik modellerde ürünlerin üretildikleri periyot ile satıldıkları periyot arasında fark olabilmesi özelliğidir. Hazırlanan tezde, üretim planlamada kullanılan lineer programlama modellerinin tümüne yakın kısmını bulmak mümkündür. Bu modeller, notasyonları, amaç fonksiyonları ve kısıt denklemleri ile birlikte sunulmuştur.
Özet (Çeviri)
Production planning is the activity of establishing production goals over some future time period, called the planning horizon. The objective of production planning is to plan the optimal use of resources to meet stated production requirements or to take advantage of potential sales opportunities. As input, production planning will use the following kinds of information; current Inventory levels; current backlog position; forecasts of future demand; current work in process; current work force levels; capacity of each production center; material availability; production standards; cost standards and selling prices; management policies. This information is gathered and analyzed periodically to develop production plans. The output from this activity may take a variety of forms, specifying the following type of things for each period of the planning horizon; quantities of each product to be produced; quantities of a given product to be produced by each of several alternative processes; quantities of each product to be produced by a given process (such as plant, department, line, machine, etc.); target inventory levels by product; work force level; overtime, additional shifts, unused capacity, etc.; quantities of material and semifinished product to be transported between stages 1n a multistage production system; subcontracting plans; purchased material requirements. The decisions made 1n production planning affect several classes of costs and revenues, such as; production costs; production rate change costs; capacity change costs; inventory holding costs; customer service and shortage losses; procurement costs. Planning can be investigated 1n three major parts according to the length of the planning horizon. - x -Long-range planning process consists of decisions such as; definition of production method, determination of customer service policy, selection of distribution channels and deciding upon production and stock capacities. This kind of decisions are made considering a 1-5 years planning horizon. The planning horizon which extends from three months up to one year 1s called as an Intermediate-range planning. Intermediate-range planning denotes the level of all activities of a firm such as employment volume, the number of shifts, additional hardware requirements, raw-material requirements and the amount of subcontracts. Short- range planning process widely contains production schedules, preparation of work programs and production control in a 1-2 week planning horizon. The models used in the main production planning which 1s also an intermediate-range planning are divided into two groups; static and dynamic models. In static models, the demand is accepted to be constant, whereas it is accepted to change by time 1n dynamic models. Production planning, according to a definition, is the whole of the activities that should be performed in order to meet future demand. If the production planner has no Idea about the level of future demand, then actually this means, he has no objective to which his plans to be directed. Therefore demand estimations are very important in production planning. Linear programming models which have a wide application range 1n production planning, have been changing into a frequently used decision making mechanism at present. So called models have to consist five major aspects in order to be applied. These aspects are respectively; definition of the goal, numerical measures of problem elements, alternative choices, linearity and mathematical formulation. When using linear programming models, it is sometimes possible to have difficulties especially in the stages of preparation of problem definition and transmitting of solutions to the application. However, linear programming models have so many advantages like; the optimal use of production factors, reaching to a higher quality in decisions, training the managers of the future and possibility of changing the mathematical solutions. Linear programming models are related with the optimization of objective function which 1s figured by structural constraints and decision variables which are - xinot negative. Thus a standard linear programming model has an objective function and constraint equations. Objective function can be formed as a maximization or a minimization. Solution methods which are used 1n linear programming models can be mainly classified 1n three groups. This mentioned groups are; graphical solution, analytical solution and consecutive numerical solution. It is possible to apply the graphical solution if the number of decision variables in the decision model is three or less. However, the analytical solution method 1s preferably used 1f the presence of the best solution is known. Because of the limitations in these methods, further solution methods which are starting at a point and finding a result by using cosecutlve numerical operations, have been developed. The first and the widely used one in these methods 1s the Simplex Algorithm. The static production planning models which have widespread application fields are', product mix models, process selection models and blending models. When a firm has different products for manufacturing and selling, the objective of the product mix models is deciding the amount of production of each product arid by using the limited resources, maximizing the income value coming from the production facilities. Meanwhile the objective of the process selection models is; determining the amount of products produced by each process subject to the constraints which are activated by resource limitations and product demands, in order to minimize the production cost. The profitability 1s not cosidered in the above mentioned models, rather than the cost minimization becomes to be the main goal. Blending models are formed for production operations in which so many raw materials are needed to be blended in order to manufacture products that have desired specifications. The objective in these models is finding out the material mix which satisfies the product specifications and has the lowest cost for production operation. Linear programming models can be applied to the single stage production systems and as well as to the multistage systems for modelizing them. The following aspects should be known at the stage of modelizing multistage systems; how to group the production operations, if there 1s more than one plant which is activating in parallel in each stage, and how many inventory points have to be defined between stages. - x11The most important dynamic models in which the demand is going to change by time are; models with only production and inventory costs, models with production rate change costs and backlogging, models related with employment level and overtime decisions, and models with mul tiproducts and multistages. The objective of the mul tiproduct, multiperiod, resource-constraint models which are also located in the group of mul tiproduct models, is obtaining the production amount in each production period and production program which contains the net inventories at the end of the period. When reaching this program, it is intended to reduce the sum of production, inventory and backlog costs to the minimum value. The most important aspect that should be paid attention when passing into the multiperiod models is that, the difference can be occured between the production period and the sales period of the products. As a firm application in this thesis, EMAS (Electrotechnical Machinery Industry and Trade A. CO.) which activates in electrotechnics and automotive industry was studied and the main production plan of the limit switches (one of the company products) for the next year was obtained by using linear programming. At the production stage, limit switches are processed in three different departments according to their types. These departments are respectively; plasticizing machinery park, pressing machinery park and revolving machinery park. The workmanships throughout the production of a limit switch are as follows; plasticizing, pressing, revolving, installing, controlling and packaging. The plasticizing, pressing and revolving workmanships were determined as the total process period that the product were being worked up in these workbenches. The model examined in the firm application is a product mix model in dynamic form. The objective is determined as maximizing the profit of limit switches group in the planning horizon. A Pareto Analysis based on the year of 1991 data was performed for 41 different products. Selecting 80/15/5 as a classification criteria, 5 products for group A, 8 products for group B and 28 products for group C are classified. Sales estimations were performed based on the data of the last four years and the results of Pareto classification. The production and workmanship capacities and the inventory level carried from the last year are actually - xiii -known. As a result of the solution of the model which was established with respect to the above mentioned data, a total profit maximizing production plan for 12 months was obtained. In the prepared thesis, 1t 1s possible to find nearly the all of the linear programming models used 1n production planning. These models were presented with their notations, objective functions and constraint equations. The thesis will be a useful reference for those who would like to use linear programming models at production stage or who are performing a research and a study on this subject. - xiv -
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