Towards a knowledge-based approach to space planning in industry

141

Applications

Towards a Knowledge-Based Approach to Space Planning in Industry

P r a s a d J o s h i a n d R . S a d a n a n d a Computer Science Division, Asian Institute of Technology, Box 2754, Bangkok 10501, Thailand

Space planning involves typically, the organization of the space under a set of constraints to meet a given set of specifications. There are quite a few mathematical models describing the space planning problem. These abstract models aim at optimizing a certain predefined criterion function. However, these approaches need clear definition of the problem domain and precise measurement of the various parameters involved. In practice, none of these axe feasible. Moreover, even when the parameters are identified, there remains the issue of search in a large problem space.

In this paper, some of the well known approaches to the space planning problems are reviewed. The expert on the space planning is usually guided by the heuristics which are acquired through experience. This effort is towards building a knowledge-based system to capture such heuristics. The study describes how a knowledge management approach could be considered for solving space planning problems.

In particular we look at this problem in the light of organizing in a shop floor in a typical wire rope industry. A LiSp-based system is developed as a prototype. The paper includes a discussion of knowiedge-based approach and optimizing techniques and concludes with a recommendation for judicious combination of the two.

Keywords: Knowledge-based system, Artificial Intelligence, Space planning, Spatial reasoning, LISP, Shop floor planning.

1. In t roduc t ion

K n o w l e d g e - b a s e d Exper t Sys tem technology has been app l i ed to a var ie ty of p rob lems . I t has especia l ly been successful in d iagnos t i c p rob lems . K n o w l e d g e - b a s e d sys tems have been deve loped for faul t de tec t ion , p r e d i c t i on and in t e rp re t a t ion [2]. There have been also research a t t empt s to use t hem in p l a n n i n g a n d des ign systems [1]. How- ever, p l a n n i n g and des ign areas are p rov ing to be mos t useful as well as cha l lenging for the knowl - edge-based exper t systems. The heur is t ic na tu re of these p r o b l e m s makes t hem sui table for Ar t i f ic ia l In te l l igence techniques bu t at the same t ime their o p e n - e n d e d na tu r e cal ls for creat ivi ty.

Space p l a n n i n g is the process of selecting, loca t ing a n d spac ing ob jec t s to c rea te layouts based on funct ional , t opo log ica l or geometr ica l cons ider - at ions. I t involves the search, ca r r ied out in the p r o b l e m space, for feas ib le so lu t ions to the p rob - lem of spa t ia l a r rangement . Space p lanning , therefore, is conce rned wi th the o rgan iza t ion of space unde r a set of cons t ra in t s to meet a given set of specif icat ions . I t a ppe a r s poss ib le to solve the spa t ia l a r r a n g e m e n t p r o b l e m s b y exhaust ive enu- m e r a t i o n of all the poss ib le a r r angemen t s and then select ing a p l an which bes t suits the specif ied

Pras~ Joshi is an Electrical Engineer- ing Graduate from the University of Poona, India and holds a M. Eng. degree in Computer Science from the Asian Institute of Technology, Bang- kok. His research interests are in knowledge-based Systems, spatial reasoning and computer vision. Presently, he is a Research Associate at AIT. Mr. Joshi is soon expected to resume his research activities at Institut National de Recherche eta Informatique et en Automatique (INRIA), France.

Elsevier Science Publishers B.V. Computers in Industry 13 (1989) 141-154

0166-3615/89/$3.50 © 1989 Elsevier Science Publishers B.V.

R. Sadananda is Associate Professor in the Division of 'Computer Science, Asian Institute of Technology, Bang- kok, Thailand. Prior tO that he has held faculty positions at various uni- versities in India and was a Senior Fulbright Fellow at the University of Texas at Austin. With the PhD in Computer Science from the Indian In- stitute of Technology, Kanpur, Dr. Sadananda has interests in artificial intelligence and computer vision. He was the Chairman of the Organising

Committee for the International Conference on Expert Sys- tems for Development, held during March, 1989 at Kathmandu, Nepal.

142 Applications Computers in Industry

requirements. However, the search space turns out to be enormous, leading to combinatorial explosion. A space planner could aid in the process. The space planner converts the user's specification of spatial requirements into a spatial arrangement. The main role of the space planner is to assign given objects in a certain space.

Numerous computer programs have been developed since the 1960's to solve the spatial allocation problem. The objective and the levels of ambition of these programs have varied widely. Many of them have aimed at producing solutions that are optimal in some sense. A survey seeking information on the usefulness of computers in facilities layout [17] indicated some issues con- cerning space planning in practice. An overwhelm- ing majority of the responses indicated that these programs were used either to generate alternative layouts or to evaluate alternative layouts. The solutions obtained from the computerized systems often represent radical departures from the conventions. A good practical example of this was offered by CORELAP [17]. This program suggested that a receiving room of a warehouse be placed in the middle of the layout. It was later found that this absurd looking idea of allowing trucks to come right in the middle considerably minimized the internal material handling. It is this characteristic of the computers which proposes creative and provocative solutions which may otherwise be tot- ally overlooked by manual methods.

The optimization approach is ideally suitable where an optimal solution is desired. Modeling of the space planning problem as an optimization problem--i.e, quadratic assignment problem-- leads to combinatorial explosion. It can be shown that the quadratic assignment problems belong to the NP-complete class of problems [12]. It is generally accepted that efficient solution of NP-complete problems is impossible in principle and only heuristic solutions can be produced.

The representation of various constraints and requirements of a space planning situation in a mathematically precise formulation is hard. Often such effort forces one to make simplifying as- sumptions which may not be always justifiable.

Algorithms have been developed over the past 20 years to solve the spatial allocation problems. These algorithms since then have formed the basis of a number of computer programs. The objective of such programs is to locate a set of objects

within a space such that some criterion function is achieved. The criterion function may be defined in terms of contiguity of the working groups, ease of communication, utilization of space and flexibility to accommodate changes taking place over a period of time. The algorithms can be classified based on the strategies they use and the criteria function employed.

Based on the strategy employed, these algorithms can be classified as "construction type"and "improvement type" algorithms [17]. The construction type algorithms start from scratch. They select each design unit to be placed and find a location for that design unit. On the other hand, improvement type algorithms take up some initial layout and change the locations of the design units so as to improve the layout.

In the construction type algorithms, the objects are located one by one to build up a solution from scratch in a step by step fashion, e.g. CORELAP [10] and ALDEP [16]. Both the systems use the activity relationship chart which gives the relative closeness ratings of various departments.

In ALDEP, a department is selected randomly and assigned in the space. Once the first department is allocated, other departments are scanned for closeness rating and the one with the highest closeness rating is assigned next. However, if there is no department which satisfies this condition, again a random selection is made. The process stops when all the departments are allocated. The number of alternative layouts produced is con- trolled by specifying the number of iterations to be performed.

In CORELAP, instead of random selection, the selection is based upon the Total Closeness Rating (TCR). TCR of the ith department is defined as:

TCR,= ~'~ V(rij ) j=]

where m is the number of departments, V is the closeness rating, and rij is the shortest distance between department i and department, j.

The first department selected is the one with the highest TCR. Other departments follow according to their closeness ratings. In case this condition is not satisfied, again TCR helps in selecting the next department.

The improvement type algorithms begin with some initial arrangement and then attempts are

Computers in Industry P. Joshi, R. Sadananda / Towards a Knowledge-Based Approach to Space Planning 143

made to improve it by exchanging activities between locations, e.g. CRAFT [3]. CRAFT tries to minimize the cost of material movement. It is assumed that the cost is a linear function of the distance travelled. All the departments with a common border between them are considered in pairs. The system tries the pairwise exchange of these departments and checks the overall cost. If it is less than the initial cost then the change of locations is made. The process stops when there is no improvement possible in the layout.

Based on the criterion function, the algorithms can be classified as "optimizers" and "satisfiers" [9]. The optimizers attempt to find the best solution as measured by some objective function. This function can be the distance between the objects in the space, e.g. CRAFT. The satisfiers attempt to find any acceptable solution which satisfies a set of constraints, e.g. GSP [6].

The earlier space planning programs used optimizing formulations which are based on the tradi- tions described in operations research literature. It was first formulated as the "quadratic assignment" problem [12]. In this formulation a set of elements of identical size must be assigned to a set of spaces. All the dements must be located in exactly one space, and each space must contain no more than one element. Optimizing formulations have been primarily concerned with the cost of circula- tion, i.e. traffic within the plan. For the quadratic assignment problem certain exact algorithms can generate an optimal solution for problems with up to 15 elements but beyond that their computational demands become unreasonable. A number of suboptimal heuristic techniques have been used which can be classified into two groups; the "additive" and the "permutational". In additive techniques elements are added one after another into an initially empty space, e.g. CO,LAP, ALDEP. In permutational techniques the elements are assigned initial locations on a plan and are inter- changed until to further improvement can be made by another interchange, e.g. CRafT.

The quadratic assignment problem consists of finding, from among the set of possible solutions, a map such that it optimizes the value of an objective function. Consider the quadratic assignment problem in terms of a mapping of a set M = (1 ,2 , 3 . . . . . m) into a set N = (1 ,2 , 3 . . . . , n } where m <~ n. Each element of M is assigned to a distinct element of N. Thus mapping is a one-to-

one assignment of dements of one set to elements of another. A space planning problem can be thought of as a mapping of a set of objects into a set of locations where each object is assigned a distinct location.

Formulation of the space planning problem as the quadratic assignment problem involves following considerations.

Obtaining One-to-One Assignment Problem. The quadratic assignment problem assumes a one-to- one assignment of the objects to the locations. The optimal solution is the one which minimizes the objective function. Space planning problems are more complex than the other quadratic assignment problems due to the effects of the imposing area requirement. The areas required by the objects are not necessarily equal, so it is not feasible in general to match objects and locations on a one-to-one basis. As a result it is necessary to regard each space as composed of some number of equal-sized modules according to the required space area. Correspondingly, each object is parti- tioned into modules equal in size to the space modules. The problem is the one-to-one assignment of object modules to space modules.

Specification of the Objective Function. The purpose of generating a spatial arrangement is to produce an assignment of objects to space that minimizes an overall objective function, subject to meeting specified space requirements. The objective function is normally some function of the travel data or the trip rates. This implies that objects that are closely interrelated will normally be located close together, while objects which are not closely related will tend to be separated.

Satisfying formulations may be further classified either as "jigsaw formulations" or as "dissection formulations". In jigsaw formulations the program attempts to locate a number of non-over- lapping elements of rigid shape into the receiving space. They employ various heuristic search techniques, for order of adding elements and the choice of the location based on their size and constrainedness, to find acceptable solutions (General Space Planner [6]). In dissection formulations the dements have no predefined shape but are formed by dividing the space with partitions. These are primarily concerned with adjacency constraints on the dement areas. They have ex-


plored the use of graph theoretic representations for generating feasible layouts [13].

Spatial Reasoning and Artificial Intelligence

The work done in the domain of spatial reasoning can be classified into two categories: • Planning, and • Diagnosis/Interpretation. Before going into the details of the two, it is worth noting some of the inherent characteristics of the two problem classes.

In case of the diagnostic problems, the lowest level of interpretation of the problem is offered (e.g. symptoms of a disease) and a higher level of interpretation is required (e.g. the disease). In case of the planning problems the highest level of the representation (the goal) is offered and a lower- level representation is required to be generated (the plan). These two problem classes differ in important ways. For example, the diagnostic problems lend themselves well to the initial bottom up strategy while planning problems lend themselves well to the initial top-down strategy. This initial difference of the strategies itself makes the planning problem more difficult. Similarly, diagnostic problems generally permit only one or a small number of solutions, while the planning problems permit an arbitrary number of different solutions. Further, diagnostic problems typically have acceptable solutions while the acceptability of the solutions to planning problems varies under different evaluation criteria.

The early planning programs like ST~PS [8] used to generate a plan of action in robot world. This system used predicates like ON(a, b) and AT(robot, x) to define the spatial relationship amongst the objects. One of the first space planning programs was the General Space Planner-- GSP. It represented the problem as a constraint directed search. Eastman [5] has described the limitations of the graphics data structures for space planning application and has introduced new representations of the space. GsP used one of those, the variable array representation. The system used a heuristic algorithm and constraints like ADJAC- ENT were checked using the procedures written in ALGOL.

Space Planner for Architecture--SPA [20] is a system implemented in POP-2 for the design of bathrooms. The system used frame-based rep-

resentation of the space and the objects. The bargaining between the goals is used as the principle means of design problem solving. Another program in architectural design is TOPOLOGY [1]. This program relies on logic programming. The space and the objects are represented using predicates. The program can answer the questions regarding a given spatial arrangement. A space planning system for the layout of a supermarket [15] used the concept of linguistic quantifiers as a measure of distance specification.

The vision systems fall in the category of interpretation/diagnosis systems e.g. ACRONYM [4] PSIEKI [2]. These systems are able to find the instances of the known objects in an image. PSIEKI uses the expected scene in the form of a map to carry out the scene analysis. The Frame-based Object Recognition and Modelling, 3D-FOR~ system., [19] uses frames to represent the objects, its geometric features and relationships. Active procedures attached to the frames dynamically compute the values as needed allowing the system to carry out an opportunistic search.

Engelmore [7] comments about the present status of spatial reasoning in AI. The task of spatial reasoning is essentially knowledge based and not a matter of special hardware in the brain. This knowledge is compiled over a period of time and is potentially "open ended" i.e. almost any aspect of the experienced world might be relevant to the problem at hand. Because of this the pro- gress will come first in the specialised problems. Generalised tools and techniques are in an early stage of development.

2. Space Planning in Industry

Defining the boundaries on a space planning problem in industries is a complex task. In fact, the boundaries differ from one problem to the next. For example, a space planner might be called on to develop a space plan for a new plant and then his next assignment might be to determine the location for a new machine. Consequently, there is a tremendous variety in the types of space planning problems one may encounter. This variety is due, in part, to the number of ways these problems develop. For example, the problem might arise because of a change in the design of the product, a significant change in the demand for


the product, changes in the production process, adoption of new safety standards or a decision to build a new plant. The problem may also develop because of the gradual changes over time that lead to bottlenecks in the production, crowded condi- tions, unexplainable delays and idle time, ex- cessive temporary storage space, obstacles to material flow, failures to meet the schedule and high cost of material handling resulting into higher production cost.

2.1. Factors Affecting Space Planning

There are certain factors which affect the space planning process but cannot be easily interpreted in mathematical terms. The planner must be suffi- ciently acquainted with these factors and give them enough consideration before putting down the rules for space planning. Some of these factors are:

(1) Loading and Unloading Facilities. The raw material must be loaded and unloaded in coordi- nation with the internal activities on the shop floor. Enough space for product movement is required during the production process.

(2) Expansion. It must be realised that the enterprize may grow and require additional facilities. It is extremely important to make plans at the very beginning for the possibility of any future growth due to increased production, new product or any other reason.

(3) Flexibility. The shop floor is planned keep- hag in mind the kind of activities it is going to support. But there are occasions when it may be necessary to alter the original plans. The layout design must be such that it minimizes the cost of redesign as need arises. Many such changes can be easily made if the floor was made flexible and these changes were anticipated in the original plan.

(4) Employee Interaction. This is an important consideration while allocating space for a shop floor. It may be desired to locate departments in such a way that the employees are grouped together. This will ensure professional interaction and therefore greater efficiency. This also reduces the manpower required for supervision.

(5) Material Handling Efficiency. In a manufac- turing plant, the material handling efficiency is of utmost importance. A lower efficiency will result in higher operational cost in the long run.

(6) Inventory Minimization. This criterion is especially important in case of storage areas on the shop floor. A good space allocation system should ensure that the inventory carded on the shop floor is kept to a minimum.

(7) Standards and Conventions. Standards like the Occupational Safety and Health Act (OSHA) have placed restrictions on the allowable noise levels. Along with this there are other standards which govern the aisle widths, ventilation, temper- ature and lighting. These standards put constraints on the space plan.

(8) Space Geometry. The space geometry can affect the spatial arrangement significantly. In case of industrial space planning problems, the shape of the department is equally important. The shape of the objects is predefined and acts as a con- stralnt in the planning. In case of an existing space plan, the location of the existing facilities, loading docks and their weight capacity, windows, lights and sewage, water and power lines also decide the new space plan.

2.2. Classification of Departments

It is often seen that the relative positions of the departments are dependent on the interactions between them. The need for departmentalisation arises from the point of view of material handling efficiency, ease of supervision, span of control and the functional responsibilities. The departments may be classified using the following criteria.

(1) Functional Similarity. The departments which perform similar functions can be classified in one group. For example, in a wire rope industry all the drawing machines can be grouped together to form the wire mill.

(2) Processing Requirement. In a shop floor, the production processes can be classified into preprocessing and the actual product processing. Based on this the departments can be formed. For example, in a wire rope industry, furnace is the preprocessing department and the ropery, a product processing department.

(3) Physical Characteristics. The departments may be classified on the basis of their physical characteristics such as the pollution created or the weight. For example, the noisy departments could be grouped, as well as the heat-producing ones. A third characteristic could be hazardous equipment, like acid tanks or electrical installations.

146 Applications Computers in lndusto,

a

g

(4) Cascaded Relationship. In this type the production process continues sequentially, with prod- ucts being processed in a step-by-step fashion (Fig. l(d)).

3. Design and Implementation of Space Planning System

3.1. Problem Formulation

d

Fig. 1. Types of interdepartment relationships: (a) bilateral; (b) multilateral; (c) star; (d) cascade.

(4) Supervision / Control Requirements. In case of the shop floor, the managerial control as well as the control over the machines forms an important criterion for grouping the departments. A good example is that of Q.C. and the other departments.

(5) Ease of Material Handling. This criterion forms and important issue. The material movement facility governs the arrangement of the departments. The weight limitations put by the material handling equipment have to be considered while forming the departments.

2.3. Types of lnterdepartment Relationships

Several types and patterns of material flow are observed in the industries. These patterns decide the interdepartment relationships. The type of these relationships could be enumerated as follows:

(1) Bilateral Relationship. In this type the material flows "from" or " to" only one department (Fig. l(a)).

(2) Material Relationship. In this type one department may send material to other departments and may receive material from other departments (Fig. l(b)).

(3) Star Relationship. In this type one department is the key department and all other departments serve it. A typical example is the assembly shop where components from various departments are assembled centrally (Fig. l(c)).

The space planning problem consists of three parts: a set objects to be located, a receiving space in which to locate these objects and a set of constraints which govern this arrangement.

A major objective in the space planning problems is maximum utilization of the space and minimization of the weighted distance between an arranged set of objects. The objective function is

rnln ~ ~ dijwij i=1 j ~ l

subject to the limitation that only one object can occupy a location. For most of the problems, this one relation requires combination with many other relations for the final objective function. The other relations could be accessibility, direct adjacency, sightlines, specific distance constraints etc.

In general, the space planning problem can be formulated as follows:

Given s - -a space; ( d 1, d 2, d3,. . . , d n }--a set of objects to be located in that space; { cl, c2, c 3 . . . . . c , ) - - a set of constraints controlling the allocation; (0~, 02, 03 . . . . . on }--a set of operators for manipulating the location or the shape of the object within the space and ao--the initial state of allocation. It is required to find a set of operators of the form

aa+t ~-.-(a B, d B, o B)

that eventually generates a state a f such that

af*-- (c 1, c2, ca,.. , c n )

The operators sequentially manipulate one or more objects at a time, generating a sequence of transformation upon the states until the final state, satisfying all the constraints, is reached,

The problem formulation describes a search tree where each node on the tree is a result of an operator being applied to a previous problem state.

Computers in Industry P. Joshi, R, Sadananda / Towards a Knowledge-Based Approach to Space Planning 147

There may be many, one or no solution to any particular problem.

3.2. Representation of Space

The representation of three-dimensional objects in two-dimensional space is that of their projection on a horizontal plane. The projection of most of the objects of irregular shape can be approxi- mated to a rectangle. Thus, representation of space reduces to that of a rectangle on a two-dimensional plane. The cartesian coordinate system offers the simplest form of representation. It provides an easy access method to address any point on the plane. But in space planning, we deal with objects of definite shape and size and not with point objects. The rectangle represented by points can be identified by its vertices and edges. But the identification of the shape now becomes difficult. We can avoid this by considering the rectangle as a set of points. But this poses the problem of computer resources in the form of memory, due to the large number of points in the rectangle. How- ever, if we group the points forming a unit area and use a number of such unit areas to form the rectangle, the representation of rectangles is achieved. Each such unit area can be accessed using cartesian coordinate system. We call each unit area a "Design Element".

Thus we arrive at the following representation of the space: the space should be divided into a set of rectangles of equal size. This can be obtained by dividing the space into a number of rows and columns (Fig. 2) forming the design dements. The size of the design dement varies depending upon the scale used. With a proper scaling factor space of any size can be represented.

((oooooo) (oooooo) (oooooo) (oooooo) (oooooo))

Fig. 3. Data st~cture.

associated with the design element provide the value of its X and Y coordinates. The value of the variable in the array represents the object located in the corresponding rectangle. The value "0" represents the empty space. The value "A" represents the object "A" in the space. This scheme provides an easy manipulation of the space and the mapping of the objects into the space. How- ever, it has certain limitations.

The dimensional accuracy is determined by the size of the design dements. Greater accuracy demands more design dements and thus increases the computer resources requirement. The space allocation operators act on one design element. Thus any increase in accuracy requires a large increase in computation time. Due to the rectan- gular shape of the design dements, it is difficult to represent objects of irregular shape. Another limitation arises if a fixed-sized array is used. This may cause wastage of memory when the space is L-shaped. This problem can be avoided if a variable-sized array representation is used. LISP provides a dynamic list data structure which can be expanded or shrunk as and when required. We can represent the space as a one-dimensional array of variable-sized lists. Each dement of the list represents a design dement. Each list represents a row of the array (Fig. 3).

3.3. Architecture of the System

3.2.1. Data Structure For the representation of the shape discussed

above the space can be considered as a two-dimensional array. Each subscripted variable of the array represents a design dement. The subscripts

The architecture of the system is that of an expert system. It consists of a knowledge base, a control structure and a working area. The examples used to illustrate the architecture are taken from the wire rope industry. Figure 4 shows the architecture of the system.

I I I I

Fig. 2. Representation of space.

Design Elements 3.3.1. Knowledge Base The knowledge base which the Space Planning

System uses contains three parts: • department information; • allocation principles and • allocation operations.


User

., Dialogue I I utput ]

I Plan

Unallocated Departments ( Status )

Allocation Principles

DepazhT~nt Information

Allocation Operations

Knowledgebase

Plan

Generator

Control Structure

Allocated Departments (History)

Working Area

Fig. 4. Architecture of the space planning system.

All the three components are domain dependent and changes in the environment shall call for modifications in the knowledge base.

Department Information. This part of the knowledge base accumulates the physical details of each and every department to be allocated in the given space. Normally, we can finalize these details after initial analysis of layout design. Each department is represented using a 'frame'. The information in the frames is stored in the form of Fg.~m-SLOT- FACET triplets. For example, a frame for "ROPERY" department is shown in Fig. 5.

To generate the space plan, two types of information are needed. First is the self descriptive information which describes the department features. In our example SaAI'E, SIZE, ALPHABET etc. are the slots of this kind. The second kind of information necessary is the interrelation of various departments, implicitly specifying the allocation operations to be applied. In our example,

( ROPERY ( SHAPE ( VALUE ( ' RECTANGLE ) ) ) ( SIZE (VALUE ( ' ( LENGTH WIDTH) ) ) ) (ALp~r(v~r.tm ('R})) (TYPE (VALUE ('PRODUCTION) ) ) (PLAN-IS (VALUE (?))) ( ~0H (vALUE ( ' FUENAC~ ) ) ) (TO (VALUE ('FG-STORE) ) ) ( OONTROL ( VALUE ( T ) ) ) )

Fig. 5. Department frame.

the FROM slot and to TO slot indicate the input to and the output from the ROPERY department. Thus the two departments FURNACE and FG-STORE need to be adjacent to the ROPERY.

The departments in the shop floor can be clas- sifted into production departments and auxiliary departments. Accordingly, the TYPE slot of the department frame is defined. In addition some of the production departments may have storage area requirements and a supervision/control area. This kind of information is supplied to the plan generator using the STORAGE and the CONTROL slots of each department frame.

All the slots in the department frame may not be instantiated initially. For example, PLAN-IS slot in the department frame gets instantiated only after the location for that department has been decided.

Allocation Principles. Allocation principles contain layout expert's rules of thumb. Ideally, rules consist of know-how affecting the planning efficiency, material movement cost and other layout planning interests. The various patterns observed and experienced by the expert are forming the material flow rule. Figure 6 shows an example of the material flow rule.

In this rule the SEQUENCE slot indicates the sequences of the material flow in the wire rope industry.


(MATERIAL- FLOW (SEQUENCE (VALUE ' ( (PICKLING-PIdmT WIRE-MILL-DRY FURNACE ROPERY FG-STORE )

(FURNACE WIRE-MILL-WET FINE-CHORDS FG-STORE ) ) ) ) )

Fig. 6. Material flow rule.

(CONTROL-RULE ( FURNACE (VALUE ' ( FURNACE-CONTROL-ROOM FURNACE-OFFICE ) ) )

(ROPER"/ (VALUE ' (ROPERY-OFFICE)) ) )

Fig. 7. Controlrule.

In addition to the material flow rule, the allocation principles also contain the control rule and the storage rule. For example the control rule takes the form shown in Fig. 7. This rule states that the FURNACE has two associated control areas and the ROPERY has one associated control area.

The storage rule takes the form shown in Fig. 8. This rule states that the WIRE-MILL-DRY has three associated storage areas.

Basically, these two rules give an idea to the Space Planning System as regards the non-production departments which must be placed in the layout. The material flow rule is used to ensure proper flow of material, which is the prime importance in any layout. At the same time the control rule and the storage rule aid the planner to allocate the supervisory and the storage areas for these departments.

Allocation Operators. The allocation operators describe the actions available with the planner, with the help of which it can perform various allocation operations. The operators which are defined are SCAN, BOUNDARY, LOCATE, CLEAN and

FREE-SPACE-P. Each of these are procedures which take on or two departments as their arguments and perform the desired task.

(1) SCAN (Dept). This operator takes the Dept to be allocated in the space and returns a possible starting location (x, y) for that department considering its orientation in the space. The locations indicated by this operator are used by the other operators.

(2) BOUNDARY (Deptl, Dept2, Side). This operator finds a location for Dept2 such that it is adjacent to Dept'. The adjacency of the two departments along the X or Y axis is decided by the value of Side which can be horizontal or vertical.

(3) LOCATE (Dept, x, y). This operator is a location operator which assigns the Dept in the space starting at location (x, y). In addition the PLAN-IS slot of the department frame also gets filled once the LOCATE operator acts on the department.

(4) CLEAN (Dept, x, y). This operator is the reverse of the locate operator. It removes Dept starting at location (x, y) from the space and also removes the value in the PLAN-IS slot of the frame.

(STORAGE-RULE (WIRE-MILL-DRY (VALUE '(WIRE-MILL-STORE DIE-SHOP WIRE-MILL-PACKAGING) ) ) ) )

Fig. 8. Storage rule.

150 Applications Computers m lndust O'

This operator is useful in case the planner decides to backtrack on any of its previous decision of locating a department.

(5) FREE-SPACE-P (xl, yl, X2, y2). This is a predicate which checks the availability of the empty space. The coordinates (xl, yl) and (x2, y2) define the boundary of the space to be checked.

3. 3.2. Control Structure The control structure of the system governs all

the actions of the system. It activates the dialogue module to build the knowledge base of the system interactively. It uses the knowledge available in the knowledge base and starts the planning process. During the allocation phase, it interacts with the working area referring to the current status and the history to decide the next step until the plan is complete. Once all the departments have been allocated the output module of the system is activated.

3.3.3. Working Area This is the component of the system which

records the status of the system, its history and the plan being generated. The working area is basically the system memory, with three kinds of data structures. The status and the history are recorded in the form of lists. The plan generated is an array. The status/history combination decides which department should be allocated next, with respect to the priorities of allocation. Thus, depending on the condition of the working area the process of planning continues.

3. 3.4. Dialogue System It was decided that the Space Planning System

should have only a meta representation of the knowledge base. The knowledge base thus needs to be built interactively. The dialogue system is designed to simplify the process for the user.

It is composed of three windows: title window, main window, and query window. All the windows are GCLISP streams and thus allow input-output operations. This feature is extremely useful for the dialogue system. The title window displays the current context of the dialogue system. It displays the input format and how to end input etc. The main window displays the questions, prompting for the answers which the user has to supply. The query window is used to query the user, e.g. "Are there any fixed facilities?" etc.

3.3.5. Output The output of the system can be obtained in

three formats; (1) array of alphabets representing space plan; (2) output using the window system of GCLISP;

or (3) graphics output using a PASCAL program. In case of the array format, a PASCAL program

is written which outputs the SPACE array on a 132 colunm printer. This output gives a true idea of the plan. It is actually the data structure used by the system. However, it is difficult to perceive the plan in this form and hence the other two formats are developed.

The second format of output uses the window system of GCLISP. Each department in the space has an associated window. The alphabet of that department is displayed in the window. The departments are shown in seven colours and the space is shown in white. This output is preferred when the size of the space is within limits of 80 × 25 design elements.

The graphics output is obtained with a program written in TURBO PASCAL version 4.0. It uses the graphics tools provided by the language. Each department is shown as a filled rectangle with corresponding alphabet written in it. The program uses an EGA card with a colour monitor having a resolution of 640 × 350 and 16 colours. In this case the entire screen represents the space. A proper scaling factor is used to enlarge or reduce the space to fit on the screen. The PASCAL program is converted into executable code and is run under the GCLISP environment. It is not possible to get a hard copy of the plan when graphics output is used.

3.4. Knowledge Acqu&ition

The purpose of knowledge acquisition was three fold: (1) to understand the space planning as it is done

in an industry; (2) to analyze the expert's knowledge; and (3) to represent and reason about this knowledge.

Once the knowledge pertaining to the wire rope industry was formalized, a representation scheme was formulated. As discussed earlier, the frame- based representation was found suitable for the kind of knowledge involved. The next step in knowledge acquisition was generalization. To


achieve generality in the knowledge base, the physical details pertaining to the wire rope industry were removed. Thus, a meta representation was arrived at, where the physical details were to be filled into the skeleton using the dialogue system.

The departments in a shop floor were found to be of three types, viz. production, control and storage. The priorities which are used for assign- ing locations for these departments are as follows: (1) production department; (2) storage associated with a production department; (3) control associated with a production department. These priorities were decided based upon inter- views with industrial engineers and other resources available. It was observed that in a production plant the material flow is most prominent amongst the production departments, followed by a production department and the associated storage area. The control area associated with the production department thus comes last.

3.5. Space Allocation Heuristics

In industrial space planning, it was observed that the material flow is of prime importance. The material can flow between two production departments or a production department and its associated storage area. Thus, to minimize the cost of material flow, it was decided that the two production departments with interconnecting material flow should be located adjacent to each other. Additionally, to facilitate storage and supervision, these areas associated with a production department should also be adjacent to the production department. Thus, if there are two production departments A and B, and C is the control area associated with A and S is the storage area associated with B then arrangement of these departments could be as shown in Fig. 9.

I C B

Fig. 9. A possible layout.

Another aspect, important in industrial space planning, is the shape of the departments. It was decided that the shape of any department shall be predefined. The present system allows only rectan- gular shape. The dimension of the department will not be altered by the system. Thus, the dimensions of the department is another by the system. Thus, the dimensions of the department is another constraint governing the space allocation. The system has the choice of deciding the orientation of the department in space. Presently, only two orientations, parallel to the X and Y axes, are supported.

3.5.1. Allocation Algorithm The system arranges the departments on the

basis of the allocation principles. The allocation starts at the top left comer of the space.

The problem is that of allocating all the departments in the material flow rule. First, the system checks for the fixed facilities and allocates them at the predefined locations. The first sequence of material flow is then selected for allocation. It allocates the first department on this sequence either at the upper left comer or the first available empty space which can accommodate this department. The control rule and the storage rule are checked for the presence of any control or the storage areas. If any of these areas exist for the department they are put in a queue for allocation. The next department selected for the allocation is the next department on that sequence. Before the next department is considered by the system, it checks if there are any departments on the queue and allocates them. Since the next department is also a production department, it has to be located adjacent to the first one. An attempt is first made to locate it such that both departments have a common vertical edge. If this fails, then a horizontal common edge is tried. In both cases, the department can have two orientations. If one orientation cannot be achieved then the other one is tried. The department is always located at the first possible location.

3.6. A Priori Knowledge of the System

As discussed earlier, the Space Planning System has meta knowledge about the space planning on the shop floor. This knowledge can be divided into distinct groups. These groups are as follows:

(1) Department Information. The system knows that there are some departments for allocation. It

152 Applications Computers in lndustrv

Oept Alpha Type I/P O/P Store Ctrl

A A Prod X C - -

B B Prod C (D E) S -

C C prod A B - O

D D Prod B X - -

E E Prod B X - -

O O Ctrl - - -

S S Store - - -

Size

5x3

10x3

5x3

4x12

7x12

2x2

3x3

Locn

(o,o) (5,o)i

Fig. 10. A sample of input data.

also knows the skeleton of the department frame. The actual values are not known and are made known dynamically.

(2) Allocation Principles. The Space Planning System knows about the three allocation principles governing the allocation of the departments. These are material flow, control and storage. Here again the skeleton of the rule frames are known and the slots get filled dynamically.

(3) Allocation Operators. The allocation operators known to the Space Planning System are general in nature. They are domain independent and thus can be applied to any space planning application. The operators like SCAN, ROTATE, LOCATE etc. are examples of the procedural knowledge the system possesses.

(4) Frames Manipulation. The knowledge base of the system is stored in the form of frames. The procedures and the functions to create the frames, to assign new values in the slots, to access the value in a slot and to remove or delete the value from slot are known to the Space Planning Sys- tem.

Apart from these groups, the control structure allows the system to know how to get certain details from the user, in the form of dialogue.

3. 7. System Requirements

In order to use the Space Planning System, meeting certain hardware and software requirements is essential. These requirements are discussed here.

The Space Planning System is designed to work on a micro computer, under the GCLISP environment. The computer should have 640 K memory, two disk drives, a colour monitor with 640 × 350 resolution and an EGA card. Optionally, a 132-col-

umn printer is essential if a hard copy of the plan is to be obtained. The system can be configured to run with a single disk drive or a hard disk.

In addition to the program, the user must be ready with the relevant details about the planning of the particular shop floor before he uses the system. The other details essential are as follows.

(1) The user should be ready with the list of departments to be allocated.

(2) The departments should be classified into production, control and storage types. He should determine which departments are to be classified as fixed facilities.

(3) The user must be ready with the input data like size, alphabet, type of the department, input from and output to departments etc. for each department.

(4) The user has to decide the material flow patterns on the shop floor to be planned.

(5) The user should assign the control and storage areas to the corresponding production departments.

(6) The user should select a proper scaling factor and decide the size of the design elements. It should be noted that only an integer number of design elements are allowed.

( A A A A A B B 8 B 8 8 B ( A A A A A B B 8 B B B B ( A A A A A B B 8 B B B B ( C C C C C e e O D O D E

C C C C C e e o o O D E C C C C C O O O D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E O 0 0 0 0 0 0 D D D D E 0 0 0

Fig. ll .

8 8 8 S S S} 8 B B S S S) B B B S S S} E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E

O 0 0 0 D D D D E E E E E E E

Space plan for the example problem.

Computers in Industry P. Joshi, RI Sadananda / Towards a Knowledge-Based Approach to Space Planning 153

(7) The user should decide the dimensions of the space in terms of the design elements.

(8) The user should also determine the dimensions of the departments in terms of the design elements.

(9) The user should decide the locations of the fixed facilities with coordinates assigned according to the design elements. The origin (0, 0) is at the upper left corner of the space.

Looking at the list of requirements, it may appear that it is difficult to use the system. But most of the data needed is available from the initial layout analysis. Some of the details like number of design elements can be easily calcu- lated. It is recommended that the user should prepare a chart containing all the data. Figure 10 shows a sample chart for the example run and Fig. 11 shows the output produced by the Space Plan- ning System.

3.8. Complexity

In general, the complexity is characterized in terms of two criteria, the time and space requirements to solve the problem. The complexity of the task undertaken by a knowledge-based system, is influenced by the characteristics of the problem at hand. The problem characteristics can be: • small solution space, reliable data and reliable

knowledge; • unreliable data or knowledge; • time-varying data; • large solution space; • interaction of subproblems; • good educated guesswork needed.

It is evident that these problem characteristics make the problem increasingly complex. The complexity of the Space Planning System can be determined along these lines. The characteristics of our problem are: (1) Reliable data in the form of the department

information. (2) Reliable knowledge pertaining to the spatial

relationships between the departments. (3) Large solution space in terms of the branching

factor associated with the search tree. (4) Heuristic search coupled with the least-com-

mitment strategy needed in order to improve the search phase of planning.

Considering these characteristics, the complexity of the present system falls mid way. The char-

35

32

28

in

~, 2o

f I I t I 2 3 ,4.

Time in sec

Fig. 12. Performance of the Space Planning System.

acteristics (1) and (2) are less complex whereas (3) and (4) make the system more complex.

Another aspect in computational complexity is the way the present system overcomes the problem of combinatorial explosion. One of the major problems associated with optimization paradigm is that of the combinatorial explosion. An exhaus- tive search formulation would lead to complexity (O(n)) of the order n!. The space planning system overcomes the problem by using expert's rules and heuristic search coupled with the least-commitment strategy. The search carried out by the system is guided by the expert's rules. In addition, search reduction occurs as a selection is made on the basis of the information available locally and then a commitment is made to that solution path. This allows the system to select only the best possible alternative. Figure 12 shows the performance curve of the Space Planning System. The knowledge-based approach does help the system in reducing the time required to allocate all the departments.

4. Conclusion

A prototype was developed in close interaction with the wire rope industry. The entire development cycle has been very encouraging. Applica- tions of the prototype to a typical industrial space planning problem demonstrated the feasibility of the system. A number of features of the system are

154 Applications Computers in lndustrv

notable . The Space P lann ing System emula tes the process of indus t r ia l space p lanning . The repre- sen ta t ion of the space is f lexible and can thus be modi f i ed and a d o p t e d to vary ing na tu re of the p rob lem. The f lexibi l i ty and mod i f i ab i l i t y of the sys tem are main ly due to the r ep resen ta t ion scheme employed and the m o d u l a r a rchi tec ture of the system. The sys tem takes in to cons ide ra t ion f ixed facil i t ies and depa r tmen t s of a p rede f ined shape.

Our Space P lann ing System does not mode l the uncer ta in ty usual ly associa ted with the spa t ia l const ra ints . In fact this is a poss ib le ex tens ion to the system. The sys tem works at the top level of p r o b l e m solving. However , an ideal space p lanning system should be hierarchical . One can th ink of an eva lua t ion modu le to evaluate the a l te rna-

tive layouts genera ted by the system. The f loor l ayou t p r o d u c e d b y the sys tem is not

c la imed to be the best solut ion. I t is a feasible solut ion, i.e. it satisfies the rules de t e rmined for the a l locat ion. In ac tua l use, the user is recom- m e n d e d to change cer ta in rules to f ind a l te rna t ive layouts . He may f ind a few feasible layouts using the system and then choose the one which he th inks the best.

References

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[11] R.C. Lee and J.M. Moore, "COREJ_AP--Computerized Re- lationship Layout Planning", J. lnd. Eng., Vol. 18, No. 3, 1967, pp. 194-200.

[12] R.S. Liggett and W.J. Mitchell, "Optimal space planning in practice", Comput. Aided Des., Vol. 13, No. 5., 1981, pp. 278-288.

[13] L.M. Pereira, "Artificial intelligence techniques in automatic layout design", in: Latombe (ed.), Artificial Intelli- gence and Pattern Recognition in Computer Aided Design, North Holland, Amsterdam, The Netherlands, 1978.

[14] R. Sadananda and Prasad Joshi, "A knowledge-based approach to space planning in industry", lnt. Conf. on Expert Systems for Development, Kathmandu, Nepal, March 1-3, 1989.

[15] R. Sadananda and Y.K. Pandey, "Linguistic quantifiers in space planning", South East Asian Regional Computer Confederation Conf. New Delhi, India, 1988.

[16] J.M. Seehof and W.O. Evans, "Automated layout design program", J. lnd Eng., Vol. 18, No. 12, 1967, pp. 690-695.

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[18] J.A. Tompkins and J.A. White, Facilities Planning, Wiley, New York, 1984.

[19] E.L. Walker, M. Herman and T. Kanade, "A framework for representing and reasoning about three dimensional objects for vision, AI Mag., Summer 1988, pp. 47-58.

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Towards a knowledge-based approach to space planning in industry

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