EXPLOITING CAUSAL STRUCTURE TO CONTROL RETRIEVAL AND REFITTING DURING PLAN REUSE

The ability to reuse existing plans to solve new planning problems can enable a domain-independent planner to improve its average case efficiency by exploiting the problem distribution and avoiding repetition of planning effort. The pay-off from plan reuse, however, crucially depends on finding effective solutions to two important underlying control problems: (i) controlling the retrieval of an appropriate plan and mapping to be reused in a new situation, and (ii) controlling the modification (refitting) of the retrieved plan so as to minimize perturbation to the applicable parts of the plan. This paper is concerned with the development of efficient domain-independent solutions to these two problems. For the retrieval, it provides a domain independent similarity metric that utilizes the plan causal dependency structure to estimate the utility of reusing a given plan in a new problem situation. For the refitting, it presents a minimum-conflict heuristic, again based on the causal dependency structure of the plan, to conservatively control the modification. The paper also discusses the implementation and evaluation of these strategies within the PRIAR plan modification framework.     

Incorporating deeply uncertain factors into the many objective search process

This paper proposes an approach for including deeply uncertain factors directly into a multi-objective search procedure, to aid in incorporating divergent quantitative scenarios within the model-based decision support process. Specifically, we extend Many Objective Robust Decision Making (MORDM), a framework for finding and evaluating planning solutions under multiple objectives, to include techniques from robust optimization. Traditional MORDM first optimized a problem under a baseline scenario, then evaluated candidate solutions under an ensemble of uncertain conditions, and finally discovered scenarios under which solutions are vulnerable. In this analysis, we perform multiple multi-objective search trials that directly incorporate these discovered scenarios within the search. Through the analysis, we have created multiple problem formulations to show how methodological choices of severe scenarios affect the resulting candidate planning solutions. We demonstrate the approach through a water planning portfolio example in the Lower Rio Grande Valley of Texas.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

A robust dynamic planning strategy for lot-sizing problems with stochastic demands

We consider the question of generating robust plans for production planning problems under uncertainty. In particular, we present an alternative approach to generate robust solutions for lot-sizing problems with stochastic demand. The proposed approach is dynamic and includes a decision rule that guides the planner. The decision rule parameters are determined so that the number of expected planning adaptations and their magnitudes are under control. The robust approach and its related models are presented together with some computational results to show how it performs compared to other approaches.     

Genetically optimised disassembly sequence for automotive component reuse

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.     

Ranking software components for reuse based on non-functional properties

One of the biggest obstacles to software reuse is the cost involved in evaluating the suitability of possible reusable components. In recent years, code search engines have made significant progress in establishing the semantic suitability of components for new usage scenarios, but the problem of ranking components according to their non-functional suitability has largely been neglected. The main difficulty is that a component’s non-functional suitability for a specific reuse scenario is usually influenced by multiple, “soft” criteria, but the relative weighting of metrics for these criteria is rarely known quantitatively. What is required, therefore, is an effective and reliable strategy for ranking software components based on their non-functional properties without requiring users to provide quantitative weighting information. In this paper we present a novel approach for achieving this based on the non-dominated sorting of components driven by a specification of the relative importance of non-functional properties as a partial ordering. After describing the ranking algorithm and its implementation in a component search engine, we provide an explorative study of its properties on a sample set of components harvested from Maven Central.     

Anytime search in dynamic graphs

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out.A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases.In this paper, we present an A^∗-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs.     

Case-based path planning for autonomous underwater vehicles

Case-based reasoning is reasoning based on specific instances of past experience. A new solution is generated by retrieving and adapting an old one which approximately matches the current situation. In this paper, we outline a case-based reasoning scheme for path planning in autonomous underwater vehicle (AUV) missions. An annotated map database is employed to model the navigational environment. Routes which are used in earlier missions are represented as objects in the map. When a new route is to be planned, the path planner retrieves a matching route from the database and modifies it to suit to the current situation. Whenever a matching route is not available, a new route is synthesized based on past cases that describe similar navigational environments. Case-based approach is thus used not only to adapt old routes but also to synthesize new ones. Since the proposed scheme is centered around reuse of old routes, it would be fast especially when long routes need to be generated. Moreover, better reliability of paths can be expected as they are adapted from earlier missions. The scheme is novel and appropriate for AUV mission scenarios. In this paper, we describe the representation of navigation environment including past routes and objects in the navigational space. Further, we discuss the retrieval and repair strategies and the scheme for synthesizing new routes. Sample results of both synthesis and reuse of routes and system performance analysis are also presented. One major advantage of this system is the facility to enrich the map database with new routes as they are generated.This work was supported in part by National Science Foundation Grant No. BCS-9017990.     

Many Plans: Multidimensional Ensembles for Visual Decision Support in Flood Management

Uncertainties in flood predictions complicate the planning of mitigation measures. There is a consensus that many possible incident scenarios should be considered. For each scenario, a specific response plan should be prepared which is optimal with respect to criteria such as protection, costs, or realization time. None of the existing software tools is capable of creating large scenario pools, nor do they provide means for quick exploration and assessment of the associated plans. In this paper, we present an integrated solution that is based on multidimensional, time‐dependent ensemble simulations of incident scenarios and protective measures. We provide scalable interfaces which facilitate and accelerate setting up multiple time‐varying parameters for generating a pool of pre‐cooked scenarios. In case of an emergency, disaster managers can quickly extract relevant information from the pool to deal with the situation at hand. An interactive 3D‐view conveys details about how a response plan has to be executed. Linked information visualization and ranking views allow for a quick assessment of many plans. In collaboration with flood managers, we demonstrate the practical applicability of our solution. We tackle the challenges of planning mobile water barriers for protecting important infrastructure. We account for real‐world limitations of available resources and handle the involved logistics problems.     

Scenario construction and reduction applied to stochastic power generation expansion planning

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduction heuristic in the context of long-term power generation expansion planning. We generate two different sets of scenarios for future electricity demands and fuel prices by statistical extrapolation of long-term historical trends. The cardinality of the first set is controlled by employing increasing length time periods in a tree structure while that of the second set is limited by its lattice structure with periods of equal length. Nevertheless, some method of scenario thinning is necessary to achieve manageable solution times. To mitigate the computational complexity of the widely-used forward selection heuristic for scenario reduction, we customize a new heuristic scenario reduction method named forward selection in wait-and-see clusters (FSWC) for this application. In this method, we first cluster the scenarios based on their wait-and-see solutions and then apply fast forward selection within clusters. Numerical results for a twenty year generation expansion planning case study indicate substantial computational savings to achieve similar solutions as those obtained by forward selection alone.     

On the Hybrid Propositional Encodings of Planning

Recently, casting planning as propositional satisfiability (SAT) has been shown to be an efficient technique of plan synthesis. This article is a response to the recently proposed challenge of developing novel propositional encodings that are based on a combination of different types of plan refinements and characterizing the tradeoffs. We refer to these encodings as hybrid encodings. An investigation of these encodings is important, because this can give insights into what kinds of planning problems can be solved faster with hybrid encodings.
Encodings based on partial–order planning and state–space planning have been reported in previous research. We propose a new type of encoding called a unifying encoding that subsumes these two encodings. We also report on several other hybrid encodings. Next, we show how the satisfiability framework can be extended to incremental planning. State–space encoding is attractive because of its lower size and causal encoding is attractive because of its highest flexibility in reordering steps. We show that hybrid encodings have a higher size and a lower flexibility in step reordering and, thus, do not combine the best of these encodings. We discuss in detail several specific planning scenarios where hybrid encodings are likely to be superior to nonhybrid encodings.     

Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans

General-purpose generative planners use domain-independent search heuristics to generate solutions for problems in a variety of domains. However, in some situations these heuristics force the planner to perform inefficiently or obtain solutions of poor quality. Learning from experience can help to identify the particular situations for which the domain-independent heuristics need to be overridden. Most of the past learning approaches are fully deductive and eagerly acquire correct control knowledge from a necessarily complete domain theory and a few examples to focus their scope. These learning strategies are hard to generalize in the case of nonlinear planning, where it is difficult to capture correct explanations of the interactions among goals, multiple planning operator choices, and situational data. In this article, we present a lazy learning method that combines a deductive and an inductive strategy to efficiently learn control knowledge incrementally with experience. We present hamlet, a system we developed that learns control knowledge to improve both search efficiency and the quality of the solutions generated by a nonlinear planner, namely prodigy4.0. We have identified three lazy aspects of our approach from which we believe hamlet greatly benefits: lazy explanation of successes, incremental refinement of acquired knowledge, and lazy learning to override only the default behavior of the problem solver. We show empirical results that support the effectiveness of this overall lazy learning approach, in terms of improving the efficiency of the problem solver and the quality of the solutions produced.     

Using tabu search with ranking candidate list to solve production planning problems with setups

This study considers production planning problems involving multiple products, multiple resources, multiple periods, setup times, and setup costs. It can be formulated as a mixed integer program (MIP). Solving a realistic MIP production planning problem is NP-hard; therefore, we use tabu search methods to solve such a difficult problem. Furthermore, we improve tabu search by a new candidate list strategy, which sorts the neighbor solutions using post-optimization information provided by the final tableau of the linear programming simplex algorithm. A neighbor solution with higher priority in the ranking sequence has a higher probability of being the best neighbor solution of a current solution. According to our experiments, the proposed candidate list strategy tabu search produces a good solution faster than the traditional simple tabu search. This study also suggests that if the evaluation of the entire neighborhood space in a tabu search algorithm takes too much computation and if an efficient and effective heuristic to rank the neighbor solutions can be developed, the speed of tabu search algorithm could be significantly increased by using the proposed candidate list strategy.     

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization.     

Workforce planning and financing on a production/capital discrete‐time model

Traditionally, companies have considered production planning and financial commitments separately. Production planning involves planning when to produce how much of a product, while the financial commitment considers which loans to take and how to repay them. In the current difficult financial environment with new challenges and with different opportunities, such as short‐term flexible loans for paying salaries, these related problems must be considered together. In this paper, we model the two processes (production and cash flows) in a single framework, using a mixed‐integer programming discrete‐time formulation. When taken individually, each of the problems has been thoroughly discussed in the literature, while the combined version that also incorporates labor financial costs and workforce sizing is more scarce. The main contribution of the paper involves new strategies for financing labor costs in strong agreement with the company's production plan and financial commitments. The new strategy relates credit ceiling with employment funding, using a sequence of flexible short‐term loans. We consider applications and propose mathematical programming based tools that can be used by companies’ managers for conducting their own solutions analysis, following their own findings and discussion of alternative scenarios.     

A Survey on Case-Based Planning

Case-based planning is the reuse of past successful plansin order to solve new planning problems.This paper presents a survey of case-based planning, in terms ofits historical roots, underlying foundations, methods andtechniques currently used, limitations, and future trends.Several authors have given overviews on case-based reasoningand specific topics such as case retrieval, case adaptation,and learning. This overview differs in focus.Its aim is to emphasize the case-based approach to planning,its methodological issues, and its relation to classical planningand the other kinds of case-based reasoning.It also provides some reference models.     

Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization

This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc.     

Automated verification of security pattern compositions

Software security becomes a critically important issue for software development when more and more malicious attacks explore the security holes in software systems. To avoid security problems, a large software system design may reuse good security solutions by applying security patterns. Security patterns document expert solutions to common security problems and capture best practices on secure software design and development. Although each security pattern describes a good design guideline, the compositions of these security patterns may be inconsistent and encounter problems and flaws. Therefore, the compositions of security patterns may be even insecure. In this paper, we present an approach to automated verification of the compositions of security patterns by model checking. We formally define the behavioral aspect of security patterns in CCS through their sequence diagrams. We also prove the faithfulness of the transformation from a sequence diagram to its CCS representation. In this way, the properties of the security patterns can be checked by a model checker when they are composed. Composition errors and problems can be discovered early in the design stage. We also use two case studies to illustrate our approach and show its capability to detect composition errors.     

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, that is, those that meet the vehicle’s motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time‐consuming pruning routines. The second one is to opportunistically check the states’ risk of collision. To evaluate the proposed approach, we use the Sparus II performing autonomous missions in different real‐world scenarios. These experiments consist of simulated and in‐water trials for different tasks. The conducted tasks include the exploration of challenging scenarios such as artificial marine structures, natural marine structures, and confined natural environments. All these applications allow us to extensively prove the efficacy of the presented approach, not only for constant‐depth missions (2D), but, more important, for situations in which the vehicle must vary its depth (3D).