in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

in this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the p-mmrcpsp while due dates are included i...

A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND ...

this paper investigates the problem of just-in-time permutation flow shop scheduling with limited buffers and linear job deterioration in an uncertain environment. the fuzzy set theory is applied to describe this situation. a novel mixed-integer nonlinear program is presented to minimize the weighted sum of fuzzy earliness and tardiness penalties. due to the computational complexities, the prop...

We consider the class of single machine scheduling problems with the objective to minimize the weighted number of late jobs, under the assumption that completion due-dates are not known precisely at the time when decisionmaker must provide a schedule. It is assumed that only the intervals to which the due-dates belong are known. The concept of maximum regret is used to define robust solution. A...

The main intent of the cloud computing to provide utilities demands users that are booming day by day. To meet requirements, existing scheduling algorithms focus on improving performance and neglecting energy consumed fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based Genetic (GA) Flower Pollination (FPA) for environments. proposed algorithm has surpa...

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A robust genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using LPT rule, known as the most appropriate dispatching rule for such problems. This application illustrate...

This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize the maximum completion time or makespan, and second objective is to minimize the maximum tardiness...