Determining optimal disposal and recovery strategies of discarded appliances under extended producer responsibility

Background. Periodized resistance training is a popular method of increasing strength and physical fitness. However, the high intensity involved in these programs often leads to significant fatigue, so optimal recovery becomes essential to achieving desired results and reducing the risk of injury. The right recovery strategy can help athletes and individuals involved in this training program balance training intensity and the necessary recovery. Study objectives. This study aims to systematically examine the various recovery strategies applied in the periodic resistance training program. Thus, this study seeks to identify the most effective recovery method to support optimal recovery and maximum performance. Materials and methods. This study uses a systematic review method by searching for relevant literature from leading scientific databases such as PubMed, Scopus, and Google Scholar. Inclusion criteria include studies examining recovery strategies in the context of periodic resistance training published in the last ten years. The study selection process is carried out in stages, starting from the initial search, abstract screening, and full-text assessment. Data from studies that met the criteria were extracted and analyzed qualitatively to identify key themes and consistent findings. Results. The analysis of selected studies found that some recovery strategies had different effectiveness depending on the type and intensity of exercise and individual characteristics. Some of the recovery strategies often mentioned include active recovery, compressions, hydrotherapy, proper nutrition, and relaxation techniques such as massage and meditation. Studies show that combinations of multiple recovery methods tend to be more effective than using a single process. For example, active recovery and proper nutrition consistently show improvements in muscle recovery and reduced fatigue. Conclusion. Optimal recovery in periodic resistance training programs requires an integrated approach tailored to individual needs. Combining various recovery strategies, including active recovery, proper nutrition, and relaxation techniques, improves muscle recovery and reduces fatigue. This study provides practical guidance for coaches and individuals involved in periodic resistance training to implement effective recovery strategies. Further studies are needed to explore the specific mechanisms of the various recovery strategies and to develop more detailed guidelines according to individual variations.

Determining optimal disassembly and recovery strategies

The introduction of extended producer responsibility makes original equipment manufacturers formally responsible for take-back, recovery and reuse of discarded products. One of their key problems is to determine to what extent return products must be disassembled and which recovery and disposal options should be applied. On a tactical management level, this involves anticipating problems like meeting (legislative) recovery targets, limited secondary end markets and investments in recycling infrastructure. In this paper, a comprehensive model is presented, which determines an optimal product recovery and disposal strategy for one product type. The objective function takes into account technical, commercial and ecological criteria as well as uncertainty on these criteria due to lack of information, in particular regarding the quality level. Optimization is done on overall net profit and occurs using a two-phased DPalgorithm. The applicability of the model is shown in a case study.

PurposeAn important consideration in the recovery strategies and performance of any given returned product is to make appropriate decisions for post-use. This paper aims to examine how the small and medium manufacturing enterprises can utilise a model-driven collaborative decision support system to evaluate product recovery strategies and performance.Design/methodology/approachAn optimisation model using a genetic algorithm (GA) approach is developed to assess product recovery plans for any returned products based on the decisions of component reuse, remanufacture and recycle potentials. The model evaluates the key decisions of cost, time, quality and waste, and proposes an optimal recovery plan for manufacturer. A case study was also conducted using the proposed model to evaluate and examine different air compressor piston-types with recovery strategies.FindingsAssessing a product recovery plan for any product is a challenge to the manufacturer due to higher operating costs associated with recovery. The nature of this challenge is complex. In this study, the results indicate that a developed optimisation model using a GA can assess the utilisation value of used products by considering suitable recovery strategies for the components and/or parts to be appropriately reused, remanufactured and recycled upon return.Research limitations/implicationsThe developed model assesses utilisation values of returns by considering both key decisions associated with returned products, and aspects of complexity of operational processes.Originality/valueThis research contributes to the practical understanding of product recovery strategies and extended producer responsibility using a case study. Also, the significance of this research is to provide a simple method of proposing an optimal recovery plan for any given returned product within a decision support system. Another innovation of the developed model is that an optimal recovery plan considers the trade-off decisions of cost, time, quality and waste aspects.

Water distribution systems (WDSs) need to be resilient against seismic hazards to ensure rapid recovery of the community following an earthquake. Topology-based resilience metrics are often used to determine the system-level performance of WDSs. However, existing topology-based resilience metrics are unable to estimate seismic performance of WDSs accurately because they do not account for the vulnerability of pipelines in the metrics. This study tailored an existing topological metric and developed a new edge-betweenness-based topological metric for evaluating the seismic resilience of a complex water distribution network. System-level performance of WDSs is compared using four performance measures including minimum cut set (MCS)–based system reliability, topological resilience metric (TRM), modified TRM, and the newly developed edge-betweenness-based TRM. These metrics were applied for four WDSs (i.e., Anytown, New York Tunnel, Jilin, and Bellingham WDSs) with unique characteristics to validate their effectiveness in estimating the seismic performance of WDSs against seismic hazards. The outcomes of these applications show that the proposed TRM can be used to determine pipelines’ seismic performance and functionality after an earthquake with an acceptable accuracy compared with existing approaches. While the topology-based resilience analysis provides information about system-level functionality, it is also vital to determine an optimal recovery sequence for damaged WDSs to maximize the functionality during the recovery process. Therefore, an easy-to-use recovery strategy is proposed to determine the optimal recovery sequence based on a repair index. The optimal recovery strategy was tested for the recovery procedure for the damaged Anytown WDS due to an earthquake, and outcomes show the system functionality is restored quickest using the proposed optimal recovery strategy.

New European government policies aim at the closure of material flows as part of integrated chain management (ICM). One of the main implementation instruments is extended producer responsibility, which makes original equipment manufacturers (OEMs) formally responsible for take-back, recovery, and reuse of discarded products. One of the key problems for OEMs is to determine a recovery strategy, i.e., determine to what extent return products must be disassembled and which recovery and disposal (RD) options should be applied. On a tactical management level, this involves anticipation of problems such as meeting legislation, limited volumes of secondary end markets, bad quality of return products, and facility investments in recycling infrastructure. In this paper, a model is presented that can be used to determine a recovery strategy for multiple-type consumer products. The objective function incorporates technical, ecological, and commercial decision criteria and optimization occurs using a two-level optimization procedure. First, a set of potential product recovery and disposal (PRD) strategies is generated for each separate product type. Secondly, optimal PRD strategies are assigned to the products within a coherent multiproduct or product group policy. The aim is to find an optimal balance between maximizing net profit and meeting constraints like recovery targets, limited market volumes, and processing capacities. A TV case is worked out to illustrate the working of the model. Also, the managerial use of the model is discussed in view of establishing an economically and ecologically sound base for achieving ICM.

The current analysis of cascading failures in command and control networks pays little attention to their roles and mechanisms, resulting in challenges in quantifying survivability evaluation metrics and limiting practical application. To address these issues, this paper designs a command and control network model with a recovery strategy to improve the scientific evaluation of critical nodes and enhance the reliability of subsequent cascading failure simulations. Two capacity parameters are introduced to analyze the nonlinear behavior between network node load and capacity, and an optimal recovery strategy is proposed. This strategy prioritizes the recovery of critical nodes, thereby minimizing the overall probability of network failure. Simulations were conducted under both random failure and deliberate attack scenarios, comparing the proposed strategy with random recovery and betweenness-priority recovery strategies to identify the optimal recovery approach. The experiments showed that the optimal recovery strategy significantly enhanced the network’s survivability and recovery efficiency, allowing for the restoration of basic network functions in the shortest possible time and reducing the impact of cascading failures. By integrating the operability and uncertainty of real-world command and control networks, this method improved the network’s recovery capability and overall stability in the face of cascading failures through scientific evaluation and strategy optimization.

Extended producer responsibility (EPR) embodies the notion that producers should be made physically or financially responsible for the environmental impacts their products have at the end of product life. The EPR concept has taken hold in Europe and is garnering wide interest in the United States, where a variant known as “shared product responsibility” or “product stewardship” is usually the preferred approach. There are several policy instruments that are consistent with EPR—product take-back mandates, advance disposal fees, deposit-refunds, recycled content standards, and more. The EPR concept itself, however, provides little guidance about which of these instruments might be appropriate under particular conditions and for particular products. Moreover, while the EPR goal is usually focused on end-of-life environmental impacts, in the United States, at least, the goal seems to have widened to include environmental impacts throughout the product life-cycle. And even a focus on end-of-life impacts leaves the question of whether EPR is intended to deal with waste volumes, the toxic constituents of waste, the method of waste disposal, or a combination of these things. In this paper, I address three main topics: appropriate goals for EPR; conditions under which EPR should be preferred over alternative non-EPR policy instruments; and specific policy instruments that are both cost-effective and consistent with EPR principles. In the discussion of the second and third topics, I focus on the issue of “design for environment.” I develop four policy “maxims” that should guide EPR policymaking. I then apply those maxims to a brief case study of electronic and electrical equipment waste.

This paper examines the recovery of a three-level manufacturing supply chain under supply and demand disruptions. The paper proposes new combined recovery strategies, which aim to cope with interruption by adjusting the supply chain structure and material flows. This study integrates both supply chain performance and supply chain capability dimensions. We develop a bi-criteria mixed integer linear programming model with profit and resilience maximization as the objective. The model combines supply-side supply expansion, manufacturer capacity impairment, and demand regulation on the demand side. In a numerical example, we find that a “reciprocal disruption overlay” occurs when supply and demand disruptions, but the supply chain still loses some profit. The results suggest that the combined recovery strategies reduce profit loss and increase supply chain resilience. Furthermore, the strategies are also the optimal recovery strategies under unilateral disruptions. This model facilitates the coordination of a disrupted supply chain and can help managers decide on the best recovery plan.

Optimal recovery strategies for manufacturing systems

The design of distributed control plane is an essential part of SDN. While there is an urgent need for verifying the control plane, little, however, is known about how to validate that the control plane offers assurable performance, especially across various failures. Such validation is hard due to two fundamental challenges. First, the number of potential failure scenarios could be exponential or even non-enumerable. Second, it is still an open problem to model the performance change when the control plane employs different failure recovery strategies. In this paper, we first characterize the validation of the distributed control plane as a robust optimization problem and further propose a robust validation framework to verify whether a control plane provides assurable performance across various failure scenarios and multiple failure recovery strategies. Then, we prove that identifying an optimal recovery strategy is NP-hard after developing an optimization model of failure recovery. Accordingly, we design two efficient failure recovery strategies, which can well approximate the optimal strategy and further exhibit good performance against potential failures. Furthermore, we design the capacity augmentation scheme when the control plane fails to accommodate the worst failure scenario even with the optimal failure recovery strategy. We have conducted extensive evaluations based on an SDN test bed and large-scale simulations over real network topologies. The evaluation results show the efficiency and effectiveness of the proposed validation framework.

Environmental accountability and protection has gained traction in the recent past. Sustainable waste management has become the point of interest for many stakeholders. Different jurisdictions have developed regulations on waste management to safeguard the planet and future generations. Extended Producer Responsibility (EPR) has emerged as the center of policy debate around the world, as it is considered a policy tool for promoting circular economy (CE) practices that transfer post-consumer waste management obligations from governments to producers. EPR ensures producers and manufacturers take responsibility for the physical end-of-life products they place on the market. The gazettement of the Sustainable Waste Management EPR Regulations, 2024, in Kenya, marked a significant regulatory milestone, mandating producer registration, take-back schemes, eco-design, and financial contributions for waste recovery and management. This review focused on the EPR framework and its implementation by synthesizing empirical studies, policy reports, and global best practices, and evaluating their implications on environmental performance, economic outcomes, and institutional capacity. It analyzed the alignment of Kenya’s EPR framework with international benchmarks, compliance levels, product design innovation, recycling rates, job creation, and financial sustainability, focusing on a number of key waste actors at policy and implementation levels. The literature consisted of 551 articles and reports extracted from key academic databases: Web of Science, Scopus, Springer, and grey sources. Boolean logic and keyword combinations, systematic screening, and content analysis was used. The PRISMA model was applied. The findings hint at limited downstream waste management effectiveness, pointing to opportunities such as enhanced waste collection and diversion, recycling, and value recovery. The results also showed existence of the primary challenge of conflict between the EPR policy intent and implementation. While the policy is intended to shift the burden of waste management from public sector to private producers, the effort is undermined by resistance to change, lack of awareness, enforcement gaps, and legal disputes. Additionally, there is limited policy and enforcement framework, misalignment of responsibility between producers, manufacturers and importers on the management of end-of-life products upstream. Consequently, strong stakeholder involvement, clear sharing of responsibilities by producers, setting waste management targets for collection, recovery, and recycling, a progressive legal framework, public education and awareness, formalization of the informal sector, adoption of differentiated EPR fees, waste segregation at source, and targeted waste stream implementation is recommended.

Disposal strategies for municipal solid waste incineration residues

Does Extended Producer Responsibility Improve Eco-Innovation: An Empirical Study of Product Take-Back Programs

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.