Employee scheduling with short demand perturbations and extensible shifts

Bürgy, Reinhard; Michon-Lacaze, Hélène; Desaulniers, Guy

doi:10.1016/j.omega.2018.10.009

Cited by 23 publications

(7 citation statements)

References 29 publications

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“…(4) Using real-life data (daily call arrivals in 6 months of 2020) and synthetic data (capabilities of devices), we experimentally verify the effectiveness and efficiency of our proposed method. Compared with five stateof-the-art methods for task scheduling, i.e., linear programming (LP [21]), integer programming (IP [22]), MIP [16]), improved particle swarm optimization (IPSO [23]), and multiobjective evolutionary algorithm-based decomposition (MOEAD [24]), we find that PACAM is at least two orders of magnitude faster than the above methods.…”

Section: Motivation Example (Internet Of Vehicle)mentioning

confidence: 95%

“…In this section, we experimentally evaluate the efficiency and effectiveness of our proposed solution PACAM against the state-of-the-art methods. We implement our algorithm in Python and adopt the Python implementation of all competitors based on the following methods: (1) LP [21], (2) IP [22], (3) MIP [16], (4) IPSO [23], and ( 5) MOEAD [24]. e solver used in this article is Gurobi solver 9.1 [25].…”

Section: Methodsmentioning

confidence: 99%

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PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Hong

Zhang

Cao

et al. 2022

Security and Communication Networks

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With the development of the smart Internet of Things (IoT), an increasing number of tasks are deployed on the edge of the network. Considering the substantially limited processing capability of IoT devices, task scheduling as an effective solution offers low latency and flexible computation to improve the system performance and increase the quality of services. However, limited computing resources make it challenging to assign the right tasks to the right devices at the edge of the network. To this end, we propose a polynomial-time solution, which consists of three steps, i.e., identifying available devices, estimating device quantity, and searching for feasible schedules. In order to shrink the number of potential schedules, we present a pairwise-allocated strategy (PA). Based on these, a capability average matrix (CAM)-based index is designed to further boost efficiency. In addition, we evaluate the schedules by the technique for order preference by similarity to an ideal solution (TOPSIS). Extensive experimental evaluation using both real and synthetic datasets demonstrates the efficiency and effectiveness of our proposed approach.

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Section: Motivation Example (Internet Of Vehicle)mentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Hong

Zhang

Cao

et al. 2022

Security and Communication Networks

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“…In reality, uncertain events, such as short demand perturbations arising from staff absenteeism or customer surge, would cause original employee schedules perform poorly. Motivated by the uncertain factors, there are a growing attentions on accounting for random employee demand in the scheduling models [15], [19]- [23], especially in the context of employee plans in retail stores [10], [24], [25]. In particular, Bürgy et al [25] studies the employee scheduling problem considering uncertain demand arising in retail stores, and their strategy to cope with uncertain demand is assigning overtime work by extending shifts to cope with a lack of employees in real-time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Motivated by the uncertain factors, there are a growing attentions on accounting for random employee demand in the scheduling models [15], [19]- [23], especially in the context of employee plans in retail stores [10], [24], [25]. In particular, Bürgy et al [25] studies the employee scheduling problem considering uncertain demand arising in retail stores, and their strategy to cope with uncertain demand is assigning overtime work by extending shifts to cope with a lack of employees in real-time. Similar to the proposed methodology used in our work, Kim and Mehrotra [21] addresses the problem of integrated nurse staffing and scheduling under demand uncertainty in a two-stage stochastic modeling fashion.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Check-in Employee Scheduling Problem

et al. 2020

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This work addresses the problem of assigning airline check-in employees to tasks related to departing flights under uncertain circumstance at an international terminal of a large airport. The uncertainty of flight departing time mainly stems from delays and traffic control. Airlines specifics the minimum and target number of staff for each flight task in each time period. Taking uncertain tasks starting time into account and introducing risk measure (e.g., conditional value-at-risk) into optimization objective function, we present a risk-averse two-stage stochastic model with the mixed-integer recourse that minimizes both staffing costs and risk measurement to respond the time uncertainty. The first-stage decision is to make personal-area allocation plan; and the particular personnel-task scheduling decisions are determined in the second stage as the actual flight departing times are realized. To solve the NP-hard problem, a progressive hedging algorithm is proposed. Numerical experiments are carried out to demonstrate the efficiency and effectiveness of the proposed approach as compared with a sample average approximation method. Finally, we utilize a real-world case to illustrate how the proposed method can be used to obtain a near-optimal solution for practical problem. INDEX TERMS Employee scheduling, stochastic programming, airport ground optimization, risk measure, progressive hedging algorithm.

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“…workforce scheduling) helps them to understand one of the core business processes in advisory firms for pursuing effectiveness and efficiency (Dodin, 1999). Furthermore, workforce scheduling is critical in many services organisations in controlling costs and satisfying customers and clients (see Burgy et al, 2019; Ernst et al, 2004; Ruiz-Torres et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Pursuing effectiveness and efficiency: Data model for workforce scheduling at Alvarino

Lim

Perdana

Wong

2020

Journal of Information Technology Teaching Cases

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This case involves actual data obtained from an interview with a partner, a manager and two associates at a firm located in Singapore, Alvarino (pseudonym). The firm is part of a global network of accountancy and business advisory firms. The network comprises of more than 100 independently owned and managed firms that straddle more than 100 geographical locations across the world. The case illustrates issues that Alvarino experienced in scheduling their staff for audit advisory engagement. As a service oriented and cost-conscious business, workforce scheduling is essential to help Alvarino’s management optimise its workforce allocation. The objective of this case is to create a data model that maps user and data requirements to optimise Alvarino’s workforce-scheduling processes.

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Employee scheduling with short demand perturbations and extensible shifts

Cited by 23 publications

References 29 publications

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Stochastic Check-in Employee Scheduling Problem

Pursuing effectiveness and efficiency: Data model for workforce scheduling at Alvarino

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