Integrated Project Scheduling and Staff Assignment with Controllable Processing Times

Table 2

Summary of related papers.

Project sched.

Staff assign.

Prec. relations

Release time

Due dates

Preemp.-nonPreemp.

Learning effect

CPTSkill

CPT𝑁^∘ofworkers

Move const.

Outsourcing staff

Project selection

Resources per task

Skill

Objective function

Exact methods and solvers

Aprox. alg.

Alfares and Bailey [28]

X

Global

X

X

NP

Data^1

Cost

^2

Heuristic

Bassett [29]

X

X

X

NP

1

X

Cost

CPLEX

Heuristic

Bellenguez and Néron [14]

X

X

X

NP

n

X

Makespan

Branch and bound

Bellenguez-Morineau and Néron [15]

X

X

X

NP

n

X

Makespan

Lower bound

Corominas et al. [20]

X

X

X

X

P

X

1

Makespan

CPLEX

Dodin and Elimam [22]

X

X

X

NP

Data^1

1

Cost

Lindo

Drexl [21]

X

X

X

NP

Data^1

1

Cost

Branch and bound

Monte Carlo

Drezet and Billaut [17]

X

X

X

X

X

NP

X

n

X

Lateness

CPLEX

Tabu search

Gutjahr et al. [25]

X

X

X

X

X

P

X

X

n

X

Multiobjective

CPLEX

Greedy procedure

Gutjahr et al. [26]

X

X

X

X

P

X

X

n

X

Multiobjective

CPLEX

NSGA-II and P-ACO

Hachicha et al. [30]

X

Data^1

Cost and preference

Lingo

Heimerl and Kolisch [31]

X

X

Non Linear

X

Cost

COIN-OR’s Ipopt

Kolisch and Heimerl [24]

X

X

X

X

X

NP

X

n

X

Cost

CPLEX

Genetic alg. and tabu search

Valls et al. [19]

X

X

X

X

X

NP

Linear

1

X

Multiobjective

Genetic alg.

Vairaktarakis [18]

X

X

X

1

X

Makespan

Lower bound

Heuristics

Wu and Sun [27]

X

X

X

P

X

X

1

Cost

CPLEX

Genetic alg.

This paper

X

X

X

X

NP

Piecewise linear

X

n

X

Makespan

CPLEX Gurobi

GRASP

1Processing times are preprocessed depending on the number of assigned employees or on the level of skills of the assigned employee(s) and then they are introduced in the model as data.
2An integer programming model is solved, although the way to do it is not explained.