A manpower allocation problem with layout considerations
18
A manpower allocation problem with layout considerations Luca Zeppetella, Elisa Gebennini, Andrea Grassi, Bianca Rimini Dipartimento di Scienze e Metodi dell’Ingegneria, Università degli Studi di Modena e Reggio Emilia 06/06/2022, Senigallia
A manpower allocation problem with layout considerations
In this study we investigate the problem of assigning tasks to operators in a facility characterized by longitudinal par- allel machines such as in a shop floor served by an overhead travelling crane. Given a master production schedule (MPS) the objective is to assign all the jobs scheduled on the machines (i.e., the tasks) to the operators in order to fill to capacity the available workforce minimizing the distance between operators and tasks. In the model we assume that one task, i.e., a particular production job processed by a particular machine, must be entirely completed by a single operator. Different levels of automation of the machines are considered, from manual machines that require a permanent employee to highly-automated machines where a single operator can oversee several machines. During the setup time or repair time of a machine the operator is considered free to operate on the remaining tasks assigned to him, if any. On the basis of the MPS the number of operators is pre-defined in the long-term planning horizon taking in consideration a fixed mean transfer time between the tasks, that are the different production jobs on different machines. This value has a huge uncertainty because it is highly influenced by the tasks allocation. In fact a simultaneous multiple allocation means a continuous back and forth of the operator between his assigned machines. The objective of the model is the maximization of the operators utilization through minimizing the operator-task distances. The backlogged work is not admitted, therefore each day is independent of the other days, so a daily staffing is modelled. The study arises from a specific real-world problem but it could be easily extended to other contexts in which the operator-task allocation is subject to spatial-layout considerations. In general, non-optimized operators’ travel times may result in production losses, i.e., machine blocking and work in progress.
Citation preview
1. Dipartimento di Scienze e Metodi dellIngegneria,Universit
degli Studi di Modena e Reggio EmiliaA manpower allocation problem
with layoutconsiderationsLuca Zeppetella, Elisa Gebennini, Andrea
Grassi, Bianca Rimini15/09/2014, Senigallia
2. Employees work or walkAmazon said: Some of the positions in
our fulfilment centresare indeed physically demanding, and some
associates maylog between 7 and 15 miles walking per shift. We are
clearabout this in our job postings and during the screening
processand, in fact, many associates seek thesepositions as they
enjoy the activenature of the work. [Financial Times, February 8,
2013] 3. A constraining layoutThe personnel scheduling is highly
influenced bythe facility layoutWe want to fill to capacity the
manpoweravailable capacity, minimizing the traveldistances in order
to increase the effectivepersonnel utilizationThe scenario is the
operator overseeing ofparallel machines at once 4. Assumptions
Decomposition of the problem in time buckets(discrete time)
Constant time bucket duration No backlogged work each day is
independent In general, its preferable that a job is
entirelycompleted by a single operator Operators can work on
several machines at thesame time (parallel machines with single
operation) 5. Input Data Facility layout Nr. of machines Nr. of
operators Days-off and breaks schedule Master Production Schedule
6. Constraints Operator to a job iff active & 1 job 1 operator
Workload assigned is limited by time bucket duration j represents
the number of changes of operator forjob j 7. Objective
functionsOBJ_1 = minimization of thechangesOBJ_2 = minimization of
thedistancesThe objective functions are solved in lexicographical
order 8. Model math formulation 9. React to UncertaintyIn case of
unexpected events the model can berun again with a revised plan, in
order to manage:- Unexpected longer setups,- Machine Failures,-
Maintenances.The solutions always balance the workloadamong the
operators, this means equity andtherefore an increased motivation
10. Case Study - scenarioSince 1972, Ghepi has been active in the
field of plastics. Ghepi is a modernfamily company and its involved
in Project Development and OrderManagement, starting with
consulting on polymers and extending to mouldsand fluid mechanics
simulation for components, component and moulddesign, manufacturing
and supply according to the customer's logisticstandards. 11. Case
study1 2555 m678 9 10 111213141516 1718 1934 12. Case study - data
19 machines 15 employees (5 employees x 3 shifts) 39 jobs 3 shifts
of 8 hours each Setups & breaks included in the plan Distances
derived from the layout 1,3 m/s operators walking speed 13.
SolutionSolution obtained with LocalSolver- high computation times
(24 hrs)- no optimal solution- no proof of how good is the
solutionbutWe are working on a further extension with IBM
ILOGCPLEX, that for quadratic problems offers differentmethods
(Dual and Primal simplex, Network optimizer, Barrieralgorithm,
Sifting and Concurrent algorithm) 14. Case study - solution 15.
Case study - solution 16. LimitationNo check on the manpower
dimensioning- If the number of the operator is not sufficient for
allthe scheduled jobs the model results in a not feasiblesolution-
Otherwise the workload is balanced among all theavailable operators
17. Further extensions Ability: binary, hierarchical and dynamic
competences Labor limitations: by laws and medical prescriptions
MOST technique to evaluate man fraction in advance Employees and
employers preferences Develop training plans to support
employeesmotivation and development 18. Thanks for the
Attention
