The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of technological developments, transforming data acquisition and treatment.

The dynamic facility layout problem (DFLP) aims to minimise the sum of handling and re-layout costs by devising an individual layout for each distinctive production period. In this paper, a hybrid particle swarm optimisation (HPSO) algorithm is proposed to find near-optimal solutions of DFLP. We use a coding and decoding technique that permits a one to one mapping of a solution in discrete space of DFLP to a PSO particle position in continuous space. The proposed PSO will further use this coding technique to explore the continuous solution space. For further enhancement, the proposed PSO is hybridised with a simple and fast simulated annealing. The developed algorithm is capable of being extended to more general cases although equal area machines and standardised handling equipments with identical unit costs are assumed for the time being. Computational results show the efficiency of the proposed algorithm compared to other heuristics.

Selecting an appropriate contractor can improve the likelihood of project success. This study intends to presents a model for selecting contractors. The model is based on a fuzzy multiple criteria model for contractor prequalification. First, a field study with experts and a review of the literature is performed to generate 22 sub-criteria along with six criteria used to evaluate contactor prequalification. Second, analytic hierarchy process (AHP) and triangular fuzzy numbers are integrated to develop a fuzzy evaluation model which prioritises the relative weights of contractor prequalification criteria. Then, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is used to rank the contractors. The results show financial stability, technical capacity and experience are the most important criteria for contractor selection. Moreover, Alamut and Folman are in the first two positions in terms of ranking for contractor selection. The proposed model and results can provide useful insights for contractor selection.