From an operational point of view, Ready-Mixed Concrete Suppliers are faced with challenging operational problems such as the acquisition of raw materials, scheduling of production facilities, and the transportation o...
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From an operational point of view, Ready-Mixed Concrete Suppliers are faced with challenging operational problems such as the acquisition of raw materials, scheduling of production facilities, and the transportation of concrete. This paper is centered around the logistical and distributional part of the operation: the scheduling and routing of concrete, commonly known as the Concrete Delivery Problem (CDP). The problem aims at finding efficient routes for a fleet of (heterogeneous) vehicles, alternating between concrete production centers and construction sites, and adhering to strict scheduling and routing constraints. Thus far, a variety of CDPs and solution approaches have appeared in academic research. However, variations in problem definitions and the lack of publicly available benchmark data inhibit a mutual comparison of these approaches. Therefore, this work presents a more fundamental version of CDP, while preserving the main characteristics of the existing problem variations. Both exact and heuristic algorithms for CDP are proposed. The exact solution approaches include a Mixed Integer programming (MIP) model and a constraint programming model. Similarly, two heuristics are studied: the first heuristic relies on an efficient best-fit scheduling procedure, whereas the second heuristic utilizes the MIP model to improve delivery schedules locally. Computational experiments are conducted on new, publicly accessible, data sets;results are compared against lower bounds on the optimal solutions. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper, we investigate the problem of scheduling a 6 DOF robotic arm to carry out a sequence of spray painting tasks. The duration of any given painting task is process dependent and fixed, but the duration of ...
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In this paper, we investigate the problem of scheduling a 6 DOF robotic arm to carry out a sequence of spray painting tasks. The duration of any given painting task is process dependent and fixed, but the duration of an "intertask", corresponding to the process of relocating and reorienting the robot arm from one painting task to the next one, is influenced by the order of tasks and must be minimized by the scheduler. There are multiple solutions for reaching any given painting task and tasks can be performed in either of two different directions. Further complicating the problem are characteristics of the painting process application itself. Unlike spot-welding, painting tasks require movement of the entire robot arm. In addition to minimizing intertask duration, the scheduler must strive to maximize painting quality and the problem is formulated as a multi-objective optimization problem. The scheduling model is implemented as a stand-alone module using constraint programming, and integrated with a larger automatic system. The results of a number of simulation experiments with simple parts are reported, both to characterize the functionality of the scheduler and to illustrate the operation of the entire software system for automatic generation of robot programs for painting. (C) 2013 Elsevier B.V. All rights reserved.
Current technological advances in communications and navigation have improved air traffic management (ATM) with new decision support tools to balance airspace capacity with user demands. Despite agreements achieved in...
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Current technological advances in communications and navigation have improved air traffic management (ATM) with new decision support tools to balance airspace capacity with user demands. Despite agreements achieved in flying reference business trajectories (RBTs) among different stakeholders, tight spatio-temporal connectivity between trajectories in dense sectors can cause perturbations that might introduce time or space deviations into the original RBTs, thus potentially affecting other 4D trajectories. In this paper, several challenging results are presented by properly tuning the Calculated Take-Off Times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories that can readily appear in dense sectors. Based on the identification of "collective microregions", a tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace was developed, together with a CTOT algorithm to sequence the departures that preserve the scheduled slots while relaxing tight trajectory interactions. The algorithm was tested by considering a realistic scenario (designed and analyzed in the STREAM project (Stream, 2013)) to evaluate relevant ATM KPIs that provide aggregated information about the sensitivity of the system to trajectory interactions, taking into account the system dynamics at a network level. The proposed approach contributes to enhancing the ATM capacity of airports to mitigate network perturbations. (C) 2014 Elsevier Ltd. All rights reserved.
The navigation constellation will have the capability of supporting Tracking Telemetry and Command (TT&C) operations by inter-satellite link (ISL). The ISL will become an important solution to reduce the shortage ...
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The navigation constellation will have the capability of supporting Tracking Telemetry and Command (TT&C) operations by inter-satellite link (ISL). The ISL will become an important solution to reduce the shortage of ground TT&C resources. The problems need to be studied urgently in the field of space TT&C network resources scheduling management are how to determine the availability of ISL and how to allocate TT&C resources of ISL. The performance and scheduling constraints of navigation constellation's ISL are analyzed, and three utilization strategies of ISL to perform TT&C operations are proposed. The allocation of TT&C resources based on ISL falls into two successive phases. Firstly, master satellite determination equation is established by using 0-1 programming model based on the availability matrix. Mathematical method is used to solve the equation to determine the master satellite and the topology of ISL. Secondly, constraint programming (CP) model is used to describe the ground TT&C resources scheduling problem with special requirements of TT&C operations based on master satellite, and a heuristic algorithm is designed to solve the CP model. The equations and algorithm are verified by simulation examples. The algorithm of TT&C resources scheduling based on ISL has realized the synthesized usage of both the ISL and ground resources on TT&C field. This algorithm can improve TT&C supports of territorial ground TT&C network for global navigation constellation, and provides technical reference for the TT&C mission planning of global constellation by using ISL. (C) 2014 IAA. Published by Elsevier Ltd. All rights reserved.
E-learning is a promising research area, as they are expected to increase enrollment and improve the quality of education. Adaptive e-learning systems, traditionally focused on content personalization, are in need to ...
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ISBN:
(纸本)9781509004799
E-learning is a promising research area, as they are expected to increase enrollment and improve the quality of education. Adaptive e-learning systems, traditionally focused on content personalization, are in need to cope with continuous changing requirements and changing environment. Indeed, the specification and the management quality attributes of such systems, supported throughout the whole lifecycle are still missing. In this paper, we propose continuous requirements monitoring that uses a constraint program to check the conformity of adaptive e-learning systems to their requirements and react properly when deviations occur at runtime. To this end, we specify system's requirements in the form of a dynamic software product line. A novel requirements engineering language that combines goal-driven requirements with features and claims is applied for the specification, from which the constraint program is automatically generated.
Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions acc...
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Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using constraint programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime. (C) 2013 Elsevier Inc. All rights reserved.
A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM...
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A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM) can be seen as supporting BPs using methods, techniques, and software in order to design, enact, control, and analyze operational processes involving humans, organizations, applications, and other sources of information. Since the accurate management of BPs is receiving increasing attention, conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative models, little work has been conducted for declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any related diagnostics. To enhance the accurate management of flexible BPs, this work presents a constraint-based approach for conformance checking over declarative BP models (including both control-flow and data perspectives). In addition, two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both the effectiveness and the efficiency of the proposed approaches, the analysis of different performance measures related to a wide diversified set of test models of varying complexity has been performed. (c) 2014 Elsevier Ltd. All rights reserved.
Sudoku is a puzzle played of an n x n grid N, where n is the square of a positive integer m. The most common size is n= 9. The grid is partitioned into n subgrids of size mxm. The player must place exactly one number ...
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Sudoku is a puzzle played of an n x n grid N, where n is the square of a positive integer m. The most common size is n= 9. The grid is partitioned into n subgrids of size mxm. The player must place exactly one number from the set N={1, . . . , n} in each row and each column of N, as well as in each subgrid. A grid is provided with some numbers already in place, called givens. In this paper, some relationships between Sudoku and several operations research problems are presented. We model the problem by means of two mathematical programming formulations. The first one consists of an integer linear programming model, while the second one is a tighter non-linear integer programming formulation. We then describe several enumerative algorithms to solve the puzzle and compare their relative efficiencies. Two basic backtracking algorithms are first described for the general Sudoku. We then solve both formulations by means of constraint programming. Computational experiments are performed to compare the efficiency and effectiveness of the proposed algorithms. Our implementation of a backtracking algorithm can solve most benchmark instances of size 9 within 0.02 s, while no such instance was solved within that time by any other method. Our implementation is also much faster than an existing alternative algorithm.
As custom multicore architectures become more and more common for DSP applications, instruction selection and scheduling for such applications and architectures become important topics. In this paper, we explore the e...
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As custom multicore architectures become more and more common for DSP applications, instruction selection and scheduling for such applications and architectures become important topics. In this paper, we explore the effects of defining the problem of finding an optimal instruction selection and scheduling as a constraint satisfaction problem (CSP). We incorporate methods based on sub-graph isomorphism and global constraints designed for scheduling. We experiment using several media applications on a custom architecture, a generic VLIW architecture and a RISC architecture, all three with several cores. Our results show that defining the problem with constraints gives flexibility in modeling, while state-of-the-art constraint solvers enable optimal solutions for large problems, hinting a new method for code generation. (C) 2014 Elsevier B.V. All rights reserved.
In spite of its tremendous economic significance, the problem of sales staff schedule optimization for retail stores has received relatively scant attention. Current approaches typically attempt to minimize payroll co...
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In spite of its tremendous economic significance, the problem of sales staff schedule optimization for retail stores has received relatively scant attention. Current approaches typically attempt to minimize payroll costs by closely fitting a staffing curve derived from exogenous sales forecasts, oblivious to the ability of additional staff to (sometimes) positively impact sales. In contrast, this paper frames the retail scheduling problem in terms of operating profit maximization, explicitly recognizing the dual role of sales employees as sources of revenues as well as generators of operating costs. We introduce a flexible stochastic model of retail store sales, estimated from store-specific historical data, that can account for the impact of all known sales drivers, including the number of scheduled staff, and provide an accurate sales forecast at a high intra-day resolution. We also present solution techniques based on mixed-integer (MIP) and constraint programming (CP) to efficiently solve the complex mixed integer non-linear scheduling (MINLP) problem with a profit-maximization objective. The proposed approach allows solving full weekly schedules to optimality, or near-optimality with a very small gap. On a case-study with a medium-sized retail chain, this integrated forecasting scheduling methodology yields significant projected net profit increases on the order of 2-3% compared to baseline schedules. (C) 2014 Elsevier B.V. All rights reserved.
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