Opreations research  (OR) ²¤¶

 

1.1 The Art and Science ¬OÃÀ³N¤]¬O¬ì¾Ç

Opreations research seeks the determination of the best (optimum) course of action of a decision problem under the restriction of limited resources.

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Operations research is the use of quantitative models to analyze and predict the behavior of systems that are influenced by human decisions.

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OR  quite often is associated with the use of mathematical techniques to model and analyze decision problems. (Science)

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Decision  problems usually include important  intangible  factors that  cannot be translated directly in terms of the  mathematical model.  (Art)

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As a problem solving technique, OR must be viewed as both a science and an art. The science aspect lies in providing mathematical techniques and algorithms for solving appropriate decision problems. OR is an art because success in the phases that precede and succeed the solution of a mathematical model largely depends on the creativity and personal ability of the decision-making analysts.

°µ¬°¤@­Ó´£¨Ñ¸Ñ¨M°ÝÃD¤èªkªº§Þ³N¡A§@·~¬ã¨s¥iµø¬°¬O¤@ªù¬JÃÀ³N¤]¬ì¾Çªº¾Ç°Ý¡A¦]¬°¦b¨D¸Ñ¬Y¨Ç¨Mµ¦°ÝÃD®É¯A¤Î¼Æ¾Ç§Þ¥©»Pµ¦²¤¡A©Ò¥H¬O¬ì¾Ç¡A¦ý¦b«Ø¥ß¼Ò¦¡¤§«e»P¨D¸Ñ¤§«áªº¸¨¹ê«h¯A¤Î¨Mµ¦¤ÀªRªÌªº³Ð³y¤O»P­Ó¤H¯à¤O¡A©Ò¥H¤]¬OÃÀ³N¡C

 

Gathering of the data for model construction¸ê°Tªº¦¬¶°

Art

model construction, solution

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Science

Model validation

¼Ò¦¡ªºÅçÃÒ

Art

Implementation of the obtained solution

µ²ªGªº¥I½Ñ¹ê¦æ

Art

 

The effect of human behavior has so influenced the decision problem that the solution obtained from the mathematical model is deemed impractical.

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Example: Elevator problem ¹q±è°ÝÃD: Solution from waiting line (queuing) model was found unsatisfactory. Problem solved by installing full-length mirrors.

 

1.2 Principal Components of Decision Problems ¨Mµ¦°ÝÃDªº¥D­n²Õ¦¨

 

Principal Components(¥D­n²Õ¦¨):objective ¥Ø¼Ð and variables ¨Mµ¦ÅܼÆ

 

An objective is the end result we desire to achieve by  selecting a specific course of action for the system under study.

 

Maximize profit, flow

Minimize cost, time, distance or

better quality (intangible) are common objectives.

 

A decision problem arises because the decision maker is  usually confronted with more than one course of action. Once an objective is defined,  the optimum decision is selected as the  course  of action  that best meets the objective. However, the 'quality' of the chosen decision depends on whether or not all the alternative courses of action are known to the decision maker.

 

The determination of possible courses of  action  represents  a crucial  aspect of the decision problem. It entails identifying the variables (governing parameters) of the system that are under the control of the decision-maker.

 

1.3 Art of Modeling ¼Ò¦¡«Ø¥ßªºÃÀ³N

 

Two basic components are essential for constructing a model:

1.    The objective of the system ¨t²Îªº¥Ø¼Ð

2.    The constraints imposed on the system ¨t²Îªº­­¨î

 

Both the objective and constraints must be expressed in terms of the controllable variables (courses of action) of the system.

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The analysis of the model should then yield the best course of action (in terms of the objective) that satisfies all the system's constraints.

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1.3.1 Types of OR models§@·~¬ã¨s©Ò±´°Qªº¼Ò¦¡

 

l        Symbolic  or  mathematical model (the most  important  type) ²Å¸¹¦¡ªº¼Ò¦¡©ÎºÙ¼Æ¾Ç¼Ò¦¡:  It assumed  that  all the relevant variables,  parameters  and  constraints as well as the objective are quantifiable. ¬O©Ò¦³¼Ò¦¡¤¤³Ì­«­nªº¤@ºØ¡A¦¹ºØ¼Ò¦¡°²³]©Ò¦³ªºÅܼơB°Ñ¼Æ¡B­­¨î»P¥Ø¼Ð¬Ò¥i¶q¤Æ¡C

 

l        Simulation model ¼ÒÀÀ¼Ò¦¡: It 'imitate' the behavior of the system over a period of time. This is achieved by specifying a number of events that are points in time whose occurrence signifies that important information pertaining to the behavior of the system can be gathered (discrete event). Once such events are defined, it is necessary to pay attention to the system only when an event occurs. The information yielding ¡¥measures of performance¡¦ for the system is accumulated as statistical observations,  which  are updated as each event takes place. ¼ÒÀÀ¼Ò¦¡¥é·Ó¨t²Îªº¦æ¬°°õ¦æ¤@¬q®É¶¡¡A¨ä°µªk¬O©w¸q¤@¨Ç¨Æ¥ó (event)¨Ó¥Nªí¨t²Îªº¦æ¬°¡A¦¬¶°¤@¬q®É¶¡¤ºµo¥Í¦¹¨Ç¨Æ¥óªº¬ÛÃö¸ê°T(¨t²Îªºª¬ºA)§Y¥iÁA¸Ñ¨t²Îªº¦æ¬°¡C¦¹¨Ç¨Æ¥ó¨Ã¤£·|³sÄòµo¥Í©Ò¥H¤@¯ëºÙ¬°Â÷´²¨Æ¥ó(discrete event)¡C¥u¦³¦b¨t²Îµo¥Í¦¹¨Ç¨Æ¥ó®É¨äª¬ºA¤~·|¦³©Ò§ïÅÜ¡A¬ö¿ý¦¹¨Ç¥i±o¥X¨t²Î¦æ¬°«ü¼Ðªºª¬ºA­È¡A³z¹L²Î­p¤ÀªR¡A¥i§ó·s¨t²Î¦æ¬°ªº«ü¼Ð­È¡C

 

l        Heuristic model ±À²z¼Ò¦¡: Sometimes the mathematical formulation may  be too  complex to allow an exact solution or the required  computation  may be impractically long. In this case, heuristics can be used to develop good (approximate) solutions. Heuristics are search procedures that intelligently move from one solution point to another with the objective of improving the value of the model objective. When no further improvements can be achieved, the best-attained solution is the approximate solution to the model. ¦³®É¼Æ¾Ç¼Ò¦¡¤Ó¹L½ÆÂø©Î«ÜÃø­pºâ¡A¦¹®É±À²z¼Ò¦¡¥i¥Î¨Ó¨D¥Xªñ¦ü¸Ñ¡C±À²z¬O¤@ºØ¦³´¼¼zªº·j´Mµ{§Ç¡A¥Ñ¤@­Ó¸Ñ´Â¥t¤@­Ó§ó¨Î¸ÑÁÚ¶i¡A©Ò¿×§ó¨Î«Y¥Ñ¼Ò¦¡¤¤©w¸qªº¥Ø¼Ð¨Ó¨M©w¡C·íµLªk¦A§ä¥X§ó¨Î¸Ñ®É¡A©Ò±oªº¸Ñ·¥¬°¨t²Îªºªñ¦ü¸Ñ¡C

 

1.3.2 Type of OR problems §@·~¬ã¨s©Ò±´°Qªº°ÝÃD

 

Sequencing

±Æ§Ç

Allocation

¤À¬£

Routing (Networking)

¸ô®| (ºô¸ô)

Replacement

§ó·s

Inventory

®w¦s

Queuing

±Æ¶¤

Competitive

Ävª§

Search

·j´M

 

¦¹¨ÇÃþ«¬¤¤¤§¨C¤@Ãþ³£¤w¦³¼Æ¾Ç¼Ò¦¡¤§«Ø¥ß¡CµM¦Ó¡A·í½ÆÂø«×¼W¥[¡A¯Â²z½×Ãø¥H¨D¸Ñ®É¡A¡u¼ÒÀÀ¡v¡]Simulation¡^¥i¯à·|¬O°ß¤@¨D¥Xµª®×ªº¤èªk¡C

 

Sequencing

  ±Æ§Ç°ÝÃD¡G

±´°Q¥H¤@­Ó¤@©w¶¶§Ç©Î¦¸²Ä©ñ¸mª««~¥H«KªA°Èªº°ÝÃD¡C

Ä´­Y¡G¦b¤@­Ó¤u§@¯¸ùØ¡AN ­Ó¤u§@¦b¤£¦Pªº¾÷¾¹ùػݤ£¦Pªº¤u§@®É¶¡¡A¨C¤@­Ó¤u§@³£¥²»Ý¥H¦P¼Ëªº¦¸§Ç¦b M³¡¾÷¾¹¤W¥[¤u¦Ó¤£¯à¸õ¹L¡A«hÀ³¸Ó¦p¦ó¦w±Æ¦U¤u§@ªº¦¸§Ç¡A¥H¨Ï¦b©Ò¦³¾÷¾¹¤W¥þ³¡¤u§@ªº¥[¤uÁ`®É¶¡¬°³Ì¤Ö?

Allocation

   ¤À°t°ÝÃD¡G

¥H¬YºØµû¦ô®Ä²v¡]measure of effectiveness¡^ªº­ì«h¨Ó¤À°t¦UºØ§@·~ªº¸ê·½¡C

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¤H­û

Job 1

Job 2

Job 3

A

2

6

3

B

8

4

9

C

5

7

8

 

Routing 

  ¸ô®|°ÝÃD¡G

±q°_ÂI¨ì²×ÂI¦³³\¦h¥i¦æªº¸ô®|¡A­n±q¤¤§ä¥X¤@±ø³Ì¨Îªº¸ô®|¡C

Replacement  §ó·s°ÝÃD¡G

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Inventory

   ®w¦s°ÝÃD¡G

¨M©w¬Y¨Ç²£«~©Î¹s²Õ¥ó¤§®w¦s¶q¦h¹è¡A»P¸É³f®É¾÷¡C

Queuing 

   ±Æ¶¤°ÝÃD¡G

¤SºÙ¡uµ¥­Ô½u¡v¡]Waiting line¡^°ÝÃD¡A¥ô¦ó¤@­Ó¯A¤Îµ¥­ÔªA°Èªº°ÝÃD§¡Äݤ§¡C

¿ì¤½¤j¼Ó»Ý´X­Ó¹q±è?»È¦æÂd¥xµ¡¤f­n¶}´X­Ó? ¥[ªo¯¸­n¦³´X½u?        

¬Y¤½¥q¦³¤Q¥x¾÷¾¹¡A¦ý¦]¦~¤[»Ý¸g±`ºû­×¡A»Ý¹µ¥Î´X­Ó¾Þ§@­û¡A´X­Ó­×²z­û?

Competitive  Ävª§°ÝÃD¡G

 

Search       ·j´M°ÝÃD¡G

 

 

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¨Ã´î¤Ö

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¶i¦Ó·¥¤p¤Æ

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1.4 A survey of OR activities at the Corporate Level ·~¬ÉÀ³¥ÎORªº½Õ¬d

 

Table 1.1 Use of OR in current activities (1972 Turban)

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¤èªk

±M®×¼Æ

¨Ï¥ÎÀW²v

²Î­p¤ÀªR

63

29

¼ÒÀÀ

54

25

½u©Ê³W¹º

41

19

¦s³f²z½×

13

6

­p¹ºµû®Ö³N

13

6

°ÊºA³W¹º

9

4

«D½u©Ê³W¹º

7

3

±Æ¶¤²z½×

2

1

±À²z³W¹º

2

1

¨ä¥L

13

6

 

Table 1.2 Relative use of OR techniques (1977 Ledbetter »PCox)

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¤èªk

±M®×¼Æ

¨Ï¥Îµ{«×

1±q¥¼¥Î

5³Ì±`¥Î

°jÂk¤ÀªR

74

3.97

½u©Ê³W¹º

78

3.36

¼ÒÀÀ

70

3.31

ºô¸ô¼Ò¦¡

69

2.14

±Æ¶¤²z½×

71

1.96

°ÊºA³W¹º

69

1.62

¹ï§½²z½×

67

1.61

 

Table 1.3 Quality of results by firms employing mathematical programming

¦U¤½¥q¨Ï¥Î¼Æ²z³W¹ºªºµ²ªG¤ÀªR (1976 Fabozzi »PValente)

 

½u©Ê³W¹º

«D°ÊºA³W¹º

°ÊºA³W¹º

µ²ªG

¼Æ¥Ø

%

¼Æ¥Ø

%

¼Æ¥Ø

%

¨Î

102

76

38

57

27

53

¥¢±Ñ

21

16

19

28

15

29

®t

6

3

6

9

3

6

¤£½T©w

7

5

4

6

6

12

¦X­p

133

100%

67

100%

51

100%

 

     Table 1.4 Educational background of OR personnel

§@·~¬ã¨s±q·~¤H­û¤§±Ð¨|­I´º (1984 Hall)

¥D­×¾Ç¬ì

¾Ç¤h

ºÓ¤h

³Õ¤h

½Ñ¾Ç¦ì¦X­p

§@·~¬ã¨s¤ÎºÞ²z¬ì¾Ç

3

24

32

12

¼Æ¾Ç¤Î²Î­p¾Ç

26

16

21

22

¥ø·~ºÞ²z

20

27

2

22

¤uµ{

34

17

29

28

¨ä¥L

17

16

16

16

¦ûÁ`¼Æ¤§¦Ê¤À²v

27

53

20

 

 

 

1.5        Phases of OR study §@·~¬ã¨sªº¶i¦æ¨BÆJ

 

a.    Problem Definition ©w¸q°ÝÃD

(1)    description  of  the goal or the  objective  of  the study,

©ú½T´y­z¥Ø¼Ð

(2)    identification of the decision alternatives of  the system,

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(3)    recognition  of the limitations,  restrictions,  and requirements of the system.

ÁA¸Ñ¨t²Îªº©Ò¦³­­¨î»P»Ý¨D

b.    Model Construction ¼Ò¦¡«Ø¥ß

c.    Model Solution     ¼Ò¦¡¨D¸Ñ

d.    Model Validation   ¼Ò¦¡ÅçÃÒ

e.    Implementation of the final results ¼Ò¦¡¸¨¹ê