Transport problems are a significant challenge for companies, i.e. due to concerns about climate change and the constant increase in raw material prices, such as for petrol. One issue in transporting bulk materials is...
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ISBN:
(纸本)9783031398209;9783031398216
Transport problems are a significant challenge for companies, i.e. due to concerns about climate change and the constant increase in raw material prices, such as for petrol. One issue in transporting bulk materials is the Stockyard Planning Problem (SPP), which plays a crucial role in mine production scheduling and bulk material transportation by using stockpiles to store and blend raw material. The SPP aims to: 1) blend superior and inferior components to achieve a desired quality, 2) transfer material from import (e.g. ocean-going ships) to interim storage (e.g. stockpiles) and then to export (e.g. docks at inland ports), and 3) find a time-/cost-optimized schedule for working steps of 1 and 2. This paper presents a novel constraint-based approach for solving the Stockyard Planning Problem (SPP) by utilizing new abstractions such as time, storage, and potential movements within a stockyard system.
In a context of labor shortage and strong global competition for talent, salary management is becoming a critical issue for companies wishing to attract, engage and retain qualified employees. This paper presents a mu...
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ISBN:
(纸本)9783031332708;9783031332715
In a context of labor shortage and strong global competition for talent, salary management is becoming a critical issue for companies wishing to attract, engage and retain qualified employees. This paper presents a multi-objective optimization model to balance the internal equity, external equity and costs trade-offs associated with the design of salary structures. Solutions are generated to estimate and explore the Pareto frontier using real compensation data from a unionized establishment in the public sector. Our work shows the interest of using combinatorial optimization techniques in the design of salary structures.
This work presents guillotine constraints for two- and three-dimensional cutting problems. These problems look for a subset of rectangular items of maximum value that can be cut from a single rectangular container. Gu...
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This work presents guillotine constraints for two- and three-dimensional cutting problems. These problems look for a subset of rectangular items of maximum value that can be cut from a single rectangular container. Guillotine constraints seek to ensure that items are arranged in such a way that cuts from one edge of the container to the opposite edge completely separate them. In particular, we consider the possibility of 2, 3, and 4 cutting stages in a predefined sequence. These constraints are considered within a two-level iterative approach that combines the resolution of integer linear programming and constraint programming models. Experiments with instances of the literature are carried out, and the results show that the proposed approach can solve in less than 500 s approximately 60% and 50% of the instances for the two- and three-dimensional cases, respectively. For the two-dimensional case, in comparison with the recent literature, it was possible to improve the upper bound for 16% of the instances.
The concept of balance plays an important role in many combinatorial optimization problems. Yet there exist various ways of expressing balance, and it is not always obvious how best to achieve it. In this methodology-...
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The concept of balance plays an important role in many combinatorial optimization problems. Yet there exist various ways of expressing balance, and it is not always obvious how best to achieve it. In this methodology-focused paper, we study three cases where its integration is deficient and analyze the causes of these inadequacies. We examine the characteristics and performance of the measures of balance used in these cases, and provide general guidelines regarding the choice of a measure.
Scheduling and resource allocation problems are widespread in many areas of today's technology and management. Their different forms and structures appear in production, logistics, software engineering, computer n...
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Scheduling and resource allocation problems are widespread in many areas of today's technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, project and human resources management, services, etc. The literature (problem classification, scheduling and resource allocation models, solutions) is vast and exhaustive. In practice, however, classical scheduling problems with fixed structures and standard constraints (precedence, disjoint, etc.) are rare. Practical scheduling problems include also logical and nonlinear constraints, and they use nonstandard criteria of schedule evaluations. Indeed, in many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as general and/or specific questions. Thus, there is a need to develop a programming framework that will facilitate the modeling and solving of a variety of diverse scheduling problems. The framework should be able to (a) model any types of constraints, (b) ask questions/criteria relating to the schedule execution mode and (c) be highly effective in finding solutions (schedule development). This paper proposes such a constraint-based declarative programming framework for modeling and solving scheduling problems which satisfies the assumptions above. It was built with the constraint Logic programming (CLP) environment and supported with Mathematical programming (MP). The functionality and effectiveness of this framework are presented with the use of an illustrative example for the resource-constrained scheduling problem with additional resources.
In confronting the "Memory Wall", the design of embedded vision systems exhibits many challenges regarding design cost, energy consumption, and performance. This paper considers a variant of the Job Shop Sch...
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In confronting the "Memory Wall", the design of embedded vision systems exhibits many challenges regarding design cost, energy consumption, and performance. This paper considers a variant of the Job Shop Scheduling Problem with tooling constraints, arising in this context, in which the completion time (makespan) is to be minimized. This objective corresponds to the performance of the produced circuit. We discuss different formulations using integer linear programming and point out their characteristics, namely the size and the quality of the linear programming relaxation bound. To solve this scheduling problem with large size, we compare various approaches, including a constraint programming model, two constructive greedy heuristics, two models of LocalSolver, a Simulated Annealing algorithm, and a Beam Search algorithm. Numerical experiments are conducted on 16 benchmark instances from the literature and 12 real-life non-linear image processing kernels for validating their efficiency.
We use constraint Satisfaction methods to enumerate and construct set-theoretic solutions to the Yang???Baxter equation of small size. We show that there are 321,931 involutive solutions of size nine, 4,895,272 involu...
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We use constraint Satisfaction methods to enumerate and construct set-theoretic solutions to the Yang???Baxter equation of small size. We show that there are 321,931 involutive solutions of size nine, 4,895,272 involutive solutions of size ten and 422,449,480 non-involutive solution of size eight. Our method is then used to enumerate non-involutive biquandles.
Context: The use of Business Process Management Systems (BPMS) has emerged in the IT arena for the automation of business processes. In the majority of cases, the issue of security is overlooked by default in these sy...
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Context: The use of Business Process Management Systems (BPMS) has emerged in the IT arena for the automation of business processes. In the majority of cases, the issue of security is overlooked by default in these systems, and hence the potential cost and consequences of the materialization of threats could produce catastrophic loss for organizations. Therefore, the early selection of security controls that mitigate risks is a real and important necessity. Nevertheless, there exists an enormous range of IT security controls and their configuration is a human, manual, time-consuming and error-prone task. Furthermore, configurations are carried out separately from the organization perspective and involve many security stakeholders. This separation makes difficult to ensure the effectiveness of the configuration with regard to organizational requirements. Objective: In this paper, we strive to provide security stakeholders with automated tools for the optimal selection of IT security configurations in accordance with a range of business process scenarios and organizational multi-criteria. Method: An approach based on feature model analysis and constraint programming techniques is presented, which enable the automated analysis and selection of optimal security configurations. Results: A catalogue of feature models is determined by analyzing typical IT security controls for BPMSs for the enforcement of the standard goals of security: integrity, confidentiality, availability, authorization, and authentication. These feature models have been implemented through constraint programs, and constraint programming techniques based on optimized and non-optimized searches are used to automate the selection and generation of configurations. In order to compare the results of the determination of configuration a comparative analysis is given. Conclusion: In this paper, we present innovative tools based on feature models, constraint programming and multi-objective techniques that en
As an important component of flow type production systems, assembly lines are widely used from automotive, appliance to apparel industry. In this research, the aim is finding the optimum task assignment that minimizes...
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As an important component of flow type production systems, assembly lines are widely used from automotive, appliance to apparel industry. In this research, the aim is finding the optimum task assignment that minimizes the cycle time under the assumption that station quantities are constant which are described as Type-2 assembly lines. Stochastic assembly lines are considered in which task times are distributed according to a statistical distribution. A new algorithm which gives optimum solution using constraint programming and Queuing Network is proposed. In this algorithm, the possible combinations are determined by constraint programming, and then, the performance measures are evaluated by Queuing Network. The method is tested with several numerical experiments from literature and the applicability is confirmed.
This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and ...
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This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and due to limited tool magazine capacity, and because it is not possible to load all available tools on the machine, tools must be switched. The ultimate goal, in this framework, is to minimize the total number of tool switching instants. We provide a mathematical programming model and two constraint programming models for the problem. Because the problem is proven to be NP-hard, we develop two heuristic approaches, and compare their performance with methods described in the literature. Our analysis indicates that our constraint programming models perform relatively well in solution quality and execution time in small-sized problem instances. The performance of our greedy approach shows potential, reaching the optimal solution in 82.5% of instances. We also statistically demonstrate that the search algorithm enhances the quality of the solution obtained by the greedy heuristic, particularly in large sets. Hence, the solution approach, i.e., the greedy heuristic and the search algorithm proposed in this study is able to quickly reach near-optimal solutions, showing that the method is appropriate for manufacturing settings requiring sudden adjustments.
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