Multiobjective Hybrid Genetic Algorithms for Manufacturing Scheduling: Part I Models and Algorithms

Production scheduling is a critical requirement in today’s production systems. The production scheduling problem focuses on the rational allocation of production resources, determining the processing order of production tasks and operations, satisfying production constraints arising from the production environment such as the number of machines and task duration, and achieving optimal production goals, such as completing all tasks in the fastest way possible. The Flexible Job Shop Scheduling (FJSP) problem is a classical production scheduling issue, an extension of the Job Shop Scheduling (JSP) problem. The JSP problem involves allocating a set of jobs to a set of machines, where each job consists of multiple operations that must adhere to a precedence order. Each machine can process only one operation at a time for each job, and the time required for each operation on the corresponding machine is predetermined. The challenge in the JSP problem is to determine an appropriate order for processing operations or assignments, based on the production objective. FJSP represents a more complex and flexible variant of this problem, where at least one operation of a task can be performed on a set of alternative machines. Given that FJSP problems are NP-hard, solutions tend to be complex, varied, and often have limited applicability. Many FJSP scenarios require targeted modeling. In this paper, I propose a modeling approach for the static scheduling problem of job shops, considering low flexibility and demonstrating its usability by comparing it with other scheduling approaches, such as those based on genetic algorithm modeling. The paper is organized as follows:The first part of this paper offers a brief introduction to the FJSP problem, including background, research motivation, and main contributions, the second part provides a literature review,including a brief review and introduction of recent solutions to the FJSP problem, the third part describes a specific modeling approach, the fourth part details the test experiments and results of the modeling approach and includes comparisons with some other approaches, the fifth part analyzes the results from the fourth part and discusses the advantages and disadvantages of the modeling approach relative to others, and the sixth part concludes with a summary of findings and recommendations.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem, as it is classified as NP-hard problem and therefore no deterministic algorithms can solve them in a reasonable amount of time. The main objective of solving this problem is to find suitable job sequences on machines to optimize the performance criteria. In this paper, a meta-heuristic approach for solving the job-shop scheduling problem (JSSP) is presented. This approach uses a hybrid genetic algorithm that is suggested by previous stuody, to generate the best solutions and then a simulated annealing algorithm to improve the quality and performance of the best solutions to produce the optimal/near-optimal solution. Ten benchmark problems adopted from the previous study are used to evaluate the performance of the proposed algorithm. The computational results validate the quality of the proposed algorithm; this is done by calculating the completion time Cmax

In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artificial Bee Colony algorithm, is proposed for a dynamic flexible job-shop scheduling (DFJSP) problem. This problem consists of n jobs that should be processed by m machines and the processing time of jobs deviates from estimated times. The objective is near-optimal scheduling after any change in tasks in order to minimise the maximal completion time (Makespan). In the proposed model, first, scheduling is done according to the estimated processing times and then re-scheduling is performed after determining the exact ones considering machine set-up. In order to evaluate the performance of the proposed model, some numerical experiments are designed in small, medium and large sizes in different levels of changes in processing times and statistical results illustrate the efficiency of the proposed algorithm.

This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and meta-heuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high-quality solutions (up to 85.94% optimum solutions was obtained). Also, the results were statistically justified by comparing with multi-rule multi-objective simulated annealing (MRMOSA), priority-based genetic approach (PriGA), two-phased genetic approach (2P-GA), and assignment genetic approach (MA-GA).  Â

Scheduling is to determine when and who to process the task in order to minimize the makespan, the cost, and the tardiness/earliness of jobs, etc. Evolutionary algorithms (EAs) are very promising approaches for the scheduling problems due to its dynamic characteristics, multiple contradicting objectives and highly nonlinear constraints. Brain storm optimization (BSO) algorithm and its variations are new emerging evolutionary algorithms. In this chapter, our focus will be on BSOs for the flexible job shop scheduling problems (FJSP). First, FJSP will be formulated as a combinatorial optimization problem. Second, approaches for FJSP will be summarized briefly. Third, BSOs will be introduced and the key issues in the application of BSOs for FJSP will be emphasized. Fourth, four BSOs will be developed to solve FJSP and the details designed for FJSP will be illustrated. Their strength and weakness will be discussed as well. Finally, a group of experiments will be conducted to compare the four BSO algorithms. The results will be analyzed statistically. Finally, a conclusion will be summarized.

Production scheduling problems are typically combinational optimization problems named bases on the processing routes of jobs on different machines. In this paper, the flexible job shop scheduling problem aimed to minimize the maximum completion times of operations or makespan is considered. To solve such an NP-hard problem, variable neighborhood search (VNS) based on genetic algorithm is proposed to enhance the search ability and to balance the intensification and diversification. VNS algorithm has shown excellent capability of local search with systematic neighborhood search structures. External library is improved to save the optimal or near optimal solutions during the iterative process, and when the objective value of the optimal solutions are the same, the scheduling Gantt charts need to be considered. To evaluate the performance of our proposed algorithm, benchmark instances in different sizes are optimized. Consequently, the computational results and comparisons illustrate that the proposed algorithm is efficiency and effectiveness.

Firstly, aimed at the existing problem of reconfigurable assembly line (RAL) balancing, the characteristics of RAL are analyzed and the special requirements of balancing are studied. Secondly, the mathematical model is proposed based on two factors, which are integrated including the cycle time and the assembly line efficiency. Then a new hybrid genetic algorithm is developed for finding optimal solution of the problems. Finally, example verification is carried out, and which indicates the validity of the proposed algorithm.

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

2D Bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem. Multiobjective versions of this well-known industrial engineering problem can occur frequently in real world application. Recently, Hybrid Evolutionary Algorithms have appear as a new area of research with their ability to combine alternative heuristics and local search mechanisms together for higher quality solutions. In this study, we propose a set of novel multiobjective hybrid genetic and memetic algorithms that make use of the state-of-the-art metaheuristics and local search techniques for minimizing the number of bins while also maintaining the load balance. We analyze the optimization time and the resulting solution quality of the proposed algorithms on an offline 2DBPP benchmark problem set with 500 instances. Using these results of exhaustive experiments, we conclude that the proposed hybrid algorithms are robust with their ability to obtain a high percentage of the optimal solutions.

With the growing emphasis on intelligent and sustainable manufacturing, the flexible job shop scheduling problem (FJSP) has emerged as a critical optimization challenge due to its capability to model complex and dynamic production environments. As an NP-hard problem, FJSP poses significantly greater complexity than the classical job shop scheduling problem, particularly in high-precision domains such as aerospace manufacturing, where traditional optimization methods often fall short. To overcome the limitations of the standard Whale Optimization Algorithm (WOA)–namely its continuous search paradigm and inadequate local search mechanisms–this paper proposes a genetic-operator-integrated whale optimization algorithm (GOI-WOA) tailored for discrete scheduling tasks. The GOI-WOA employs a two-layer discrete encoding strategy to represent operations and machine assignments, and introduces genetic operators, a critical-path-based neighborhood search, and a temporal-entropy-based local search to enhance solution diversity and local exploitation. Furthermore, an elite information-sharing mechanism is incorporated to facilitate the diffusion of high-quality solutions within the population. Comprehensive experiments conducted on standard benchmark instances demonstrate that GOI-WOA consistently outperforms baseline WOA and other state-of-the-art methods in terms of makespan minimization, convergence stability, and computational efficiency. These results validate the effectiveness and robustness of GOI-WOA as a competitive solution framework for complex manufacturing scheduling problems.

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access the jobs at various processing velocities depending on the product families. To satisfy the various jobs, the machines need to be set up as the numerous tools to conduct consecutive products. This study aims to propose a novel approach to address the TFT-LCD module assembly scheduling problem by simultaneously considering the following multiple and often conflicting objectives such as the makespan, the weighted number of tardy jobs, and the total machine setup time, subject to the constraints of product families, non-identical parallel machines, and sequence-dependent setup times. In particular, we developed a multiobjective hybrid genetic algorithm (MO-HGA) that hybridizes with the variable neighborhood descent (VND) algorithm as a local search and TOPSIS evaluation technique to derive the best compromised solution. To estimate the validity of the proposed MO-HGA, experiments based on empirical data were conducted to compare the results with conventional approaches. The results have shown the validity of this approach. This study concludes with a discussion of future research directions.