Journal Of Industrial Engineering International
Volume:2 Issue: 1, Mar2006

This paper focuses on formulating capacity-price trade off problem in Yield Management for manufactur-ing 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 cus-tomers to take advantage of using the unfulfilled capacity in discounted offers. The authors present a frame-work for customer segmentation and lead-time demand management to change standard production and ca-pacity 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 de-velop an innovative two-stage dynamic programming model to help practitioners to using the benefit of a dy-namic model with reasonable computational effort. To formulate the problem in a general framework, the au-thors 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 parame-ters of the model may be supported by sales and typical production planning data base.

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 accom-plish each activity is a discrete quantity and deterministic. The activity duration time is assumed to be a dis-crete 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 gener-alized for stochastic case. Solutions of generalized methods are relatively better than random solutions. How-ever, 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 algo-rithm represented using this method

Nonlinear Knapsack Problems (NKP) are the alternative formulation for the multiple-choice knapsack problems. A powerful approach for solving NKP is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. Despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the problems grows rap-idly. In this paper the authors developed a procedure for improving the computational efficiency of the dy-namic programming for solving KNP. They incorporate three routines; the imbedded state, surrogate con-straints, 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 pro-posed approach with the general dynamic programming approach is also presented.

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 bod-ies 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 multi-period portfolio selection problems and illustrate the model properties with numerical examples.

This paper presents a genetic algorithm (GA) for solving a generalized model of single-item resource-constrained aggregate production planning (APP) with linear cost functions. APP belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. We linearize a linear mixed-integer model of APP subject to hiring/firing of workforce, avail-able 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 NP-hard class of APP, the real-world sized problems cannot optimality be solved within a reasonable time. In this paper, we develop the proposed genetic algo-rithm with effective operators for solving the proposed model with an integer representation. This model is optimally solved and validated in small-sized 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 achiev-ing to near optimal solutions within a reasonably computational time.

Classical deterministic inventory models consider the demand rate to be either constant or time-dependent but independent from the stock status. However, for a certain type of inventory, the demand rate may be in-fluenced by the stock level. Also in many real-life problems, some products such as fruits, vegetables, phar-maceuticals and volatile liquids continuously deteriorate to evaporation, obsolescence, spoilage, etc. In this paper, a multi-deteriorating inventory model with shortage in fuzzy form is formulated and solved where the demand’s pattern has a linear trend. In this paper, we present a multi-objective inventory model of deteriorat-ing 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 non-linear programming and solved using Fuzzy Non-Linear Programming (FNLP) method. The implementation of the model is presented with some numerical examples and finally the results of two fuzzy models are compared.