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Author: Suresh P. Sethi
Publisher:
ISBN:
Category : Inventory control
Languages : en
Pages : 17
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Book Description
A simple stochastic production-inventory model with quadratic cost functions is analyzed in detail. The inventory process is assumed to be driven by a white noise process resulting into an Ito stochastic differential equation. Both finite and infinite horizon versions of the problem are treated by a methodology based on the theory of stochastic integrals and differentials. Particular attention is given to illustrate the methodology, which is quite general and capable of dealing with more complicated problems. The paper concludes with some remarks in connection with the relationship of the results of this paper to the results in the deterministic case. (Author).
Author: Suresh P. Sethi
Publisher:
ISBN:
Category : Inventory control
Languages : en
Pages : 17
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Book Description
A simple stochastic production-inventory model with quadratic cost functions is analyzed in detail. The inventory process is assumed to be driven by a white noise process resulting into an Ito stochastic differential equation. Both finite and infinite horizon versions of the problem are treated by a methodology based on the theory of stochastic integrals and differentials. Particular attention is given to illustrate the methodology, which is quite general and capable of dealing with more complicated problems. The paper concludes with some remarks in connection with the relationship of the results of this paper to the results in the deterministic case. (Author).
Author: Meenakshi Prajapati
Publisher:
ISBN:
Category : Production planning
Languages : en
Pages : 66
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Book Description
Production planning plays a vital role in the management of manufacturing facilities. The problem is to determine the production loading plan consisting of the quantity of production and the workforce level - to fulfill a future demand. Although the deterministic version of the problem has been widely studied in the literature, the stochastic production planning problem has not. The application of production planning models could be limited if the stochastic nature of the problem, for example, uncertainty in future demand, is not addressed. This study addresses such a stochastic production planning problem under uncertain demand and its application in an enclosure manufacturing facility. The thesis first addresses the forecast of the demand where seasonal fluctuation is present. A decomposition model is utilized in the forecast and compared with other forecasting methods. Although forecast models could be used to improve the accuracy of forecast, error and uncertainty still exists. To deal with this uncertainty, a two stage stochastic scenario based production planning model is developed to minimize the total cost consisting of production cost, labor cost, inventory cost and overtime cost under uncertain demand. The model is solved with data from a local manufacturing facility and the results are compared with various deterministic production models to show the effectiveness of the developed stochastic model. Parametric analysis are performed to derive managerial insights related to issues such as overtime usage and inventory holding cost and the proper selection of scenarios under pessimist, neutral and optimist forecasts. An extension of the stochastic model, i.e., a robust model is also solved in an effort to minimize changes in the solutions under various scenarios. The stochastic production planning model has been implemented in the manufacturing facility, provided guidance for material acquisition and production plans and has dramatically increased the company’s bottom line. As a result, it’s estimated an approximately annual savings of $340,000 in inventory cost can be achieved for the company in the next few years.
Author: Stefan Voß
Publisher: Springer Science & Business Media
ISBN: 3540247645
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
An easy-to-read introduction to the concepts associated with the creation of optimization models for production planning starts off this book. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation, fairly sophisticated models for supply chain management are developed. Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages.
Author: James MacGregor Smith
Publisher: Springer
ISBN: 9781461467786
Category : Business & Economics
Languages : en
Pages : 373
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Book Description
This handbook surveys important stochastic problems and models in manufacturing system operations and their stochastic analysis. Using analytical models to design and control manufacturing systems and their operations entail critical stochastic performance analysis as well as integrated optimization models of these systems. Topics deal with the areas of facilities planning, transportation, and material handling systems, logistics and supply chain management, and integrated productivity and quality models covering: • Stochastic modeling and analysis of manufacturing systems • Design, analysis, and optimization of manufacturing systems • Facilities planning, transportation, and material handling systems analysis • Production planning, scheduling systems, management, and control • Analytical approaches to logistics and supply chain management • Integrated productivity and quality models, and their analysis • Literature surveys of issues relevant in manufacturing systems • Case studies of manufacturing system operations and analysis Today’s manufacturing system operations are becoming increasingly complex. Advanced knowledge of best practices for treating these problems is not always well known. The purpose of the book is to create a foundation for the development of stochastic models and their analysis in manufacturing system operations. Given the handbook nature of the volume, introducing basic principles, concepts, and algorithms for treating these problems and their solutions is the main intent of this handbook. Readers unfamiliar with these research areas will be able to find a research foundation for studying these problems and systems.
Author: Kai Huang
Publisher:
ISBN:
Category : Approximation theory
Languages : en
Pages :
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Book Description
In this thesis, we study a series of closely related multi-stage stochastic programming models in production planning, from both a modeling and an algorithmic point of view. We first consider a very simple multi-stage stochastic lot-sizing problem, involving a single item with no fixed charge and capacity constraint. Although a multi-stage stochastic integer program, this problem can be shown to have a totally unimodular constraint matrix. We develop primal and dual algorithms by exploiting the problem structure. Both algorithms are strongly polynomial, and therefore much more efficient than the Simplex method. Next, motivated by applications in semiconductor tool planning, we develop a general capacity planning problem under uncertainty. Using a scenario tree to model the evolution of the uncertainties, we present a multi-stage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multi-stage model allows for revision of the capacity expansion plan as more information regarding the uncertainties is revealed. We provide analytical bounds for the value of multi-stage stochastic programming over the two-stage approach. By exploiting the special simple stochastic lot-sizing substructure inherent in the problem, we design an efficient approximation scheme and show that the proposed scheme is asymptotically optimal. We conduct a computational study with respect to a semiconductor-tool-planning problem. Numerical results indicate that even an approximate solution to the multi-stage model is far superior to any optimal solution to the two-stage model. These results show that the value of multi-stage stochastic programming for this class of problem is extremely high. Next, we extend the simple stochastic lot-sizing model to an infinite horizon problem to study the planning horizon of this problem. We show that an optimal solution of the infinite horizon problem can be approximated by optimal solutions of a series of finite horizon problems, which implies the existence of a planning horizon. We also provide a useful upper bound for the planning horizon.
Author: George Liberopoulos
Publisher: Springer Science & Business Media
ISBN: 3540290575
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
Author: Nico Dellaert
Publisher:
ISBN: 9783642466731
Category : Production planning
Languages : en
Pages : 172
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Book Description
Author: Vibhor Vineet
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Nico Dellaert
Publisher: Springer Science & Business Media
ISBN: 3642466729
Category : Business & Economics
Languages : en
Pages : 167
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Book Description
In this book, production rules are studied for situations which share some important elements. The most important one is that the products are manufactured according to customer specifications and they will not be manufactured unless they have been ordered. Other elements are the set-ups on the machines, which make a clustering of orders necessary, the backlogging of late orders, a production process with one bottle-neck and a stochastic demand. The purpose is to find a simple production rule, which offers possibilities for a simple adaptation to the varying wishes or to additional complications. The production rules under consideration are well-known heuristics such as the Silver-Meal heuristic and the Wagner-Whitin heuristic, which can be adapted for backlogging and for the stochastic demand. Also a new, simple rule is introduced: the (x,T)-rule. To determine and compare the performance of the production rules different techniques have been used, such as simulation, dynamic programming and markovian models. Among the situations that have been considered are situations with capacity restrictions, overtime possibilities and situations where the clients react to the performance of their orders.
Author: Alexander Hübl
Publisher: Springer
ISBN: 3658191201
Category : Business & Economics
Languages : en
Pages : 147
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Book Description
Alexander Hübl develops models for production planning and analyzes performance indicators to investigate production system behaviour. He extends existing literature by considering the uncertainty of customer required lead time and processing times as well as by increasing the complexity of multi-machine multi-items production models. Results are on the one hand a decision support system for determining capacity and the further development of the production planning method Conwip. On the other hand, the author develops the JIT intensity and analytically proves the effects of dispatching rules on production lead time.
