Three-objective Model Research Articles

Prevention of post-pandemic crises: A green sustainable and reliable healthcare supply chain network design for emergency medical products

The COVID-19 pandemic happened exactly when no one expected it and many people died due to the lack of medical equipment. Although pulse oximeters and thermometers were only one of the virus detection equipment, the lack of that equipment could lead to people not knowing about the disease and then the death of those people. Given that these medical devices are very vital in the era of a pandemic, and much less in the later, it is required to provide the best conditions for their use in a closed-loop healthcare supply chain network in post-pandemic. In this paper, a scenario-based two-stage stochastic programming three-objective model for designing a green closed-loop supply chain network is presented. Cost minimization, reliability maximization, and critical response maximization are the objectives of the proposed model. The third objective function is considered for the critical response based on the choice of transportation method. The greenhouse gas emissions for all supply chain elements are considered uncertain and controlled in several possible scenarios. By using an accelerated Benders decomposition algorithm, the mathematical model was solved in different dimensions and the result was analyzed and evaluated in different scenarios. The results demonstrate that the acceleration of Benders bonds reduces the convergence speed of the algorithm by 41%. Our study provides managerial insights into sustainability and reliability, enhancing healthcare supply chain resilience during and after pandemics.

Designing a sustainable disruption-oriented supply chain under joint pricing and resiliency considerations: A case study

Supply chains must adopt an appropriate mechanism to deal with interrelated risks. Sustainable development's economic, environmental and social aspects are critical concerns of supply chains. This study presents a three-objective model to design a forward supply chain network considering interrelated operational and disruptive risks. Several mitigative and preventive resilience strategies are implemented to cope with mentioned risks. Also, the optimal prices for products and parts are determined via a joint pricing strategy to enhance supply chain's profitability in disruption incidence. A stochastic robust optimization method is employed to deal with parameters’ uncertainty. The proposed model is applied to an industry of manufacturing chemical processing equipment to validate the formulation and derive practical insights. Results revealed that simultaneously managing disruption risks and uncertainties can improve sustainability goals and reduce the costs associated with risk exposure and protection in the under-study supply chain.

Research on Multiobjective Optimal Operation Strategy for Wind-Photovoltaic-Hydro Complementary Power System

To address the problems of wind and solar generation volatility and lose of wind and photovoltaic resources, on the basis of the complementary property of wind-solar-water, the topology structure of the wind-solar-water synergy power generation system is constructed. Taking the minimum grid fluctuation index, the minimum wind-photovoltaic-hydro discard rate and the greatest economic effectiveness of the power station as the goal functions and considering various constraints of the wind, photovoltaic, and hydrostation units, a triobjective optimization running model of the wind-photovoltaic-water synergy system is established. Meanwhile, this essay suggests an IMOSSA on the basis of tent chaotic sequence and random wandering strategy to settle the described triobjective optimization issue. Taking Hubei Pankou as an example for simulation analysis, after choosing the best scheme, IMOSSA compared with MOSSA, MOGWO, and NSGA-II, the volatility of sunny days is reduced by 12.39%, 19.5%, and 36.71%, respectively; the wind-photovoltaic abandonment rate is reduced by 11.17%, 22.5%, and 38.03%, respectively, while in the rainy days the volatility is reduced by 8.09%, 18.34%, and 47.03%, respectively; the wind-photovoltaic abandonment rate is reduced by 14.84%, 16.86%, and 40%, respectively. Therefore, it is possible to demonstrate the validity of the proposed three-objective model and the efficiency of the IMOSSA in solving the issue. The efficiency of the optimization operation approach suggested in this research is confirmed by the case study, providing a new idea for the large-scale consumption of new energy in high-proportion hydropower grids.

A Multi-Objective Mathematical Model for the Population-Based Transportation Network Planning

The current article introduces a three-objective model for the problem of location, allocation, and routing, taking into account the travel times depending on the population on the route. Objective functions include minimizing the total network transit time, maximizing travel attractiveness for travel applicants, and balanced allocation of travel applicants to each service area. In this research, a general form of the proposed three-objective mathematical planning model is formulated. The proposed model seeks to select a suitable place or places as places of service, allocate people or vehicles in each area to these places and determine the transportation route of each person or vehicle in each area to reach the service provider in question. Getting a direct impact of random factors and population flow on the random travel times of each bow so that all three goals are met simultaneously. The cuckoo optimization algorithm is developed to solve this model. A sample problem is also examined to show the validity and behavior of the proposed model. The numerical results indicate the high performance of the cuckoo optimization algorithm.

Multi-Objective Feature Selection With Missing Data in Classification

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study.

A Robust Multiobjective Mathematical Model Optimizing Stock Portfolio

The present paper investigates the problem of capital portfolio selection under uncertain conditions and uses a robust optimization approach for modeling. The model provided in this paper is a three‐objective model that aims to maximize returns, maximize liquidity, and minimize risk. The data extracted from the site of the Tehran Stock Exchange are as follows. These data are related to twenty shares from July 2020 to July 2021. The robust approach used in this research has been analyzed by the real data of the Tehran Stock Exchange and then the optimal portfolio for different robust costs has been formed by solving the robust model. In the following section, the relevant model is solved through real stock market data and using the goal programming approach, and the results are investigated and analyzed.

Optimal Sizing of Isolated Microgrid Containing Photovoltaic/Photothermal/Wind/Diesel/Battery

The establishment of isolated microgrid is of great significance in solving power supply problems in offshore islands or remote mountainous areas. Aiming at the isolated microgrid containing photovoltaic, photothermal, wind, diesel, and energy storage, a three-objective sizing optimization model of the microgrid is proposed considering comprehensive economy cost, deficiency of power supply probability (DPSP), and renewable energy discard rate (REDR). The three-objective sizing optimization model was solved by the improved multiobjective grey wolf optimization algorithm. An island was taken as an example to optimize the sizing of the microgrid, and the rationality of the proposed three-objective model was verified. The feasibility of the improved multiobjective grey wolf optimization (IMOGWO) was verified by comparing with the multiobjective grey wolf optimization (MOGWO). Three representative solution sets and a set of compromise solution sets are obtained by simulation, and the results satisfied the load demand. And the DPSP and the REDR are reduced by 7.55% and 6.29% by using the IMOGWO. The designed and analyzed hybrid renewable energy system model might be applicable to around the world having similar climate conditions.

The study of multi-objective supplier selection problem by a novel hybrid method: COA/ε-constraint

Today, paying attention to the interests of suppliers in supply chain management strategies is one of the important points in the success of long-term and strategic relationships with suppliers. Not paying enough attention to these points sometimes causes irreparable damage to the overall structure of the organization. In response to this need, researchers have developed and proposed different models according to different approaches. This research has presented a special model with the approach of answering these problems. This approach, which is based on the cuckoo optimization algorithm (COA), can solve the problems in multi-objective methods in addition to single-objective problems. This method based on the COA and the e-constraint method named COA/e-Constraint. The general approach of this method is to turn a multi-objective problem into a single-purpose problem, which is associated with increased efficiency. The model studied in this paper, with the aim of creating coordination between buyers and suppliers in the problem of supplier selection, is a three-objective model of cost, quality and delivery time, which is implemented to evaluate the performance of the proposed method. The results show the superiority of the proposed method over similar approaches in terms of creating a Pareto frontier.

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

In recent years, people have paid more and more attention to traditional manufacturing’s environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model’s effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.

Scheduling different types of vehicles in distribution centers with fixed due dates and packed shipments

This paper proposes a novel non-dominated sorting genetic algorithm-based method for scheduling simultaneously different types of vehicles with different capacities in distribution centers working as cross dock. In this paper, a three-objective model is proposed, which minimizes operational time, lateness and earliness of delivering products.In many of real life cases, packs of products with different number of products must be unloaded instead of arbitrary number of products because of products’ nature and/or physical limitations. It means that the optional amount of unloading is not prohibited in many of real life cases unlike to previous researches; therefore, scheduling vehicles by considering this limitation is different from previous works that allow optional amount of unloading.Another advantage of this paper is consideration of the frequent unloading pattern of inbound vehicles. This consideration better synchronizes the inbound and outbound vehicles compared to non-frequent pattern, and as a result, it helps to reduce the operational time of cross docking as well as the shipping cycle.This paper proposes a new scheduling method for considering the mentioned novelties. Furthermore, Taguchi design is used for regulating the proposed algorithm’s parameters. Several numerical examples are solved by the proposed method, and the obtained results are compared to multi-objective particle swarm optimization. The numerical results show the superiority of the proposed method.

Joint optimisation for time consumption and energy consumption of multi‐application and load balancing of cloudlets in mobile edge computing

Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited; a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater

In this research, cropping pattern optimization models have been developed to maximize the benefits and minimize the potential negative effects of quantitative-qualitative conjunctive use of unconventional surface water (treated wastewater) and groundwater in agricultural irrigation. Three objective functions were considered: maximizing the benefits from crop patterns, reducing nitrogen leaching, and improving the rate of aquifer recharge. The developed models use particle swarm optimization (PSO) integrated with an additive weighting method and a multi-objective particle swarm optimization (MOPSO) algorithm for different single- and three-objective optimization scenarios in the Varamin irrigation network in Iran, and then the results of the models were compared. The solutions resulting from the three-objective model using the PSO algorithm with the additive weighting method indicated that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 49%, and 20%, respectively. Meanwhile, the conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 35% and 88%, respectively. The solutions resulting from the three-objective model using the MOPSO algorithm, forming Pareto front, and then using TOPSIS to select the optimal solution from among the non-dominated solutions showed that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 47% and 15%, respectively. The objectives of conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 36% and 89%, respectively. The difference between the objective function values of the two algorithms is about 0.4% to 4%, which shows the proximity of the models in finding the optimal solution. The results of this research can be used to optimize the use of water resources, increase farmers’ benefits, and decrease nitrogen leaching from irrigation and drainage networks.

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective—the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO.

Portfolio Optimization of Financial Services Stocks in the Nigerian Stock Exchange

Portfolio Optimization of Financial Services Stocks in the Nigerian Stock Exchange C. P. Nnanwa, T. C. Urama, P. O. Ezepue Abstract This paper attempts to maximize the expected return and minimize the variance of the portfolio by using Markowitz's portfolio selection model and a three-objective linear programming model to allocate different percentage of weight to different assets to obtain an optimal/feasible portfolio of the financial sector of the Nigerian stock exchange (NSE). An equally weighted portfolio was constructed using the daily closing prices from the financial services sector of NSE, The mean and the standard deviation of the data from the sector of the market served as our constrains in the three-objective model used. Additionally, three portfolios were constructed with the aims of maximizing the returns and minimizing the standard deviation (variance) and maximizing the Sharpe ratio. With the result from the simulation and analysis, these portfolios were compared alongside with the original equally weighted portfolio and the result of the comparison form the basis for our recommendations provided to the investors and market practitioners in the sector of NSE. Full Text: PDF DOI: 10.15640/arms.v4n2a1

Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm

The development of an efficient emergency response plan to provide critical daily supplies to affected people facilitates efficient relief operations. In this study, we propose a multi-objective stochastic programming model for developing an earthquake response plan, which integrates pre-and post-disaster decisions. This three-objective model attempts to maximize the total expected demand coverage, to minimize the total expected cost, and to minimize the difference in the satisfaction rates between nodes. We develop a new multi-objective particle swarm optimization (MOPSO) algorithm to solve this model. Genotype-phenotype-based binary particle swarm optimization (PSO) and continuous PSO are designed to deal with the binary location and other continuous decision variables, respectively. A new strategy is employed to select two types of guides in order to enhance the search ability. Furthermore, a new adaptive inertia weight strategy and two mutation operators are used to ensure that the diversity is sufficient and to regulate the exploration and exploitation capacities, respectively. We present an illustrative real-world case study and some randomly generated instances for computational applications. The results obtained by the proposed MOPSO algorithm were compared with those obtained using a modified time-variant MOPSO, the non-dominated sorting genetic algorithm, strength Pareto evolutionary algorithm, and the exact solution method where possible.