Mobile Workforce Scheduling Problem with Multitask-Processes

Goel, Asvin; Gruhn, Volker; Richter, Thomas

doi:10.1007/978-3-642-12186-9_9

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The authors remark that the presence of dependencies between tasks often make local neighbourhood search methods difficult to implement. Goel et al (2010) proposed an MILP model for scheduling mobile workforce, where time windows and precedence relations are simultaneously considered, and suggested an iterative solution algorithm. Starkey et al (2016) defined the mobile workforce management problem as grouping places into worker areas served independently by travelling engineers, motivated by the telecommunications field.…”

Section: Mobile Workforce Managementmentioning

confidence: 99%

New general mixed-integer linear programming model for mobile workforce management

2021

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A mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

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Section: Mobile Workforce Managementmentioning

confidence: 99%

New general mixed-integer linear programming model for mobile workforce management

2021

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“…jobs involving physical presence at a specific customer or patient [33], or no location restrictions, e.g. electronic work that can be accessed using IT solutions [5], [34]. In many situations it is also clear which workers or organisational roles can be involved in each activity.…”

Section: Organisational Perspectivementioning

confidence: 99%

“…Nowadays, the work in many organisations such as hospitals and financial institutions is done according to predefined business processes [4], [5]. These processes specify which activities need to be executed, in what order and by whom [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Human Performance-Aware Scheduling and Routing of a Multi-Skilled Workforce

Eck

Fırat

Nuijten

et al. 2017

CSIMQ

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Abstract. Planning human activities within business processes often happens based on the same methods and algorithms as are used in the area of manufacturing systems. However, human behaviour is quite different from machine behaviour. Their performance depends on a number of factors, including workload, stress, personal preferences, etc. In this article we describe an approach for scheduling activities of people that takes into account business rules and dynamic human performance in order to optimise the schedule. We formally describe the scheduling problem we address and discuss how it can be constructed from inputs in the form of business process models and performance measurements. Finally, we discuss and evaluate an implementation for our planning approach to show the impact of considering dynamic human performance in scheduling.

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