Sequencing problems are among the most prominent problems studied in operations research, with primary application in, e.g., scheduling and routing. We propose a novel approach to solving generic sequencing problems u...
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Sequencing problems are among the most prominent problems studied in operations research, with primary application in, e.g., scheduling and routing. We propose a novel approach to solving generic sequencing problems using multivalued decision diagrams (MDDs). Because an MDD representation may grow exponentially large, we apply MDDs of limited size as a discrete relaxation to the problem. We show that MDDs can be used to represent a wide range of sequencing problems with various side constraints and objective functions, and we demonstrate how MDDs can be added to existing constraint-based scheduling systems. Our computational results indicate that the additional inference obtained by our MDDs can speed up a state-of-the art solver by several orders of magnitude, for a range of different problem classes.
Contractors attempt to manage optimal collection of human capital that can optimize the company's performance. The skills and competency of human resources contribute considerably to the competitive advantage of t...
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Contractors attempt to manage optimal collection of human capital that can optimize the company's performance. The skills and competency of human resources contribute considerably to the competitive advantage of the firm. Yet the skill shortage problem encourages utilizing strategies that bring more flexibility to workforce management. Multiskilling has been suggested as a potential strategy that assists workforce assignment across multiple projects. However, it brings new challenges for company workforce managers. This study contributes to the construction project management body of knowledge by proposing a simulation framework for assigning multiskilled, mainly managerial, workforces across multiple projects of one company. Meanwhile, it is a combination of a staffng and scheduling decision model, incorporating workers' competency status. A cohesive mathematical model using the cost objective function optimizes the entire allocation process toward minimizing resource usage and fluctuations, minimizing costs, and maximizing social sustainability. Ultimately, a validation example incorporating diverse workers' competency data is provided to exemplify how the optimization model performs in an academic setting compared with three commonly used methods in workforce assignment. This approach provides insight into simulating workforce assignments across multiple projects regarding competency. (C) 2020 American Society of Civil Engineers.
This study presents a logical based approach with constraint programming technique for applying rule-based hybrid strategies in the optimal planning of the micro cogeneration systems. Moreover, in this paper, a novel ...
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This study presents a logical based approach with constraint programming technique for applying rule-based hybrid strategies in the optimal planning of the micro cogeneration systems. Moreover, in this paper, a novel operation strategy that relied on the comparison of loss of supply probabilities for reducing the dependency on auxiliary systems and minimizing the production of redundant energy in micro cogeneration systems is also introduced. The presented approach is applied to evaluate the effectiveness of five operation strategies namely two basic, two common hybrid and the proposed hybrid strategy under three single optimization criteria for the total annual cost (TAC), primary energy consumption (PEC) and greenhouse gas generation (GHG) as well as one integrated criterion based on Utopia tracking method for the combination of these criteria. Three energy matching parameters are exploited to assess the performance of each strategy in matching the cogenerated energy carriers with the building demand requirements. The analysis of the obtained results for four types of residential buildings indicates that the common hybrid strategy which maximizes the exploitation of cogeneration system, and the proposed strategy as the most suitable strategies for integrated criterion have been able to achieve the best compromise solutions with over 55%, 14% and 9% reductions in TAC, PEC and GHG respectively. While the proposed strategy can achieve higher reductions for TAC (up to 4.6%) as well as higher overall performance for energy matching (up to 2.5%) with the same limitations bounds for GHG and PEC in comparison to this common hybrid strategy. By analyzing the model uncertainties, the feed-in tariff for power generation with near 60% decrement in TAC is found as the most dominant economic parameter for sensitivity analysis while the capacity of the micro cogeneration unit with over 3% increment is detected as the most affected outcome for stochastic analysis.
String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the past few years given the massive use of strings in...
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String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the past few years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This article reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the past few decades to solve string constraints.
This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-ba...
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This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-based objectives, setup and transport resource constraints were not considered simultaneously with this policy, limiting the resulting schedule's applicability in practice. For this reason, we propose a novel constraint programming (CP) model enriched by an efficient variable and value ordering strategy specifically designed for the FJSSP-LS with resource constraints. We also present a CP-based iterative improvement method, CP-based Large Neighbourhood Search (CP-based LNS), that focuses on exploring large neighbourhoods through the CP model. Both models are initially tested for the FJSSP and have been shown to provide the best solutions to well-known benchmark instances. Next, they are used for the FJSSP-LS, and the proposed CP-based LNS improves the objective function value by 4.68 percent on average compared to the CP model for the generated test problems.
Background: Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various le...
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Background: Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods: In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results: We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://***/compneuro/
A hybrid flow shop group scheduling problem (HFGSP) involves two distinct sub-problems, the arrangement of groups and the configuration of jobs within each group. Constructing its mathematical model based on the class...
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A hybrid flow shop group scheduling problem (HFGSP) involves two distinct sub-problems, the arrangement of groups and the configuration of jobs within each group. Constructing its mathematical model based on the classification of these sub-problems can help better understand the problem's characteristics. However, the existing literature fails to establish MILP models of HFGSP based on the interrelation between each subproblem and the respective process constraints satisfied by each subproblem. To address this gap, our study first categorizes all the constraints in the HFGSP according to several factors: group arrangement, job arrangement within the group, process requirements between groups, process requirements between jobs from the same group, process requirements between adjacent stages, and the relationship between the decision variables of completion time and the objective constraint. Following this classification, we construct 48 available MILP models of HFGSP. Additionally, we also propose an efficient constraint programming (CP) model of HFGSP. Numerous experiments are conducted to verify the correctness and performance of all 48 MILP models across different test instances, analyze the complexities of all models, and excavate their intrinsic characteristics. Through our evaluation for the 48 models, we observe that models 24 and 48 exhibit superior performance. This highlights the effectiveness of the hybrid modeling approach, which synergistically combines sequence-based modeling for groups and position-based adjacent modeling for jobs within each group. The utilization of this hybrid modeling approach enables the construction of high-quality MILP models for addressing the HFGSP problem. Our intention is to bridge the gap between MILP modeling of HFGSP and problem-specific algorithms, thereby providing a comprehensive understanding of the problem and offering potential solutions.
Manufacturing Resources Planning (MRPII) systems are unable to prevent capacity problems occurring on the shop floor because of the fixed lead time and backward scheduling logic. For this reason, a new breed of concep...
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Manufacturing Resources Planning (MRPII) systems are unable to prevent capacity problems occurring on the shop floor because of the fixed lead time and backward scheduling logic. For this reason, a new breed of concepts called APS (Advanced Planning and Scheduling) systems emerged which include finite capacity planning at the shop floor level through constraint based planning. In this paper, we present a constraint programming (CP) model to show how optimization models could be used in this context. We also present a two phase heuristic to solve this complicated APS problem. While jobs are assigned to the best eligible machines to smooth the workload on the machines in the first phase, a constraint based scheduling heuristic schedules jobs once they are assigned to eligible machines in the second phase. We provide numerical tests and discuss the results for both the model and the heuristic. The concluding remarks and suggestions for future research are stated in the final section of the paper.
In this article, we propose an explicit integer optimization formulation for the design of reliable and robust (to uncertainty in reliability data) sensor networks. The robustness is achieved by incorporating simultan...
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In this article, we propose an explicit integer optimization formulation for the design of reliable and robust (to uncertainty in reliability data) sensor networks. The robustness is achieved by incorporating simultaneous occurrence of different kinds of uncertainty in the failure rate data in the optimization formulation. We show the use of constraint programming to solve these combinatorial problems to global optimality and also evaluate the globally optimal pareto front between robustness and cost of these sensor networks. Such tradeoffs help the designer in making informed choices for the selection of sensor networks. The applicability of the proposed work has been demonstrated on a case study taken from literature.
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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