In the literature, line balancing is mostly investigated in deterministic environments. But production systems inevitably contain stochastic situations. In this study, the stochastic type-II assembly line balancing pr...
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In the literature, line balancing is mostly investigated in deterministic environments. But production systems inevitably contain stochastic situations. In this study, the stochastic type-II assembly line balancing problem (ALBP) is considered. Firstly, a chance-constrained nonlinear mixed integer programming (MIP) formulation is developed from the well-known deterministic form. Then, a new linearized stochastic model is proposed by using some transformation approaches to reduce model complexity, and the model is solved. Finally, constraint programming (CP) models for deterministic ALBPs, nonlinear chance-constrained stochastic ALBPs and linearized chance-constrained stochastic ALBPs are developed, respectively. Problems from the literature are utilized to test the effectiveness of the proposed models and the results are compared with a bidirectional heuristic algorithm. The numerical results show that the CP models are more effective and successful for solving the stochastic ALBP. Some managerial implications are also suggested for industrial environments that consistently face ALBPs.
The advancement of technology and the empowerment of the industry have made humans and robots more closely tied together, known as human-robot collaboration. A sector that specifically utilises this collaboration is t...
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The advancement of technology and the empowerment of the industry have made humans and robots more closely tied together, known as human-robot collaboration. A sector that specifically utilises this collaboration is the printed circuit boards industry. Therefore in this industry, proper allocation of tasks to humans and robots is crucial. This study investigates this type of allocation to minimise makespan. A constraint programming based (CP) approach is developed to solve the problem as the main novelty of this study. A single board problem, as the basic model, is developed by adding more assumptions including different groups of tasks, no-wait scheduling of tasks, multi-agent planning, and multiple boards sequencing. Then, different experimental instances are generated and solved to analyse the performance of CP and the sensitivity of idle time and makespan to key parameters of the problem. The superiority of the computational results of CP over mathematical programming is evident.
Embedded systems are built for specific purposes and are optimized to meet different kind of constraints, such as performance, timing, power and cost. The design process therefore involves different optimization activ...
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Embedded systems are built for specific purposes and are optimized to meet different kind of constraints, such as performance, timing, power and cost. The design process therefore involves different optimization activities. In this paper, we discuss the use of constraint programming (CP) technology for these optimization problems. The main advantages and disadvantages of applying CP to embedded system design problems are discussed on two examples, scheduling and mapping. Based on these examples modelling capabilities of CP and basic solving methods are discussed. We have identified CP modelling capability as an important factor for problem formalization and their uniform representation. We have also, using several experiments, show efficiency of the models and solving process. Finally, we have also pointed out difficulties with CP technology that are mostly related to search methods that, for more realistic problems, must be carefully selected or even new methods must be developed. (C) 2019 Elsevier B.V. All rights reserved.
Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a constraint programming approach capable of automating the short-term scheduling process in a c...
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Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a constraint programming approach capable of automating the short-term scheduling process in a cut-and-fill mine. The approach extends previous work by accounting for fleet travel times, and thus captures an important aspect of the real-world machine scheduling problem. We introduce two models: one that directly solves the original interruptible scheduling problem, and one that is based on solving a related uninterruptible scheduling problem and transforming its solution back to the original domain. Large Neighborhood Search is also employed with a domain-specific neighborhood definition that helps to find high-quality schedules faster. Problem instances derived from an operational mine are used to demonstrate the efficacy of our approach. (C) 2020 Elsevier Ltd. All rights reserved.
Nowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with i...
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Nowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with inter-model constraints representing the existing relations among domain concerns, and with non-functional properties modeled as attributes attached to product features. Multiple techniques use constraint programming to automate the cumbersome task of manually configuring a suitable product. Currently, there are some proposals to improve the performance of constraint solvers when configuring single-model product lines, however configuration scenarios with multiple interrelated and attributed models are not yet targeted. This paper proposes and evaluates three search heuristics used to configure optimal products regarding multi-objective criteria. Results are compared against the default search strategy of the Choco constraint solver. We evaluated the performance for configuring optimal products in four state-of-the-art product lines and 130 generated variability models representing multi-product lines that scale up to 6400 features and 960 constraints. As a result, we observe that the proposed heuristics perform better than the default solver strategy when the variability models scale in terms of features. In contrast, the default strategy and one of the proposed heuristics perform better as the number of interdependencies between variability models increases. (C) 2018 Elsevier Inc. All rights reserved.
ABSRACT The main idea of constraint programming (CP) is to determine a solution (or solutions) of a problem assigning values to decision variables satisfying all constraints. Two sub processes, an enumeration strategy...
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ABSRACT The main idea of constraint programming (CP) is to determine a solution (or solutions) of a problem assigning values to decision variables satisfying all constraints. Two sub processes, an enumeration strategy and a consistency, run under the constraint programming main algorithm. The enumeration strategy which is managing the order of variables and values to build a search tree and possible solutions is crucial process in CP. In this study problem-based specific variable selection rules are studied on a mixed model assembly line balancing problem. The 18 variable selection rules are generated in three main categories by considering the problem input parameters. These rules are tested with benchmark problems in the literature and experimental results are compared with the results of mathematical model and standard CP algorithm. Also, benchmark problems are run with two CP rules to compare experimental results. In conclusion, experimental results are shown that the outperform rules are listed and also their specifications are defined to guide to researchers who solve optimization problems with CP.
Complexity of surgery scheduling reduces surgical staff efficiency and patient satisfaction. Nurses and surgeons face extreme workload every day, whose schedules can be improved by correct scheduling of surgeries. Thi...
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Complexity of surgery scheduling reduces surgical staff efficiency and patient satisfaction. Nurses and surgeons face extreme workload every day, whose schedules can be improved by correct scheduling of surgeries. This paper addresses integrated scheduling of operating rooms, nurses, and surgeons aiming to minimize makespan and maximize patient satisfaction and surgical teams' affinity. A constraint programming model and a hybrid method of NSGA-II and multi-objective dragonfly algorithm (MODA) are developed. Results indicate that high-quality solutions for problem instances with up to 150 surgeries are obtained by the CP model in less than 500 seconds. Superiority of the proposed meta-heuristic method compared to NSGA-II, MODA, SPEA-2, and a hybrid of NSGA-II and SPEA-2 is also shown.
The job shop scheduling problem is one of the most studied optimization problems to this day and it becomes more and more important in the light of the fourth industrial revolution (Industry 4.0) that aims at fully au...
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The job shop scheduling problem is one of the most studied optimization problems to this day and it becomes more and more important in the light of the fourth industrial revolution (Industry 4.0) that aims at fully automated production processes. For a long time exact methods like constraint programming had problems to solve real large-scale problem instances and methods of choice were to be found in the area of (meta-) heuristics. However, developments during the last decade improved the performance of state-of-the-art constraint solvers dramatically, to the point that they can be applied also on large-scale instances. The presented work's main target is to elaborate the performance of state-of-the-art constraint solvers with respect to industrial-size job shop scheduling problem instances. To this end, we analyze and compare the performance of two cutting-edge constraint solvers: OR-Tools, an open-source solver developed by Google and recurrent winner of the MiniZinc Challenge, and CP Optimizer, a proprietary constraint solver from IBM targeted at industrial optimization problems. In order to reflect real-world industrial scenarios with heavy workloads like found in the semi-conductor domain, we use novel benchmarks that comprise up to one million operations to be scheduled on up to one thousand machines. The comparison is based on the best makespan (i.e. completion time) achieved and the time required to solve the problem instances. We test the solvers on single-core and quad-core configurations.
No-wait Integrated Scheduling Problem (NISP) describes a real-life process of the non-standard products where the consideration is given to the great structure differences, processing parameter differences, no-wait co...
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No-wait Integrated Scheduling Problem (NISP) describes a real-life process of the non-standard products where the consideration is given to the great structure differences, processing parameter differences, no-wait constraint, and the need for further deep processing after assembly of jobs. To deal with the dynamic orders of non-standard products, the scheduling algorithm to be design should be a dynamic algorithm with the ability to deal with the above conditions. At first, the dynamic scheduling problem is transformed to a series of continuous static scheduling problem by adoption of window-based event-driven strategy, thus establishing constraint programming model targeted at minimal total tardiness and thereby proposing a hybrid method of Heuristic Algorithm and constraint programming (HACP) for the problem. In order to enhance the ability to response the dynamic orders of non-standard products, HA-CP adopts heuristic algorithm to generate a prescheduling solution at each dynamic event moment, so that jobs that fall into the window period are labelled as dispatched jobs, while the remaining jobs are labelled as jobs to be dispatched. To improve solution quality, the jobs to be dispatched are mapped into an operation-based constraint programming model, then, during the execution interval of dispatched jobs, constraint programming solver starts to solve the jobs to be dispatched and update the current solution if the solver gets a better solution within the execution interval. The above procedures are repeated until all jobs are scheduled. Finally, the results of simulation experiment show that the proposed algorithm is effective and feasible.
constraint Answer Set programming (CASP) combines Answer Set programming (ASP) and constraint programming (CP) to offer a powerful framework for solving complex problems. While there exists various ways to represent C...
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