With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate ber...
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With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects-cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods-in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year's operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.
Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) wh...
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Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) which generated the low-level sequences are not made available. This de facto 'raw sequence' exchange format allows published origami designs to be re-attempted in the laboratory by other groups, but effectively stops designs from being significantly modified or re-purposed for new future applications. To make the raw sequence exchange format more accessible to further design and engineering, in this work we propose the first algorithmic solution to the inverse problem of converting staple/scaffold sequences back to a 'guide schematic' resembling the original origami schematic. The guide schematic can be used to aid the manual re-input of an origami into a CAD tool like caDNAno, hence recovering a high-level editable design file. Creation of a guide schematic can also be used to double check that a list of staple strand sequences does not have errors and indeed does assemble into a desired origami nanostructure prior to costly laboratory experimentation. We tested our reverse algorithm on 36 diverse origami designs from the literature and found that 29 origamis (81 %) had a good quality guide sche-matic recovered from raw sequences. Our software is made available at https://***.
Intensional sets are sets given by a property rather than by enumerating their elements. In a previous work, we have proposed a decision procedure for a first-order logic language which provides restricted intensional...
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Intensional sets are sets given by a property rather than by enumerating their elements. In a previous work, we have proposed a decision procedure for a first-order logic language which provides restricted intensional sets (RISs), i.e. a sub-class of intensional sets that are guaranteed to denote finite-though unbounded-sets. In this paper, we show how RIS can be exploited as a convenient programming tool also in a conventional setting, namely the imperative O-O language Java. We do this by considering a Java library, called JSetL, that integrates the notions of logical variable, (set) unification and constraints that are typical of constraint logic programming languages into the Java language. We show how JSetL is naturally extended to accommodate for RIS and RIS constraints and how this extension can be exploited;on the one hand, to support a more declarative style of programming and, on the other hand, to effectively enhance the expressive power of the constraint language provided by the library.
In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down...
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In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down methodologies for effectively breaking down complex requirements into precise, actionable sub-requirements are scarce, especially compared to the wealth of bottom-up verification techniques. Addressing this gap, we introduce a formal decomposition for contract-based design that guarantees the correctness of decomposed requirements if specific conditions are met. Our (semi-)automated methodology augments contract-based design with reachability analysis and constraint programming to systematically identify, verify, and validate sub-requirements representable by continuous bounded sets-continuous relations between real-valued inputs and outputs. We demonstrate the efficacy and practicality of a correct-by-construction approach through a comprehensive case study on a cruise control system, highlighting how our methodology improves the interpretability, tractability, and verifiability of system requirements.
Real world assembly lines have a characterisation of variability in arrival, service and departure processes. Modelling these variabilities and their interactions, and the optimisation of a line have not been achieved...
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Real world assembly lines have a characterisation of variability in arrival, service and departure processes. Modelling these variabilities and their interactions, and the optimisation of a line have not been achieved yet. The purpose of this research is to provide an analytical solution framework for finding the best combinations of task assignment under variability. A queueing-based decomposition model that considers all variations sources has been proposed for the performance evaluation of a stochastic assembly line. A closed, nonlinear constraint programming model has been developed. Mathematical relations from the variability sources are established to measure the overall system performance. Numerical experiments which are conducted on several numerical examples demonstrate that the approach is a viable and an effective solution method. The results also indicate that changes in the coefficient of variance of either the service or arrival process, alter both the task assignment combinations, station workloads and line performance. [Submitted: 10 July 2021;Accepted: 19 January 2022]
t-wise coverage is one of the most important techniques used to test configurations of software for finding bugs. It ensures that interactions between features of a Software Product Line (SPL) are tested. The size of ...
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t-wise coverage is one of the most important techniques used to test configurations of software for finding bugs. It ensures that interactions between features of a Software Product Line (SPL) are tested. The size of SPLs (of thousands of features) makes the problem of finding a good test suite computationally expensive, as the number of t-wise combinations grows exponentially. In this article, we leverage constraint programming's search strategies to generate test suites with a high coverage of configurations. We analyse the behaviour of the default random search strategy, and then we propose an improvement based on the commonalities (frequency) of the features. We experimentally compare to uniform sampling and state of the art sampling approaches. We show that our new search strategy outperforms all the other approaches and has the fastest running time.
Scheduling aircraft turnarounds at airports requires the coordination of several organizations, including the airport operator, airlines, and ground service providers. The latter manage the necessary supplies and team...
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Scheduling aircraft turnarounds at airports requires the coordination of several organizations, including the airport operator, airlines, and ground service providers. The latter manage the necessary supplies and teams to handle aircraft in between consecutive flights, in an area called the airport 'apron'. Divergence and conflicting priorities across organizational borders negatively impact the smooth running of operations, and play a major role in departure delays. We provide a novel simulation-optimization approach that allows multiple service providers to build robust plans for their teams independently, whilst supporting overall coordination through central scheduling of all the involved turnaround activities. Simulation is integrated within the optimization process, following simheuristic techniques, which are augmented with an efficient search driving mechanism. Two tailored constraint-based feedback routines are automatically generated from simulation outputs to constrain the search space to solutions more likely to ensure plan robustness. The two simulation components provide constructive feedback on individual routing problems and global turnaround scheduling, respectively. Compared to the state-of-the-art approach for aircraft turnaround scheduling and routing of service teams, our methodology improves the apron's on-time punctuality, without the need for the involved organizations to share sensitive information. This supports a wider applicability of our approach in a multiple-stakeholder environment.
To tackle the balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations, If-then rules and AND/OR graphs are adopted as modelling tools to replace precedence graphs...
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To tackle the balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations, If-then rules and AND/OR graphs are adopted as modelling tools to replace precedence graphs that have limitations in representing alternative precedence relations. Mixed integer linear programming (MILP) and constraint programming (CP) models are established respectively. Moreover, an iterative decomposition method is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test those methods. The computational results reveal that the performance of CP is better than MILP especially when more OR relations exist. And the iterative decomposition method outperforms MILP and CP in terms of solution quality and time.
Time-Sensitive Networking (TSN) collectively defines a set of protocols and standard amendments that enhance IEEE 802.1Q Ethernet nodes with time-aware and fault-tolerant capabilities. Specifically, the IEEE 802.1Qbv ...
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Time-Sensitive Networking (TSN) collectively defines a set of protocols and standard amendments that enhance IEEE 802.1Q Ethernet nodes with time-aware and fault-tolerant capabilities. Specifically, the IEEE 802.1Qbv amendment defines a timed-gate mechanism that governs the real-time transmission of critical traffic via a so-called Gate Control List (GCL) schedule encoded in each TSN-capable network device. Most TSN scheduling mechanisms are designed for homogeneous TSN networks in which all network devices must have at least the TSN capabilities related to scheduled gates and time synchronization. However, this assumption is often unrealistic since many distributed applications use heterogeneous TSN networks with legacy or off-the-shelf end systems that are unscheduled and/or unsynchronized. We propose a new scheduling paradigm for heterogeneous TSN networks that intertwines a network calculus worst-case interference analysis within the scheduling step. Through this, we compromise on the solution's optimality to be able to support heterogeneous TSN networks featuring unscheduled and/or unsynchronized end-systems while guaranteeing the real-time properties of critical communication. Within this new paradigm, we propose two solutions to solve the problem, one based on a constraint programming formulation and one based on a Simulated Annealing metaheuristic, that provide different trade-offs and scalability properties. We compare and evaluate our flexible window-based scheduling methods using both synthetic and real-world test cases, validating the correctness and scalability of our implementation. Furthermore, we use OMNET++ to validate the generated GCL schedules.
Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust ...
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Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust if it absorbs some level of unforeseen events when at most a certain number of activities are delayed. Taking advantage of constraint programming, we present two new filtering algorithms for a constraint that models cumulative scheduling problems in robust contexts where up to r out of n tasks can be concurrently delayed while keeping the schedule valid. We adapt the overload-checking and edge-finding filtering rules for this framework. We show that our robust versions of these algorithms run in & UTheta;(r2nlog(n)) and O(r2znlog(n)), respectively, where z denotes the number of distinct capacities of all tasks. This achievement implies that the complexities of the state-of-the-art algorithms for these techniques are invariable when r is constant. Experiments illustrate that our algorithms scale, with respect to n and r. As a practical application, the experimental results on a special case of crane assignment problem also verify a stronger filtering for these methods in terms of backtrack numbers as well as computation times when used in conjunction with time tabling. Finally, in order to show that our CP-based algorithms improve to solve a robust scheduling problem, we make a comparison against temporal protection as an external robust scheduling approach.
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