2006
2
2
2
73
1

Capacity price decisions, a manufacturing yield management perspective
http://jiei.azad.ac.ir/article_511102.html
1
This paper focuses on formulating capacityprice trade off problem in Yield Management for manufacturing industry by drawing motivation from the remarkable success of Yield Management (YM) implementation in airlines. In the current practice, there is no alternative and procedure for the manufacturer, as well as customers to take advantage of using the unfulfilled capacity in discounted offers. The authors present a framework for customer segmentation and leadtime demand management to change standard production and capacity planning problem to Yield Management problem. For a planning period of T, the authors formulate the model with the objective of optimizing both price and capacity utilization factors, simultaneously. They develop an innovative twostage dynamic programming model to help practitioners to using the benefit of a dynamic model with reasonable computational effort. To formulate the problem in a general framework, the authors devise a demand model with an independent probability function structure. The authors also identify some important challenges and devise a set of rules to assist decision makers in manufacturing. The parameters of the model may be supported by sales and typical production planning data base.
0

1
18


M
Modarres
Professor, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Iran


J
Nazemi
Assistant Professor, Islamic Azad University, Science and Research Branch, Tehran, Iran
Iran
Revenue management
Yield management
Capacity planning
Pricing
Order booking
Assemble to order
Make to order
Dynamic programming
1

Splittable stochastic project scheduling with constrained renewable resource
http://jiei.azad.ac.ir/article_511103.html
1
This paper discusses the problem of allocation of constrained renewable resource to splittable activities of a single project. If the activities of stochastic projects can be split, these projects may be completed in shorter time when the available resource is constrained. It is assumed that the resource amount required to accomplish each activity is a discrete quantity and deterministic. The activity duration time is assumed to be a discrete random variable with arbitrary experimental distribution. Solving stochastic mathematical programming model of problem is very hard. So, here some existing methods for deterministic problems have been generalized for stochastic case. Solutions of generalized methods are relatively better than random solutions. However, the authors developed the new algorithm that may improve the solutions of generalized methods and project Completion Time Distribution Function (CTDF). Comparison of solution of a method with random solutions is a common assessment method in literature research. Hence, the efficiency of the proposed algorithm represented using this method.
0

19
30


S.S
Hashemin
Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Science and Research Branch,
Tehran, Iran
Iran


S.M.T
Fatemi Ghomi
Professor, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
Project Scheduling
Constrained resource
Renewable
stochastic
Splittable
Completion time
Distribution function
1

A dynamic programming approach for solving nonlinear knapsack problems
http://jiei.azad.ac.ir/article_511104.html
1
Nonlinear Knapsack Problems (NKP) are the alternative formulation for the multiplechoice knapsack problems. A powerful approach for solving NKP is dynamic programming which may obtain the global optimal solution even in the case of discrete solution space for these problems. Despite the power of this solution approach, it computationally performs very slowly when the solution space of the problems grows rapidly. In this paper the authors developed a procedure for improving the computational efficiency of the dynamic programming for solving KNP. They incorporate three routines; the imbedded state, surrogate constraints, and bounding scheme, in the dynamic programming solution approach and developed an algorithmic routine for solving the KNP. An experimental study for comparing the computational efficiency of the proposed approach with the general dynamic programming approach is also presented.
0

31
37


E
Jahangiri
Assistant Professor, Islamic Azad University, Science and Research Branch, Tehran, Iran
Iran


F
GhassemiTari
Associate Professor, Sharif University of Technology, Tehran, Iran
Iran
Discrete optimization
Multiplechoice knapsack
Imbedded state
Surrogate constraint
1

Primal and dual robust counterparts of uncertain linear programs: an application to portfolio selection
http://jiei.azad.ac.ir/article_511105.html
1
This paper proposes a family of robust counterpart for uncertain linear programs (LP) which is obtained for a general definition of the uncertainty region. The relationship between uncertainty sets using norm bodies and their corresponding robust counterparts defined by dual norms is presented. Those properties lead us to characterize primal and dual robust counterparts. The researchers show that when the uncertainty region is small the corresponding robust counterpart is less conservative than the one for a larger region. Therefore, the model can be adjusted by choosing an appropriate norm body and the radius of the uncertainty region. We show how to apply a robust modeling approach to single and multiperiod portfolio selection problems and illustrate the model properties with numerical examples.
0

38
52


P
Hanafizadeh
Assistant Professor, Department of Industrial Management, Allame Tabataba'ee University, Tehran, Iran
Iran


A
Seifi
Associate Professor, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Iran


K
Ponnambalam
Professor, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
Iran
Robust Optimization
Linear programming
Data uncertainty
Portfolio Selection
1

Solving a generalized aggregate production planning problem by genetic algorithms
http://jiei.azad.ac.ir/article_511106.html
1
This paper presents a genetic algorithm (GA) for solving a generalized model of singleitem resourceconstrained aggregate production planning (APP) with linear cost functions. APP belongs to a class of production planning problems in which there is a single production variable representing the total production of all products. We linearize a linear mixedinteger model of APP subject to hiring/firing of workforce, available regular/over time, and inventory/shortage/subcontracting allowable level where the total demand must fully be satisfied at end of the horizon planning. Due to NPhard class of APP, the realworld sized problems cannot optimality be solved within a reasonable time. In this paper, we develop the proposed genetic algorithm with effective operators for solving the proposed model with an integer representation. This model is optimally solved and validated in smallsized problems by an optimization software package, in which the obtained results are compared with GA results. The results imply the efficiency of the proposed GA achieving to near optimal solutions within a reasonably computational time.
0

53
64


R
TavakkoliMoghaddam
Associate Professor, Department of Industrial Engineering , Faculty of Engineering, University of Tehran, Iran
Iran


N
Safaei
Research Scholar, Department of Industrial Engineering Iran University Science and Technology, Tehran, Iran
Iran
Aggregate Production Planning
Linear mixinteger programming
Genetic Algorithm
1

A multiobjective inventory model for deteriorating items with backorder and stock dependent demand
http://jiei.azad.ac.ir/article_511107.html
1
Classical deterministic inventory models consider the demand rate to be either constant or timedependent but independent from the stock status. However, for a certain type of inventory, the demand rate may be influenced by the stock level. Also in many reallife problems, some products such as fruits, vegetables, pharmaceuticals and volatile liquids continuously deteriorate to evaporation, obsolescence, spoilage, etc. In this paper, a multideteriorating inventory model with shortage in fuzzy form is formulated and solved where the demandâ€™s pattern has a linear trend. In this paper, we present a multiobjective inventory model of deteriorating items in fuzzy environment with the consideration of shortage in the problem formulation. The demand here is assumed with a linear trend and the shortage is allowed for all items. The objectives of maximizing net profit of the inventory system and minimizing the total annual cost of deteriorated items are considered subject to the total cost and the storage area. The vagueness in the objectives is expressed by fuzzy linear membership functions and the resulted fuzzy models are transferred into a nonlinear programming and solved using Fuzzy NonLinear Programming (FNLP) method. The implementation of the model is presented with some numerical examples and finally the results of two fuzzy models are compared.
0

65
73


A.H
Sarfaraz
Assistant Professor, Islamic Azad University, Science and Research Branch, Tehran, Iran
Iran


S
Alizadeh Noghani
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran


S.J
Sadjadi
Associate Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran


M.B
Aryanezhad
Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran
Multiobjective programming
Fuzzy inventory
Fuzzy nonlinear programming
deteriorating items