The parallel batch-processing machine scheduling problem is widely present in industries such as manufacturing, service, and healthcare, and becomes more complex when incorporating flexible preventive maintenance (FPM). This paper presents a mixed-integer programming (MIP) model and a multi-objective artificial bee colony (MOABC) algorithm to tackle the unrelated parallel batch-processing machine scheduling problem with flexible preventive maintenance (UPBPM-FPM). The objective is to simultaneously minimize the makespan, earliness and tardiness, and total energy consumption, providing a comprehensive solution to optimize both scheduling efficiency and energy use while incorporating preventive maintenance considerations. The MOABC algorithm integrates three key innovations: (1) a novel processing power-feature information (PP-FI) heuristic to generate high-quality initial solutions, (2) a hybrid selection strategy combining the hypervolume index and roulette wheel approach to improve diversity and convergence, and (3) a set of random and goal-oriented neighborhood search methods to enhance Pareto frontier. Experimental results demonstrate that the MOABC algorithm outperforms three classical algorithms, NSGA-III, ABC, and PSO, in terms of convergence, diversity, and robustness of the Pareto solutions. This study provides a robust framework for energy-efficient scheduling in complex manufacturing environments.

In real-world markets, supply chain costs often fluctuate over time due to the contango and backwardation effects, making multi-period supply chain planning complex and critical. This paper presents a multi-period supply chain optimization model that explicitly incorporates these effects into cost forecasting and decision-making. A multi-period supply chain model is developed, considering the cost uncertainty introduced by contango and backwardation. An integrated polynomial regression fuzzy method is proposed to address this problem by predicting future fluctuations in purchasing, ordering, and logistics costs. A mixed-integer linear programming (MILP) model is formulated to minimize the total supply chain cost across multiple periods. Moreover, improving the hybrid genetic algorithm (IHGA) is proposed to solve this problem. The performance of the proposed IHGA is triggered by integrating trust region, quasi-Newton, and pattern search methods. Response Surface Methodology (RSM) determines the optimal parameter settings and hybridization structure. A real-world case study involving surgical instrument manufacturing companies validates the proposed approach. The results highlight optimal supplier selection and order allocations for each period, and performance comparisons reveal that the IHGA outperforms traditional algorithms in terms of cost efficiency, computational time, and convergence behavior.

The significance of project scheduling and sequencing has increased considerably in recent years, driven by the rising customer demand for highly personalized solutions. Companies have to consider multiple criteria while executing multiple projects simultaneously to meet the customer demands. Therefore, this study focuses on the multi-objective multi-project scheduling and sequencing problem (MP-SSP). A simulation-based mathematical model is developed and integrated with a multi-objective genetic algorithm. The objectives of this model are to minimize the project execution cost, project completion time and project lateness simultaneously while maximizing the resource utilization in the stochastic environment. Goal attainment programming is introduced in the simulation integrated multi-objective genetic algorithm (SIHMO-GA) to increase the effectiveness of the algorithm. Further, response surface methodology (RSM) has been used to find the optimum parameters of the proposed SIHMO-GA. The effectiveness of the proposed SIHMO-GA is evaluated through a real-world case study by comparing it with simulation-optimization approaches, namely the multi-objective genetic algorithm (MOGA) and goal attainment programming. Gap analysis indicates that the SIHMO-GA provides best trade off values of the above-mentioned conflicting objectives under a stochastic environment. This study supports practical scheduling and sequencing of multiple projects in a stochastic environment by generating solutions that maximize profit, enhance resource utilization, and ensure customer satisfaction through timely project delivery.