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Chapter 2 : Model of scheduling problemPowerPoint Presentation

Chapter 2 : Model of scheduling problem

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Chapter 2: Model of scheduling problem

- Components of any model:
- Decision variables
- What we can change to optimize the system, i.e., model output

- Parameters
- model input values, i.e., values that cannot be changed

- Objective function
- e.g. profit, quality to be maximized
- e.g. costs, time to be minimized

- Constraints
- determine which decision variable values are allowed

- Three basic types of decision variables:
- Sequence
- permutation of the jobs

- Schedule
- allocation of the jobs in a more complex environment

- Scheduling policy
- determines the next job, given the current state of the system

- Other variables are regarded as dynamic data, or as auxiliary variables:
- Completion time (Cij)
- Lateness (Lj)

- Tardiness (Tj)
- Makespan (Mj)

- Typical scheduling parameters:
- Number of resources (m machines, operators)
- Configuration and layout
- Resource capabilities
- Number of jobs (n)
- Job processing times (pij)
- Job release and due dates (resp. rij and dij)
- Job weight (wij) or priority
- Setup times

- Objectives and performance measures:
- Throughput, makespan (Cmax, weighted sum)
- Due date related objectives (Lmax, Tmax, ΣwjTj)
- Work-in-process (WIP), lead time (response time), finished inventory
- Total setup time
- Penalties due to lateness (ΣwjLj)
- Idle time
- Yield
- Multiple objectives may be used with weights

- Precedence constraints (linear vs. network)
- Routing constraints
- Material handling constraints
- (Sequence dependent) Setup times
- Transport times
- Preemption
- Machine eligibility
- Tooling/resource constraints
- Personnel (capability) scheduling constraints
- Storage/waiting constraints
- Resource capacity constraints

prec

sjk

prmp

Mj

- Single-machine vs. parallel-machine
- Flow shop vs. job shop

Processing characteristics:

- Sequence dependent setup times and costs
- length of setup depends on jobs
- sijk: setup time for processing job j after k on machine i
- costs: waste of material, labor

- Preemptions
- interrupt the processing of one job to process another with a higher priority

Generic notation of scheduling problem

Machine Job Objective

characteristics characteristics function

for example:

Pm | rj, prmp | ΣwjCj (parallel machines)

1 | sjk | Cmax (sequence dependent

setup / traveling salesman)

Q2 | prec | ΣwjTj (2 machines w. different speed, precedence rel., weighted tardiness)

- Deterministic models
- input matches realization

- vs.
- Stochastic models
- distributions of processing times, release and due dates, etc. known in advance
- outcome/realization of distribution known at completion

Chapter 3: general-purpose scheduling procedures

- Dispatching rules
- Composite dispatching rules
- Adaptive search (Handouts)
- Mathematical programming (Appendices A3, A4)
- Branch-and-bound
- Beam-Search

- Static rules
- not time-dependent
- are function of job and/or machine data

- vs.
- Dynamic rules
- time-dependent
- e.g.: “minimum slack first”-priority rule

- Local rules
- e.g. only consider current job, machine

- vs.
- Global rules
- “look ahead”-strategy

- Schedule generation scheme:
- mechanism by which schedule is constructed
- serial scheme
- schedule job-for-job (n stages)
- vs.
- parallel scheme (at most T stages)
- decision set (all candidates to be scheduled at stage t)
- completed set of jobs (jobs that are completed at stage t)
- remaining set of jobs (not allowed to be scheduled at t)
- active set of jobs (scheduled, but not completed at stage t)

- Service in random order (SIRO)
- Earliest release date (ERD)
- Earliest due date (EDD)
- Minimum slack (MS)
- Weighted shortest processing time (WSPT)
- Note: WSPT is optimal in single-machine setting (Smith’s rule)
- Longest processing time (LPT)

- Shortest setup time (SST)
- Least flexible job (LFJ)
- Greatest resource demand (GRD)
- Critical path rule (CP)
- Largest number of successors (LNS)
- Shortest queue at the next operation (SQNO)

- Dependent on release and due dates:
- ERD, EDD, MS
- Dependent on processing times:
- LPT, SPT, WSPT, CP, LNS
- Miscellaneous:
- SST, LFJ, SQNO

Dispatching in more complicated situations

- Dispatching+routing over non-identical machines
- Batch and dispatch
- Dispatching with preemption
- Dispatching with setups

More advanced dispatching rules

- Look-ahead with multiple job planning (e.g. including jobs with future release dates, dispatching with a waiting option, worst case slack rule)
- Adaptive search techniques
- Composite rules

Composite priority rule that is mixture of 3 basic priority rules:

- ATC ( apparent tardiness rule ) is comb. of:
- 1. Weighted Shortest Processing Time First
- 2. Earliest Due Date First
- 3. Minimal slack
- ATCS ( ATC with setups )
- 4. Shortest Setup Time First

Composite dispatching: rules: Apparent Tardiness Cost (ATC)

ATC combines MS rule and WSPT rule

k1=due date scaling par.

(look-ahead parameter)

average pj of the remaining jobs

k1 function of Due Date Range factor:

Composite dispatching: rules:Apparent Tardiness Cost with Setups (ATCS)

ATCS combines MS rule, WSPT rule and SST rule:

k1=due date scaling par.

k2=setup time scaling par.

k1 and k2 functions of:

- Due Date tightness
- Due Date Range
- Setup Time Severity

Dispatching rules: multiple passes rules:

drawback of priority rules:

may yield bad solutions

SOLUTION: Use multiple passes

Multi-pass priority rule based methods:

1. Multi-priority rule procedures (repeat dispatching procedure with different disp. rules)

2. Sampling procedures (each job has a probability to be dispatched)

Dispatching rules: sampling rules:

Random sampling (each job has the same probability):

Pj=1/n

Biased random sampling (based on priority of job):

first sort jobs on non-increasing priority vj

[j] is the position of job j in the list

Probability that job is selected:

Parameterized regret-based random sampling:

accounts for relative difference in priorities of jobs

random sampling

deterministic

Dispatching rules: sampling (cont.) rules:

- Parameterized regret-based random sampling:
- regret factor (vj=priority):
- probability:
- =0: random sampling
- =: deterministic activity selection
- Kolisch and Drexl:
- the more passes, the higher should be taken
- this method is called the “Adaptive Search”-method

normalization

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