This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.

This paper deals with Flow-shop Sequence-Dependent Group Scheduling and worker assignment problem. Flow-shop allows the process of a set of families of products applying the group technology concept to reduce setup costs, lead times, and work-in-process inventory costs. The worker assignment problem deals with assigning workers to workstations considering their different abilities and learning effect. The proposed model in this paper considers different objectives. The decision problems in this cellular manufacturing system are the jobs scheduling within of own group, the group scheduling and the workers assignment to the machines. The aim of this paper is to consider a more realistic profile of heterogeneous workers introducing the learning effect in the joint group scheduling and workers assignment problem. A mathematical model and an evolutionary procedure has been developed to solve this problem. A benchmark of test cases having different numbers of machines, groups, jobs, worker skills and learning index, has been taken into account to compare the efficiency of the proposed algorithm with two well known procedures.

The industry has expressed significant concern regarding the issue of parallel machines and the influence of learning and deterioration. This research investigates non-identical parallel machine scheduling, taking into account the simultaneous consideration of learning effects, deterioration, and past-sequence-dependent setup times. Due to the existence of uncertain parameters in real-world scenarios, the processing times and due dates are assumed to be triangular fuzzy numbers. A fuzzy nonlinear mathematical model with two objective functions is presented and solved using the fuzzy Chance Constraint Programming approach. The two objectives are the summation of earliness and tardiness, as well as makespan. To achieve an efficient near-optimal Pareto front for the problem, a hybrid NSGA-II and VNS multi-objective meta-heuristic is proposed and the results are discussed. Finally, the augmented ε-constraint method is utilized to address issues with small dimensions. The computational analysis demonstrates the effectiveness of this proposed algorithm in tackling problems, especially those with substantial dimensions.

In today’s world, due to rapid changes in the market, scheduling as one of the most fundamental issues of competitive production, plays a very important role in maintaining the competitive position and survival of manufacturing organizations, therefore, development of scheduling models in order to improve the timing criteria is of great importance. In this research, we put the development of a no-wait flow-shop scheduling model alongside with the effect of learning into consideration to minimize the cost of consumption of resources. Finding the correct sequence of two machines’ performance and optimized allocation of the resources for any performance on each machine, were considered as the main goal in this study. To solve the problem, metaheuristic genetic algorithms, particle swarm optimization, imperialist competitive algorithms, optimization of the whale and the League Champions algorithms, have been used. The statistical comparisons and also using of TOPSIS Multi-Criteria Decision Making method, indicate high level of efficiency of the League Champions algorithm with the utility weight of 0.9516.

Recently, learning effects have been studied as an interesting topic for scheduling problems, however, most researches have considered single or two-machine settings. Moreover, learning factor has been considered for job times instead of setup times and the same learning effect has been used for all machines. This paper studies the m-machine no-wait flowshop scheduling problem considering truncated learning effect in no-wait flowshop environment. In this problem, setup time is a function of job position in the sequence with a learning truncation parameter and each machine has its own learning effect. In this paper, a mixed integer linear programming is proposed for the problem to solve such problem. This problem is NP-hard so an improved genetic algorithm (GA) and a simulated annealing (SA) algorithm are developed to find near optimal solutions. The accuracy and efficiency of the proposed procedures are tested against different criteria on various instances. Numerical experiments approve that SA outperforms in most instances.

Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.