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The purpose of this paper is to investigate the multi-skilled workers assignment problem in complex assembly systems such as aircraft assembly lines. An adaptive binary particle swarm optimization (A-BPSO) algorithm is proposed, which is used to balance the workload of both assembly stations and processes and to minimize the human cost.

Firstly, a cycle time model considering the cooperation of multi-skilled workers is constructed. This model provides a quantitative description of the relationship between the cycle time and multi-skilled workers by means of revising the standard learning curve with the “Partition-And-Accumulate” method. Then, to improve the accuracy and stability of the current heuristic algorithms, an A-BPSO algorithm that suits for the discrete optimization problems is proposed to assign multi-skilled workers to assembly stations and processes based on modified sigmoid limiting function.

The proposed method has been successfully applied to a practical case, and the result justifies its advantage as well as adaptability to both theory and engineering application.

A novel cycle time model considering cooperation of multi-skilled workers is constructed so that the calculation results of cycle time are more accurate and closer to reality. An A-BPSO algorithm is proposed to improve the stability and convergence in dealing with the problems with higher dimensional search space. This research can be used by the project managers and dispatchers on assembly field.

The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).

Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.

Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.

SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.

The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

This paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters.

The approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM).

The results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables.

The authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell.

This paper aims to evaluate two operational modes of the worker allocation problem (WAP) in the multiple U-line system (MULS). Five objectives are optimised simultaneously for the most complicated operational modes, i.e. machine-dominant working and fixed-station walking. Besides, the benefits of using multiline workstations (MLWs) are investigated.

The elite non-dominated sorting differential evolutionary III (ENSDE III) algorithm is developed as a solution technique. Also, the largest remaining available time heuristic is proposed as a baseline in determining the number and utilisation of workers when the use of MLWs is not allowed.

ENSDE III outperforms the cutting-edged multi-objective evolutionary algorithms, i.e. multi-objective evolutionary algorithm based on decomposition and non-dominated sorting differential evolutionary III, under two key Pareto metrics, i.e. generational distance and inverted generational distance, regardless of the problem size. The best-found number of workers from ENSDE III is substantially lower than the upper bound. The MULS with MLWs requires fewer workers than the one without.

Although this research has extended several issues in the basic model of multiple U-line systems, some assumptions were used to facilitate mathematical computation as follows. The U-line system in this research assumed that all lines were produced only a single product. Besides, all workers were well-trained to gain the same skill. These assumptions could be extended in the future.

The implication of this research is the benefits of multiline workstations (MLWs) used in the multiple U-line system. Instead of leaving each individual line to operate independently, all lines should be working in parallel through the use of MLWs to gain benefits in terms of worker reduction, balancing worker’s workload, higher system utilisation.

This research is the first to address the WAP in the MULS with machine-dominant working and fixed-station walking modes. Worker’s fatigue due to standing and walking while working is incorporated into the model. The novel ENSDE III algorithm is developed to optimise the multi-objective WAP in a Pareto sense. The benefits of exploiting MLWs are also illustrated.

This study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.

Firstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.

Numerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.

The findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.

The equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.

This research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

Line–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.

First, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.

Computations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.

In this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

Rabbit chase (RC) is used as one of the most effective techniques in manufacturing systems, as such systems have high level of adaptability and increased productivity in addition to providing uniform workload balancing and skill improving environment. In assembly systems, RC inspires the development of walking worker assembly line (WWAL). On the other hand, U-type assembly lines (UALs) may provide higher worker utilization, lower space requirement and more convenient internal logistics when compared to straight assembly lines. In this context, this study aims to improve assembly line performance by generating RC cycles on WWAL with respect to task assignment characteristics of UAL within reasonable walking distance and space requirement. Therefore, a novel line configuration, namely, segmented rabbit chase-oriented U-type assembly line (SRCUAL), emerges.

The mathematical programming approach treats SRCUAL balancing problem in a hierarchical manner to decrease computational burden. Firstly, segments are generated via the first linear programming model in the solution approach for balancing SRCUALs to minimize total number of workers. Then, stations are determined within each segment for forward and backward sections separately using two different pre-emptive goal programming models. Moreover, three heuristics are developed to provide solution quality with computational efficiency.

The proposed mathematical programming approach is applied to the light-emitting diode (LED) luminaire assembly section of a manufacturing company. The adaptation of SRCUAL decreased the number of workers by 15.4% and the space requirement by 17.7% for LED luminaire assembly system when compared to UAL. Moreover, satisfactory results for the proposed heuristics were obtained in terms of deviation from lower bound, especially for SRCUAL heuristics I and II. Moreover, the results indicate that the integration of RC not only decreased the number of workers in 40.28% (29 instances) of test problems in U-lines, but also yielded less number of buffer points (48.48%) with lower workload deviation (75%) among workers in terms of coefficient of variation.

This study provides convenience for capacity management (assessing capacity and adjusting capacity by changing the number of workers) for industrial SRCUAL applications. Meanwhile, SRCUAL applications give the opportunity to increase the capacity for a product or transfer the saved capacity to the assembly of other products. As it is possible to provide one-piece flow with equal workloads via walking workers, SRCUAL has the potential for quick realization of defects and better lead time performance.

To the best of the authors’ knowledge, forward–backward task assignments in U-type lines have not been adapted to WWALs. Moreover, as workers travel overall the line in WWALs, walking time increases drastically. Addressing this research gap and limitation, the main innovative aspect of this study can be considered as the proposal of a new line design (i.e. SRCUAL) which is sourced from the hybridization of UALs and WWAL as well as the segmentation of the line with RC cycles. The superiority of SRCUAL over WWAL and UAL was also discussed. Moreover, operating systematic for SRCUAL was devised. As for methodical aspect, this study is the first attempt to solve the balancing problem for SRCUAL design.

The paper aims to present a systematic literature review to analyze interrelated enablers of Industry 4.0 for implementation. Industry 4.0 is an integrated manufacturing strategy embedded with disruptive technologies. Adapting these technologies with the present industrial scenario is dependent on understanding the dynamics of various critical enablers in the existing literature. In this paper, an effort has been taken to validate and reinforce these enablers by experts in the field of Industry 4.0 for implementation.

A mixed-methodology is designed in this paper. A text mining approach with an expert’s linguistic assessment method is planned to discover the enablers from literature 2010 to 2019. The most critical enablers and their dependencies on other enablers are studied by using correlation analysis.

The research explores the power driving enablers in three groups: technology, features and requirements for implementing Industry 4.0 in the existing factory. In each group, a high degree of associated and dependent enablers is fragmented in detail.

This paper will benefit the research communities and practitioners to understand the significance of an integrated ecosystem of Industry 4.0 technologies, features and requirements for implementation.

The text mining approach integrated with expert’s linguistic assessment to explore the pairwise relationship among the enablers using word correlation is a novel approach in this paper. Moreover, to best of the authors’ knowledge, this is the first-ever attempt to conduct a structured literature review combined with text analysis and linguistic assessment to identify the enablers of Industry 4.0 for implementation.

Recently, there has been increasing focus on the development of multi-skilled workforce in project management. The purpose of this paper is to investigate a multi-skill project scheduling problem (MSPSP), which combines project scheduling and multi-skill personnel assignment. The distinct features of skill evolution and cooperation effectiveness are considered in the problem to maximize the total project effectiveness and skill development simultaneously.

The Bi-objective non-linear integer programming (LIP) models are formulated for the problem using three types of skill development objective function: number of experts, total skill increment and “bottleneck” skill increment. Non-linear models are then linearized through several linearization techniques, and the ε-constraint method is used to convert the bi-objective models into single-objective models.

A construction project case is used to validate the proposed models. In comparison with models that do not consider skill evolution and cooperation effectiveness, the models proposed in this paper offer more realistic solutions and show better performance with regard to both project effectiveness and skill development.

This research extends the current MSPSP by considering skill evolution based on the “learning effect” as well as the influence of cooperation in an activity-based team, which are common phenomena in practice but seldom studied. LIP models formulated in this paper can be solved by any off-the-shelf optimization solver, such as CPLEX. Besides, the proposed LIP models can offer better project scheduling and personnel assignment plan, which would be of immense practical value in project management applications.