Dynamic order acceptance and scheduling problem with sequence-dependent setup time

This paper studies the order acceptance and scheduling problem under a single machine environment when the orders come stochastically during the planning horizon and a sequence-dependent setup time is required between the processing of different types of orders. The objective is to maximise the expe...

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Published inInternational journal of production research Vol. 53; no. 19; pp. 5797 - 5808
Main Authors Xu, Lei, Wang, Qian, Huang, Simin
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 02.10.2015
Taylor & Francis LLC
Subjects
Acceptance
Decision making models
Decisions
Dynamic programming
Dynamical systems
Dynamics
Heuristic
Mathematical models
Mathematical programming
Opportunity costs
order acceptance
Production scheduling
Scheduling
sequence-dependent setup
Setup times
stochastic demand
Stochastic models
Studies
Online AccessGet full text
ISSN0020-7543
1366-588X
DOI10.1080/00207543.2015.1005768

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Abstract This paper studies the order acceptance and scheduling problem under a single machine environment when the orders come stochastically during the planning horizon and a sequence-dependent setup time is required between the processing of different types of orders. The objective is to maximise the expected revenue subject to the due date constraints. The problem is formulated as a stochastic dynamic programming model. A rule based on the opportunity cost of the remaining system capacity for the current system state is proposed to make the order acceptance decisions. The remaining system capacity is estimated by a heuristic which generates a good schedule for the accepted orders. Its opportunity cost is estimated by both mathematical programme and greedy heuristic. Computational experiments show that the profit generated by the integrated dynamic programming decision model is much higher than the widely used first-come-first-accept policy in industries and the benefit increases with the length of planning horizon, the arrival rate and the length of lead time. Acceptance decision based on mathematical programming outperforms greedy heuristic by about 7% and its computational time is short. It also shows that the quality of the solutions generated by the opportunity cost based order acceptance rule is satisfactory.
AbstractList This paper studies the order acceptance and scheduling problem under a single machine environment when the orders come stochastically during the planning horizon and a sequence-dependent setup time is required between the processing of different types of orders. The objective is to maximise the expected revenue subject to the due date constraints. The problem is formulated as a stochastic dynamic programming model. A rule based on the opportunity cost of the remaining system capacity for the current system state is proposed to make the order acceptance decisions. The remaining system capacity is estimated by a heuristic which generates a good schedule for the accepted orders. Its opportunity cost is estimated by both mathematical programme and greedy heuristic. Computational experiments show that the profit generated by the integrated dynamic programming decision model is much higher than the widely used first-come-first-accept policy in industries and the benefit increases with the length of planning horizon, the arrival rate and the length of lead time. Acceptance decision based on mathematical programming outperforms greedy heuristic by about 7% and its computational time is short. It also shows that the quality of the solutions generated by the opportunity cost based order acceptance rule is satisfactory.
Author Xu, Lei
Wang, Qian
Huang, Simin
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SubjectTerms Acceptance
Decision making models
Decisions
Dynamic programming
Dynamical systems
Dynamics
Heuristic
Mathematical models
Mathematical programming
Opportunity costs
order acceptance
Production scheduling
Scheduling
sequence-dependent setup
Setup times
stochastic demand
Stochastic models
Studies
Title Dynamic order acceptance and scheduling problem with sequence-dependent setup time
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