Engineering Applications of Articial Intelligence 14 (2001) 265267

Preface

Articial intelligence and soft computing for planningand scheduling: how to eciently solve

more realistic problems...

Planning and scheduling have always had at the sametime close and loose links. In its Articial Intelligencemeaning, Planning can be dened as building a sequenceof activities to reach a goal. Scheduling aims atpositioning activities through time, taking into accountvarious types of constraints, including resource con-straints. These two elds are apparently complementary,but have for a long time been considered by dierentresearch communities, interested in dierent problemsand often using dierent methods. This is less and lesstrue: the interest of the two communities for more or lessnew methods, related to Articial Intelligence, toOperational research or to Combinatorial Optimisationaccording to the opinions, has somehow strengthenedthe links between these two elds. In both cases the useof these methods has spectacularly increased during thelast years, and they have allowed to solve ecientlymore realistic problems.Classifying or giving labels to these methods is a

tricky } and perhaps futile } task, since the denitionof the boxes where to put them often needs to beadjusted according to their content. Some of thesemethods aim at solving Combinatorial Optimisationproblems, and can for instance be applied to Opera-tional Research, e.g. meta-heuristics like Tabu search orSimulated Annealing. If it is considered that a Combi-natorial Optimisation method optimises a criterion, thislabel is not adequate since these methods do notnecessarily lead to an optimum. Other methods can beconsidered as coming from the Articial Intelligencearea, considered as a set of methods and techniquesallowing to imitate human abilities, like Fuzzy Logic,Neural Networks, Case-Based Reasoning or evenConstraint Propagation. Nevertheless, most of thesetechniques set into question the commonly usedboundaries between existing elds: Genetic Algorithmsare a multi-heuristic approach to Combinatorial Opti-misation problems like Tabu Search and Simulated

Annealing, but is also relevant from Articial Intelli-gence or at least from Articial Life. The Multi-agentparadigm is often seen as an implementation ofdistributed Articial Intelligence but it can be consid-ered as well as an extension of analysis/design methodsfor computer science, like object-oriented methods...Whatever the classication (and the term SoftComputing may provide an exit to this problem, evenif not very pertinent for optimisation methods), the useof these methods has opened new horizons to theplanning and scheduling research. This special issueaims at illustrating these new possibilities.AI techniques have been intensively applied in order

to dene computerised methods for building plans. Inreal applications, Planning is often characterised by anincomplete observability of the considered system stateand by the uncertainty of the eects of an action: thesecharacteristics have so rapidly led to use of probabilisticapproaches. Nevertheless, an important gap could benoticed in the literature between the ambition tosolve dicult and complex problems and the trivialillustrating examples which were used. The arrival tomaturity of the above mentioned solving methods (andothers) has changed this, and AI planning is now animportant challenge for the future by its expected abilityto give more exibility to automated systems. Mobilerobots are an important application area of AIplanning, and a representative example has beenincluded in this special issue. Nevertheless, many otherelds, from the strategic to the operational level,are now accessible: we have tried to illustrate some ofthem here.Scheduling has always been an important area of

Operational Research but also, with the necessity tooptimise manufacturing productivity and lead times, avery active eld in industrial software design. Therefore,until the last decade, a poor fertilisation could benoticed between OR techniques and industrial sched-

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ulers: many OR were oriented on nding the optimalsolution according to a single performance criterion,whereas mainly heuristic approaches were used inindustrial products in order to support the workshopmanager by providing quickly an acceptable solution.Acceptable means providing a sucient satisfactionfor several performance criteria, but also satisfyingtemporal or technological complex constraints whichcould hardly be taken into account by OR techniques.New elds of scheduling can now be tackled and, as weshall see, wider sets of constraints can be takeninto account and new problems can be now addressed,like hoist scheduling or real time scheduling. Theselection of articles made for this special issue does notpretend to build an exhaustive panel of the use of the AIand Soft Computing techniques for Planning andScheduling. Its only ambition is to illustrate how morerealistic problems can be addressed using these methods.Both review and application papers have been chosen:the rst ones in order to give a global view on newresearch directions; the later ones for showing practi-cally how the methods can successfully solve real worldproblems.The article of van Wezel and Jorna is a good

introduction to this special issue. Under the pretext ofa reection on planning} the planning activity involvesseveral activities that must be planned. . . Are these twolevels similar? } it situates opportunistically planningand scheduling, gives a cognitive perspective on plan-ning and situates the contribution of Articial Intelli-gence on that eld.Most of the following articles show that articial

intelligence or soft computing techniques do not onlyprovide solving methods, but rst of all modelling toolsallowing to better describe the defaults of the informa-tion in real world applications. R!egis Sabbadin gives areview on methods that can be used when planning isseen as a multi-stage decision under uncertainty. Heoutlines the interest of Possibilistic Markov DecisionProcesses compared to stochastic ones, with an aca-demic illustration on robot movements. Extensions topartially observable environments are suggested, show-ing that planning does not anymore require thesimplifying hypothesis which set into question itsapplication to real complex problems. Other approachesto Planning are possible: it is seen by Miguel et al. as aConstraint Satisfaction Problem (CSP), which is now-adays a very promising and ecient approach. Hardconstraints being considered as too restrictive formodelling real world problems, Planning in a dynamicalenvironment is seen as a dynamic exible CSP. A hotreal world planning application for the next future isaddressed by Pasquier et al.: a eet of vehicles planningtheir journey opportunistically according to a changingdemand. A heuristic solution based on the Blackboardapproach is suggested here.

In the article of Aylett et al., AI planning technologiesare used to generate plant operating procedures for achemical process plant. An important eort is requiredto generate such procedures manually: an automatedgeneration is made possible by a set of methods thatcombine knowledge-based inference systems and con-straint propagation.Chang and Angkasith describe in their article an

operational short term planning problem: the sequen-cing of cutting operations. Applications of neuralnetworks to planning and scheduling are often basedon perceptron models, which require to get solvedexamples in order to teach the network. This articleshows that the learning phase can be made useless byusing the natural property of Hopeld neural networksto minimise their energy function. In that case, the pointis to model the problem in order to make possible ananalogy between the energy of the network and thefunction to optimise: as a matter of fact, modelling theproblem according to the chosen solving method isalways a crucial phase in the use of the above mentionedmethods.The short paper of Lazansky et al. is a bridge between

planning and scheduling: choices are made on theactivities to be planned, but time and resource capacitiesare also managed by the suggested system. Like often,the use of the multi-agent approach is justied by thedistributed nature of the problem. The multi-agentapproach provides so a paradigm allowing to solve theproblem by its modelling: the agents represent the actorsof the manufacturing planning (project planner, projectmanager, production agents) and the discussions whichare necessary for production planning are modelled bycommunication strategies between agents.Chanas and Kasperski address here a pure scheduling

problem. Again, the used method } here fuzzy logic }allows to better model reality by describing uncertainprocessing times and exible due dates. The ability offuzzy logic to model both imprecise/uncertain data andpreferences has been intensively used in schedulingduring the last years. The article of Fargier andLamothe, dealing with the dicult problem ofhoist scheduling, also shows that fuzzy constraints notonly allow to better represent the exibility of realworld constraints, but also make the solving methodmore ecient. Increased modelling capacities are notalways synonym to increased diculties to solve theproblem.I hope that this panorama will illustrate both the great

variety of problems that can now be addressed in theelds of Planning and Scheduling, and the importantevolution of the so-called Articial Intelligence or soft-computing techniques: even if the rst idea of ArticialIntelligence was to imitate human reasoning throughknowledge-based systems, most of the actual applica-tions have their eciency in their ability to learn from or

Preface / Engineering Applications of Articial Intelligence 14 (2001) 265267266

to use less structured information. The growing interestin Knowledge Capitalisation, Return of Experiment andData Mining show that this tendency is far from beingat its end.

B. GrabotENITLGP, 47, Avenue d Azereix, BP 1629,

F-65016 Tarbes, FranceE-mail address: bernard@enit.fr

Preface / Engineering Applications of Articial Intelligence 14 (2001) 265267 267