The Trade Off Between Diversity and Quality for Multi-objective Workforce Scheduling

Cowling, Peter I.; Colledge, Nic; Dahal, Keshav; Remde, Stephen

doi:10.1007/11730095_2

Cited by 24 publications

(24 citation statements)

References 12 publications

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“…This objective is to maximise the total priority of tasks scheduled while minimising travel time and cost. Values are scaled so that this expression is a realistic representation of solution quality for a large class of problems encountered in practice (Cowling et al, 2006).…”

Section: (3)mentioning

confidence: 99%

“…Tasks require between one and three skills and resources possess between one and five skills. The problems reflect realistic problems Trimble have identified and are generated using a commercial problem instance generator (Cowling et al, 2006). The size of the problems was chosen to be solvable in a reasonable amount of CPU time.…”

Section: Computational Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

An empirical study of hyperheuristics for managing very large sets of low level heuristics

Remde

Cowling

Dahal

et al. 2012

Journal of the Operational Research Society

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Section: (3)mentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

An empirical study of hyperheuristics for managing very large sets of low level heuristics

Remde

Cowling

Dahal

et al. 2012

Journal of the Operational Research Society

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“…Focusing on task-based adaptation, [20] near-optimal resource allocations and reallocations of human tasks were presented. Workforce scheduling research investigates the impact of weights on the multiple, at times contradicting, objectives in real work workforce scheduling [8]. Staff scheduling related to closed systems was discussed in [5,10].…”

Section: Related Workmentioning

confidence: 99%

QoS-Based Task Scheduling in Crowdsourcing Environments

Khazankin

Psaier

Schall

et al. 2011

Service-Oriented Computing

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Abstract. Crowdsourcing has emerged as an important paradigm in human-problem solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement as solutions based on software services only. Another benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource certain tasks to the crowd based on workload variations. The paper addresses the monitoring of crowd members' characteristics and the effective use of monitored data to improve the quality of work. Here we propose the extensions of standards such as Web Service Level Agreement (WSLA) to settle quality guarantees between crowd consumers and the crowdsourcing platform. Based on negotiated agreements, we provide a skill-based crowd scheduling algorithm. We evaluate our approach through simulations and show that our approach clearly outperforms a skill-agnostic scheduling approach.

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“…In real world situations it might be necessary to consider multiple and potentially conflicting objectives, typically resulting in the combined schedule evaluation function ϕ being complex and highly dynamic (see [46] and [47] for the discussion of related approaches).…”

Section: Computational Experimentsmentioning

confidence: 99%

Extending the RCPSP for modeling and solving disruption management problems

2008

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Abstract. This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem for the comprehensive description of disruption management problems. This conceptual framework employs the concept of alternative activities to consider both the temporal shift of activities or the reallocation of resources and switches from one valid process variant to another one. Activities can be serialized or parallelized, process steps can be inserted or removed and durations as well as resource requirements can be modified dynamically during optimization. Focusing on the domain of the aircraft turnaround as the most important airport ground process, we illustrate how the Extended RCPSP (x-RCPSP) can be applied for decision support. A specific evolutionary algorithm is presented that identifies good-quality solutions to relatively large disruption management problems within only a few seconds. The results of the evaluation illustrate fast convergence on good or optimal schedules and serve as a basis for the development of future problem solving algorithms.

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The Trade Off Between Diversity and Quality for Multi-objective Workforce Scheduling

Cited by 24 publications

References 12 publications

An empirical study of hyperheuristics for managing very large sets of low level heuristics

An empirical study of hyperheuristics for managing very large sets of low level heuristics

QoS-Based Task Scheduling in Crowdsourcing Environments

Extending the RCPSP for modeling and solving disruption management problems

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