This paper examines the challenge of integrated production and distribution, aiming to deliver products to customers precisely on time. Customers, situated within the transportation network, have predefined requirements regarding demand volume and ti...

Today's competitive environment confronts employees with constant pressure to reduce costs and optimize the use of resources. In this regard, this paper aims to present a model of resource allocation, based on the satisfaction of user requirements fr...

The problem of job scheduling in parallel machine environments, where both processing times and setup times are characterized by stochastic variability, has been investigated with a focus on enhancing the efficiency of resource allocation in complex production systems. Job scheduling, as a critical component of operations research and systems engineering, plays a vital role in the optimization of large-scale, flexible manufacturing and service environments. In this study, a stochastic scheduling model has been formulated to minimize the maximum completion time (denoted as $Ct_{\textit{max}}$), under the simultaneous influence of probabilistic job durations and setup times associated with tool preparation. The problem has been addressed using two prominent metaheuristic algorithms: Genetic Algorithm (GA) and Simulated Annealing (SA). These methods were selected due to their demonstrated capacity to navigate large, non-deterministic search spaces efficiently and their adaptability to multi-constraint scheduling problems. A comparative analysis has been conducted by applying both algorithms under identical initial conditions, with algorithmic performance evaluated in terms of solution quality, computational efficiency, and robustness to input variability. The model incorporates key practical considerations, including randomized setup times which are often neglected in conventional deterministic scheduling models, thereby improving its relevance to real-world industrial settings. The formulation of the problem allows for additional constraints and objectives to be flexibly integrated in future research, including resource conflicts, machine eligibility constraints, and energy-aware scheduling. Empirical results suggest that while both algorithms are effective in deriving near-optimal schedules, notable differences exist in convergence behavior and sensitivity to parameter tuning. The findings offer critical insights into the comparative strengths of GA and SA in managing the stochastic nature of parallel machine scheduling problems. By advancing a robust metaheuristic framework that accounts for real-world uncertainties, this study contributes to the ongoing development of intelligent scheduling systems in systems engineering, manufacturing logistics, and automated production planning.

The problem with flexible job planning (FJSP) is a modification o f the classic job booking problem. This paper deals with the problem o f flexible job deployment and processing o f operations on one machine from a set o f alternative machines. The problem o f deploying flexible jobs in real time is one o f a group o f difficult NP problems (Non-deterministic polynomial time). The motive o f this paper is to show the application o f meta-heuristic algorithms on the example of flexible job schedules and present the appropriate method to future studies. To solve this problem, two meta-heuristic algorithms were used: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The aim o f the paper is to achieve the speed o f the conversion solution in a series o f iterations, minimizing the total time o f deployment of jobs on an alternative set of machines, as well as minimizing the total schedule time. The problem o f deploying flexible jobs has great application, and with this test we propose an algorithm for solving this problem.

Efficient prioritization of production jobs is a critical task in manufacturing systems, especially in environments with limited resources, multiple customer orders, and conflicting operational objectives. Production managers must determine which jobs should be processed first while considering several criteria such as due date urgency, profit margin, processing time, customer importance, and setup time. This decision- making problem can be effectively addressed using multi-criteria decision-making (MCDM) methods. In this paper, a hybrid SWARA–MULTIMOORA model is proposed for prioritizing production jobs. The SWARA (Step-wise Weight Assessment Ratio Analysis) method is applied to determine the relative importance of evaluation criteria based on expert judgment, while the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) method is used to rank production jobs. The proposed approach is illustrated through a case study involving five production orders in a manufacturing environment. The results demonstrate that the model provides a systematic, transparent, and practical decision-support tool for production managers, enabling more effective scheduling decisions and improved utilization of manufacturing resources. The proposed methodology can be easily integrated into existing production planning and scheduling systems.