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A Hybrid Approach to Decision Support for Resource-Constrained Scheduling Problems

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Intelligent Decision Technologies 2016 (IDT 2016)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 56))

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Abstract

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.

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Authors and Affiliations

  1. Department of Information Systems, Kielce University of Technology, Kielce, Poland

    Paweł Sitek & Jarosław Wikarek

  2. Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark

    Izabela Nielsen & Peter Nielsen

Authors
  1. Paweł Sitek
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  2. Izabela Nielsen
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  3. Jarosław Wikarek
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  4. Peter Nielsen
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Corresponding author

Correspondence to Paweł Sitek .

Editor information

Editors and Affiliations

  1. Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

Appendix A Data Instances for Illustrative Example (Sets of Facts)

Appendix A Data Instances for Illustrative Example (Sets of Facts)

%machine (#M).

machine (M1). machine (M2). machine (M3). machine (M4).

machine (M5). machine (M6). machine (M7). machine (M8).

%product (#I).

product (A). product (B). product (C). product (D).

product (E). product (F). product (G).

%technology (#I,#M,C).

technology (A,M1,1). technology (A,M3,2). technology (A,M5,2).

technology (A,M7,3). technology (B,M2,2). technology (B,M3,2).

technology (B,M4,1). technology (C,M1,2) technology (C,M2,4).

technology (C,M3,2). technology (D,M5,2). technology (D,M6,2).

technology (D,M7,5). technology (D,M8,2). technology (E,M1,2).

technology (E,M2,1). technology (E,M3,2). technology (F,M4,2).

technology (F,M5,2). technology (F,M6,2). technology (G,M3,1).

technology (G,M5,2). technology (G,M8,2).

%resources (#R,L,K).

resources (R1,12,40). resources (R2,12,30).resources (R3,12,30).

resources (R4,12,20). resources (R5,12,20).

%allocation (#R,#M,#I,D)

allocation (O1,M1,A,2). allocation (O3,M3,A,2).

allocation (O1,M5,A,1). allocation (O4,M7,A,1).

allocation (O3,M2,B,2). allocation (O2,M3,B,1).

allocation (O4,M4,B,1). allocation (O3,M1,C,2).

allocation (O2,M2,C,1). allocation (O1,M3,C,2).

allocation (O1,M5,D,2). allocation (O2,M6,D,2).

allocation (O3,M7,D,1). allocation (O4,M8,D,1).

allocation (O2,M1,E,1). allocation (O5,M2,E,1).

allocation (O3,M3,E,2). allocation (O4,M4,F,2).

allocation (O5,M5,F,2). allocation (O1,M6,F,2).

allocation (O3,M3,G,1). allocation (O2,M5,G,2).

allocation (O1,M8,G,2).

%precedence (#P,#M,#M).

precedence (A,M1,M2). precedence (A,M2,M3).precedence (A,M3,M7).

precedence (B,M2,M3). precedence (B,M3,M4).precedence (C,M1,M2).

precedence (C,M2,M3). precedence (D,M5,M6).precedence (D,M6,M7).

precedence (D,M7,M8). precedence (E,M1,M2).precedence (E,M2,M3).

precedence (F,M4,M5). precedence (F,M5,M6).precedence (G,M3,M5).

precedence (G,M5,M8).

Order (#P,quantity).

order(A,2). order(B,2). order(C,1). order(D,1).

order(E,1). order(F,1). order(G,1).

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Sitek, P., Nielsen, I., Wikarek, J., Nielsen, P. (2016). A Hybrid Approach to Decision Support for Resource-Constrained Scheduling Problems. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-39630-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39629-3

  • Online ISBN: 978-3-319-39630-9

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