Planning and scheduling are challenging processes, particularly for projects with continuously evolving requirements and constraints such as design-build and turnkey projects. In the literature, schedule optimization ...
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Planning and scheduling are challenging processes, particularly for projects with continuously evolving requirements and constraints such as design-build and turnkey projects. In the literature, schedule optimization models can only handle predefined activities and fixed constraints. To better support dynamic projects, this paper proposes a flexible constraint programming (CP) framework that optimizes schedules both at the early planning stage and immediately before construction. At the early planning stage, the model selects among alternative network paths and construction methods to determine the most suitable work packages for the project. Later as more constraints become refined, the optimization model helps to meet the persistent milestones, deadlines, and resource limits, using a variety of activity-crashing strategies without changing the committed construction methods. Experimenting with a case study proved the flexibility of the model, its unique support for planning, and its ability to consider evolving project constraints. The proposed model contributes to developing automated decision support systems for cost effectively meeting the evolving schedule constraints.
Graphical model processing is a central problem in artificial intelligence. The optimization of the combined cost of a network of local cost functions federates a variety of famous problems including CSP, SAT and Max-...
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Graphical model processing is a central problem in artificial intelligence. The optimization of the combined cost of a network of local cost functions federates a variety of famous problems including CSP, SAT and Max-SAT but also optimization in stochastic variants such as Markov Random Fields and Bayesian networks. Exact solving methods for these problems typically include branch and bound and local inference-based bounds. In this paper we are interested in understanding when and how dynamic programming based optimization can be used to efficiently enforce soft local consistencies on Global Cost Functions, defined as parameterized families of cost functions of unbounded arity. Enforcing local consistencies in cost function networks is performed by applying so-called Equivalence Preserving Transformations (EPTs) to the cost functions. These EPTs may transform global cost functions and make them intractable to optimize. We identify as tractable projection-safe those global cost functions whose optimization is and remains tractable after applying the EPTs used for enforcing arc consistency. We also provide new classes of cost functions that are tractable projection-safe thanks to dynamic programming. We show that dynamic programming can either be directly used inside filtering algorithms, defining polynomially DAG-filterable cost functions, or emulated by arc consistency filtering on a Berge-acyclic network of bounded-arity cost functions, defining Berge-acyclic network decomposable cost functions. We give examples of such cost functions and we provide a systematic way to define decompositions from existing decomposable global constraints. These two approaches to enforcing consistency in global cost functions are then embedded in a solver for extensive experiments that confirm the feasibility and efficiency of our proposal. (C) 2016 Elsevier B.V. All rights reserved.
In code generation, instruction selection chooses processor instructions to implement a program under compilation where code quality crucially depends on the choice of instructions. Using methods from combinatorial op...
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In code generation, instruction selection chooses processor instructions to implement a program under compilation where code quality crucially depends on the choice of instructions. Using methods from combinatorial optimization, this paper proposes an expressive model that integrates global instruction selection with global code motion. The model introduces (1) handling of memory computations and function calls, (2) a method for inserting additional jump instructions where necessary, (3) a dependency-based technique to ensure correct combinations of instructions, (4) value reuse to improve code quality, and (5) an objective function that reduces compilation time and increases scalability by exploiting bounding techniques. The approach is demonstrated to be complete and practical, competitive with LLVM, and potentially optimal (w.r.t. the model) for medium-sized functions. The results show that combinatorial optimization for instruction selection is well-suited to exploit the potential of modern processors in embedded systems.
We introduce a propagator for pairs of SUM constraints, where the expressions in the sums respect a form of convexity. This propagator is parametric and can be instantiated for various concrete pairs, including DEVIAT...
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We introduce a propagator for pairs of SUM constraints, where the expressions in the sums respect a form of convexity. This propagator is parametric and can be instantiated for various concrete pairs, including DEVIATION, SPREAD, and the conjunction of LINEAR(<=) and AMONG. We show that despite its generality, our propagator is competitive in theory and practice with state-of-the-art propagators. (C) 2016 Elsevier B.V. All rights reserved.
This paper addresses a process-to-machine reassignment problem arising in cloud computing environments. The problem formulation has been posed as the ROADEF/EURO challenge 2012. Our presented approach is basically a l...
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This paper addresses a process-to-machine reassignment problem arising in cloud computing environments. The problem formulation has been posed as the ROADEF/EURO challenge 2012. Our presented approach is basically a large neighborhood search that iteratively improves a given solution. In each iteration only a subset of processes is considered for reassignment and the new assignments are evaluated by a constraint program. In this paper we present our general solution approach. Furthermore, we evaluate different process selection strategies and other optimization means to improve the performance on larger instances. In addition, we present a simple way to compute tight lower bounds of the necessary costs.
Nowadays,global competitiveness,dynamic demand changes,short product manufacturing cycle and customer oriented productions have forced manufacturing companies to form supply chain networks,in order to secure market op...
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Nowadays,global competitiveness,dynamic demand changes,short product manufacturing cycle and customer oriented productions have forced manufacturing companies to form supply chain networks,in order to secure market opportunities,and to manufacture products *** therefore have to develop and adopt cost-effective methodologies to integrate partner selection and production/distribution planning decisions,in particular when selection of companies to form the supply chain network depends on the BOM structures of the products. In this research,a novel mathematical model is developed to describe the characteristics of a supply chain network operating under a multi-stage,multi-product and multi-period manufacturing *** algorithm based on the technique of constraint programming(CP) is also developed to optimize the formation of such a supply chain network and its manufacturing and distribution activities cost-effectively.A new forward-testing propagation technique is developed and implemented to facilitate the search *** obtained from solving a set of randomly generated problems clearly show that the developed CP algorithm furnished with the forward-testing propagation technique performs well when compared with the genetic ***,this optimization approach can also be used as a general optimization technique to solve other logistics systems design problems.
This paper presents a mathematical model and a solution approach to solve the hot rolling scheduling *** problem is formulated as a constraint satisfaction problem with conflicting objectives and process *** target of...
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ISBN:
(纸本)9781612848334
This paper presents a mathematical model and a solution approach to solve the hot rolling scheduling *** problem is formulated as a constraint satisfaction problem with conflicting objectives and process *** target of the problem is to find a slab sequence which satisfies all constraints presented in the model,and the total of the rolling length of the slab sequence need to be close to the upper bound on the length rolled in a rolling *** model of the problem is tested with practical production *** on the constraint programming,the model is easily established and solved *** computational results of the problem show that the method for this model is feasible,and the results meet the requirements of practical applications.
Within the constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few stu...
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Within the constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. The results obtained by measuring several evaluation metrics confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research.
Open forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems where problem const...
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For variants of the single-mode resource-constrained project scheduling problem, state-of-the-art exact algorithms combine a Branch and Bound algorithm with principles from constraint programming and Boolean Satisfiab...
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For variants of the single-mode resource-constrained project scheduling problem, state-of-the-art exact algorithms combine a Branch and Bound algorithm with principles from constraint programming and Boolean Satisfiability Solving. In our paper, we propose new exact approaches extending the above principles to the multi-mode RCPSP (MRCPSP) with generalized precedence relations (GPRs). More precisely, we implemented two constraint handlers cumulativemm and gprecedencemm for the optimization framework SCIP. With the latter, one can model renewable resource constraints and GPRs in the context of multi-mode activities, respectively. Moreover, they integrate domain propagation and explanation generation techniques for the above problem characteristics. We formulate three SCIP-models for the MRCPSP with GPRs, two without and one with our constraint handler gprecedencemm. Our computational results on instances from the literature with 30, 50 and 100 activities show that the addition of this constraint handler significantly strengthens the SCIP-model. Moreover, we outperform the state-of-the-art exact approach on instances with 50 activities when imposing time limits of 27 s. In addition, we close (find the optimal solution and prove its optimality for) 289 open instances and improve the best known makespan for 271 instances from the literature.
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