In this work, we introduce a generalized flexible job-shop scheduling problem in which, besides the classical constraints of the flexible job shop scheduling problem other hard constraints such as machine capacity, ti...
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In this work, we introduce a generalized flexible job-shop scheduling problem in which, besides the classical constraints of the flexible job shop scheduling problem other hard constraints such as machine capacity, time lags, holding times, and sequence-dependent setup times are taken into account. This problem is inspired by a real situation observed in a seamless rolled ring manufacturer. We propose a mixed integer linear programming (MILP) and a constraint programming (CP) models to represent the problem. Additionally, we develop a metaheuristic based on a Greedy Randomized Adaptive Search Procedure (GRASP) able to tackle efficiently large instances of the problem. The results show that CP outperforms the MILP and the proposed GRASP outperforms the CP when solving instances with more than 100 jobs.
Batch scheduling is a common problem faced in industrial scheduling when groups of related jobs must be processed consecutively or simultaneously on the same resource. Motivated by the composites manufacturing industr...
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ISBN:
(纸本)9783030589424;9783030589417
Batch scheduling is a common problem faced in industrial scheduling when groups of related jobs must be processed consecutively or simultaneously on the same resource. Motivated by the composites manufacturing industry, we present a complex batch scheduling problem combining two-stage bin packing with hybrid flowshop scheduling. We propose five solution approaches: a constraint programming model, a three-phase logic-based Benders decomposition model, an earliest due date heuristic, and two hybrid heuristic/constraint programming approaches. We then computationally test these approaches on generated problem instances modelled on real-world instances. Numeric results show that the heuristic approaches perform as well as or better than the exact models, especially on large instances. The relative success of a simple heuristic suggests that such problems pose an interesting challenge for further research in mathematical and constraint programming.
Cellular manufacturing system (CMS) is a novel production system adaptable to the make-to-order production. The present study focuses on scheduling CMS aimed at maximizing total profits as a function of the revenues e...
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Cellular manufacturing system (CMS) is a novel production system adaptable to the make-to-order production. The present study focuses on scheduling CMS aimed at maximizing total profits as a function of the revenues earned from sales as well as energy consumption cost and order tardiness penalties. The components to be considered in the problem in hand include the time-dependency of energy price, price elasticity of demand, and speed-based power consumption of machines. Two linearization approaches are used to determine order quantities. The first chooses lot sizes from a continuous range while the second chooses them among prespecified discrete levels. Especially developed mathematical models are used to solve the problem in either approach. For the second linearization approach, a constraint programming model, and a hybrid algorithm based on the fixand-optimize and variable neighborhood search metaheuristic (FOVNS, for short) are additionally developed. Changing the branching procedure as a technique and three dominance rules are also proposed to improve the performance of the CP and FOVNS models while their effectiveness is examined using the full factorial design of experiments. Also, the parameters of the FOVNS are tuned using the Taguchi method. Exact methods are found capable of optimizing medium-size problems in less than an hour while FOVNS is able to optimize large-size ones in 822 seconds on average with a deviation of 1.8% from the optimal solution. Statistical analysis show that considering a time-dependent energy price in the scheduling decreases the energy cost by about 40%.
This paper describes the implementation of Nutmeg, a solver that hybridizes mixed integer linear programming and constraint programming using the branch-and-cut style of logic-based Benders decomposition known as bran...
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In this paper, we present the results of solving the Maximum Clique Problem using declarative modeling approaches. Our goal is to create a single reusable model that can be utilized to compare several solvers supporte...
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ISBN:
(纸本)9781728198095
In this paper, we present the results of solving the Maximum Clique Problem using declarative modeling approaches. Our goal is to create a single reusable model that can be utilized to compare several solvers supported by MiniZinc. For the comparison, we have used data sets from the DIMACS benchmarks. We present the performance results of the solvers used in experiments using single as well as 16-threaded configurations on data set instances of 125, 250, 500, 1000, and 2000 nodes. Our initial results revealed that the Gurobi solver handles the small data sets better than the Gecode or COIN-OR, but the Gecode solver is better over the larger data sets. We found that COIN-OR was not able to find a solution for data sets of more than 500 nodes, on the single thread configuration, within our chosen time limit of 120s.
Despite the significant progress made in scheduling in the past years, industrial problems with several hundred tasks remain intractable for some variants of the scheduling problems. We present techniques that can be ...
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ISBN:
(纸本)9783030589424;9783030589417
Despite the significant progress made in scheduling in the past years, industrial problems with several hundred tasks remain intractable for some variants of the scheduling problems. We present techniques that can be used to leverage the power of constraint programming to solve an industrial problem with 800 non-preemptive tasks, 90 resources, and sequence-dependent setup times. Our method involves solving the traveling salesperson problem (TSP) as a simplification of the scheduling problem and using the simplified solution to guide the branching heuristics. We also explore large neighborhood search. Experiments conducted on a dataset provided by our partner from the textile industry show that we obtain non-optimal but satisfactory solutions.
This paper presents a new approach to describe the analog power-down synthesis problem by combining two state-of-the art constraint programs to a unified, homogeneous constraint optimization problem that, in contrast ...
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ISBN:
(纸本)9781728171838
This paper presents a new approach to describe the analog power-down synthesis problem by combining two state-of-the art constraint programs to a unified, homogeneous constraint optimization problem that, in contrast to the previous approach, allows trade-offs between the two design goals "matching" and "area". Furthermore, enhanced symmetry constraints are incorporated by the new method. Experimental results show the efficacy of the proposed method.
We present a new constraint programming (CP) model to optimize the transition cost of Fixed Job Scheduling (FJS), which improves our previous approach based on per-resource constraints by orders of magnitude. Our new ...
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ISBN:
(纸本)9781643681016;9781643681009
We present a new constraint programming (CP) model to optimize the transition cost of Fixed Job Scheduling (FJS), which improves our previous approach based on per-resource constraints by orders of magnitude. Our new model relies on a much tighter relaxation which encompasses all resources to directly propagate on the global cost, thanks to the MinWeightAllDiff optimization constraint. We also present several strategies which exploit the optimal matching computed by the MinWeightAllDiff constraint to efficiently guide the search. The resulting CP solver, using parallel cooperation between the strategies, consistently outperforms a state-of-the-art MIP solver on real instances of an FJS application, the Gate Allocation Problem, at Paris-Charles-de-Gaulle international airport.
The profitability of any assembly robot installation depends on the production throughput, and to an even greater extent on incurred costs. Most of the cost comes from manually designing the layout and programming the...
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ISBN:
(纸本)9783030589424;9783030589417
The profitability of any assembly robot installation depends on the production throughput, and to an even greater extent on incurred costs. Most of the cost comes from manually designing the layout and programming the robot as well as production downtime. With ever smaller production series, fewer products share this cost. In this work, we present the dual arm assembly program as an integrated routing and scheduling problem with complex arm-to-arm collision avoidance. We also present a set of high-level layout decisions, and we propose a unified CP model to solve the joint problem. The model is evaluated on realistic instances and real data. The model finds high-quality solutions in short time, and proves optimality for all evaluated problem instances, which demonstrates the potential of the approach.
The traditional approach to Model Expansion (MX) is to reduce the theory to a propositional language and apply a search algorithm to the resulting theory. Function symbols are typically replaced by predicate symbols r...
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ISBN:
(纸本)9781479929733
The traditional approach to Model Expansion (MX) is to reduce the theory to a propositional language and apply a search algorithm to the resulting theory. Function symbols are typically replaced by predicate symbols representing the graph of the function, an operation that blows up the reduced theory. In this paper, we present an improved approach to handle function symbols in a ground-and-solve methodology, building on ideas from constraint programming. We do so in the context of FO(·)~(IDP), the knowledge representation language that extends First-Order Logic (FO) with, among others, inductive definitions, arithmetic and aggregates. An MX algorithm is developed, consisting of (i) a grounding algorithm for FO(·)~(IDP), parametrised by the function symbols allowed to occur in the reduced theory, and (ii) a search algorithm for unrestricted, ground FO(·)~(IDP). The ideas are implemented in the IDP knowledge-base system and experimental evaluation shows that both more compact groundings and improved search performance are obtained.
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