RETRACTED ARTICLE: Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system

Renewable energy policy and deployment of renewable energy technologies: The role of resource curse

Due to the increasing emission of greenhouse gases and global warming, the development of renewable energy has become very important. The availability of fossil fuels and the low cost of their extraction compared to renewable energy projects reduce the motivation of countries, especially countries that have abundant natural resources, to develop this technology. Renewable energy deployment has become crucial in response to rising greenhouse gas emissions and global warming. Policies supporting renewable energy play a significant role in this. This study examines the effect of such policies on the deployment of renewable energy technologies, considering the role of natural resources. Two groups of countries were analysed: 20 oil developed countries and 20 oil developing countries. Given the availability of data and the achievement of balanced panels to evaluate short-term and long-term relationships between variables, in current research Data from 2010 to 2020 was used, and various panel data estimators such as Feasible Generalized Least Squares and Generalized Method of Moments were employed. The Quantile estimator was also used to assess the accuracy of the results. The findings suggest that renewable energy policies consistently lead to increased deployment of renewable energy technologies, regardless of a country's group. Of course, this positive effect is different according to the level of development in countries. Due to the higher efficiency of renewable energy policy, developed oil countries have more capacity to support renewable energy projects than oil developing countries. The abundance of natural resources in oil developed countries did not negatively impact renewable energy capacity, but in oil developing countries, the "resource curse" hindered the development of installed renewable energy.

Production planning approaches: a review from green perspective

There is an increasing concern about incorporating green criteria into production planning approaches. Production planning models that ignore green parameters may generate outcomes that are unfriendly to the environment. The relevant literature has suggested a flourishing trend towards the integration of green parameters into production planning approaches. The earlier reviews have most commonly analyzed the green production planning approaches from an "energy efficiency" perspective. Literature on the integration of other green criteria is also available. However, such studies are rarely reviewed. Along with "energy efficiency," the study in hand reviews the production planning strategies from another green perspective which is "low-carbon emissions." The first objective of this study is to review the medium and short-term production planning approaches from the aforementioned green criteria and provide a classification scheme. Second, new research avenues are identified to facilitate researchers in incorporating green schemes in production planning approaches. This study explored various databases for articles published on green production planning approaches. Consequently, 84 articles published between 2011 and 2022 were considered for the review. This review pointed out that most of the studies on green production planning considered "energy efficiency" and studies on "carbon emissions" were overlooked. Furthermore, green concepts were mostly integrated into the short-term production planning level and comparatively few studies were found for the medium-term. This study will help researchers to analyze green production planning in terms of modeling approaches, objective functions, uncertainties, solution approaches, etc.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.

Development of an Improved Water Cycle Algorithm for Solving an Energy-Efficient Disassembly-Line Balancing Problem

Nowadays, there is a great deal of interest in the development of practical optimization models and intelligent solution algorithms for solving disassembly-line balancing problems. Based on the importance of energy efficiency of product disassembly and the trend for green remanufacturing, this paper develops a new optimization model for the energy-efficient disassembly-line balancing problem where the goal is to minimize the energy consumption generated during the disassembly-line operations. Since the proposed model is a complex optimization problem known as NP-hard, this study develops an improved metaheuristic algorithm based on the water cycle algorithm as a recently developed successful metaheuristic inspired by the natural water cycle phenomena of diversion, rainfall, confluence, and infiltration operations. A local search operator is added to the main algorithm to improve its performance. The proposed algorithm is validated by the exact solver and compared with other state-of-the-art and recent metaheuristic algorithms. A case study in a turbine reducer with different parameters is solved to show the applicability of this paper. Finally, our results confirm the high performance of the proposed improved water cycle algorithm and the efficiency of our sensitivity analyses during some sensitivity analyses.

Optimisation of Preventive Maintenance Regime Based on Failure Mode System Modelling Considering Reliability

Today, the ability to maintain a continuous complex system operation is viewed as a key attribute for ensuring uninterrupted revenue contribution and the survival of a business. Many industrial organisations have come to understand that by having an effective plan of maintenance, the efficiency and reliability of a system can be improved, while costs can be minimised and revenue-generating production can continue. The novelty of this paper is based on using failure modes and effects analysis (FMEA) to develop a holistic preventive maintenance schedule for a complete system. A system can be modelled as a series and parallel arrangement of subsystems and components, and failure of different components of the system can be determined from their life expectancy. The objective is to ensure continuity of production output while maintaining a high level of system reliability and minimising the total maintenance costs. The reliability of a complete system is analysed using the Weibull failure-time distribution. By using the exhaustive search optimisation method, the maintenance cost is minimised by determining the optimal replacement interval for each FMEA block, subject to system reliability. Based on the results obtained from a case study, it is shown that the approach in this paper can ensure the continuity of production output during maintenance activities, reduce system maintenance costs, and achieve maximum system reliability. This holistic approach can be applied to any form of complex system, and at every step of the process.

Repositioning and Optimal Re-Allocation of Empty Containers: A Review of Methods, Models, and Applications

Managing empty-container movements is one of the most challenging logistics problems in the shipping field. With the growth of global trade imbalance, the repositioning process has become necessary, immediately after emptying a container. The main contribution of this research paper is to enrich the most frequently used methods, models, and applications in the literature, for relaxing the empty-container-repositioning problem. The article presents practices that vary between organizational policies, technical solutions, and modelling applications. A review of optimization models has been used for comparisons, based on specified criteria, such as the time frame, inputs, outputs, scale of the project, and value. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was applied through the online database Web of Science (WOS). It gives a comprehensive description of all the relevant published documents. On the basis of conducting a brief systematic review, future research opportunities have been determined, considering the emerging phenomena in container transport chains.

Impact of Unreliable Subcontracting on Production and Maintenance Planning Considering Quality Decline

Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model that properly coordinates production, subcontracting, and maintenances strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. Such a set of characteristics has not been addressed before in the literature. A simulation–optimization approach was proposed to address such a stochastic model. A numerical case study was performed as an illustration of the approach and a comprehensive sensitivity analysis was performed to analyze the impact of several costs. Furthermore, the effect of the availability of the subcontractor and the producer was analyzed. The main finding of the study showed that the integrated model led to significant economic cost savings compared to other approaches that address such policies in isolation. The results also indicated that quality deterioration had a strong impact on the subcontracting rate and that the proposed joint control policy adequately coordinated these three key functions. The level of subcontracting participation was directly defined by its availability and the subcontracting cost.

Attracting Potential Customers in E-Commerce Environments: A Comparative Study of Metaheuristic Algorithms

Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget.

A fuzzy rule-based multi-criterion approach for a cooperative green supplier selection problem

Multi-criterion decision-making models are widely used in supplier selection problems. This study contributes to a green supplier selection problem considering the green manufacturing, green transportation, and green procurement. This study contributes to reverse logistics, eco-design, reusing, recycling, and remanufacturing with their high impact on the industries. In addition to the logistics costs and transportation costs, the carbon emissions are considered. With regard to the game theory, this paper uses a cooperative green supplier selection model. If transportation requirements of two or more companies are combined, it will help manufacturers to have less [Formula: see text] emissions with lower cost. After creating the optimization model to consider the uncertainty, this cooperative game theory model is established in a fuzzy environment. In this regard, a fuzzy rule-based (FRB) system is deployed and the set of fuzzy IF-THEN rules is considered. The proposed FRB model is contributed for the first time in the area of green supplier selection problem. Finally, some sensitivity analyses are conducted in a numerical example to evaluate the proposed model. With regard to the findings, although the cost of CO2 emission of horizontal cooperation is increased, the cost saving of companies is increased. It means our total cost is optimal in a logistic network using the cooperative game theory. The results also indicate that horizontal cooperation in logistic network causes less cost and benefits for each company.