Production Scheduling and Rescheduling with Genetic Algorithms

Bierwirth, Christian; Mattfeld, Dirk C.

doi:10.1162/evco.1999.7.1.1

Cited by 238 publications

(127 citation statements)

References 2 publications

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“…NEH for flows shop scheduling [136], and shifting bottleneck [119] for job shop scheduling, are developed to search for "good enough" solutions within a reasonable computational times. Meta-heuristics such as tabu search [106], genetic algorithm [16], particle swarm optimisation [126] have also been applied extensively to solve production scheduling problems.…”

Section: Production Schedulingmentioning

confidence: 99%

“…Other heuristics based on understandings of problem domains have been also proposed in the literature such as shifting bottlenecks [5]. More general techniques based on meta-heuristics such as tabu search [106] and genetic algorithm [16] have been developed to deal with different production scheduling problems and show promising results. However, it is noted that designing a good heuristic is not a trivial task and it can be very time consuming and requires a lot of problem domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Section: Production Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

View full text Add to dashboard Cite

show abstract

“…This new starting solution must be first updated to the new problem, by deleting the operations already started. Starting with an already "good" solution, together with the reduction in the search space, will decrease the runtime needed to provide the new solution [11]. It is also necessary to adjust different algorithm parameters, like for example crossover operator and mutation rate in the case of genetic algorithms, to adapt to the new situation.…”

Section: Optimisation Engine Design and User Interactionmentioning

confidence: 99%

Energy Efficient Production Planning

Upton

Quilligan²,

García-Santiago

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. The introduction of energy efficiency as a new goal into already complex production plans is a difficult challenge. Decision support systems can help with this problem but these systems are often resisted by end users who ultimately bear the responsibility for production outputs. This paper describes the design of a decision support tool that aims to increase the interpretability of decision support outputs. The concept of 'grey box' optimisation is introduced, where aspects of the optimisation engine are communicated to, and configurable by, the end user. A multi-objective optimisation algorithm is combined with an interactive visualisation to improve system observability and increase trust.Keywords: visualisation, optimisation, energy efficiency, manufacturing. IntroductionEnergy efficient manufacturing is a key research challenge for both industry and academia. Systemic energy waste is closely tied to strategic production decisions and therefore poses a complex operations-research problem. An example of this involves switching idle machine into a low-power mode. While this strategy is an effective way to save energy, it is not a straightforward task in many industrial environments. Energy savings are often subservient to production targets and decisions about changing machine states involve weighing up a complex set of goals and constraints. These include hard metrics such as production capacity, predicted inventory and product priorities as well as soft constraints such as technician skill level, engineering requests and machine recovery risks. Operations managers currently apply human expertise to cope with this complexity. In high product mix factories this problem can become very challenging and even before energy-saving is considered. Optimisation algorithms can be applied to reduce the problem space associated with this decision and to highlight energy saving opportunities; however an algorithmic approach is challenged by soft constraints and unpredictable changes in goals. In addition operations managers tend to be wary of decision support tools due to their perceived brittleness and lack of transparency [1].

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“…The development of techniques for incrementally managing schedules in response to execution status updates has received attention in the AI community (Smith, 1994;Zweben et al, 1994;Bierwirth and Mattfeld, 1999;Chien et al, 2000;El Sakkout and Wallace, 2000;Kramer and Smith, 2004). The approach taken in the DS descends from Smith (1994) and combines the use of strong domain heuristics with local repair-based search techniques to strike a balance between optimization and solution stability.…”

Section: Article In Pressmentioning

confidence: 99%

“…The approach taken in the DS descends from Smith (1994) and combines the use of strong domain heuristics with local repair-based search techniques to strike a balance between optimization and solution stability. Other work (e.g., Zweben et al, 1994;Bierwirth and Mattfeld, 1999;Chien et al, 2000), places greater reliance on broader searchbased processes, which also provides a basis for efficient, ''anytime'' scheduler response but with less emphasis on minimizing change.…”

Section: Article In Pressmentioning

confidence: 99%

Airlift mission monitoring and dynamic rescheduling

Wilkins

Smith

Kramer

et al. 2008

Engineering Applications of Artificial Intelligence

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We describe the Flight Manager Assistant (FMA), a prototype system, designed to support real-time management of airlift operations at the USAF Air Mobility Command (AMC). In current practice, AMC flight managers are assigned to manage individual air missions. They tend to be overburdened with associated data monitoring and constraint checking, and generally react to detected problems in a local, myopic fashion. Consequently, decisions taken for one mission can often have deleterious effects on others. FMA combines two key capabilities for overcoming these problems: (1) intelligent monitoring of incoming information (for example, weather, airport operations, aircraft status) and recognition of those situations that require corrective action and (2) dynamic rescheduling of missions in response to detected problems, both to understand the global implications of changed circumstances and to determine appropriate rescheduling actions. FMA builds on two existing technologies: an execution-monitoring framework previously applied to small-unit operations and control of robots, and a dynamic scheduling tool that is transitioning into operational use in AMC's Tanker/Airlift Control Center. FMA's dynamic-mediation module provides for collaborative mission management by different planning and execution offices by structuring communication for decision making. r

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Production Scheduling and Rescheduling with Genetic Algorithms

Cited by 238 publications

References 2 publications

Genetic programming for production scheduling: a survey with a unified framework

Genetic programming for production scheduling: a survey with a unified framework

Energy Efficient Production Planning

Airlift mission monitoring and dynamic rescheduling

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