MANUFACTURING MODELS

Transcription

1 MANUFACTURING MODELS Manufacturing models In manufacturing models: Resource is called a machine Task is called as job A job may be a single operation or a collection of operations to be done in several different machines. There are five classes of manufacturing models: Project planning and scheduling Job shop models Flexible manufacturing systems Lot scheduling Supply-chain models 27 1

2 Project planning and scheduling Project scheduling is important for large projects. A large project consists of a number of jobs with precedence constraints. Examples: construction of an aircraft large consulting project Goal: minimize completion time of last job (makespan) The critical path (set of jobs that determine the makespan) can be identified. 28 Job shop models Job shop scheduling (include single machine and parallel machine models). Jobs Minimize makespan or the number of late jobs. Mostly for make-to-order manufacturing systems. Also in services. 1 1, 2, 3 2 2, 1, 4, 3 3 1, 2, 4 Machine Sequence 29 2

3 Flexible manufacturing systems Production systems with automated material handling. Material handling or conveyor system controls the movement of jobs and timing of their processing. Mostly for mass production systems. Maximize throughput. Examples: automotive industry consumer electronics industry 30 Lot scheduling For medium and long term production planning. Processes are continuous. Switching between products incurs a setup cost. Minimize total inventory and setup costs. Examples: process industries, e.g. oil refineries paper mills 31 3

4 Supply-chain models In general, are an integration of job-shop and lot scheduling, including transportation costs. Material Flow Information Flow 32 Manufacturing models revisited Discrete models: project scheduling, job shop or flexible assembly systems. Formulated as an integer programming or disjunctive programming. Continuous models: lot scheduling. Formulated as a linear or nonlinear programming 33 4

5 Jobs and machines machines i = 1,,m jobs j = 1,,n Static data: Processing time p ij processing time of job j on machine i Q j = 1/p j, machine s production rate of job j Release date r j earliest time at which job j can start its processing Due date d j committed shipping or completion date of job j Weight w j importance of job j relative to the other jobs in the system 34 Modeling parameters Dynamic data: Starting time S ij time when job j starts its processing on machine i Completion time C ij time when job j is completed on machine i Model representation: machine configuration characteristics objectives 35 5

6 Completion time Resources Job 2 r 2 = 7 d 2 = 11 w 2 = 4 (?) i S i2 p i2 C i Time C4 C1 C2, C Completion time Resources Job 3 r 3 = 0 d 3 = 11 w 3 = 4 (?) i S i3 p i3 C i Time C4 C1 C2, C

7 Machine environment ( ) Single machine 1 Identical machines in parallel P m Job j can be processed in any of the m machines Machines in parallel with different speeds Q m Unrelated machines in parallel R m Open shop O m Jobs have to be processed in all m machines but in some the processing time may be zero 38 Machine environment ( ) Flow shop F m m machines in series and each job has to be processed on each of the m machines Flexible Flow shop FF c Generalization of F m and P m c stages in series with a number of parallel machines in each stage 39 7

8 Machine environment ( ) Job shop J m m machines and each job has its own predetermined route to follow If job may visit a machine more than once, it has recirculation Flexible job shop FJ c Generalization of J m and P m c work centers with a number of parallel machines in each work center 40 Constraints ( ) Release date r j job j may not be processed before r j Sequence dependent setup times s jk Setup time between job j and job k Preemptions prmp It is possible to interrupt job j Recirculation recrc In FJ c, when a job visits a machine more than once 41 8

9 Objective functions ( ) Lateness L j Tardiness T j L C d j j Tj max( L j,0) j Makespan C max C max max( C 1,, C ) j Maximum lateness L max L max max( L 1,, L ) j Total weighted tardiness j T j 42 Dispatching solutions Scheduling solutions are found using heuristics: Algorithm that is able to produce a simple and acceptable solution Usually it provides a fast good solution, but not the best one (optimal solution) 43 9

10 Single machine Single machine: When there is a single bottleneck in a multi-machine environment, that bottleneck is scheduled first. Earliest Due Date (EDD) orders the jobs in increasing order of their due dates. Minimize maximum lateness among all jobs Short Processing Time first (SPT) minimize the average number of jobs waiting for processing. 44 Exercise single machine 45 10

11 Solution a) Brut force: P 4 = 4! = 24 possible different sequences Consider Job Sequence #1: Job 1, w = 6 Job 2, w = 11 Job 3, w = 9 Job 4, w = Total Cost of Sequence #1 is given by J JS#1 = 6 x x x x 19 = 336 Follow the same procedure for the remaining and find out the optimal sequence such that the cost is minimal 46 Solution b) Higher Priority (HP) first Job Sequence HP: Job 2, w = 11 Job 3, w = 9 Job 1, w = 6 Job 4, w = Total Cost of Sequence HP is given by J HP = 11 x x x x 19 = 348 Worst than previous case! 47 11

12 Solution c) Shortest Processing Time (SPT) first Job Sequence SPT: Job 1, w = 6 Job 4, w = 5 Job 2, w = 11 Job 3, w = Total Cost of Sequence SPT is given by J SPT = 6 x x x x 19 = 349 Worst than previous cases! 48 Parallel machine Machines do not have to be identical. Machines can be old or new, or can be people

13 Exercise parallel machine 50 Solution a) Considering combinations of all possible different sequences, gives C 5 11 = 462 b) Longest Processing Time (LPT) first: whenever a machine is free, the next job with the longest processing time is assigned. Reasoning: it is easier to distribute short processing time jobs in the long run

14 Solution c) Possible solution M1: Job 1, p j = 9 M1: Job 9, p j = 5 M1: Job 11, p j = 5 M2: Job 2, p j = 9 M2: Job 10, p j = 5 M3: Job 3, p j = 8 M3: Job 7, p j = 6 M4: Job 4, p j = 8 M4: Job 8, p j = 6 M5: Job 5, p j = 7 M5: Job 6, p j = 7 C LPT max = 19 d) If C Optimal max = 15, then C LPT max = 19 is worse. 52 Solution c) Possible Solution p j / # machines = 75 / 5 = 15 :-) Fill in each machine at a time to complete 15 time units M1: Job 1, pj = 9 M1: Job 7, p j = 6 M2: Job 2, pj = 9 M2: Job 8, p j = 6 M3: Job 3, pj = 8 M3: Job 5, p j = 7 M4: Job 4, pj = 8 M4: Job 6, p j = 7 M5: Job 9, pj = 5 M5: Job 10, p j = 5 M5: Job 11, p j = 5 C max LPT =

15 Other machine configurations Flow shop: Flexible flow shop: 54 Other machine configurations Job shop: (can have recirculation) Flexible job shop (several machines in parallel) Supply- chain 55 15

16 Processing characteristics Precedence constraints one or more jobs may have to be completed before another job is allowed to start its processing Machine eligibility constraints in parallel machine environment, M j denotes the subset of machines that can process job j 56 Processing characteristics Workforce constraints workforce consists of several pools each pool consists of operators with a specific skill set number of operators in pool l is denoted by W l Routing constraints specify the operations for each job and the machines at which these operations must be processed. Material handling constraints are fixed for automated (e.g., robotized) work centers and adjustable for manual tasks

17 Processing characteristics Sequence-dependent setup times and costs length of setup (reconfiguration or cleaning) depends on jobs s ijk : setup time for processing job k after job j on machine i c ijk : setup costs, e.g. waste of raw material, labor Storage space and waiting time constraints amount of space available for Work-In-Progress (WIP) is limited. Completion time Start time Buffer space 58 Processing characteristics Make-to-Stock and Make-to-Order D j demand rate fixed and constant. Items are produced for inventory and do not have tight dates. Make-to-Order jobs have specific due dates; amount produced is determined by the costumer. Preemptions interrupt the processing of a job to process another one with higher priority. Preemptive resume processing done is not lost Preemptive repeat processing must be completely repeated Transportation constraints, etc

18 Performance measures and objectives Througput is frequently determined by the bottleneck machines, for which the utilization should be maximized. Makespan the time when the last job leaves the system C max max( C1, C2,, C n ) where C j is the completion time of job j. Minimizing makespan tends to maximize throughput and balance load. 60 Performance measures and objectives Due date related objectives Lateness L j C j d j where d j is the due date of job j. Maximum lateness (minimize worst performance) L max max( L 1,, L n ) 61 18

19 Due date related objectives Tardiness T j max( C j d j,0) Objective function Weighted Tardiness n T j j 1 n j 1 w j T j 62 Due date related objectives Lateness Tardiness L j T j C j C j d j Late or Not d j In practice 1 U j C j C j d j d j 63 19

20 Performance measures and objectives Work-In-Process (WIP) inventory costs Minimizing WIP also minimizes average throughput (lead) time, which is the time it takes a job to transverse the system. Equivalent to minimize the average number of jobs in the system. Minimizing average throughput time is closely related to minimize the sum of completion times: n j 1 C j n j 1 w C j j 64 Other costs and concepts Setup costs Finished goods inventory costs Transportation costs In Just-In-Time (JIT) concepts, it is important to minimize the total earliness. A job should be completed just before its committed shipping, avoiding inventory and handling costs. Robustness. A schedule is robust when the necessary changes in case of disruption (e.g. machine breakdown, rush order) are minimal

21 SERVICE MODELS Introduction Impossible to store goods It is not possible to get back the lost time in a hotel room. Resource availability (e.g. people, rooms or trucks) often varies May even be part of the objective function Saying no to a customer is common No available seats on that flight (even if there are some!) Try to book a restaurant for 8 or 9 PM 67 21

22 Reservation systems and timetabling Reservation systems A job j has a duration p j and the starting and completion times are usually fixed in advance. Example: in a car rental agency, a job is the reservation of a car for a given period. Timetabling (rostering) A job or activity j with a duration p j, which has to be scheduled in a time window in the interval: [earliest possible starting time r j, latest possible completion time d j ] Examples: exam scheduling, scheduling operating rooms 68 Service models Tournament scheduling and broadcast television models Tournament scheduling parallel machine problem, where all the jobs have the same processing time. Transportation scheduling Examples: airlines, railroads, shipping. Job trip or flight leg; machine ship, plane or vehicle. Trip k incurs a cost c k and generates a profit k. Objective: minimize total cost or maximize total profit

23 Workforce scheduling Shift scheduling in service facilities Example: call center Time interval i requires a staffing of b i (integer). Objective: minimize total cost. Crew scheduling in transportation. Depends on the specific tasks to be done Crew scheduling is often intertwined with other schedules (e.g. routing and scheduling of planes or trucks). 70 Activities Examples of activities: meetings to be attended by certain people game to be played by 2 teams flight leg to be covered by a plane personnel position to be occupied in a given time period Data: duration processing time p ij earliest possible start time release time r j latest possible finishing time due date d j priority level weight w j 71 23

24 Resources Machines: classroom, hotel, rental car, stadium, operating room, plane, ship, airport gate, dock, railroad track, person (nurse/pilot) Synchronization of resources may be important Need a plane and a pilot Classroom, video projector equipment, professor, students Characteristics of resources Classroom: capacity, equipment, cost, accessibility. Truck: capacity, refrigeration, speed Person: specialist (surgeon, nurse) with skills (languages) 72 Operational characteristics Time windows (release dates and due dates) Capacity requirements and constraints Preemptions many activities are difficult to preempt (e.g. operations flight leg or game). Setup times and turnaround times Setups between consecutive meetings or trips. Example: Runway in an airport is a resource, takeoff and landing are activities. Necessary idle time between activities

25 Operational characteristics Operator and tooling requirements Workforce scheduling constraints Shift patterns, break requirements Union and safety rules 74 Objectives Combination of two types of objectives: one concerning the timings and other concerning the utilization of resources. General objective: minimize the total cost of all the assignments. Makespan Setup costs Earliness and Tardiness costs Personnel costs cost ideal departure time time 75 25

26 Computational complexity Easy problems: Sort n numbers Solve a system of linear equations Hard problems: Schedule a factory, deliver packages, schedule buses, 76 Computational complexity f (n): the number of basic operations needed to solve the problem with input size n Easy: f (n) is polynomial in n O(n), O(n log n), O(n 2 ), Hard: f (n) is exponential in n O(2 n ), 77 26

27 Hard vs Easy O(n) O(n log n) O(n 2 ) O(2 n ) ,000, Hard vs Easy O(n) O(n log n) O(n 2 ) O(2 n ) ,125,899,906,842, ,000,

28 Hard vs Easy O(n) O(n log n) O(n 2 ) O(2 n ) ,125,899,906,842, X ,000, X Hard vs Easy operations required in worst case Age of universe: seconds Fastest Computer today: operations/sec If it is going to take times the age of the universe to schedule a factory? May be it is possible to do it in a reasonable time in most cases! May be a good (but not the best) solution can be obtained in a reasonable amount of time! 81 28

29 P and NP problems The efficiency of an algorithm is measured by the maximum (worst-case) number of computational steps needed to obtain an optimal solution. Problems which have a known polynomial algorithm are said to be in class P; the effort of the algorithm is bounded by a polynomial function of the size of the problem. For NP (non-deterministic polynomial problems) no simple algorithm yields optimal solutions in a limited amount of computer time