Annualized Hours, Multiskilling, and Overtime on Annual Staffing Problem: A Two-Stage Stochastic Approach

Solano, Andrés Porto; Lusa, Amaia; Henao, César Augusto; Solano, Roberto Porto

doi:10.1007/978-3-031-29382-5_12

Cited by 1 publication

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“…The objective of the MPAP is to cost-effectively design a workforce training plan that addresses key aspects, such as: (i) determining the number of single-skilled employees (those trained for a one task type) and multiskilled employees (those trained for two or more task types), (ii) specifying the types of tasks in which each employee should be trained, and (iii) devising a weekly work-hour distribution for each employee based on their trained skills. 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]).…”

Section: Introductionmentioning

confidence: 99%

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

Henao,

Porto,

González

2024

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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.

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