TY - GEN
T1 - A hybrid approach to decision support for resource-constrained scheduling problems
AU - Sitek, Paweł
AU - Nielsen, Izabela
AU - Wikarek, Jarosław
AU - Nielsen, Peter
PY - 2016
Y1 - 2016
N2 - Resource-constrained scheduling problems are commonly found in various areas, such as project management, manufacturing, transportation, software engineering, computer networks, and supply chain management. Its problem models involve a large number of constraints and discrete decision variables, including binary and integer. In effect, the representation of resource allocation, for instance, is often expressed using binary or integer decision variables to form several constraints according to the respective scheduling problem. It significantly increases the number of decision variables and constraints as the problem scales; such kind of traditional approaches based on operations research is insufficient. Therefore, a hybrid approach to decision support for resource-constrained scheduling problems which combines operation research (OR) and constraint logic programming (CLP) is proposed. Unlike OR-based approaches, declarative CLP provides a natural representation of different types of constraints. This approach provides: (a) decision support through the answers to the general and specific questions, (b) specification of the problem based on a set of facts and constraints, (c) reduction to the combinatorial solution space. To evaluate efficiency and applicability of the proposed hybrid approach and implementation platform, implementation examples of job-shop scheduling problem are presented separately for the three environments, i.e., Mathematical Programming (MP), CLP, and hybrid implementation platform.
AB - Resource-constrained scheduling problems are commonly found in various areas, such as project management, manufacturing, transportation, software engineering, computer networks, and supply chain management. Its problem models involve a large number of constraints and discrete decision variables, including binary and integer. In effect, the representation of resource allocation, for instance, is often expressed using binary or integer decision variables to form several constraints according to the respective scheduling problem. It significantly increases the number of decision variables and constraints as the problem scales; such kind of traditional approaches based on operations research is insufficient. Therefore, a hybrid approach to decision support for resource-constrained scheduling problems which combines operation research (OR) and constraint logic programming (CLP) is proposed. Unlike OR-based approaches, declarative CLP provides a natural representation of different types of constraints. This approach provides: (a) decision support through the answers to the general and specific questions, (b) specification of the problem based on a set of facts and constraints, (c) reduction to the combinatorial solution space. To evaluate efficiency and applicability of the proposed hybrid approach and implementation platform, implementation examples of job-shop scheduling problem are presented separately for the three environments, i.e., Mathematical Programming (MP), CLP, and hybrid implementation platform.
KW - Constraint logic programming
KW - Decision support
KW - Hybridization
KW - Mathematical programming
KW - Resource-constrained scheduling problem
KW - Decision support
KW - Constraint logic programming
KW - Resource-constrained scheduling problem
KW - Mathematical programming
KW - Hybridization
UR - http://www.scopus.com/inward/record.url?scp=84977104089&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-39630-9_9
DO - 10.1007/978-3-319-39630-9_9
M3 - Article in proceeding
AN - SCOPUS:84977104089
SN - 978-3-319-39629-3
T3 - Smart Innovation, Systems and Technologies
SP - 101
EP - 113
BT - Intelligent Decision Technologies 2016
PB - Springer
T2 - 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016
Y2 - 15 June 2016 through 17 June 2016
ER -