Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help an...
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Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.
The authors survey the research and development in Sweden in constraint programming, which is rapidly becoming the method of choice for some kinds of constraint problems, such as scheduling and configuration.
The authors survey the research and development in Sweden in constraint programming, which is rapidly becoming the method of choice for some kinds of constraint problems, such as scheduling and configuration.
Due to the increased demand for efficient recycling systems for end-of-life (EOL) products, the role of disassembly lines in reverse supply chains has become crucial. Parallel disassembly lines can handle multi-type E...
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Due to the increased demand for efficient recycling systems for end-of-life (EOL) products, the role of disassembly lines in reverse supply chains has become crucial. Parallel disassembly lines can handle multi-type EOL products and consist of two or more lines. However, previous research has primarily focused on two-line disassembly systems and has not fully addressed the optionality of common stations. To address this gap, this study proposes three exact methods for optimizing multi-line parallel disassembly systems with optional common stations, partial disassembly mode, and AND/OR precedence relations. Firstly, a mixed-integer linear programming (MILP) model is formulated that optimizes three objectives: weighted line length, additional profits, and hazard evaluation. Secondly, two constraint programming (CP) models are developed with different solution methodologies to provide more extensive applications and efficient solutions. An illustrative example shows that production mode can significantly reduce line length and workstations, and computational results demonstrate that both CP methods outperform the MILP model in terms of solution quality and computational efficiency. Specifically, the CP-I method demonstrates a higher level of stability and efficiency in most instances, while the CP-II method excels in optimizing line length and station utilization. These results illustrate the potential for optimizing multi-line disassembly systems with optional common stations to enhance production flexibility in remanufacturing processes.& COPY;2023 Elsevier Inc. All rights reserved.
In fuzzy mathematical programming literature, most of the transformation approaches were mainly focused on integer linear programs (ILPs) with fuzzy parameters/variables. However, ILP-based solution approaches may be ...
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In fuzzy mathematical programming literature, most of the transformation approaches were mainly focused on integer linear programs (ILPs) with fuzzy parameters/variables. However, ILP-based solution approaches may be inadequate for solving large-scaled combinatorial fuzzy optimization problems, like project scheduling under mixed fuzzy-stochastic environments. Moreover, many real-life project scheduling applications may contain different types of uncertainties such as fuzziness, stochasticity, and dynamism simultaneously. Based on these motivations, this paper presents a novel constraint programming (CP)-based transformation approach for solving a multi-objective and multi-mode, fuzzy-stochastic resource investment project scheduling problem (FS-MRIPSP) which is a well-known NP-complete problem. In fact, the proposed approach mainly depends on a bound and decomposition principle which divides fuzzy components of the problem into the crisp middle, lower, and upper level problems. Thus, it reduces the problem dimension and does not need to use any standard fuzzy arithmetic and ranking operations directly. Furthermore, the stochastic nature of the problem is also taken into account by using a multi-scenario-based stochastic programming technique. Finally, a weighted additive fuzzy goal program is embedded into the proposed CP-based transformation approach to produce compromise fuzzy project schedules that trade-off between expected values of project makespan and total resource usage costs. To show the validity and practicality of the proposed approach, a real-life application is presented for the production-and-operations management module implementation process of an international Enterprise Resource Planning software company. The fuzzy-stochastic project schedules generated by the proposed CP-based approach are also compared to the results of a similar ILP-based method. Computational results have shown that the CP-based approach outperforms the ILP-based method in te
This paper relates the author's personal experience with constraint programming and gives a personal assessment of the relationships between constraint programming and operations research.
This paper relates the author's personal experience with constraint programming and gives a personal assessment of the relationships between constraint programming and operations research.
We propose a constraint programming approach for the optimization of inventory routing in the liquefied natural gas industry. We present two constraint programming models that rely on a disjunctive scheduling represen...
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We propose a constraint programming approach for the optimization of inventory routing in the liquefied natural gas industry. We present two constraint programming models that rely on a disjunctive scheduling representation of the problem. We also propose an iterative search heuristic to generate good feasible solutions for these models. Computational results on a set of large-scale test instances demonstrate that our approach can find better solutions than existing approaches based on mixed integer programming, while being 4-10 times faster on average. (C) 2014 Elsevier B.V. All rights reserved.
This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among the scheduled jobs. ...
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This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among the scheduled jobs. Sequence-dependent setup times, precedence relations, machine eligibility restrictions and release dates are incorporated into the problem as operational constraints to reflect real-world manufacturing environments. The considered problem is in NP-hard class of problems, which cannot be solved in deterministic polynomial time. Our aim in this study is to develop an exact solution approach based on constraint programming (CP), which shows good performance in solving scheduling problems. In this regard, we propose a CP model and enrich this model by adding lower bound restrictions and redundant constraints. Moreover, to achieve a reduction in computation time, we propose two branching strategies for the proposed CP model. The performance of the CP model is tested using randomly generated and benchmark instances from the literature. The computational results indicate that the proposed CP model outperforms the best solutions with an average gap of 15.52%.
Although operations in container terminals are highly interdependent, they are traditionally optimized by decomposing the overall problem into a sequence of smaller sub-problems, each focusing on a single operation. R...
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Although operations in container terminals are highly interdependent, they are traditionally optimized by decomposing the overall problem into a sequence of smaller sub-problems, each focusing on a single operation. Recent studies, however, have demonstrated the need and potential of optimizing these interdependent operations jointly. This paper proposes the Integrated Port Container Terminal Problem (IPCTP) that considers the joint optimization of quay crane assignment and scheduling, yard crane assignment and scheduling, yard location assignments, and yard truck assignment and scheduling. The IPCTP aims at minimizing the turnover times of the vessels and maximize terminal throughput. It also considers inbound and outbound containers simultaneously and models the safety distance and the interference constraints for the quay cranes. To solve the IPCTP, the paper proposes several constraint programming (CP) models. Computational results show that CP provides exact solutions in acceptable time to IPCTP instances derived from an actual (small) container terminal in Turkey. For hard IPCTP instances, the CP model can be generalized in a two-stage optimization approach to produce high-quality solutions in reasonable times. (C) 2020 Elsevier B.V. All rights reserved.
In this paper, the timetabling problem for a typical high school environment was modeled and solved using a constraint programming (CP) approach. In addition,. operations research (OR) models and local search techniqu...
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In this paper, the timetabling problem for a typical high school environment was modeled and solved using a constraint programming (CP) approach. In addition,. operations research (OR) models and local search techniques were also used in order to assist the CP search process by effectively reducing the solution search space. Relaxed models that can be solved using minimum cost matching algorithms were used in order to calculate problem lower bounds at various instances of the solution process. These bounds were in turn used to prioritize the search options of the CP process. The use of minimum cost matching model in the search process is an economical and efficient mechanism for the creation of effective search strategies and it is a competitive manner of introducing problem domain information in the CP environment. By including in the solution process a sequence of local search steps, the solution quality was further improved. Several large problems were solved and actual computational results for specific problem instances are presented.
Proper scheduling of jobs is essential for modern production systems to work effectively. The hybrid flow shop scheduling problem is a scheduling problem with many applications in the industry. The problem has also at...
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Proper scheduling of jobs is essential for modern production systems to work effectively. The hybrid flow shop scheduling problem is a scheduling problem with many applications in the industry. The problem has also attracted much attention from researchers due to its complexity. This study addresses the hybrid flow shop scheduling problem (HFSP), which considers unrelated parallel machines at each stage and the machine eligibility constraints. HFSP is a well-known NP-hard problem with the aim of minimizing the makespan. Owing to the complexity of the problem, this study develops constraint programming (CP) models for the HFSP and its extensions: the no-wait HFSP, the blocking HFSP, the HFSP with sequence-dependent setup times, the no-wait HFSP with sequence-dependent setup times, and the blocking HFSP with sequence-dependent setup times. We also propose two mixed-integer linear programming models (MILP) for no-wait and blocking HSFPs with sequence-dependent setup times. The performances of the CP models were tested against their MILP counterparts using randomly generated instances and benchmark instances from the literature. The computational results indicated that the proposed CP model outperformed the best MILP solutions for benchmark instances. It is also more effective for finding high-quality solutions for larger problem instances.
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