Abstract
Management 3.0 is a new concept that intends to revolutionize the way managers and leaders act within companies to offer a more happy, collaborative, and productive work environment. This paper aims to analyze the management 3.0 phenomenon and establish a research agenda in the field. A systematic review was conducted considering 215 published studies in the field between 2010 and 2019. The findings reveal that management 3.0 is an emerging area and one that has grown in 2019 and involves multidisciplinary research teams from management, leadership, information technology, and psychology.

Abstract
In this paper, we address the Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. We propose both sequential and parallel Relaxation Adaptive Memory Programming approaches for the CFLP, combining a Lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. Computational experiments of the effectiveness of this approach are presented and discussed.

Abstract
The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.

Abstract
Agriculture plays a significant role in the labor force and GDP of Mozambique. Nonetheless, the energy source massively used for water pumping in irrigation purposes is based on fossil fuels (diesel oil). Despite the water availability and fertile soils in Moamba, Mozambique, farmers struggle with the high cost of fuels used in the pumping systems. This study was sought to analyze the feasibility of utilizing a solar photovoltaic system as a means to reduce the environmental impact caused by the diesel pumps and simultaneously alleviate the expenses regarding the use of non-environmentally friendly technologies. Site observations and interviews were undertaken in order to obtain local data regarding the water demand, current energy systems costs and distances from the source to the irrigated fields. CLIMWAT 2.0 was used for climate data acquisition and analysis. The environmental benefits, the cost effectiveness and local climate conditions show that the PV system is feasible in Moamba. Furthermore, parameters such as hydraulic energy, incident solar energy, pump efficiency and total system efficiency were used to predict the performance of the system. The results obtained are important to analyze the implementation of such energy systems.

Abstract
In this paper, we address the Single Source Capacitated Facility Location Problem (SSCFLP) which considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. We propose a Relaxation Adaptive Memory Programming (RAMP) heuristic for solving the SSCFLP to efficiently explore the relation between the primal and the dual sides of this combinatorial optimisation problem. Computational experiments demonstrated that the proposed heuristic is very effective in terms of solution quality with reasonable computing times.

Abstract
The management of urban solid waste represents a great challenge to humanity. The current scenario of pollution due to waste that is still being incorrectly disposed of has brought us to an alarming situation. To progress and overcome the barriers, the sector needs changes and innovations. Waste management is not only the responsibility of municipalities; it must also involve people. This chapter presents a technological solution that fosters people's involvement in waste management practices. Through the use of this platform, users can register the waste produced and evaluate their performance in recycling management according to several types of residues considering the targets set by the municipalities. This approach may be relevant for the implementation of pay-as-you-throw models in municipalities. © 2021, IGI Global.