Application of chance-constrained programming for stochastic group shop scheduling problem

In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to...

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Vydáno v:International journal of advanced manufacturing technology Ročník 42; číslo 3-4; s. 321 - 334
Hlavní autoři: Ahmadizar, Fardin, Ghazanfari, Mehdi, Fatemi Ghomi, Seyyed Mohammad Taghi
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer-Verlag 01.05.2009
Springer Nature B.V
Témata:
Algorithms
Ant colony optimization
CAE) and Design
Completion time
Computer-Aided Engineering (CAD
Constraints
Engineering
Heuristic methods
Industrial and Production Engineering
Integer programming
Job shop scheduling
Job shops
Linear programming
Mechanical Engineering
Media Management
Original Article
Production scheduling
Random variables
Schedules
Uncertain release dates and processing times
Total weighted completion time
Chance-constrained programming
Group shops scheduling
Heuristic algorithm
Ant colony algorithm
ISSN:0268-3768, 1433-3015
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Shrnutí:In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to find a job schedule which minimizes the total weighted completion time. We solve this problem based on the chance-constrained programming. First, the problem is formulated in a form of stochastic programming and then prepared in a form of deterministic mixed binary integer linear programming such that it can be solved by a linear programming solver. To solve the problem efficiently, we develop an efficient hybrid method. Exploiting a heuristic algorithm in order to satisfy the constraints, an ant colony optimization algorithm is applied to construct high-quality solutions to the problem. The proposed approach is tested on instances where the random variables are normally, uniformly, or exponentially distributed.
Bibliografie:ObjectType-Article-1
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-008-1594-2