Lot streaming strategy enables to divide jobs into smaller lots and scheduling them in order to diminish production time and deliver products more quickly in multi-stage manufacturing systems. Production leveling (heijunka) indicates determination of the size and order of sublots belonging to products for a time period and it ensures to obtain a production pattern relevant to the addressed period. In the Just In Time production environment, it is aimed to reach a balanced production schedule eliminating wastes, increasing flexibility, and minimizing workload differences among workstations by means of this concept. In this paper, it has been aimed to determine an optimal production pattern for each of the unrelated parallel machines under a production leveling based lot streaming strategy by considering also sequence-independent setup times, machine eligibility, stage skipping, and work shift constraints. In this context, initially, a mixed integer linear programming model including all constraints of the addressed hybrid flowshop scheduling problem has been developed and a numerical example has been presented for a better explanation. Since it is not possible to obtain a solution in a reasonable time as the problem size increases for this problem, four genetic algorithm based meta-heuristic approaches differ according to encoding and decoding structures have been proposed. Some significant parameters utilized in these meta-heuristic approaches have been calibrated through the Taguchi method for small, medium, and large-sized instances separately. Finally, the experimental analysis results obtained using randomly generated test instances have been presented to evaluate the performance of the mathematical model and the proposed meta-heuristic approaches.
This study develops a two-echelon green supply chain model with pre-sales comprising a retailer and a manufacturer, aiming to investigate how customer demand is influenced by regular-sale commission rate, advertising effort, green level, and lead time. The goal of this study is to determine the best choices that maximize individual and total supply chain profits under centralized, decentralized, profit-sharing, and cost-sharing contracts. These choices include commission rates for regular sales, lead times, green investment levels, and promotional initiatives. A Stackelberg game-theoretic technique is adapted to model leader–follower interactions within the supply chain. Numerical simulations and scenario comparisons are used to validate the model and assess the impact of key variables. Findings reveal that cost-sharing and profit-sharing significantly improve overall supply chain profitability, with centralized decision-making yielding the most efficient outcomes. Additionally, shorter lead times further improve green supply chain efficiency, especially under larger pre-sale scales, which helps mitigate fairness concerns among channel members and ultimately increases product sales and market penetration. The model offers valuable tips for both firms and policymakers seeking sustainable supply chain practices. Figure 1 displayed a graphical abstract.
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
