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ISEN 315. Production Systems Planning

Description: Principles, models and techniques for planning, analysis and design of integrated production systems; optimization principles, including linear programming, unconstrained and equality constrained optimization and dynamic programming applied to production planning; topics to include capacity expansion models, learning curves, aggregate planning models, deterministic and stochastic inventory, MRP and project scheduling.

PREREQUISITES

ISEN 220, STAT 211, and MATH 304 or ISEN 420

COURSE OBJECTIVES

1.       Learn formulations, models, and analytical procedures for the study of production planning and operations management problems.

2.        Learn fundamental principles of inventory control.

3.        Be able to develop optimization models for capacity, production, and inventory decisions.

4.        Improve systems thinking and modeling skills.

TEXTBOOK AND ADDITIONAL COURSE MATERIAL

Text: Production and Operations Analysis by S. Nahmias (4^th edition)

Additional Reading:

Chapters 2 and 3 of Introduction to Operations Research by F. S. Hillier and G. J. Lieberman (6^th Edition)
Chapter 1 of Applied Probability and Stochastic Processes by R. M. Feldman and C. Valdez-Flores

TOPICAL OUTLINE

Introduction to Production and Operations Strategy (Chapter 1)
Nonlinear Optimization (Lecture notes)
Forecasting (Chapter 2)
Aggregate Planning (Chapter 3)
Linear Programming (Supplement 1)
Inventory Control: Known Demand (Chapter 4)
Nonlinear Programming (Lecture notes)
Probability (Review)
Inventory Control: Uncertain Demand (Chapter 5)
Material Requirements Planning (Chapter 6)
Assembly Line Balancing (Chapter 7)
Project Scheduling (Chapter 8)

CLASS SCHEDULE

Tuesday and Thursday: 12:45 - 2:00 p.m.

PROFESSIONAL COMPONENT

This course provides fundamental concepts and theory for the treatment of the principles, models, and techniques for the planning, analysis, and design of production and service systems. Course topics include forecasting, inventory management, production planning, project scheduling, and materials requirement planning problems with an emphasis on analytical modeling approaches and optimization methods used to obtain their solutions. These approaches and methods emphasize the decision-making process in operational planning, analysis, and design of production or service facilities. The course is aimed at developing a better understanding of production and operations management problems, and providing foundations for mathematical modeling/ programming methods needed to solve these problems.

PROGRAM OUTCOMES

A: An ability to apply knowledge of mathematics, science, and engineering.

Outcome Definition - Students are able to apply general principles, theories, concepts, and/or formulas from calculus, differential equations, linear algebra, statistics, probability, engineering sciences, and systems engineering to conduct industrial engineering analyses of a diversified range of problems.

Course Experience - Specialized knowledge and analytical techniques are developed to enhance the decision-making process production planning/control and operations management. Specific topics of ISEN 315 in which knowledge of mathematics, science and engineering is applied are shown below:

·          Calculus in nonlinear optimization models for capacity and inventory decisions.

·          Series expansions for modeling time value of money and cash flows for capacity decisions.

·          Linear algebra (e.g., rank, basis, basic feasible solutions) in linear programming models for aggregate planning.

·          Random variables, probability density, and distribution functions for modeling random demand.

·          Manufacturing processes: description, selection, and applications.

·          Calculation of capacity, e.g., machine and manpower, requirements.

·          Flow and product analysis for MRP.

B.   An ability to identify, formulate, and solve engineering problems.

Outcome Definition - Students are able to recognize opportunities for applying industrial engineering tools to improve operational aspects of complex production, distribution, and service systems. They can define and formulate the important elements of an industrial engineering problem in a concrete, quantitative language of engineering and mathematics. Furthermore, students are able to apply engineering, statistical, and mathematical methods to analyze the problem formulations and develop appropriate solutions that improve the operation of the system.

Course Experience - ISEN 315 emphasizes the importance of quantitative decision making for production and operations management problems as one of the fundamental functions of industrial engineering. The course is aimed at providing an overview of the models and techniques available for some of the fundamental problems arising in this context. Specific formulations studied in this course are:

·          Capacity expansion models under economies of scale (Nonlinear Optimization Models.)

·          Time Series and regression analysis for forecasting.

·          Statistical analysis for demand data.

·          Aggregate workforce, capacity, and production planning models (Linear Continuous and Linear Discrete Optimization Models.)

·          Multi-item linear programming formulations for requirements planning.

·          Deterministic demand inventory optimization formulations (Nonlinear and Dynamic Optimization Models.)

·          Stochastic demand inventory control models (Nonlinear Optimization Models).

·          Network formulations for dynamic lot-sizing.

·          Linear programming formulations for project scheduling.

C.      An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Outcome Definition - Students have knowledge of state-of-the art computerized procedures for decision-making including, but not limited to, spreadsheet analysis, optimization software, and general-purpose computer languages.

Course Experience - The following techniques, skills, and tools are studied and used:

·          Optimization procedures.

·          Statistical analysis and probability.

·          C or C++ programming for coding (voluntary).

·          Excel Solver for linear and nonlinear optimization models.

·          Maple for Calculus, linear algebra, and statistical analysis.

Course info in pdf format

PREPARED BY: Sila Çetinkaya                                          DATE: 9-18-03