Proposed Solution Methodology Research Articles

Analytical frequency-domain solution for Euler-Bernoulli frames with semi-rigid connections

This paper presents a novel method for analyzing the dynamic response of plane Euler-Bernoulli frames with semi-rigid connections subjected to arbitrary external loads and bending moments. The proposed solution methodology is the Green’s Functions Stiffness Method (GFSM) in the frequency domain. The GFSM is a mesh reduction method closely related with the Finite Element Method (FEM) sharing with it key components such as shape functions, fixed end forces, and stiffness matrices. By capitalizing on the strengths of both FEM and Green’s Functions, the GFSM facilitates the derivation of closed-form solutions for structural analysis. The formulation is initially established in the frequency domain and is later transformed into the time domain using the fast Fourier transform algorithm. To illustrate the applicability of the method, an example involving a one-bay, one-storey plane frame with semi-rigid connections is presented.

Packaging process optimization for multihead weighing machines with vertical and diagonal double-layered systems: A bi-level approach

Bi-level programming offers a modeling approach for solving problems that involve two decision makers, each with their own objective. This research addresses a bi-level programming application to multihead, vertical, and diagonal double-layered machines. In these machines, hoppers are distributed across two levels: the upper level consists of weighing hoppers that weigh the product coming from the feeding hoppers. The weighed product is then deposited into the hoppers on the lower level, known as booster hoppers, located beneath them in a vertical or diagonal position, depending on the machine type. Simultaneously, they receive a new product to weigh. In the proposed model, weighing hoppers are associated with the leader’s decision variables and optimize the target weight, ensuring it is as close as possible to the weight stated on the label, while also being higher. Meanwhile, booster hoppers are assigned to the follower’s decision variables, optimizing hopper priority. Hopper priority is measured by the time elapsed from the moment the product arrives at the weighing hoppers until it is loaded onto a pack (measured in the number of processed packs). This model has been tested with different filling and combination strategies, considering factors such as the number of heads in the machine, product type (fusilli and ravioli), among others. The results demonstrate that the model successfully reduces the standard deviation of the produced packages for specific combinations of factors. Furthermore, it was observed that response times on each machine increase as the number of heads or the number of hoppers to be combined grows. This is a result of the increased number of combinations required to find the optimal solution. However, this heightened complexity leads to greater effectiveness and more accurate results in terms of the precision of the content delivered to the customer. The proposed solution methodology identifies the optimal machine configuration for minimizing weight deviation in each package. Additionally, it prioritizes the utilization of machines in the lower level of decisions. Consequently, we achieve optimal operating conditions that minimize package overweight, material overcost, and appropriately select the weighing machines. Managerial insights are provided to underscore that customer satisfaction and improved business performance are outcomes of this study.

Combating COVID-19 by placing facilities maintaining social distancing

In this paper, we introduce and study the problem of facility location along with the notion of ‘social distancing’. The input to the problem is the road network of a city where the nodes are the residential zones, edges are the road segments connecting the zones along with their respective distance. We also have the information about the population at each zone, different types of facilities to be opened and in which number, and their respective demands in each zone. The goal of the problem is to locate the facilities such that the people can be served and at the same time the total social distancing is maximized. We formally call this problem as the Social Distancing-Based Facility Location Problem. We mathematically quantify social distancing for a given allocation of facilities and proposed an optimization model. As the problem is NP-Hard, we propose three solution methodologies. The first one is a simulation-based approach, the second one is a greedy heuristic, and the third one is a local search heuristic. To validate the proposed solution methodologies, we collect the data from the Food Corporation of India for the city of Kolkata, which happens to be the largest city of eastern India. With this dataset, we perform an extensive set of experiments. From the results, we observe that the proposed local search heuristic allocates facilities that lead to minimum average queue length and greedy heuristic allocates facilities that lead to the maximum social distancing.

Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare

Crowdsourced delivery and other sharing economy platforms attract freelance workers by offering them flexibility in scheduling their own work hours. Those platforms, however, have been criticized for the lack of protection they offer workers. Since workers are treated as independent contractors, they do not receive minimum wage and other protection measures under labor law. In this paper, we examine the integration of driver compensation guarantees in a platform’s dynamic matching decisions. We study the problem of designing dynamic matching policies that guarantee a particular level of compensation for active workers over a time period, while maintaining work hour flexibility. We model three types of policies, that are either wage-based or utilization-based. We propose an MDP model to capture the dynamic and stochastic nature of the problem, then design a value function approximation algorithm to efficiently solve the large-scale MDP model. Extensive computational testing is conducted to assess the performance of the proposed solution methodology and the compensation guarantees, using synthetic and real-world datasets. Our findings suggest that the utilization policy results in the highest earning for drivers, though at the expense of longer empty miles from drivers’ origins to the pickup locations of matched orders. On the other hand, the effective wage policy leads to shorter average distance to pickup, but slightly lower earning to drivers. Both policies result in only a slight decrease in platform profit as compared to the base case, and exhibit lower dispersion in the distribution of driver earning while active. In contrast, the nominal wage policy shows a comparable trend to the base-case policy in terms of average driver earnings, suggesting minimal benefits for drivers.

Last mile delivery routing problem using autonomous electric vehicles

This paper presents a study on the application of Autonomous Delivery Vehicles (ADVs) in last-mile delivery for urban logistics. Specifically, we focus on a routing problem using multi-stop ADVs, with the goal of minimizing route and vehicle usage costs while satisfying several constraints associated with load and battery capacities, maximum route duration for ADVs, and maximum walking distance for customers. We refer to this problem as the Autonomous Delivery Vehicle Routing Problem (ADVRP) and present its mixed-integer linear programming formulation. Due to the NP-hardness of the problem, we propose a two-phase metaheuristic approach that first clusters customers and determines stopping locations for the ADVs, followed by a phase that determines the optimal routes for the ADVs using hybrid variable neighborhood search and simulated annealing. To evaluate our proposed solution methodology, we conduct computational experiments on various related Vehicle Routing Problems (VRPs) from the literature and newly generated ADVRP instances. The results show that the proposed two-phase metaheuristic approach can produce high-quality solutions with minimal computational effort while outperforming an exact solver in 26 medium- and large-sized instances of ADVRP and reaching optimal solutions in most VRP instances and related problems. Furthermore, we conduct sensitivity analyses on selected problem parameters and present a case study in Istanbul, Turkey to provide managerial insights for implementing ADVs in urban logistics.

Multi-response optimization for laser transmission welding of PMMA to ABS using Taguchi-based TOPSIS method

Recently, the usage of laser transmission welding (LTW) technology in the automotive industry has been rising for joining thermoplastic parts regarding its superior properties in comparison to other welding technologies. However, specifying the process parameters is a crucial step to obtain satisfactory welding quality for the vehicle parts. In this context, this paper addresses the LTW process of Polimetil metakrilat (PMMA) to Akrilonitril bütadien stiren (ABS) materials for the production of taillights in an automotive company and proposes a new multi-response Taguchi-Based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to optimize machining parameters in the laser welding process. In the proposed solution methodology, the main effects of the parameters on different outputs are identified by using the [Formula: see text] orthogonal array of the Taguchi method. Regarding the best parameter set for each output, the best parameter set for multi-response is identified by using the TOPSIS method, in which alternatives are evaluated through the interrelated quality measures. To optimize welding process in taillight production, PMMA and ABS samples with the dimensions of [Formula: see text] in width, [Formula: see text] in length, and [Formula: see text] in thickness are used in the experiments. The samples are welded by using LPKF Twinweld 3D 6000® laser welding machine. For the welding process, laser power, welding speed, and pressure force are taken into account as the input parameters to optimize three responses: weld strength, breaking strain, and weld width. To identify the best process parameter values, the Taguchi Method is initially employed to calculate the main effects of LTW parameters for each output. Then, the TOPSIS method is carried out to evaluate a number of alternative parameter sets generated through the Taguchi results. As a consequence of the TOPSIS ranking scores, the best parameter set that jointly optimizes three outputs of the LTW process is identified for the taillight production as [Formula: see text] power, [Formula: see text] speed, and [Formula: see text] pressure force. Based on the conducted experiments, this parameter setting achieves the highest weld strength, breaking strain level, and above-average weld width. The results of the experiments show that the proposed methodology is capable of optimizing LTW parameters for a multi-response with fewer experiments in the joining of plastic vehicle parts.

A decomposition algorithm for multi‐item production planning with independent random demand

AbstractProduction planning in a multiproduct setting where the demands for different items are independent random variables that are also featured with a dynamic behavior over the planning horizon is a challenging task. With a particular focus on maintenance facilities, this study proposes a multistage stochastic programming (MSP) model for operations planning under independent random demand of faulty components in the modular‐structured devices (e.g., gas turbines) received for repair and overhaul services. A Lagrangian relaxation‐based decomposition heuristic is also developed to efficiently solve the problem for real‐size instances. This heuristic relies on decomposing the MSP model into submodels corresponding to component STs and coordinating them via a subgradient algorithm to obtain a high‐quality feasible solution. Our numerical experiments conducted on a range of problem instances endorse the significant value of incorporating demand uncertainty and the effectiveness of the proposed solution methodology in overcoming computational complexity.

Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique

The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (An∗), Wolbachia-uninfected adult female mosquitoes (Fn∗), Wolbachia-infected aquatic mosquitoes (Aw∗), and Wolbachia-infected adult female mosquitoes (Fw∗). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots.

Optimizing system reliability through selective maintenance allocation: A novel multi‐objective programming approach using neutrosophic fuzzy concept

AbstractSelective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied.

Optimal allocation of demand response considering transmission system congestion

The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed nature throughout the grid. Aggregators can be created to manage a set of such demand response resources, but deciding how to allocate an aggregator’s resources is an important problem. One of the aspects that needs more attention is the impact of the transmission system on these decisions. In this paper, we propose a short-term optimization model for allocating demand response(DR) resources as well as generation resources to supply external demand that is offered after the scheduling decision is made. The DR resources will only be available for use after the scheduling decision is made. Finally, our work also considers the impact of congestion in the transmission system when allocating DR. We propose the use of a semidefinite relaxation to provide a good initial point to solve our model with the aim of guaranteeing that we will find an optimal solution. Results from numerical tests with the IEEE 96-RTS and the ACTIVSG500 test grids are reported. We found that DR resources mitigates congestion management, allowing the generators to supply more of the external demand that is offered. Besides that, we observe that using our proposed solution methodology, we were able to obtain optimal solution for both cases studies, which is not the case when solving the original formulation for the ACTIVSG500 grid.

A battery energy management system to improve the financial, technical, and environmental indicators of Colombian urban and rural networks

This paper addresses the problem of managing battery energy in urban and rural alternating current networks, aiming at improving their financial, technical, and environmental indicators. To this end, a mathematical model was formulated that proposes as objective functions the optimization of energy operational costs of the grid, the minimization of power losses associated with energy transport, and the reduction in CO2 emissions related to the production of energy. This model also considers the set of constraints involved in the operation of an alternating current network in an environment with distributed energy resources (Photovoltaic generators + Batteries storage systems). A master–slave strategy that combines a parallel version of the Vortex Search Algorithm (VSA) and an Hourly Power Flow method based on Successive Approximations (HPFSA) was proposed as the solution methodology. To create the test scenarios, urban and rural electrical networks documented in the specialized literature were adapted using generation and demand data of an average day of operation in Medellín-Antioquia (Urban) and Capurganá-Chocó (Rural). These data represent the energy behavior of an urban and a rural network within the Colombian territory. In relation to the distributed energy devices, the integration of three Photovoltaic Distributed Generators (PV DGs) and three lithium-ion batteries of different types into the electrical networks was considered. In addition, the energy production costs and CO2 emissions of the local network and the diesel fuel in Colombia were determined, as well as the maintenance costs associated with the batteries and the PV DGs. With the purpose of validating the effectiveness of the proposed method in terms of solution, repeatability, and processing times, two comparison methods reported in the specialized literature were employed to solve the problem addressed in this paper. In the urban and rural networks under study, the proposed solution methodology achieved the best results in terms of solution quality, repeatability, and processing time.

Sustainable Ecosystem Management via H<sub>∞</sub>-Control Theory of Stochastic Systems

In this paper, aiming to provide accurate protocols for management of sustainable ecosystems, a design methodology of H∞-controller for hunter-prey model under exposure to exogenous disturbance and stochastic noise is presented. Along the development, solution procedure of the stochastic Hamilton-Jacobi-Isaacs equation via Successive Galerkin’s Approximation is described. Utilizing the proposed solution methodology of Hamilton-Jacobi-Isaacs equation, H∞-controller of hunter-prey model was successfully designed. Robustness and performance against exogenous disturbance of the designed H∞-controller is validated and confirmed by numerical simulations including Monte-Carlo simulation by Simulink software on MATLAB.

Optimal Phase-Balancing in Three-Phase Distribution Networks Considering Shunt Reactive Power Compensation with Fixed-Step Capacitor Banks

The black hole optimization (BHO) method is applied in this research to solve the problem of the optimal reactive power compensation with fixed-step capacitor banks in three-phase networks considering the phase-balancing problem simultaneously. A master–slave optimization approach based on the BHO in the master stage considers a discrete codification and the successive approximation power flow method in the slave stage. Two different evaluations are proposed to measure the impact of the shunt reactive power compensation and the phase-balancing strategies. These evaluations include a cascade solution methodology (CSM) approach and a simultaneous solution methodology (SSM). The CSM approach solves the phase-balancing problem in the first stage. This solution is implemented in the distribution network to determine the fixed-step capacitor banks installed in the second stage. In the SSM, both problems are solved using a unique codification vector. Numerical results in the IEEE 8- and IEEE 27-bus systems demonstrate the effectiveness of the proposed solution methodology, where the SSM presents the better numerical results in both test feeders with reductions of about 32.27% and 33.52%, respectively, when compared with the CSM. To validate all the numerical achievements in the MATLAB programming environment, the DIgSILENT software was used for making cross-validations. Note that the selection of the DIgISLENT software is based on its wide recognition in the scientific literature and industry for making quasi-experimental validations as a previous stage to the physical implementation of any grid intervention in power and distribution networks.

Logistics of temporary testing centers for coronavirus disease

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability.This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem.We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted.

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (Ploss) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of Ploss in both radial and mesh systems. It provided the best reduction in minimum Ploss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

Investigation of heat and mass transfer for an MHD hybrid nanofluid flow over a rotating disk: a numerical investigation with supervised neural network

The key intention behind this research work is to investigate the heat and mass transfer phenomenon in the flow of hybrid nanofluid over a revolving disk with magnetohydrodynamic effects under consideration (MHD-HNRD). The flow model of the problem has been formulated in the form of PDEs which are further transmuted into ODEs with proper transforming equations. The influence of Magnetic parameter, Brinkman number, Prandtl number, Rotation parameter, and Concentration of the nanoparticles on velocities and thermal profile has been studied and reference data sets for the MHD-HNRD model are computed with the help of the renowned Lobatto-IIIA solver by varying these parameters. This reference solution is then subjected under the proposed solution methodology based on Levenberg–Marquardt Supervised Neural Networks (LMSNNs). An increase in heat transfer rate has been observed with the larger magnetic force and rotation rate. Graphical and numerical results based on mean-square errors (MSEs), time series response, error distribution plots, and regression plots endorse the precision, validity, and consistency of the proposed solution methodology. MSEs up to the level of 10–12 and performance learning curves predict the smoothness of the curves with the number of epochs confirming the accuracy, convergence, and validity of the designed solver.

Optimal Selection and Integration of Batteries and Renewable Generators in DC Distribution Systems through a Mixed-Integer Convex Formulation

The problem concerning the optimal placement and sizing of renewable energy resources and battery energy storage systems in electrical DC distribution networks is addressed in this research by proposing a new mathematical formulation. The exact mixed-integer nonlinear programming (MINLP) model is transformed into a mixed-integer convex model using McCormick envelopes regarding the product between two positive variables. Convex theory allows ensuring that the global optimum is found due to the linear equivalent structure of the solution space and the quadratic structure of the objective function when all the binary variables are defined. Numerical results in the 21-bus system demonstrate the effectiveness and robustness of the proposed solution methodology when compared to the solution reached by solving the exact MINLP model. Numerical results showed that the simultaneous allocation of batteries and renewable energy resources allows for the best improvements in the daily operating costs, i.e., about 53.29% with respect to the benchmark case of the 21-bus grid, followed by the scenario where the renewable energy resources are reallocated while considering a fixed location for the batteries, with an improvement of 43.33%. In addition, the main result is that the difference between the exact modeling and the proposed formulation regarding the final objective function was less than 3.90% for all the simulation cases, which demonstrated the effectiveness of the proposed approach for operating distributed energy resources in monopolar DC networks.

SOĞUK ZİNCİR LOJİSTİĞİNDE GEZGİN SATIN ALICI PROBLEMİ İÇİN İKİ AŞAMALI BİR ÇÖZÜM YÖNTEMİ

Bu çalışma, soğuk zincir lojistiğinde bozulabilir gıdaların satın alınması işlemini dikkate alarak literatürde iyi bilinen gezgin satın alıcı probleminin genişletilmiş bir halini sunmaktadır. Taşıma işlemlerinin bozulabilir ürün şartlarına dayalı gerçekleştirildiği bu problem, soğuk zincir lojistiğinde gezgin satın alıcı problemi (SZL-GSAP) olarak adlandırılmıştır. SZL-GSAP, sıcaklık kontrollü bir araç ile taşınması gereken çeşitli ürün taleplerinin belirli sayıda marketlerden satın alınması işlemini dikkate almaktadır. Problemin amacı, satın alıcı için sıcaklık kontrollü aracın enerji maliyetinin, ürün bozulma maliyetinin ve satın alma maliyetinin toplamını minimize edecek satın alma ve rota planını elde etmektir. Problem, doğrusal olmayan bir karma tamsayılı programlama modeli olarak formüle edilmiş ve iki aşamalı bir yöntem kullanılarak çözülmüştür. Çözüm yönteminin ilk aşamasında, bir çözüm kurucu yöntem kullanılarak problem için kaba bir sonuç elde edilmektedir. Daha sonra ilk aşamada elde edilen çözüm, GUROBI çözücüsü kullanılarak belirli bir zaman kısıtı içerisinde geliştirilmektedir. Sayısal uygulamalarda, önerilen çözüm yönteminin performansı, farklı büyüklükte örnekleri içeren bir problem seti üzerinde analiz edilmiştir.

Optimal Selection of Conductor Sizes in Three-Phase Asymmetric Distribution Networks Considering Optimal Phase-Balancing: An Application of the Salp Swarm Algorithm

This paper presents a new methodology to simultaneously solve the optimal conductor selection and optimal phase-balancing problems in unbalanced three-phase distribution systems. Both problems were represented by means of a mathematical model known as the Mixed-Integer Nonlinear Programming (MINLP) model, and the objective function was the minimization of the total annual operating costs. The latter included the costs associated with energy losses, investment in conductors per network segment, and phase reconfiguration at each node in the system. To solve the problem addressed in this study, a master–slave methodology was implemented. The master stage employs a discrete version of the Salp Swarm Algorithm (SSA) to determine the set of conductors to be installed in each line, as well as the set of connections per phase at each of the nodes that compose the system. Afterward, the slave stage uses the three-phase version of the backward/forward sweep power flow method to determine the value of the fitness function of each individual provided by the master stage. Compared to those of the Hurricane-based Optimization Algorithm (HOA) and the Sine Cosine Algorithm (SCA), the numerical results obtained by the proposed solution methodology in the IEEE 8- and 25-node test systems demonstrate its applicability and effectiveness. All the numerical validations were performed in MATLAB.