Growing concern in the management of energy due to the increasing energy costs, has forced managers to optimize the amount of energy required to provide products and services. This research integrates an energy-based resource investment project-scheduling problem (RIP) under a multi-skilled structure of the resources. The proposed energy based multi skilled resource investment problem (EB-MSRIP...

Surgical theater is one of the most expensive hospital sources that a high percentage of hospital admissions are related to it. Therefore, efficient planning and scheduling of the operating rooms (ORs) is necessary to improve the efficiency of any healthcare system. Therefore, in this paper, the weekly OR planning and scheduling problem is addressed to minimize the waiting time of elective pati...

Investment managers are multi-objective decision-makers (DMs) who make portfolio decisions by maximizing profits and minimizing risks over a multi-period planning horizon. Portfolio decisions are complex multi-objective problems which include both tangible and intangible factors. We propose an integrated multi-objective framework for project portfolio selection with respect to both the profits ...

Network representation can reveal the topological dysfunctions of brain function [1]. Recently brain network construction of whole brain voxels has been introduced using the epsilon neighbor method [2, 3]. However, this method has a difficulty in regional network interpretation. Thus we propose the voxel-based network construction on gray matter of FDG-PET image via the epsilon neighbor method ...

One of the most challenging issues in multi-objective problems is finding Pareto optimal points. This paper describes an algorithm based on Benders Decomposition Algorithm (BDA) which tries to find Pareto solutions. For this aim, a multi-objective facility location allocation model is proposed. In this case, an integrated BDA and epsilon constraint method are proposed and it is shown that how P...

It is well known that it very difficult to solve constrained multiobjective optimization problems. Such problems not only need optimize the objective function but also consider constraints. The epsilon constraint handling method commonly used, which releases degree of violations by defining a gradually decayed epsilon. However, for solutions whose overall greater than epsilon, original cannot g...

This paper discusses methods for generating or approximating the Pareto set of multiobjective optimization problems by solving a sequence of constrained single-objective problems. The necessity of determining the constraint value a priori is shown to be a serious drawback of the original epsilon-constraint method. We therefore propose a new, adaptive scheme to generate appropriate constraint va...