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The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO₂ emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.

An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.

The results from this research suggest that CO₂ emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.

The design of water distribution networks is a computationally complex process and often requires significant CPU time to arrive at an optimal solution. This problem is significant in the case of larger networks, especially when all the failed states need to be simulated. Simpler measures of reliability could be adopted in the future.

Although a significant amount of research had been undertaken in the area of optimal water distribution network design, only limited research includes environmental impacts as a design objective. This paper not only includes environmental aspects but also considers reliability. The model proposed in this research is a useful tool for engineers for considering various alternatives before choosing the best design.

Distribution network design (DND) has become an important strategic decision for supply chain managers with increasing competitive nature of the industry nowadays. The purpose of this paper is to propose a web-based decision support system (DSS) for fuzzy distribution network optimization. For this purpose, a web-based DSS using fuzzy linear programming model is proposed to solve DND problem under uncertainty and a framework is created to optimize a distribution network.

In this study, the fuzziness in distribution network optimization is addressed. Fuzzy linear programming is used in a DSS to consider the uncertain and imprecise data. A web-based DSS architecture is presented. Furthermore, as an application, distribution network optimization is conducted for a company in the ceramics industry.

By using this DSS, the optimal transshipment amounts in the distribution network and the required facility and distribution centers can be determined for different fuzziness levels. In fact, for different uncertainty levels of input parameters, the planner can understand the range of optimum network planning costs. Based on the results of this study, planners will be able to decide how to develop the distribution network under uncertain demand.

Reviewing previous research in the related literature revealed that there are no studies presenting a web-based DSS using fuzzy linear programming model to solve this type of problems under uncertainty.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

The purpose of this paper is to attempt to realize a distribution network optimization in supply chain using grey systems theory for uncertain information.

There is much uncertain information in the distribution network optimization of supply chain, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in supply chain, however grey information of the supply chain has not been covered. In the distribution problem of supply chain, grey demands are taken into account. Then, a mathematics model with grey demands has been constructed, and it can be transformed into a grey chance‐constrained programming model, grey simulation and a proposed hybrid particle swarm optimization are combined to resolve it. An example is also computed in the last part of the paper.

The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about distribution of supply chain, but grey chance‐constrained programming, grey simulation and particle swarm optimization can be combined to resolve the grey model.

The method exposed in the paper can be used to deal with distribution problems with grey information in the supply chain, and network optimization results with a grey uncertain factor could be helpful for supply chain efficiency and practicability.

The paper succeeds in realising both a constructed model of the distribution of supply chain with grey demands and a solution algorithm of the grey mathematics model by using one of the newest developed theories: grey systems theory.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (P^loss_total), reduction of voltage deviation and balancing of current through feeder sections.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.

A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.

The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.

The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Distribution network protection is a complicated problem and mal-operation of the protective relays due to false settings make the operation of the network unreliable. Besides, obtaining proper settings could be very complicated. This paper aims to discuss an innovative evolutionary Lightning Flash Algorithm (LFA) which is developed for solving the relay coordination problems in distribution networks. The proposed method is inspired from the movements of cloud to ground lightning strikes in a thunderstorm phenomenon. LFA is applied on three case study systems including ring, interconnected and radial distribution networks. The power flow analysis is performed in Digsilent Power Factory software; then the collected data are sent to MATLAB software for optimization process. The proposed algorithm provides optimum time multiplier setting and plug setting of all digital overcurrent relays in each system. The results are compared with other methods such as particle swarm optimization and genetic algorithm. The result comparisons demonstrate that the proposed LFA can successfully obtain proper relay settings in distribution networks with faster speed of convergence and lower total operation time of relays. Also, it shows the superiority and effectiveness of this method against other algorithms.

A novel LFA is designed based on the movements of cloud to ground lightning strikes in a thunderstorm. This method is used to optimally adjust the time multiplier setting and plug setting of the relays in distribution system to provide a proper coordination scheme.

The proposed algorithm was tested on three case study systems, and the results were compared with other methods. The results confirmed that the proposed method could optimally adjust the relay settings in the electric distribution system to provide a proper protection scheme.

The practical implications can be conducted on distribution networks. The studies provided in this paper approve the practical application of the proposed method in providing proper relay protection in real power system.

This paper proposes a new evolutionary method derived from the movements of cloud to ground lightning strikes in thunderstorm. The proposed method can be used as an optimization toolbox to solve complex optimization problems in practical engineering systems.

Interactive‐graphic systems and operations research methodologies are increasingly being combined to produce efficient, versatile and powerful tools that enhance the decision‐making process. The possible contribution of such tools to the planning of distribution systems for industrial firms and freight carrier service networks and operations is examined and compared.

This paper deals with a new formulation of the optimal operation of electrical distribution networks problem in regular working state. In the new deregulated energy market providing reliable and economical service to customers is a primary task. The multiobjective formulation of the reconfiguration and compensation problem used in this paper considers as a primary object also the minimisation of the load nodes unavailability (UA) expressed in probabilistic terms. Therefore, the objectives to be attained through the optimisation strategy are: minimal power losses operation, minimum UA of the load nodes, load balancing among the HV/MV transformers, and voltage profile regularisation. The application carried out uses an evolutionary algorithm and a particular normalisation technique for the multiple objectives formulation. In the considered automated network, the remote control of capacitor banks and tie‐switches is possible and their layout is the optimisation variable. After a brief description of the optimal reconfiguration and compensation problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, the test results on a large MV distribution network are reported and discussed.