Abstract:Flexibility is a main mean to create resilient supply chains. The most flexible resources are often human resources but creating high, homogenous skill levels is not cost efficient. Heterogenous labour provides an alternative. The literature on Dual Resource Constrained (DRC) shops modelled heterogeneous labour with multi-functionality and efficiency matrices that indicate if a worker can perform at a station and according to which level of efficiency. However, this literature typically considered these matric… Show more
“…Therefore, as multiskilled employees can be transferred from tasks with staffing surplus to those facing a staffing shortage, solving the MPAP enables the design of a workforce that can flexibly adapt to fluctuating demand patterns (e.g., [3,4,6,31,45,46]). In turn, an optimal training plan not only improves demand coverage but also minimizes labor costs arising from mismatches between staffing levels and staff demand ( [5,8,10,12,13,18,26,38]).…”
In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand.
“…Therefore, as multiskilled employees can be transferred from tasks with staffing surplus to those facing a staffing shortage, solving the MPAP enables the design of a workforce that can flexibly adapt to fluctuating demand patterns (e.g., [3,4,6,31,45,46]). In turn, an optimal training plan not only improves demand coverage but also minimizes labor costs arising from mismatches between staffing levels and staff demand ( [5,8,10,12,13,18,26,38]).…”
In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand.
This paper shows the effectiveness of labour transfers in addressing premature idleness caused by controlled order release. Controlled order release restricts order entry to the shop floor and is commonly employed in high-variety manufacturing where it results in benefits such as stable work-in-progress. However, it can increase waiting times when orders are blocked from release, while capacities are idling. This issue, known as premature idleness, negatively impacts delivery performance. Previous studies have primarily focused on addressing premature idleness through input control by releasing new orders to idling workstations. This approach overlooks the potential of output control during premature idleness, transferring labour to assist at other workstations in a dual resource constrained setting. Using simulation, this study demonstrates that output control significantly improves delivery performance—in terms of mean tardiness and percentage tardy—and reduces total and shop floor throughput times. Importantly, this result proves robust, even when the efficiency of the assisting worker is severely limited. Shop-level performance improves despite the efficiency loss of the worker. The impact of the where-rule is minimal, while the efficacy of the priority dispatching rule depends on the joint efficiency of collaborating workers. Finally, we show that combining input control and output control enhances performance, providing opportunities for further research on the role of both control approaches in high-variety manufacturing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.