ABSTRACT The utilization of multi-head packaging systems improves packages production processes, minimizing margins of error in their content. This paper proposes an exact enumerative algorithm for packaging indivisible products using multi-head batch weighers. The optimization algorithm and its translation into programming language are presented. Validation experiments involved calculating process variability measures and conducting an experimental analysis with real data. The results indicate that adjusting the label’s target weight value and considering the probability distribution of process data can significantly reduce variability. This approach minimizes overweight packages while enhancing productivity and overall process efficiency compared to random packaging methods. Additionally, it was observed that the number of hoppers in the weigher does not influence process variability when only a few hoppers are used during packaging. Consequently, machines with fewer hoppers can reduce both acquisition and operational costs. In summary, the proposed enumerative algorithm significantly improves operating conditions and ensures product savings.

ABSTRACT Industry 4.0 and the advancement in automation boosted the capacity of production industries. However, demand for mass customization birthed the need for flexible production lines. As such, human-workers’ finesse combined with cobots’ ability provides manufacturing flexibility and productivity. However, this integration introduces new cognitive demands, which, if unmanaged, can compromise operator well-being and system performance. This systematic review consolidates current approaches of assessing cognitive ergonomics in human-robot collaboration (HRC) and advances a novel four-factor taxonomy of variables influencing cognitive stress: Cobot Motion Variables (CMV), Cobot Attribute Variables (CAV), Collaborative Workplace Design (CWD), and Collaborative Task Design (CTD). By mapping these categories to established assessment methodologies physiological, subjective, and behavioral, we demonstrate how categories inform assessment measures due to variable characteristics, holding potential for robust study of future HRC workplaces eccentric to cognitive factors and cognitive stress standards. Features highlight task-pace, robot-speed, and task-complexity as leading factors of Cognitive Stress.

ABSTRACT This study presents key perspectives and challenges in relation to artificial intelligence in green production and industrial engineering. Numerous studies have examined the benefits of artificial intelligence for achieving operational efficiency and improved environmental performance; however, few studies have identified future perspectives and challenges. In this study, content, bibliographic and cluster analyses are conducted to extract and cluster attributes related to artificial intelligence in green production and challenges in industrial engineering from the Scopus database, and meta-analysis is used to validate the topics and refine the attributes and clusters. The entropy weighted method and fuzzy Delphi method are utilized to identify key challenges. Fuzzy decision-making trials and laboratory evaluations are performed to analyze perspectives that should be prioritized on the basis of cause-and-effect interrelationships. The results reveal that data management and analysis as well as waste and risk management are key future perspectives that must be addressed.

ABSTRACT This study addresses key challenges in modern two-echelon supply chains, including quality management, lead time, and environmental factors. A nonlinear multi-factor lead-time reduction function is developed to better reflect real-world conditions, which is unexplored in existing literature. In addition, the impact of inspection errors on inventory decisions remains unaddressed in fuzzy environments with multiple constraints, leading to inefficiencies in handling defective products. To bridge this gap, this study incorporates two types of errors in quality inspection and models all associated supply chain costs using type-2 trapezoidal fuzzy numbers, providing a comprehensive framework for managing uncertainty. The model also considers renewable energy sources and external carbon emission factors, addressing sustainability concerns in supply chain operations. The fmincon optimization technique evaluates the model under multi-factor and traditional lead-time reduction strategies to achieve best optimal solutions. Numerical results show improved cost efficiency under the best-of-two strategy for supply chain decision-makers.

ABSTRACT This paper presents a reference model for the Vehicle Routing Problem with Time Windows and Limited Resources (VRP-TR) imposed by fleet size and vehicle passenger capacity when transporting service teams (STs) to customers at the start of a time window and collecting them at its end. The proposed VRP-TR problem jointly addresses resource feasibility and routing optimization for transporting STs , allowing flexible pickup by different vehicles and prioritizing customer coverage before cost minimization. The adopted model first checked whether the contractor’s limited resources can meet all customer requests within the time horizon, then finds routes minimizing costs. Its hierarchical objective, capacity-driven feasibility, and declarative character enable adaptation to varying operational constraints, yielding solutions and flexibility beyond existing approaches. The model supports both planning service delivery for geographically dispersed customers and designing resource structures for service providers, including home healthcare and maintenance services. Experimental results demonstrate its applicability to real-scale scenarios.

ABSTRACT This study explores a two-period pricing and inventory control model, employing both competitive and cooperative policies with a reference price effect for two retailers. With increasing consumer demand for fresh products and the perishable nature of such items, effective pricing and inventory management are critical for maximizing profitability. The proposed model examines how retailers can determine optimal selling prices and replenishment times while minimizing holding, ordering, and deterioration costs. A game-theoretic approach is employed to derive equilibrium pricing and replenishment strategies, considering the reference price effect on consumer behavior. The results indicate that an increase in the deterioration rate reduces retailer profits by negatively impacting the demand function. Moreover, adjustments to the reference price directly influence the profitability of each retailer.

ABSTRACT The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of selecting an appropriate forecasting solutionbecomes a daunting task. This complexity arises due to the distinct features inherent to eachdataset. Research to tackle this issue has been performed since the eighties but recent development of demand forecasting has opened new perspectives. This research aims to enhance automatic forecasting model selection by proposing a novel architecture that acts as a double deep reinforcement learning agent, automatically selecting a forecasting model from the forecasting committee at the time of prediction. Moreover, a novel early-stopping approach based on average reward convergence has been introduced to expedite training time. To evaluate the model’s performance, an empirical study was conducted utilizing grocery sales datasets and snack demands datasets. The experimental results demonstrate the robustness of the proposed approach when compared to state-of-the-art methods.

ABSTRACT Amid growing emphasis on sustainability and customer-driven market, supply chain must balance environmental responsibility with efficiency. Present study develops a joint economic lot-sizing model integrating outsourcing, green technology investment, carbon emission regulations, and customization strategies in a two-level supply chain comprising a manufacturer and a retailer. The manufacturer produces fresh products and outsources defective repairs, while the retailer customizes part of the output to meet individual preferences. Both chain-partners invest in emission-reduction technologies to comply with carbon tax policies. Considering price-sensitive demand, the model optimizes customization cost, emission-reduction investment, selling price, and production cycle to maximize centralized profit. The novelty lies in the integrated treatment of outsourcing, carbon control, and customization under trade credit. Results suggest improving quality to reduce defectives, moderating customization, and setting optimal credit periods to enhance profitability.

ABSTRACT In recent times, sustainable supply chain management has been a prominent area of research due to the growing concern over carbon emissions and their environmental impact. There has been limited research on sustainable supply chain management that considers the three pillars of sustainability simultaneously, viz. economic, environmental, and social. This study addresses this gap and provides a multi-objective optimization framework for designing a sustainable, uncertain, closed-loop supply chain network while considering three sustainability dimensions. The maximum entropy method has been utilized to deal with uncertainties. Lagrangian relaxation, linear relaxation, and Monte Carlo simulation techniques have been used to solve the model for computational purposes. The results demonstrate that the proposed approach may help improve supply chain efficiency by balancing cost reduction, environmental responsibility, and social impact. This study contributes to the literature on sustainable supply chains by offering a comprehensive decision-making framework that simultaneously integrates the three sustainability pillars.