Equipment scheduling problem under disruptions in mail processing and distribution centres

This paper addresses the production and workforce scheduling problem under disruptions in United States Postal Service mail processing facilities. These facilities contain a large variety of equipment and employ a non-homogeneous workforce that work on shifts of various lengths and start times. Disruptions such as demand fluctuation and absenteeism happen and may significantly change demand and the size of workforce. How to adjust production plans and workforce schedules through the use of overtime and flexible employees in the face of these disruptions to meet the service commitment is a challenging problem yet to be solved. This problem is modelled as a large-scale integer program, which contains equipment scheduling, shift scheduling and overtime management, and break assignment modules. Problems of realistic size are solved efficiently through an LP-based decomposition algorithm. Comprehensive experiments have been designed to investigate the effects of the use of overtime, the control of absenteeism, and the importance of integrating equipment and workforce scheduling simultaneously. The model integrates seamlessly with other research studies and provides the necessary and critical tools to manage the resources in a facility on a routine basis.

[1]  Jonathan F. Bard,et al.  Staff scheduling at the United States Postal Service , 2003, Comput. Oper. Res..

[2]  Jonathan F. Bard,et al.  Staff scheduling in high volume service facilities with downgrading , 2004 .

[3]  Jonathan F. Bard,et al.  Equipment scheduling at mail processing and distribution centers , 2005 .

[4]  Stephen E. Bechtold,et al.  Implicit modeling of flexible break assignments in optimal shift scheduling , 1990 .

[5]  Oded Berman,et al.  Optimal workforce configuration incorporating absenteeism and daily workload variability , 1993 .

[6]  Jeremy F. Shapiro,et al.  Chapter 8 Mathematical programming models and methods for production planning and scheduling , 1993, Logistics of Production and Inventory.

[7]  Xiangtong Qi,et al.  Disruption management in production planning , 2005 .

[8]  George L. Nemhauser,et al.  Handbooks in operations research and management science , 1989 .

[9]  Jonathan F. Bard,et al.  Equipment Selection and Machine Scheduling in General Mail Facilities , 1994 .

[10]  Jonathan F. Bard Selecting the appropriate input data set when configuring a permanent workforce , 2004, Comput. Ind. Eng..

[11]  Jonathan F. Bard,et al.  DESIGN OF SEMI-AUTOMATED MAIL PROCESSING FACILITIES , 1993 .

[12]  Jean-François Cordeau,et al.  Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling , 2000, Transp. Sci..

[13]  Jonathan F. Bard,et al.  Workforce planning at USPS mail processing and distribution centers using stochastic optimization , 2007, Ann. Oper. Res..

[14]  Jeremy F. Shapiro,et al.  Mathematical programming models and methods for production planning and scheduling , 1988 .

[15]  Jonathan F. Bard,et al.  Comparative approaches to equipment scheduling in high volume factories , 2006, Comput. Oper. Res..

[16]  Jonathan F. Bard,et al.  Solving large-scale tour scheduling problems , 1994 .

[17]  Matteo Fischetti,et al.  Local branching , 2003, Math. Program..

[18]  Jonathan F. Bard,et al.  Weekly staff scheduling with workstation group restrictions , 2007, J. Oper. Res. Soc..

[19]  Andreas T. Ernst,et al.  Staff scheduling and rostering: A review of applications, methods and models , 2004, Eur. J. Oper. Res..

[20]  François Soumis,et al.  An integrated aircraft routing, crew scheduling and flight retiming model , 2005, Comput. Oper. Res..

[21]  Jonathan F. Bard,et al.  Weekly scheduling in the service industry: an application to mail processing and distribution centers , 2005 .

[22]  Richard C. Larson,et al.  Scheduling workforce and workflow in a high volume factory , 1997 .

[23]  J. C. Goodale,et al.  Schedule Recovery: Unplanned Absences in Service Operations , 2003, Decis. Sci..