The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

In recent years, various issues such as industrial waste and emissions of greenhouse gases have led to serious environmental pollution. Industrial managers nowadays need to regard cutting carbon emissions as one of their principal responsibilities in relation to the environment, as industry is a major source of carbon emissions. Two prominent regulatory approaches to reducing carbon emissions from operations are the carbon tax and the cap-and-trade system. The existing literature on inventory studies has often considered the market-expanding effects of greening efforts. Nevertheless, a number of additional factors exert influence on greening efforts, with the cost reduction effect representing a critical one. This paper develops an inventory system in which each time a lot of items is received, a proportion of items are found to be of imperfect quality; to identify these, the retailer carries out a 100% inspection of goods received. Following this inspection, the saleable items are added to the inventory in the warehouse in batches of equal size, rather than one by one, and the retailer allows backordering to meet demand. Carbon emissions are incurred at every stage, including ordering, purchasing, repairing, transporting, and holding, so advanced green technology is employed to reduce them. Imperfect products can be sold to a second-hand market or sent to a repair shop. The model discussed in this paper calculates, for both options, the most cost-effective lot size for orders, shortage quantity, scale of green investment and number of batches.

This research focused on intervening in the optimization algorithm used by the Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) tool to analyze small-angle scattering (SAS) profiles using the Rigid-Body model. CREASE uses the genetic algorithm (GA) with dynamic adaptation as its optimization algorithm. The aim is to evaluate the performance of CREASE by replacing the GA with a Harmony Search (HS)-based metaheuristic, specifically the Nobel Global Harmony Search (NGHS), in the analysis of SAS profiles of low-concentration solutions vesicles-assembled amphiphilic macromolecules. Results showed that NGHS achieved similar accuracy to GA but with higher efficiency, achieving similar quality solutions with only one-sixth, and in some cases one-tenth, the number of fitness function evaluations used by GA. Besides, CREASE-NGHS achieved SAS profile analysis convergence with less than half the number of fitness function evaluations, saving computational resources and facilitating a more complete analysis. In addition, NGHS addressed some shortcomings of the GA optimization process and facilitated its use and adaptation to distinct types of samples for users with little experience in optimization.

Evolving market trends, characterized by an increasing demand for personalized products with short life cycles and variable demands, pose a significant challenge to the industry. One of the industry's strategies is to adopt robotic assembly systems to improve productivity and increase system flexibility. The widespread adoption of robots in assembly processes is evident; however, success is not guaranteed with implementation alone. Equally critical is addressing assembly planning and scheduling problems in robotic systems. To facilitate understanding, this review offers, in Section 2, a classification of robotic assembly systems, with an emphasis on a new layout termed the robotic matrix-structure assembly system. Section 3 classifies the planning and scheduling problems applied to the robotic assembly systems. In Section 4, we discuss the approaches and techniques used to formulate and solve the planning and programming challenges. Finally, statistical data are presented to illustrate current research trends and identify gaps for future research.

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

As an integral part of production planning control, order release management is critical to enhance the competitiveness and production efficiency of companies. Previous literature shows limited application of optimization-based models in assembly job shops, primarily due to the intricate nature of product structures and assembly operations. Therefore, based on the idea of the allocated clearing function (ACF) model, we introduce material flow constraints and complex assembly structure constraints during the assembly stage, proposing the assembly job shop allocated clearing function (AACF) model. The performance of the AACF model and the rule-based mechanisms in terms of cost and timing measures are compared through experiments containing 6 factors and 96 scenarios. The results show that the AACF model performs better in terms of cost management, service level and order due date deviation. In addition, a sensitivity analysis of the objective function parameters is performed to confirm the robustness of the AACF model. Finally, a case application in a real assembly shop illustrates the feasibility and validity of the proposed AACF model.

Assembly buildings, in the context of the low-carbon transformation of the construction industry, achieve superior outcomes in terms of carbon emission reduction, enhancement of building uniformity, and optimization of resource utilization as compared to traditional buildings. However, the supply chain for assembly building is marked by a significant susceptibility to risk and a need for timely fulfillment of requirements. This paper examines the risk of disruption and capacity limitations in the assembly building supply chain resulting from supply disruptions. It establishes a three-tier supply chain for assembly buildings, including primary component suppliers, backup suppliers, assembly manufacturers, and retailers. The study compares the optimal decision-making and coordination strategies of the supply chain members under centralized, decentralized, and joint agreements. The supply chain dual-source procurement decision coordination model is constructed by incorporating capacity constraints and analyzing the effects of supply disruption probability, repurchase coefficient, revenue sharing coefficient, cost, and other parameters on the expected profits of the supply chain members using arithmetic simulation. Research has indicated that when the likelihood of a disturbance occurring rises, the anticipated financial gain for the main provider decreases, while the predicted financial gain for the secondary supplier increases. The implementation of a collaborative agreement between the assembly maker and the parts backup provider would result in much greater anticipated profits compared to the decentralized decision-making approach. The impact of the revenue sharing coefficient on the predicted earnings of retailers and assembly manufacturers is more significant compared to the repurchase coefficient. The selection bias between NA and NB techniques under capacity constraints mostly arises from the assertiveness of the wholesale asking prices of inexpensive component suppliers, leading assembly manufacturers to increasingly prefer the NA option. This paper's research successfully achieves the contractual coordination of the assembly building supply chain, enhances the resilience of the assembly building supply chain, and promotes the long-term sustainable development of the assembly building supply chain.

With the organic integration of the Internet and remanufacturing industry, traditional manufacturers can recycle used products and sell products (including new and remanufactured products) through e-commerce retail platforms. A recycling and remanufacturing supply chain with three members (manufacturer, e-commerce retail platform, third-party recycler) is constructed in this paper. Manufacturer has remanufacturing capabilities, and the e-commerce retail platform can provide logistics service. In response to the organic integration of the Internet and remanufacturing industry, we mainly consider the pricing decisions of consumer preferences for online sales models and recycled products’ quality. Based on the impact of consumer used product recycling promotion activities on supply chain, a pricing game model was constructed for three recycling channels: manufacturer, e-commerce retail platform, and third-party recycler. Optimal pricing decision, logistics service level, used product recycling promotion intensity index, and recycling rate were obtained. Research has shown that consumer preferences can significantly improve supply chain logistics service level, pricing, market demand, and profits; Strengthening consumer awareness of remanufacturing used products and improving recycled products’ quality can not only lower consumer purchase prices and expand consumer demand, but also increase profits of supply chain members.

The stochastic contextual bandit problem, recognized for its effectiveness in navigating the classic exploration-exploitation dilemma through ongoing player-environment interactions, has found broad applications across various industries. This utility largely stems from the algorithms’ ability to accurately forecast reward functions and maintain an optimal balance between exploration and exploitation, contingent upon the precise selection and calibration of hyperparameters. However, the inherently dynamic and real-time nature of bandit environments significantly complicates hyperparameter tuning, rendering traditional offline methods inadequate. While specialized methods have been developed to overcome these challenges, they often face three primary issues: difficulty in adaptively learning hyperparameters in ever-changing environments, inability to simultaneously optimize multiple hyperparameters for complex models, and inefficiencies in data utilization and knowledge transfer from analogous tasks. To tackle these hurdles, this paper introduces an innovative transfer learning-based approach designed to harness past task knowledge for accelerated optimization and dynamically optimize multiple hyperparameters, making it well-suited for fluctuating environments. The method employs a dual Gaussian meta-model strategy—one for transfer learning and the other for assessing hyperparameters’ performance within the current task —enabling it to leverage insights from previous tasks while quickly adapting to new environmental changes. Furthermore, the framework’s meta-model-centric architecture enables simultaneous optimization of multiple hyperparameters. Experimental evaluations demonstrate that this approach markedly outperforms competing methods in scenarios with perturbations and exhibits superior performance in 70% of stationary cases while matching performance in the remaining 30%. This superiority in performance, coupled with its computational efficiency on par with existing alternatives, positions it as a superior and practical solution for optimizing hyperparameters in contextual bandit settings.

Increasing customer apprehensions regarding the security and nutritional value of agricultural goods are compelling governments and industries to implement traceable, transparent, and reputable logistics management systems. Blockchain-based agricultural logistics management systems guarantee the permanence of data once it is uploaded but cannot cope with the risk of data being falsified before uploading to the blockchain. In this work, we developed a collaborative game model between government bodies and agricultural enterprises based on the evolutionary game theory and explored the influencing factors of enterprises following the rules to share the real traceability information through numerical simulation using MATLAB. The findings show that government incentives and penalties promote positive behavior, and consumer and media supervision contribute to supply chain transparency, but firms tend to share truthful information only when it benefits them. This study builds upon existing research on the impact of social variables on both members' decision-making behavior. It highlights the positive roles of consumers and the media in the supervision of agricultural product traceability, which can help to raise public awareness of social responsibility and thus promote positive interaction in the market.