Javad Taheri
Chamran Gas Operation Department, Operational Management Unit, Khuzestan Gas Company (KHGC), National Iranian Gas Company (NIGC), Ahvaz, Iran
PhD Graduate, Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
Phone: +986152424200
Address: P.O. Box 161, Modares Blvd., Shahid Bandar Square, Ahvaz, Iran
PhD Graduate, Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
Phone: +986152424200
Address: P.O. Box 161, Modares Blvd., Shahid Bandar Square, Ahvaz, Iran
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Papers by Javad Taheri
imperfect items under inflationary conditions with considering inspection errors. The
previous imperfect quality inventory studies, however, have mostly had the emphasis on
developing cost-minimizing models that do not consider imperfect inspection processes
and related defect sales return issues despite their practical significance. In this paper,
we assume that some produced items might not be perfect and the first stage inspector
of product quality control might make some inspection errors during the separation of
defective and perfect items. Thus, this study proposes a profit maximizing inventory model
with incorporating both imperfect production quality and two-way imperfect inspection,
i.e., Type-one inspection error of falsely screening out a proportion of no defects and
disposing of them like defects and Type-two inspection error of falsely not screening out
a proportion of defects, thereby passing them on to customers, resulting in defect sales
returns. In addition, this model includes one more stage of inspection that is after the
rework process and there is no inspection error in this stage. The purpose of this model
is to determine the important factors of an inventory system to optimize the present value
of the total profit in the finite time horizon. Finally, a numerical example is provided to
solve the presented inventory model using our proposed innovative approach, which is
further clarified through a sensitivity analysis.
and uncertainty environment. The mentioned method was intended to produce
an optimum order/production quantity as well as taking care of imperfect processes. The imperfect proportion of the received lot size was described by an
imperfect inspection process. That is, two-way inspection errors may be committed by the inspector as separate items. Thus, this survey was aimed to
maximize the benefit in the traditional inventory systems. The incorporation
of both defects and defective classifications (Type-I&II errors) was illustrated,
in a way that the defects were returned by the consumers. Moreover, this inventory model had an extra step in the scope of inspection; which occurred
after the rework process with no inspection error. To get closer to the practical circumstances and to consider the uncertainty, the model was formulated
in the fuzzy environment. The demand, rework, and inspection rates of the
inventory system were considered as the triangular fuzzy numbers where the
output factors of the inventory system were obtained via nonlinear parametric
programming and Zadeh’s extension principle. Finally, this scenario was illustrated through a mathematical model. The concavity of the objective function
was also calculated and the total profit function was presented to clarify the
solution procedure by numerical examples.
quality process and preventive maintenance to establish the inspection policy
and optimum inventory level for production items with considering uncertainty
environment. The shortage occurs because of preventive maintenance and is
considered as partial backlogging. Through the production process, at a random moment, the production of items from the state in-control turns into an
out-of-control mode, so that parts of the defective product are manufactured
an in-control state and outside of the control process mode. The online item
inspection process begins after a time variable through the production period.
The human inspection process has also been considered for the classify of defective goods. Uninspected products are accepted to the customer/buyer with
minimal repair warranty and the defective items classify by the inspector at
fixed cost before being shipped subject to salvaged items. Also, the inspection
process of manufactured goods includes a human inspection error. Therefore,
two types of classification errors (Type I & II) are considered to be more realistic than the proposed model. The input parameters of the model are considered
as a triangular fuzzy environment, and the output parameters of the model are
solved by the Zadehs extension principle and nonlinear parametric programming. As a final point, a numerical example by graphical representations is
obtainable to illustrate the proposed model.