Transportation planning in a multi-stage supply chain must reconcile shipment continuity with economic and environmental performance. We develop a three-stage mixed-integer model using representatives of interval-valued trapezoidal fuzzy assessments. Two objectives sum stagewise actual-to-reference transportation and damage cost ratios; the others measure pollution and activated-link time. Prescribed transit times determine the temporal coefficient adjustments. Componentwise signed-distance ranking defines the numerical representatives. Positive denominator bounds support an exact bilinear formulation of the selected ratios, and goal programming determines a compromise. We establish a conditional Pareto-selection result and show that linear, normalized hyperbolic and parabolic memberships share the same minimizers under equal deviation weights. Feasible payoff plans determine the aspiration anchors. Two fully specified networks illustrate the formulation, while twelve additional synthetic instances vary size and capacity. For the two illustrative networks, both compromise-selection stages meet the stated numerical optimality tolerance. Expenditure benchmarks minimize pollution among cost-optimal plans, providing a consistent economic and environmental comparison. Comparisons with monetary objectives and exact deliveries show how reference policies and excess shipment affect the selected compromise. The model addresses static planning with prescribed temporal coefficients; its optimization guarantees concern the selected representatives.
In this paper, we develop a comprehensive analytical framework for a fractional-order fluid queueing system with priority-based retrial arrivals, multiple exponential vacations, N-policy server activation and Poisson-driven catastrophic resets. This model integrates Caputo fractional derivatives of order α∈ (0,1) with respect to the buffer level to capture memory-dependent fluid accumulation, while incorporating a two-class priority discipline ( Class 1≻Class 2≻Retrial ) and orbit-based retrial mechanisms. Further, using the supplementary variable method combined with Laplace-generating function techniques, we derive the steady-state Laplace transform of the buffer content distribution, along with explicit expressions for the mean buffer occupancy, variance, tail asymptotics and transient dynamics governed by Mittag-Leffler relaxation kernels. Moreover, the key theoretical contributions of this work include: (i) a closed-form characterization of the power-law tail decay ℙ{C > w}∼ w^-α induced by fractional spatial memory; (ii) a rigorous sensitivity analysis demonstrating that increasing α strictly reduces the expected buffer content and convergence time; (iii) optimization formulas for the N-policy threshold N^* and buffer capacity K^* , balancing holding costs against overflow penalties under fractional-catastrophic dynamics and (iv) a unified analytical framework that recovers classical queueing-fluid models as special limiting cases. In addition, extensive numerical experiments, supported by figures and summary tables generated using MATLAB R2024a, validate the theoretical findings and reveal that fractional memory amplifies tail risks, prolongs transient convergence and necessitates larger safety margins for quality-of-service guarantees. Finally, the proposed model provides practical insights for the design and operation of energy-aware cloud computing centers, self-similar communication networks, resilient manufacturing systems and patient-flow management in healthcare operations, where long-range dependence and sudden disruptions coexist.
Blockchain technologies are perceived as groundbreaking solutions that effectively support a wide array of organizational domains. One such domain that remains profoundly unexplored is the ability of supply chains to self-organize. This paper aims to explore the role of blockchain in enhancing the capability of supply chains to self-organize. Using the tenets of Complex Adaptive Systems (CAS) theory, we describe how the main features of blockchain, smart contracts, and consensus mechanisms can affect the way supply chains self-organize. The study employs a narrative literature review approach, focusing particularly on integrating the constructs of blockchain and self-organization within the supply chain framework. To do that, we followed the IMRaD approach (Introduction, Methods, Results, and Discussion). The results indicate that blockchain technologies have the potential to enable supply chains to self-organize. Although the application of blockchain in the supply chain context is still in its infancy, the study shows that there are numerous opportunities to improve internal processes, enabling the supply chain to become self-organized. The potential areas of application include heterogeneous agents and interactions, which can be supported by a distributed ledger, decentralization, and autonomy, enhanced by blockchain decentralization and control by influence. Moreover, the study advances CAS theory from abstract organizational concepts into concrete operational reality. Rather than merely digitizing traditional centralized structures, blockchain introduces “control by influence”, an algorithmically mediated coordination model operating in the middle ground between rigid top-down hierarchy and complete, unguided autonomy. By redistributing custody, ownership, and data access rights across network nodes, blockchain fundamentally alters governance logic. Another key theoretical and managerial finding of this study lies in how spontaneity functions across different agent compositions. While strict consensus rules maximize deterministic efficiency for fully digital agents, they also strip away contextual adaptability. Conversely, consensus mechanisms enhance spontaneity when human-inclusive agents remain in the loop. Managers need therefore to strategically delineate where full automation creates value and where human involvement is necessary to maintain strategic flexibility and relationship management.
Micro, small and medium-sized enterprises (MSMEs) experiencing competition in the manufacturing sector have considerable difficulty in keeping up product quality while at the same time managing process losses. The present study looks at reducing defects in a flexographic label-printing MSME by using a structured Six Sigma DMAIC improvement approach. Its objective is to find the main causes of product rejection, determine the reasons behind these causes, and set up specific improvement actions which can enhance process quality and operational performance. The production and defect data from the label-printing process were systematically analysed in order to identify the critical defects, prioritise the underlying causes and put into effect corrective actions. It was found that three defects: misregistration, inconsistent ink and cutting out were responsible for about 84
This paper presents the new Poisson-weighted exponential minification integer-valued autoregressive (NPWE-MINAR) process, motivated by the need to overcome the zero-absorbing property of conventional binomial and negative binomial thinning operators in discrete minification models and to provide greater flexibility for modeling overdispersed count time series. Various stochastic properties, particularly those related to conditional stochastic measures such as transition probabilities, the conditional probability generating function, conditional mean and variance, and the autocorrelation function, are analyzed. Two estimation methods are explored, and the accuracy of the resulting estimates is evaluated through simulation study. The practical usefulness of the NPWE-MINAR model was demonstrated through the analysis of a time series dataset, where it exhibited either superior or comparable performance to the conventional INAR model.
