Multiple-criteria ABC (MCABC) analysis is conducted using a dominance-based rough set approach. ABC analysis, a well-known technique for inventory planning and control, divides stock-keeping units (SKUs) into three classes according to annual dollar usage. But MCABC analysis offers more managerial flexibility by including other criteria, such as lead time and criticality, in the classification ...

In this paper, we consider the extension of multi-objective constraint optimization algorithms to the case where there are additional tradeoffs, reducing the number of optimal solutions. We focus especially on branch-and-bound algorithms which use a mini-buckets algorithm for generating the upper bound at each node (in the context of maximizing values of objectives). Since the main bottleneck o...

The Dominance-based Rough Set Approach (DRSA) is an extension of Rough Sets Theory to handle multicriteria classification problems by authorizing preferenceordered attributes. The DRSA assumes the existence of a single decision table while real-world decision problems imply generally several experts with different decision tables. The objective of this paper is to propose an algorithm for the a...

A dominance-based rough set approach (DRSA) to multiple criteria ABC analysis (MCABC) is designed and compared to other approaches using a practical case study. ABC analysis is a well-known inventory planning and control approach, which classifies inventory items, or stock-keeping units (SKUs), based solely on their annual dollar usage. Recently, it has been suggested that MCABC can provide mor...

Financial performance is vital for information technology (IT) companies to survive intense global competition. Because of the complexity in the business environment and the rapidly advancing technologies, companies lack specific guidance to understand the implicit relationship among crucial financial indicators for improving prospects in a contextual approach. To resolve the aforementioned con...

A standard requirement of rationality is that preferences obey stochastic dominance. In this paper, we investigate a new variety of dominance violation from the domain of uncertainty. We find that subjects systematically value a packed prospect, $x if one of two mutually exclusive events E1 or E2 obtains, less than an unpacked and dominated prospect, $x if E1 obtains, and $x−ε if E2 obtains. We...

We use the notion of the rough set diagram introduced by Wasilewska and Vigneron 8] to present a general decision procedure for validity of equations in rough boolean algebra. First, we establish equivalence of validity in rough boolean algebra to validity in so called simple rough boolean algebra. Second, we propose a decision method for simple rough boolean algebra, which is to construct and ...

Many real world problems deal with ordering of objects instead of classifying objects, although most of research in data analysis has been focused on the latter. One of the extensions of classical rough sets to take into account the ordering properties is dominance-based rough sets approach which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this p...

Dominance-based Rough Sets Approach (DRSA) is a generalized model of the classical Rough Sets Theory (RST) which may handle information with preference-ordered attribute domain. The attribute set in the information system may evolve over time. Approximations of DRSA used to induce decision rules need updating for knowledge discovery and other related tasks. We firstly introduce a kind of domina...

Dominance-based rough sets approach (DRSA) is an effective tool to deal with information with preference-ordered attribute domains and decision classes. Any information system may evolve when new objects enter into or old objects get out. Approximations of DRSA need update for decision analysis or other relative tasks. Incremental updating is a feasible and effective technique to update approxi...