An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems

Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

With the advancement of science and technology in recent years, the rate of product upgrading by end users has increased, resulting in a high number of End-Of-Life (EOL) products. For environmental protection and economic benefits, disassembly lines are set up to recycle them. A disassembly line balancing problem arises and has attracted widespread attention. In this paper, we design a model to address a class of disassembly line balancing problems in which the main optimization objective is to maximize profits, while keeping carbon emission in a tolerable range. A discrete whale optimization algorithm is proposed to search their high-quality solutions. Its efficiency is verified by comparing its solutions with their analytic solutions obtained from a commercial optimization solver and other swarm intelligence algorithms. Experimental results show that it is significantly faster than CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while guaranteeing its solutions quality.

Many literatures in forward supply chain management point out the importance of product design. In this research, we follow the same concept and apply it to closed-loop supply chain. We consider two closed-loop supply chain structures, Parallel and Mix. In the Parallel structure, the assembly and disassembly lines are separated while in Mix structure they are performed by the same production line. We study how the usage of component commonality, location of common component, and degree of delayed differentiation impact the mean and variance of the cycle time for forward supply chain under these two structures.KeywordsClosed-Loop Supply ChainComponent Commonality

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In present era of environmentally conscious manufacturing, End-of-life (EOL) product disassembly is increasingly used for the product recovery process. However, the low efficiency in disassembly process put a great challenge in front of manufacturers to achieve the environmental goals set for the product. To achieve the efficient utilization in part recycling processes at the end of its life, disassembly line has important role to play. Thus, much emphasis is given by the research community to reduce the company reliance on the availability of human experts to improve the intelligence in the disassembly process. This research aims to optimize the number of machine required to disassemble the parts in a disassembly line. The complexity while disassembly increases in case of defects in the tasks to be allocated on the workstation. In this paper an evolutionary approach named Psycho-Clonal is proposed to solve the disassembly line-balancing problem in the presence of task failure. The proposed algorithm is inspired by artificial immune systems (AIS), which is further enhanced by the authors by incorporating Maslow’s need hierarchy theory work as a meta-heuristic. The main attributes of the algorithm are the various need levels, immune memory, and affinity maturation. Need levels incorporated in the algorithm help in satisfying the model constraints, thus empowering the algorithm to work for a multi-objective, multi-constraints problem. Immune memory helps in preserving the best solution and affinity maturation helps in exploring and exploiting the search space.Copyright © 2005 by ASME

The ever-increasing concerns of the growth in the volume of waste tires, reduction in natural resources and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. A CLSC is a network with a holistic view of the entire forward and reverse logistic system with the aim of transferring the EOL products up the stream from the end users for value recovery such as reuse, repair, remanufacturing, or recycling.There are many factors that are considered when designing a closed-loop supply-chain network, such as uncertainty in the parameters, costs, environmental impacts, social dimensions, governmental regulations, domestic and international competition, disruption and operational risks, reliability of the network, technological advancements, etc. In the real world, the CLSC network design is subject to a variety of uncertainties which can be random and fuzzy. Designing a reliable and environmentally conscious CLSC which considers the risks and uncertainties of the parameters in the network is necessary for a successful supply-chain network. The government regulations could have a significant impact on the supply chain network design, its management and the long/short-term investment decisions. Ever-changing restrictions, mandates and tax policies can affect corporation's competitive advantage, market share, and operational costs. Tariffs and caps imposed on imported goods, intended to limit the volume of imports, protect domestic employment and reduce competition among domestic industries. One of the challenges facing the recovery of EOL products is the uncertainty associated with the conditions of the EOL products after being returned. Designing intelligent products that can collect valuable information during their life cycles is something that has been studied in the past decade. This information can be used to extend the life span of the products and improve the value recovery of EOL products. In this dissertation, we take a closer look at the challenges facing the tire industry and obtain a solution that creates a win-win situation for companies in the tire network as well as the environment. We evaluate the available recovery options for EOL tires, identify the uncertain parameters in the logistics network and find the solution for dealing with these uncertainties using robust modeling for the logistics network. In the first model, we introduce a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty with the objective of reducing costs, reducing environmental impacts, and increasing the social responsibilities and the reliability of the network. Second, we study the impact of tariffs and government regulations on the profitability of the network. Third, we evaluate the impact of using sensor embedded tires on the design of CLSC network. The analysis is done using a novel Robust Fuzzy/ Possibilistic Stochastics Programming (RFSP) model that controls scenario-based uncertainty (disruption risks) and fuzzy-based uncertainty (operational risks) in the network simultaneously and provides more reliable results for the managers and the decision makers. We evaluate the possibility and feasibility of expanding the existing forward logistic network at different capacity levels to cope with the market demand under various scenarios as well as the feasibility of establishing a new reverse logistic network at different capacity levels to cope with the return rate of EOL tires. A case example is considered to illustrate the implementations and validation of the proposed models. --Author's abstract

BackgroundDue to the growing awareness of environmental protection and their economic benefits, there has been rapid development in the closed-loop supply chain (CLSC) in recent years, with a general occurrence of CLSCs led by collectors. MotivationEven with government support such as subsidies, the operation of CLSC still requires immense investment, such as purchasing recycling equipment. Nevertheless, the unprecedented economic recession during the post-pandemic era has heightened the risk of insufficient funding for CLSC. This highlights the importance of understanding the complexities involved in CLSC financing strategies, as they are crucial for ensuring the sustainability and resilience of CLSC in the face of challenges. MethodsTo investigate the equilibrium decisions and financing strategy selection of CLSCs, we developed and analyzed a series of three-stage Stackelberg models, considering practical scenarios involving capital constraints, environmentally friendly green activities, government subsidies for End-of-Life (EOL) recycling, as well as introducing common trade credit financing and bank financing for generality. ConclusionThe findings demonstrate: (1) Under trade credit financing, it is always beneficial for the collector granting credit. Interest income can motivate the collector to recover more EOL products. (2) In addition to financing interest rates, government subsidies and remanufacturing cost savings can affect the financing strategy selection of the capital-constrained manufacturer. Our study provides guidance for managers in selecting optimal financing strategies under specific conditions. (3) The total profit of CLSC under trade credit financing is always higher and we propose a revenue-sharing contract to realize the win-win status with the optimal profit of CLSC. (4) Furthermore, our results underscore the importance of government recycling subsidies, remanufacturing cost savings, and the positive impact achieved by reducing recycling difficulties and enhancing customer awareness of green consumption.

Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil

For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multi-objective optimization algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence-based EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in real-world design optimization problems.

Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.

Parameter estimation of the soil water retention curve model with Jaya algorithm

In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms.