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The purpose of this paper is to investigate factors that influence effective workplace designs for knowledge workers.

During spring 2016, the employees in a large institution for research and higher education, a large consultancy company and a medium-sized consultancy company (in total 4367 employees) in Norway received invitations to participate in an anonymous online survey about workplaces and facilities. In all, 1,670 employees answered the survey (38.2 per cent response rate). The data have been analyzed with IBM SPSS version 23, among others through use of exploratory factor analysis and two-way ANOVA.

Most respondents at the institution for research and higher education have cell offices. Most respondents in the two consultancy companies have open and flexible offices. This paper indicate the respondents’ preferences or perception of their workstation and the workplace’s fit for their tasks is affected both by the respondents’ type of office and how much time they spend at their workstation during the week. There are also possible age or generation effects.

One methodical weakness in the present paper is that two-way ANOVA has been applied on survey data. Experiments are usually arranged to provide almost equal numbers of observations in each category. This is usually not possible with survey data. However, despite this weakness, the present paper provides several findings that challenge some of the workplace research’s taken for givens.

The present paper indicates that facility managers and others responsible for office and workplace design are advised to take the employees’ tasks and work patterns into consideration when designing workplaces and providing offices and workstations to their end-users. The present paper also indicates that employees require different kinds of support facilities and services depending on what kind of offices and workplaces they have.

This is a large N empirical study among knowledge workers in three organizations, one public administration and two private enterprises. The present paper indicate that provision of offices and workstations with supporting facilities should be differentiated according to the end-users’ work tasks and work patterns.

Parallel two-sided assembly lines are usually designed to produce large-sized products such as trucks and buses. In parallel two-sided assembly lines, both left and right sides of the line are used for manufacturing one or more products on two or more assembly lines located parallel to each other. The purpose of this paper is to develop a new mathematical model for the parallel two-sided assembly line balancing problem that helps to evaluate and validate the balancing operations of the machines such as removal of tools and fixtures and reallocating the operators.

The proposed approach is explained with the help of an example problem. In all, 22 test problems are formed using the benchmark problems P9, P12, P16 and P24. The results obtained are compared among approaches of the task(s) shared, tool(s) shared and both tool(s) and task(s) shared for effect on efficiency as the performance measure. The solution presented here follows the exact solution procedure that is solved by Lingo 16 solver.

Based on the experiments, line efficiency decreases when only tools are shared and increases when only tasks are shared. Results indicate that by sharing tasks and tools together, better line efficiency is obtained with less cost of tools and fixtures.

According to the industrial aspect, the result of the study can be beneficial for assembly of the products, where tools and tasks are shared between parallel workstations of two or more parallel lines.

According to the author’s best knowledge, this paper is the first to address the tools and tasks sharing between any pair of parallel workstations.

The purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.

This paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.

Computational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.

The developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

Recent developments in workstations and software that make it possible to extend the utility of existing library systems are discussed. These developments provide an excellent foundation on which to base future library automation, especially in the context of a research library. Further, this technology suggests that the concept of a “scholar's workstation” is technically feasible. Some experiences with the use of this technology to explore ideas central to the development of a scholar's workstation are also reported.

The purpose of this paper is to present a mixed integer programming model for simple assembly line balancing problems (SALBP) with Type 1 when the annual demand and task durations are uncertain and encoded with grey numbers.

Grey theory and grey numbers are used for illustrating the uncertainty of parameters in an SALBP, where the objective is to minimize the total number of workstations. The paper proposes a 0-1 mathematical model for SALBP of Type 1 with grey demand and grey task durations.

The uncertainty of the demand and task durations are encoded with grey numbers and a well-known 0-1 mathematical model for SALBP of Type 1 is modified to find the minimum number of workstations in order to meet both the lower and upper bounds of the uncertain demand. The results obtained from the proposed mathematical model show a task-workstation assignment that does not distribute precedence relations among tasks and workstations and the sum of task durations in each single workstation is less than or equal to the grey cycle time.

The grey theory and grey numbers have not been previously used to identify uncertainties in assembly line balancing problems. Therefore, this study provides an important contribution to the literature.

This paper attempts to reveal the interrelationships between the workstation loading and system entry policies and in particular to investigate the adverse or positive effects of workstation loading on the objective which the entry policy is trying to achieve. This analysis is made through a computer simulation study of an existing flexible assembly plant for printed circuit boards. Dynamic performance indicators are defined and are shown to be crucial in revealing the effect of workstation loading. The analysis of the simulation results shows that in general, the workstation policy is beneficial when it takes into account an important local system parameter which is not considered by the entry policy, and furthermore, when it is able to attain at least the value of the dynamic performance indicator dictated by system entry policy.

Multi-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.

A mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.

The experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.

For assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.

This paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem.

Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed.

The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study.

The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

This paper aims to analyse the role of government-led innovative knowledge management platforms in innovation knowledge management, social network effects and innovative resource clusters in the context of academician workstations in China. Specifically, this paper empirically studies the impact of academician workstations on corporate innovation capabilities and the mechanisms behind this impact.

This study uses the propensity matching score method and difference-in-differences method to test the relationship between academician workstations and corporate innovation capabilities. Baron and Kenny’s (1986) mediation method is used to test two potential mechanisms.

Academician workstations significantly improve corporate innovation capabilities because of their contribution to knowledge and innovation management. The facilitation effects are stronger in non-state-owned firms, high-tech firms and firms in industries with low levels of competition. Further, academician workstations enhance corporate innovation capabilities through their funding effect.

This paper encourages policymakers to create a better market environment and stable support policies to facilitate sustainable scientific and technological innovation.

To the best of authors’ knowledge, this study is among the first to empirically analyse the impacts of innovative knowledge management platforms on corporate innovation. It enriches the theoretical perspective of innovation platforms and provides an excellent research perspective for effectively analysing the impacts of innovation platforms. This study also contributes to the literature on the determinants of innovation.

This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line.

To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time.

The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy.

This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.