Joint managed inventory is an advanced supply chain inventory management tool, which will effectively tackle the complicated problem between the inventory cost of supply chain and service level. The research on inventory model and its’ control under JMI environment is a hot issue at present. In this paper, the authors deeply discuss the question of the inventory time costs about the multi-produ...

the sensitivity analysis for multi-attribute decision making (madm) problems is important for two reasons: first, the decision matrix as the source of the results of a decision problem is inaccurate because it sorts the alternatives in each criterion inaccurately. second, the decision maker may change his opinions in a time period because of changes in the importance of the criteria and in the ...

an integrated model considers all parameters and elements of different deficiencies in one problem. this paper presents a new integrated model of a supply chain that simultaneously considers facility location, vehicle routing and inventory control problems as well as their interactions in one problem, called location-routing-inventory (lri) problem. this model also considers stochastic demands ...

the inventory routing problem (irp) arises in the context of vendor-managed systems. this problem addresses jointly solving an inventory management problem and a vehicle routing problem. in this paper, we consider a multi-depot multi-vehicle inventory routing problem in which suppliers produce one type of product to be delivered to customers during a finite time horizon. we propose a two-phase ...

Dominance-based Rough Set Approach (DRSA) has been proposed to deal with multi-criteria classification problems, where data may be inconsistent with respect to the dominance principle. In this paper, we consider different measures of the quality of approximation, which is the value indicating how much inconsistent the decision table is. We begin with the classical definition, based on the relat...

Classification aims to map a data instance to its appropriate class (or label). In associative classification the mapping is done through an association rule with the consequent restricted to the class attribute. Eager associative classification algorithms build a single rule set during the training phase, and this rule set is used to classify all test instances. Lazy algorithms, however, do no...