Time-dependent Vehicle Routing Problem is one of the most applicable but least-studied variants of routing and scheduling problems. In this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. To deal with the traffic congestions, we also considered that the vehicles are not forced to come...

with the increase in population and consequent increasing needs of society, land use planning is of particular importance. land use planning due to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. but use of these algorithms should be examined according to the type of issues. in the study, addition to introd...

This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...

Obtaining a well distributed non-dominated Pareto front is one of the key issues in multi-objective optimization algorithms. This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts. The basic idea is to replace the crowding distance method by a maximin technique. The proposed technique is deployed in wel...

A kind of unrelated parallel machines scheduling problem is discussed. The memberships of fuzzy due dates denote the grades of satisfaction with respect to completion times with jobs. Objectives of scheduling are to maximize the minimum grade of satisfaction while makespan is minimized in the meantime. Two kind of genetic algorithms are employed to search optimal solution set of the problem. Bo...

In this paper, we propose a new genetic algorithm for multi-objective optimization problems. That is called “Neighborhood Cultivation Genetic Algorithm (NCGA)”. NCGA includes the mechanisms of other methods such as SPEA2 and NSGA-II. Moreover, NCGA has the mechanism of neighborhood crossover. Because of the neighborhood crossover, the effective search can be performed and good results can be de...

This paper considers a scheduling problem of a set of independent jobs on unrelated parallel machines (UPMs) that minimizesthe maximum completion time (i.e., makespan or ), maximum earliness ( ), and maximum tardiness ( ) simultaneously. Jobs have non-identical due dates, sequence-dependent setup times and machine-dependentprocessing times. A multi-objective mixed-integer linear programmi...

The purpose of this problem is to choose a set of project activities for crashing, in a way that the expected project time, cost and risk are minimized and the expected quality is maximized. In this problem, each project activity can be performed with a specific executive mode. Each executive mode is characterized with four measures, namely the expected time, cost, quality and risk. In this pap...

In decentralized construction projects, costs are mostly related to investment, material, holding, logistics, and other minor costs for implementation. For this reason, simultaneous planning of these items and appropriate scheduling of activities can significantly reduce the total costs of the project undertaken. This paper investigates the decentralized multiple construction projects schedulin...

Very recently, the first mathematical runtime analyses for NSGA-II, most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that NSGA-II optimizes OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, time such an advantage of proven ...