Daniel Mora-Melià

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.

Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations. Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for each case. The results demonstrate the validity of the method. Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a “good solution” is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained.

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes.Specifically, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of peoples dedication. This new problem is named the Multiple Team Formation Problem (MTFP).Second, an optimisation model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimisation model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most efficient in almost all cases.Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research. HighlightsOptimisation of human resource allocation in multiple simultaneous projects.Time-fraction allocations are now allowed.Comparison of CP, LS and VNS algorithm performance.Proposal of multiple options for future research.

Transient phenomena during the emptying process of a single pipe with water–air interaction

ABSTRACT Emptying pipelines can be critical in many water distribution networks because subatmospheric pressure troughs could cause considerable damage to the system due to the expansion of entrapped air. Researchers have given relatively little attention to emptying processes compared to filling processes. The intricacy of computations of this phenomenon makes it difficult to predict the behaviour during emptying, and there are only a few reliable models in the literature. In this work, a computational model for simulating the transient phenomena in single pipes is proposed, and was validated using experimental results. The proposed model is based on a rigid column to analyse water movement, the air–water interface, and air pocket equations. Two practical cases were used to validate the model: (1) a single pipe with the upstream end closed, and (2) a single pipe with an air valve installed on the upstream end. The results show how the model accurately predicts the experimental data, including the pressure oscillation patterns and subatmospheric pressure troughs.

EXPLAINING THE BAYES’ THEOREM GRAPHICALLY

The Bayes’ theorem on conditional probabilities is normally presented to students in introductory courses/modules on Statistics and Probability. This because most STEM students will make use of conditional probabilities in their professional lives with or without noticing. However, maybe because of the unfamiliar notation or because of the variety of ways in which this theorem can be formulated, most students have trouble understanding it. Moreover, when it comes to practical applications and problem exercises, most students (who have generally memorised its manifold ways of rearranging the conditional probabilities formula along with a few applications) struggle even more to come up with correct solutions. By means of a completely graphical approach, this paper presents an alternative way of explaining the Bayes’ theorem to STEM students. By means of diagrams and schematics the students can see the conditional probabilities represented as areas in a square. Simple geometric operations with these areas (additions and multiplications mostly) allow them, not just to understand this theorem far quicker, but to apply it confidently in almost any possible problem configuration. Overall, this paper offers an alternative or complementary way of explaining this important theorem more clearly to students that take probability courses by conveying it graphically instead of with the traditional mathematical formulae. Through a representative case study, this paper deals provides first-hand evidence about how confusing to understand the Bayes’ theorem might be at first even in simple problems, and how the understanding of this theorem is dramatically improved when presenting it graphically.

MAKING THE MOST OF PRE-CLASS ASSIGNMENTS

When teaching a course, the lecturer or teaching instructor may need that students read some material before coming to the next face-to-face session. Counting on students that have done their assignments and that have done them well, allows the lecturer to make quicker and deeper progress in the contact session. It also raises motivation and allows devoting more time to hands-on practice rather than lecturing, the latter being a method that has proven to reach very low retention levels among students. However, getting the students to read and work on their assignments is easier said than done, and many lecturers feel compelled to set in-class tests and quizzes in order to lure the students to fulfil their homework tasks. However, in-class tests and quizzes are also time-expensive, both inside and outside the classroom, and they are not exempt of other disadvantages. In this paper, we will go over some methods that Teaching and Learning research has found to promote high levels of student understanding and retention, but that are generally too time-consuming to implement them on a regular basis. Also, drawing on the authors’ experience and other research studies, we will present some alternative methods that are almost as effective as the former but require significantly lower time resources.