Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of...

Data envelopment analysis is a nonparametric technique checking efficiency of DMUs using math programming. In conventional DEA, it has been assumed that the status of each measure is clearly known as either input or output. Kao and Hwang (2008) developed a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divided into two stages. The first stag...

The allocation of surgeries to operating rooms (ORs) is a challenging combinatorial optimization problem. There is also significant uncertainty in the duration of surgical procedures, which further complicates assignment decisions. In this article, we present stochastic optimization models for the assignment of surgeries to ORs on a given day of surgery. The objective includes a fixed cost of o...

A Two-phase model for configuring a cellular manufacturing system is proposed. In phase (I), for the first time, number of cells is considered as a decision variable. In phase (II), pursing two different objectives, one minimization of underload and the other, maximization of similarity of parts within a group, the design procedure is performed. As one cannot have these two objects together, a ...

We study the problem of minimizing the makespan in a two-stage assembly flow shop scheduling problem with uniform parallel machines. This problem is a generalization of the assembly flow shop problem with concurrent operations in the first stage and a single assembly operation in the second stage. We propose a heuristic with an absolute performance bound which becomes asymptotically optimal as ...

We examine several formulations of fuzzy rule selection for the design of fuzzy rulebased classification systems in our twostage approach. The first stage is heuristic rule extraction where a large number of candidate rules are extracted. The second stage is evolutionary rule selection where fuzzy rule-based systems are constructed by choosing a small number of candidate rules. Rule selection i...

This two-part paper presents modelling and scheduling approaches of flexible manufacturing systems using Petri nets (PNs) and artificial intelligence (AI)-based heuristic search methods. In Part I, PN-based modelling approaches and basic AI-based heuristic search algorithms were presented. In Part II, a new heuristic function that exploits PN information is proposed. Heuristic information obtai...