From: AAAI Technical Report FS-96-03. Compilation copyright © 1996, AAAI (www.aaai.org). All rights reserved.
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`1 Introduction.
`
`Products generally become more complex with more variants and shorter life cycles.
`Wrongly configured products means increased expenses, unsatisfied customers and
`stressed sales people therefore the need for good sales tools to assist the configuration
`process is evident.
`salesPLUS [2] exploits the recent development in constraint satisfaction algorithms to
`evolve a new methodology for interactive configuration.
`
`2 Methodology.
`
`2.1 Objects & Resources.
`
`The most fundamental part of the configuration data is the object. All the items or parts
`that can go into a product must be represented as objects. Product features may also be
`represented as objects e.g., the screen resolution on a PC although it derives from the
`combination of Monitor and Videocard.
`
`The objects have a number of attributes.
`
`Object :: {Type, id, Resources, Menu position, Description, Detault—value,...}
`
`A resource is a property or measure of some objects. Examples of a resource are
`"Price". "Memory", "Weight", etc. Objects can have positive or negative resource values.
`Hence it is possible to have a provider/consumer principle, e.g., devices use certain
`amounts of power whereas power—supply's provide more power.
`
`2.2 Constraints.
`
`The representation of a configuration problem is naturally done with constraints
`governing the objects. These constraints may stem from physical limits (as space limits)
`and from technical restrictions (as "supports only“) and from marketing demands (only sell
`some combinations). This is the contents of the configuration problem. This product
`information can typically be found in a printed configuration guide and transformed on a
`one to one basis into constraints.
`
`Two types of constraints are supported: boolean algebra and finite domain arithmetic.
`Examples from a UNIX workstation.
`
`Storage >= 300
`"The system should entail at least 300 Mb of storage (resource)."
`
`FiAM_8Mb + FtAM_32Mb + FtAM__64Mb <= 2
`"The model can only accommodate two optional memory options."
`
`impossible( Mode|_A, CDFtOM )
`"Model A does not support CDROM."
`
`The representation of UNIX workstations consists of only 30-40 constraints.
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`3 Consistency check.
`
`The constraints only state facts and there is no meta reasoning dealing with which rules
`to use. Since each rule represents a real world constraint they can mentally be checked
`individually.
`-
`salesPLUS has a consistency checker that can detect inconsistencies in individual
`constraints, bound objects and most important inconsistency in the overall configuration
`data.
`
`Explanation and traces of conclusions can also be made for testing purposes.
`
`4 Deduction methods of inference engine.
`
`4.1 Constraints.
`
`with traditional programming as with production rules systems the programmer has to
`master the general information flow. As a result, these systems are very intricate and they
`are hard to develop, test and maintain when either incomplete information or unordered
`information flow has to be dealt with. in contrast to constraint programming where you
`‘deal with problem contents only.
`The basic step in the configuration process is the following: Given the partial
`configuration selected so far, the existence of a corresponding valid configuration has to
`be verified (consistency), and the elements that can still be selected have to be known
`(domain reduction). There is both unordered information flow and incomplete information
`available in a typical configuration problem.
`The inference engine in salesPLUS is a State of the Art constraint solver [1]. It handles
`both boolean algebra and finite domain arithmetic constraints based upon interval calculus
`and the patented Beologic array inference technology.
`Having a pure constraint representation allows the user to add additional constraints at
`runtime like "The computer should have more than 32 Megabytes of memory", and
`salesPLUS uses this information to reduce the set of possible machines.
`Having asserted the additional constraints it is now possible to minimize the price
`resource with an optimization algorithm. The resulting computer will be the cheapest one
`that have more than 32 Mbyte of memory. This strong feature is impossible in traditional
`configuration systems.
`
`4.2 Time Complexity.
`
`The inference engine exhibits a nice and poiite policy towards the user. it has
`guaranteed response time that can be user selected independent of the platform.
`The inference engine's job is to check that no inconsistency occurs between the
`constraints and the assignments. Determining solvability to a set of objects that must
`satisfy a set of constraints is NP-complete i.e. the execution time grows exponentially with
`the size of the problem.
`'
`The inference engine may therefore use longer time than desired to establish all
`conseq uences. The engine will then give control to the user when the specified time (1-2
`seconds) is used or sooner.
`What the inference engine can deduce, increases with the number of assignments of
`objects. This is so because the search space only depends on the number of unbound
`objects.
`For ‘'large'' products this means that an option may seem to be free, but when you select
`it the inference engine will find that it is not possible and assert this. In practise this is not
`a problem with the products that have been modelled.
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`The concept of the inference engine allows it to take advantage of future hardware
`improvements, and it will not be slow on weak computers.
`
`5 Comparison with traditional configuration Systems.
`
`‘ 5.1 Representation.
`
`Configuration problems are by nature NP—compiete. Traditional configuration systems
`deal with this problem in several ways.
`
`* For expert system rules that deals with the execution of the application are added to
`reduce the problem to a simple linear/polynomial problem. Adding these rules is very bad
`for maintainability because they are not founded in the problem contents but only in the
`context.
`
`%
`
`saIesPLUS does not have rules that deal with execution issues.
`
`* For decision trees the user _is forced to make his choices in a predefined order. This
`reduces the problem by avoiding unordered information flow. This yields an application
`.that is very constraining for the user. Imagine that a customer wants a red car. The
`system will prompt for model,engine,.. etc. And when it comes to the colour it might not be
`possible to select red at all.
`%
`salesPLUS gives full freedom in the order of choices.
`
`* Batch mode validation does not allow for interactive configuration therefore the problem
`is reduced by avoiding incomplete information. Batch mode means that a user picks all the
`parts that he assumes appropriate. Then the validation algorithm produces a list of
`_
`missing and conflicting parts. Here the user has to put up with an iterative process.
`imagine that the customer specifically said that he wanted a part and the configuratcr then
`says that it is not possible. Then he has to start all over again.
`%
`sa!esPLUS has an interactive mode where the status of resources are displayed
`dynamically such as price, storage and weight to give the user better overview.
`
`5.2 Maintenance.
`
`Maintenance of the product model is an extremely important feature of a configuration
`system where product changes may occur on a monthly basis or even more frequent.
`Traditional configuration tools have difficulties with rapid changing product models. For
`expert systems it is known that rule bases with up to 10000 rules exists [5] which
`unfortunately makes it hard for even experienced engineers to change the rules.
`Easy maintenance in sa|esPLUS is achieved by keeping the number of constraints
`small. A single constraint often replaces many production rules and a company's products
`should be split into several knowledge bases. It is therefore possible for even quite
`complicated products to be represented by less than 100 constraints.
`Before release of any configuration system a test must be made to see if errors occur in
`the configuration data. With the product range decomposed it is only necessary to test the
`changed products. Whereas if you have one big knowledge base. you might have
`introduced side effects and the whole product range must be tested again.
`Concurrent product configuration data maintenance is possible with salesPLUS.
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`6 Discussion.
`
`The reason for the progress made in salesPLUS is the recent development in constraint
`satisfaction algorithms [3][4] that allows them to successfully solve real world problems.
`The representation in salesPLUS is restricted to constraints on boolean and finite
`domain. It does not allow expressions like "expl cos(x))". However most configuration
`tasks that sales people are able to perform with the printed documentation and sufficient
`time is suitable for salesPLUS. With sa|esPLUS it has been established that you can make
`configurations for cars, trucks, PABX's, computers, doors, coffee automates, custom
`installation guides, water pumps, hydraulic proportional valves, airplanes etc.
`
`7 Example: Car Configuration.
`
`The configuration problem is from SAAB Automobiles 1992 with the models from the 900
`series. We will show how products can be modelled within the salesPLUS representation.
`
`in the example only the objects and constraints will be considered in depth except for the
`Object "Models". Therefore issues related to menu positioning, ID, Button names,
`- Descriptions, object lD's and Languages are omitted. A graphics tool, salesPLUS Definer
`exists that helps the user to declare objects, resource, menus etc in an easy way and
`enables users to make rules by pick and place. This tool will not be described here.
`
`?.1 Declarations.
`
`First the objects are declared.
`
`The 3 models are SAAB 900, Cabriolet and Turbo Cabriolet. They are declared as
`an object "Models" of the type ONEOF.
`
`The 4 engines are 2.0i, 2.1i, 2.08 and Turbo. They are declared as one obje
`"Engine" of the type ONEOF
`--
`
`Available accessories are Automatic gearbox, ABS—bral<es. Air Bag, Air-condition,
`Audio system, Automatic air-conditioning, Eec. mirrors/windows and Cruise
`control. These are declared as eight objects of the type SINGLE.
`
`Available Sunshine roofs are Manual steel, Electric steel and Electric glass. The
`sunroofs are declared as one object "Sunroof" of the type ATMOSTONE.
`
`Available Trims are Velour Jet~Tuff Horizon, Velour pique Parallel, Leather
`Contour and Leather Suede Contour. The trims are declared as one object "Trim"
`of the type ONEOF.
`
`Available trim Colors are Labrador, Marine, Puma, Angora, Buffalo and Pamir.
`These are declared as one object "TrimColor" of the type ON EOF.
`
`Available standard paints are Cirrus white, Black, Embassy blue, Cherry red and
`Talladega red. The paints are declared as one object "Stdpaint" of the type
`ATMOSTONE.
`
`Available metallic paints are Citrin beige, Platana grey, Le Mans blue, Scarabe
`green and Monte Carlo yellow. Declared as one object "Metalpaint" of the type
`ATMOSTONE.
`
`Object that do not relate to physical items is Leather and delivery time. Leather is
`declared as an objects of the type SINGLE. Delivery time is declared as one
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`object of the type EN UM [0-35].
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`Declared resources are Price, Weight and Horse power. Declared menus are
`Accessories, Trims, Paints, Accessories at Dealer.
`
`7.2 Constraints.
`
`SAAB configuration guidelines:
`
`‘A
`‘B
`
`*C
`*D
`*E
`"F
`*6‘:
`‘H
`
`_ ‘l
`
`It is impossible to have both Air conditioning and automatic Air conditioning.
`The turbo cabriolet comes with Turbo engine, metallic paint, leather trim and
`cruise control.
`
`Ordinary cabriolets comes with 2.1 litre engine.
`Ordinary cabriolets cannot have the paint Marine.
`Models with red, grey or green paints cannot be ordered with Marine trim.
`Models with beige or green paints cannot be ordered with Puma trim.
`Cars with Turbo engine should be ordered with ABS brakes.
`There cannot be ordered sunroof for cabriolets.
`
`Delivery times are 14 days for the SAAB 900, 21 days for the Cabriolet and 35
`days for the Turbo Cabriolet.
`
`These configuration guide lines yields the following knowledge base. Note the almost one
`to one correspondence between the written guidelines and the constraints. In the
`constraints the objects identifiers are used.
`
`Knowledge base:
`
`NEW: Leather <> Trim[ Leather_Ccntour] or Trim[ Leather_Suede];
`"Sub grouping of leather trim types"
`NEW: Leather -:> TrimColor[ Pamir] or TrimColor[ Buffalo];
`"Sub grouping of leather trim color"
`lmpossible( Aircondition, Automatic_AirCondition);
`Model[ Turbo_Cabriolet] -> Engine[ Turbo] and
`Metaipaint and Leather and cruise__control;
`8D: Model[ Cabriolet] -> Engine[ s21] and not TrimColor[ Marine];
`Stdpaint[ Cherry_red] or Stdpaint[ Talladega_red] or
`Metalpaint[ Citrin_beige] or Metalpaint[ Scarabe_green] -> not TrimColor[ Marine];
`Metalpaint[ Citrin_beige] or Metalpaint[ P|atana_grey] -> not TrimColor[ Puma];
`WARNING (Engine[ Turbo] and not ABS);
`Model[ Cabriolet] or Model[ Turbo_Cabriolet] -> not Sunroof;
`EW: Metalpaint or Stdpaint;
`"You must pick one type of paint"
`
`zgmmmomy
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`I: DeIivery_time == 14 * Model[ SAAEL900] + 21 * Model[ Cabriolet]
`+ 35 * Model[ Turbo_Cabriolet];
`
`A warning constraint is a constraint that can be overruled but for most cases this is not
`desirably. For cars the boolean constraints are used mostly but for other products like
`PABX's the configuration mode! consists mainly of arithmetic constraints because most
`options can come in different numbers.
`
`7.3 Fluntime Sales Tool.
`
`With these declarations and constraints the end user sales tool is shown in fig. 1.
`The leftmost column of buttons is standard functions that salesPLUS offers the user. The
`
`resources are dynamically displayed at the bottom of the window. The objects are shown
`above the resources. Two selections have already been made in the window namely
`Models = "SAAB 900" and Engine = "2.0s" .The box menu "Paints" is used to group
`logically related objects. The sub menu "Accessories at dealer” is shown when activated.
`With the objects you can configure in the naive way by picking as desired. In the
`opened list for "Metallic Paints" you will notice that the option "Monte Carlo Yellow" is
`surrounded by # which means that this option is not available any more due to the
`_ constraints. However if you insist on this paint; select it and the system will resolve which
`earlier selections that have to be overruled.
`
`The runtime system of salesPLUS holds a number features that is not described in
`any length here like Trace, Total list, Discounts. Finish, Default, Response times,
`Load/save resource, Load/Save configuration etc.
`
`IE
`
`QEDacr: Demo
`
`- BS Brake:
`
`- it Bag
`
`-ir Condition
`
`Bilrin Beige
`Ejgggng §[e5I_
`Le Mans Blue
`Scargbe Glegn
`Iflulgnte Calla Yellagfl
`
`Horse Power:105 hp
`
`‘uH"eiglh:1322 kg
`
`
`
`Status
`
` |
`
`Fig-Pa9geb'fi3é$=$ sol Example
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`8 Implementation.
`
`in salesPLUS a Definer environment exists that runs on MS-Windows, and a
`executable runtime Customizer environment that runs on MS-DOS, MS—Windows and
`MOTIF. The runtime interface is build upon an ANSI-C API set, that can easily be ported
`to any platform. SalesPLUS does not demand any proprietary application such as
`database etc. The inference engine uses a compiled version of the constraints for efficient
`execufion.
`
`References.
`
`[1] CLP(B). Logic Programming with Boolean Constraints. Final report.
`Henri Beringer, Centre Scientifique IBM France. Dec. 1991.
`
`[2] Concept of the BEOLOGIC Product Configurator. Beoiogic A18, 1994.
`
`‘[3] Constraint satisfaction in Logic Programming. Pascal Van Hentenryck. The MIT Press.
`1989.
`
`[4] Algorithms for Constraint-Satisfaction Problems: A Survey. Vipin Kumar. Al Magazine.
`Spring 1992.
`
`[5] Expert System for Configuration at DIGITAL: XCON and beyond. Communication ACM
`(vol. 31-32) Dec. 1988.
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