Mixed-integer/linear and constraint programming approaches for activity scheduling in a nuclear research facility

Abstract

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Heuristic and metaheuristic methods for the multi‐skill project scheduling problem with partial preemption

“…At each iteration of the algorithm, all the activities within the time window are selected according to their preemption type to be rescheduled, and all resources and technicians used by them are released and can be redistributed using an exact technique. For our numerical experiments we test and compare both Constraint Programming (CP) and Mixed-Integer Linear Programming (MILP) models presented in Polo-Mejía et al (2020) for the MSPSP-PP, the latter formulation is recalled in Section 2. For ensuring that the subproblem is solved in reasonable time, we limit the maximal solution time allowed to the solver.…”

“…The skills mastered for each technician are also randomly generated, going from 5 up to 10 skills for each of them. For ensuring the feasibility of an instance, we use a trial and error approach, generating first the instance with the random generator and proving then its feasibility (or not) using the CP Optimizer solver and the CP model presented in Polo-Mejía et al (2020). The proposed heuristics are coded in the C++ programming language.…”

“…In some industrial environments, such as nuclear research facilities described in Polo-Mejía et al (2020) that inspired our study, scheduling activities need to take into account a set of skills and clearances required to execute the activities and to match them with those that some technicians master. In this context, where regulations require the presence of a group of technicians having a set of well-defined skills for the execution of an activity, the multi-skill project scheduling problem (MSPSP) is important, see for example Bellenguez-Morineau and Néron (2007); Bellenguez-Morineau (2008); Montoya et al (2014); Correia and Saldanha-da Gama (2015); Almeida et al (2016); Young et al (2017); Almeida (2018); Almeida et al (2019).…”

“…The MSPSP-PP, in the same way as the standard MSPSP, is NP-hard (Polo-Mejía et al, 2018a), and the use of exact methods can be prohibitive to solve large-sized industrial instances, as shown in Polo-Mejía et al (2020). The industrial applications of scheduling models require, most of the time, to be able to obtain high-quality solution in limited time.…”

Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem

“…Some other researchers have also studied the new extension problems of the Constrained Project Scheduling Problem and applied them in many fields of science and finance. Polo-Mejia et al [19] developed a scheduling algorithm to manage nuclear laboratory operations. To solve the problem of the dense sensors in wireless sensor networks, Wan et al [20] proposed an energy-saving scheduling algorithm, which arranges some redundant sensors into the sleep mode to reduce the data transmission collision and energy dissipation.…”