A general quantity discount and supplier selection mixed integer programming model
ABSTRACT Recently, new models and heuristics for exploiting quantity discounts have been proposed that are applicable in classical
purchasing as well as in an e-business environment and can be implemented as part of an advanced planning system. These models
can now handle both the single- and multi-item case with fixed cost to be shared among several products ordered at the same
point in time from a single supplier. Furthermore, the supplier selection problem is addressed, i.e., how to best exploit
quantity discounts over time offered by several suppliers. Last but not least, additional constraints on the buyer’s or on
the supplier’s side may be included. While so far only purpose-built heuristics have been proposed for this generalized problem,
we present a linear mixed integer programming (MIP) model, which not only represents the all-units discount but also the incremental discount
case. Furthermore, the objective function chosen resolves (former) conflicts among proponents of a purely cost oriented and
a cash flow oriented modeling approach. Computational tests show that our model yields near optimal solutions within a given
CPU time limit by making use of a standard MIP solver.
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ABSTRACT: The globalization of production systems has led organizations to consider the supplier management function as a strategic decision. There is signifcant research work addressing decision making and supplier management. However, there is a lack of research in the case of multiple suppliers with different characteristics and attributes and able to satisfy a product demand defined by a fixed lot size. This kind of demand arises in production environments organized by processes such as the pharmaceutical and food industry. This research aims at fulfilling this lack of research in supplier management. The proposed methodology is based on the formulation of a Mixed Integer Linear Programming model for optimizing the decisions of supplier selection and order quantity allocation in the case of fixed lot size. A numerical example shows the applicability and usefulness of the proposed model. Furthermore, a sensitivity analysis shows that the model is sensitive to changes in parameters. Finally, the results also show that the model is practical and can be easily adapted by organizations that purchase products in fixed-size lots.Ingeniería y Desarrollo. 07/2013; 31(1):1-21.
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ABSTRACT: This paper is about creating a hybrid QFD-based approach in which the best supplier is selected considering changing customer needs. In most previous studies employing a QFD approach, the possibility of changing customer needs is ignored. On the other hand, supplier selection is a challenging problem that could have been addressed by such a QFD. This paper attempts to create a hybrid QFD-based approach in which the internal relations between the elements are considered. It connects the new QFD to suppliers’ qualifications to create a hybrid supplier selection process. The best suppliers are selected based on the priorities of customer needs for each level of the product improvement plan. When a product is to be developed, the proposed methodology seems to create an efficient solution for supplier selection problem with respect to quality factorsInternational Journal of Industrial Engineering Computations. 07/2014; 5(4).
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ABSTRACT: A pharmaceutical company raised the question whether an increased product portfolio could still be manufactured on the existing machinery. The proportional lot-sizing and scheduling problem (PLSP) seemed to be most appropriate to answer this question. However, although there are papers dealing with a multi-level PLSP none allows a zero lead time offset which is a prerequisite for the case considered here.In this paper we will extend and modify an existing mixed integer linear programming (MIP) model formulation in two ways: first, we will extend the single-level single machine PLSP to a multi-level single machine PLSP (PLSP-ML-SM) with a zero lead time offset. Second, we will describe a new and more compact model formulation incorporating period overlapping setup times and batch size constraints. Based on the real-world application several test instances have been generated to provide insights into those characteristics which make instances of the PLSP-ML-SM difficult to solve by a standard MIP solver.European Journal of Operational Research - EJOR. 01/2011; 209(3):241-252.