This study addresses the multi-crane scheduling problem with temporal constraints (MCSPTC) arising in steelmaking shops, where multiple rail-guided overhead cranes transport materials on shared tracks and must strictly avoid interference. The problem requires jointly determining task assignment and sequencing decisions while satisfying temporal constraints on task start times, including time windows and precedence relations. In practice, manufacturing execution system data are updated frequently, requiring high quality solutions to be generated repeatedly within short computation times. However, existing heuristics lack quality guarantees, while exact models do not scale to industrial-size instances within practical time limits. To tackle these challenges, we formulate the MCSPTC as a sequential decision process and propose a Monte Carlo tree search (MCTS)–based solution framework. The proposed approach performs look-ahead planning at each decision point using a schedule construction mechanism. To improve search efficiency, we develop a feasibility-based action pruning strategy that eliminates actions inevitably leading to infeasible schedules prior to node expansion. We further tailor the value backup and final action selection rules to account for the tight temporal feasibility and the varying size of the feasible action space induced by temporal constraints. Our numerical results suggest that the proposed MCTS framework yields better solutions than existing heuristics within short computation times. In particular, the proposed method improves upon the heuristic baselines by approximately 5
A fair distribution of profits among primary partners is crucial to promote long-term relationships in global supply chains. In this paper, a fair decision-making is studied in a multi-tier supply chain under propagated disruptions. Instead of a common game-theoretic framework, the fair optimization theory approach is applied to determine an equitably efficient distribution of expected profits among primary partners of a supply chain. A novel stochastic mixed integer program is developed. The fair profit distribution is optimized along with optimal selection of recovery supply portfolios and coordination of production across the supply chain for each disruption scenario to mitigate the impact of the ripple effect. In the supply chain a collaborative partnership is applied to ensure that the expected service level of manufacturer is not less than the service level of each supplier. This study’s contribution and novelty lie in the development of a computationally efficient optimization model for a fair allocation of expected profits in multi-tier supply chains under the ripple effect. The proposed fair optimization approach integrates a multi-tier structure of supply chain, propagated disruptions across the supply chain, recovery portfolio decisions, and the collaborative service requirements. The computational findings indicate that if manufacturer’s objective is to maximize its individual profit, then the associated profits of all primary suppliers may converge to their respective maxima as well as to the fair profit distribution. Moreover, the fair profit distribution under collaborative partnership may ensure coordinated production across the supply chain. Overall, this study demonstrates that supply chain fairness can be a driver of supply chain resiliency. Both, a better coordination of the manufacturer and primary suppliers production, and an efficient collaboration with backup suppliers may improve the supply chain resilience.
In today's volatile manufacturing landscape, the need for resilience challenges the lean paradigm's emphasis on minimal inventory. This study investigates this tension by proposing a discrete-event simulation (DES) framework that integrates value stream mapping (VSM) to quantitatively assess how lean redesigns affect the efficiency-resilience trade-off. The framework is applied to the seminal TWI Industries case study. A detailed DES model, developed using ARENA simulation software, compares a production system's current state with a VSM-guided lean future state under multiple disruption scenarios, including supplier shortages and subcontractor failures. Results confirm substantial efficiency gains from lean implementation, with a 33
This study develops a multi-scenario coordinated scheduling framework for green port-integrated energy systems that extends capacity-based planning by explicitly incorporating renewable output variability. The study examines how renewable energy integration costs in port environments are affected by output variability, in addition to average generation levels. Based on a mixed-integer linear programming model, the framework integrates conventional generators, renewable sources, energy storage systems, and grid interactions. A scenario taxonomy is established across load intensity, solar irradiance, and wind speed to evaluate multiple scenario combinations. Five experiments investigate baseline operational economics, renewable variability impacts, load intensity effects, carbon pricing mechanisms, and energy storage coordination. The results indicate that renewable energy integration costs are affected more by output variability than by average generation levels; that the relative importance of the economic and reliability functions of energy storage changes as load intensity increases; and that storage performance depends on matching capacity-to-power ratios to port-specific load fluctuation patterns. The study decomposes the value of energy storage into economic value from peak-valley arbitrage and reliability value from avoided load shedding. It also develops a load–storage configuration principle showing that performance depends on aligning capacity-to-power ratios with port-specific load dynamics rather than increasing capacity alone. The findings suggest that ports should consider both the quantity and variability of energy supply and use storage to balance cost, reliability, and environmental objectives under changing market conditions.
Digital logistics environments increasingly require human-centric training approaches that support procedural reliability and operational performance. Virtual Reality (VR) has been adopted for immersive training in such contexts, yet evidence regarding its relative effectiveness compared with conventional instructional methods remains mixed. This study presents a controlled experimental comparison of VR-based and text-based instruction for manual order picking tasks, integrating objective performance measures with structured self-reported evaluations. Results indicated no statistically significant differences between training conditions in task completion time or error rates. Participants in the VR condition reported statistically higher ratings of confidence, perceived effectiveness, and perceived applicability to real-world operational settings. At a ten-day follow-up, perceived knowledge retention remained high and comparable across conditions, while expectations of future performance improvement were statistically higher in the VR group. This pattern suggests that the benefits of VR may have been expressed more strongly through perceived engagement, spatial awareness, and user experience than through immediate objective performance differences. VR may be positioned as a complementary instructional modality associated with higher perceived competence and readiness within digital intralogistics environments, rather than as a replacement for conventional instruction. Despite limitations related to sample composition and reliance on self-reported measures, the study provides controlled empirical evidence on instructional modality differences in manual order picking training.
