Local branching is a general purpose heuristic method which searches locally around the best known solution by employing tree search. It has been successfully used in Mixed Integer programming (MIP) where local branch...
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ISBN:
(纸本)9783939897170
Local branching is a general purpose heuristic method which searches locally around the best known solution by employing tree search. It has been successfully used in Mixed Integer programming (MIP) where local branching constraints are used to model the neighborhood of an incumbent solution and improve the bound. The neighborhoods are obtained by linear inequalities in the MIP model so that MIP searches for the optimal solution within the Hamming distance of the incumbent solution. The linear constraints representing the neighborhood of incumbent solutions are called local branching constraints and are involved in the computation of the problem bound. Local branching is a general framework to effectively explore solution subspaces, making use of the state-of-the-art MIP solvers.
A new approach is proposed to tackle integrated decision making associated to supply chains. This procedure enables reliable decisions concerning the set of order demands along a supply chain. This is accomplished by ...
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ISBN:
(纸本)9783540766308
A new approach is proposed to tackle integrated decision making associated to supply chains. This procedure enables reliable decisions concerning the set of order demands along a supply chain. This is accomplished by means of supply chain scheduling simulations, based on the use of constraint programming. The definition of time windows for all tasks poses as an indication that no infeasibility was found during supply chain analysis. Scheduling of orders along the supply chain is treated as a constraint satisfaction problem. It suffices to identify any feasible schedule to state that simulated decisions are acceptable. The main contribution of this work is the integration of constraint programming concepts within a decision-support system to support supply chain decisions.
The rise of mass-individualization has underscored the significance of Mixed-Model Assembly Lines (MMALs) for producing diverse products on the same line. The Car Sequencing Problem (CSP) tackles short-term balancing ...
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ISBN:
(纸本)9783031581120;9783031581137
The rise of mass-individualization has underscored the significance of Mixed-Model Assembly Lines (MMALs) for producing diverse products on the same line. The Car Sequencing Problem (CSP) tackles short-term balancing in an MMAL by emphasizing the use of spacing rules to manage the space between each pair of work-intensive products that possess specific characteristics. In this study, we tackle two challenges within the CSP context. The first challenge involves exploring CSP with cross-ratio constraints that takes into account the dependency between different characteristics. As the second challenge, we study the CSP under two states where spacing rule violations are not allowed (hard) and allowed (soft). We develop two constraint programming models for the mentioned states and evaluate the performance of the models using several real-world assembly lines' instances. The findings enhance understanding of each model's strengths and weaknesses. Given the inherent complexity of real-world problems, the soft model may find more practical and effective application. This research enriches the realm of problem-solving in MMALs by offering valuable insights and introducing the main challenges in the CSP.
Scheduling tasks on reconfigurable hardware is a well-known problem. Yet, the adoption of advanced scheduling strategies for reconfigurable systems is still low. We argue that a pragmatic solution not relying on low-l...
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ISBN:
(纸本)9781665497473
Scheduling tasks on reconfigurable hardware is a well-known problem. Yet, the adoption of advanced scheduling strategies for reconfigurable systems is still low. We argue that a pragmatic solution not relying on low-level features like partial reconfiguration is feasible. Our theoretical framework describes reconfigurable hardware in a simple and abstract way. The constraints of a schedule are used to derive a constraint programming formulation. We present two heuristic algorithms based on list scheduling and on clustering, respectively. The model is evaluated and compared to partial reconfiguration using parameters from a previously observed LU decomposition on an FPGA. The losses are compared to a conventional, optimal approach. It can be integrated into existing technologies to aide the adoption of high-level FPGA programming environments.
Safety-critical Real Time Embedded Systems (RTESs) are usually subject to strict timing and performance requirements that must be satisfied for the system to be deemed safe. In this paper, we use effective search stra...
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ISBN:
(纸本)9781479923663
Safety-critical Real Time Embedded Systems (RTESs) are usually subject to strict timing and performance requirements that must be satisfied for the system to be deemed safe. In this paper, we use effective search strategies whose goal is finding worst case scenarios with respect to deadline misses. Such scenarios can in turn be used to test the target RTES and ensure that it satisfies its timing requirements even under worst case conditions. Specifically, we develop an approach based on constraint programming (CP) to automate the generation of test cases that reveal, or are likely to, task deadline misses. We evaluate it through a comparison with a state-of-the-art approach based on Genetic Algorithms (GA). In particular, we compare CP and GA in five case studies for efficiency, effectiveness, and scalability. Our experimental results show that, on the largest and more complex case studies, CP performs significantly better than GA. Furthermore, CP offers some advantages over GA, such as it guarantees a complete search when there is sufficient time, and, being deterministic, it doesn't rely on parameters that potentially have a significant effect on the search and therefore need to be tuned. Hence, we conclude that our results are encouraging and suggest this is an advantageous approach for stress testing of RTESs with respect to timing constraints.
p-Hub location-allocation problem is one of the most interesting subjects in the location theory. Hubs act as switching points to reduce the transportation cost. In this study, two new solution methods, a constraint p...
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p-Hub location-allocation problem is one of the most interesting subjects in the location theory. Hubs act as switching points to reduce the transportation cost. In this study, two new solution methods, a constraint programming (CP) based model and a hybrid of k-means and genetic algorithm (KGA), are developed to generate exact and approximate solutions, respectively. The proposed CP formulation is more understandable and straightforward in comparison with the MIP model. The experimental results indicate that the CP model uses the memory of the computer (RAM) more efficiently, which enables us to solve the medium size problems. But, in terms of run time, this method cannot be superior to the MIP model. The CP formulation is also extended for the multi allocation p-hub location problem. K-means algorithm, a well-known algorithm for clustering data, is used to generate initial solutions of GA. Furthermore, a new adaptive crossover operator, which is based on the k-means algorithm, is proposed. The experimental results indicate that the KGA algorithm is superior to the GA, regarding time, objective value, and quality of solution measures.
The final step of the typical process of developing educational and psychological tests is to place the selected test items in a formatted form. The step involves the grouping and ordering of the items to meet a varie...
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The final step of the typical process of developing educational and psychological tests is to place the selected test items in a formatted form. The step involves the grouping and ordering of the items to meet a variety of formatting constraints. As this activity tends to be time-intensive, the use of mixed-integer programming (MIP) has been proposed to automate it. The goal of this article is to show how constraint programming (CP) can be used as an alternative to automate test-form generation problems with a large variety of formatting constraints, and how it compares with MIP-based form generation as for its models, solutions, and running times. Two empirical examples are presented: (i) automated generation of a computerized fixed-form;and (ii) automated generation of shadow tests for multistage testing. Both examples show that CP works well with feasible solutions and running times likely to be better than that for MIP-based applications.
This paper is about Set Partitioning formulation and resolution for a particular case of VRP, the Dial-a-ride Problem. Set Partitioning has demonstrated to be useful modeling this problem and others very visible and e...
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ISBN:
(纸本)9783540730545
This paper is about Set Partitioning formulation and resolution for a particular case of VRP, the Dial-a-ride Problem. Set Partitioning has demonstrated to be useful modeling this problem and others very visible and economically significant problems. But the main disadvantage of this model is the need to explicitly generate a large set of possibilities to obtain good solutions. Additionally, in many cases a prohibitive time is needed to find the exact solution. Nowadays, many efficient metaheuristic methods have been developed to make possible a good solution in a reasonable amount of time. In this work we try to solve it with Low-level Hybridizations of Ant Colony Optimization and constraint programming techniques helping the construction phase of the ants. Computational results solving some benchmark instances are presented showing the advantages of using this kind of hybridization.
Last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers are considered. We focus on settings where a fleet with several homogeneous trucks work in parallel to collaborative ...
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ISBN:
(纸本)9783031629112;9783031629129
Last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers are considered. We focus on settings where a fleet with several homogeneous trucks work in parallel to collaborative drones, able to combine with each other to optimize speed and power consumption for deliveries. A heuristic for the min-max vehicle routing problem is coupled with constraint programming models, leading to an effective method able to provide several state-of-the-art solutions for the instances commonly adopted in the literature.
This paper presents a novel method of designing selected aspects of messaging-based integration solutions. The method uses constraint programming to find appropriate communication channels, components' deployment ...
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ISBN:
(纸本)9783642155758
This paper presents a novel method of designing selected aspects of messaging-based integration solutions. The method uses constraint programming to find appropriate communication channels, components' deployment parameters and integration services in order to create a solution that meets specified functional and non-functional requirements. The method has been evaluated using a prototype implementation and compared to authors' earlier work that used action-based planning techniques to reach similar goals.
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