Strategies for the Coupling of Autonomous Control and Central Planning: Evaluation of Strategies Using Logistic Objectives Achievement and Planning Adherence

Autonomous control methods are a promising approach for manufacturing companies to cope with dynamic influences. An important issue for the acceptance of autonomous control is to provide sufficient planning accuracy. Therefore, transferring autonomous control into practical application requires the harmonization of autonomous control with commonly used, central planning methods. This paper introduces coupling strategies with different degrees of adherence to planning parameters for the harmonization of central planning and autonomous control. The simulation based evaluation considers both logistic objectives achievement and the adherence to predefined planning parameters. Besides varying the level of dynamic influences, the simulation study also analyzes the impact of production schedules with different levels of temporal flexibility by inserting additional idle time into the schedule. The results serve as a guideline for the selection of suitable coupling strategies depending on dynamic influences and user specific requirements to the strategies' performance.

[1]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[2]  D. S. Palmer.  Sequencing Jobs Through a Multi-Stage Process in the Minimum Total Time—A Quick Method of Obtaining a Near Optimum , 1965 .

[3]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[4]  J. Curry,et al.  Rescheduling parallel machines with stepwise increasing tardiness and machine assignment stability objectives , 2005 .

[5]  David G. Dannenbring,et al.  An Evaluation of Flow Shop Sequencing Heuristics , 1977 .

[6]  Bernd Scholz-Reiter,et al.  Evaluation System for Autonomous Control Methods in Coupled Planning and Control Systems , 2015 .

[7]  Jeffrey W. Herrmann,et al.  Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods , 2003, J. Sched..

[8]  Frank Werner,et al.  1 An Evaluation of Sequencing Heuristics for Flexible Flowshop Scheduling Problems with Unrelated Parallel Machines and Dual Criteria , 2005 .

[9]  F. Musharavati.  RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

[10]  K. Windt,et al.  Allocation Flexibility-A New Flexibility Type as an Enabler for Autonomous Control in Production Logistics , 2008 .

[11]  Bernd Scholz-Reiter,et al.  Autonomous Control of a Shop Floor Based on Bee's Foraging Behaviour , 2007, LDIC.

[12]  B. Scholz-Reiter,et al.  The Influence of Production Networks ’ Complexity on the Performance of Autonomous Control Methods , 2006 .

[13]  Bernd Scholz-Reiter,et al.  Dynamic flexible flow shop problems—Scheduling heuristics vs. autonomous control , 2010 .

[14]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[15]  Jatinder N. D. Gupta,et al.  A Functional Heuristic Algorithm for the Flowshop Scheduling Problem , 1971 .

[16]  J. Christopher Beck,et al.  Slack-based Techniques for Robust Schedules , 2014 .

[17]  Till Becker,et al.  A classification pattern for autonomous control methods in logistics , 2010, Logist. Res..

[18]  R. A. Dudek,et al.  A Heuristic Algorithm for the n Job, m Machine Sequencing Problem , 1970 .

[19]  Heinrich Kuhn,et al.  A taxonomy of flexible flow line scheduling procedures , 2007, Eur. J. Oper. Res..