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A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Zeng, Chuangfeng Liu, Jianjun Li, Qinsong Guangdong Univ Technol Sch Electromech Engn State Key Lab Precis Elect Mfg Technol & Equipment Guangdong CIM Prov Key Lab Guangzhou Peoples R China

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem's strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

关键词： Integrated production and distribution scheduling Flexible assembly flow shop Batch delivery Auxiliary resource constraint programming Mixed-integer programming

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Advanced constraint programming formulations for additive manufacturing machine scheduling problems

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年第3期76卷 590-605页

作者： Cakici, Eray Kucukkoc, Ibrahim Akdemir, Mustafa IBM Deutschland GmbH Ehningen Germany Balikesir Univ Balikesir Turkiye

In comparison with traditional subtractive manufacturing techniques, additive manufacturing (AM) enables fabricating complex parts through a layer-by-layer process. AM makes it possible to produce one-piece and lightweight functional products, which are traditionally made from several parts. This paper introduces constraint programming (CP) models to minimise makespan in single, parallel identical and parallel unrelated AM machine scheduling environments for selective laser melting. Alternative CP formulations are explored to improve efficiency. The proposed CP model significantly benefits from the introduction of interval variables to replace binary assignment variables, and pre-definitions to narrow the search space, resulting in increased search performance. A computational study has been conducted to compare the performance of our proposed CP model with both a mixed-integer programming and a genetic algorithm from existing literature, evaluating improvements made to its search capability. Computational results indicate that the proposed CP model can obtain high-quality solutions in a timely manner even for several large-size instances.

关键词： Scheduling constraint programming production

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An exact constraint programming method for the multi-manned assembly line balancing problem with assignment restrictions

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Possan Junior, Moacyr Carlos Michels, Adalberto Sato Magatao, Leandro Fed Univ Technol Parana UTFPR Grad Program Elect & Comp Engn CPGEI Curitiba Brazil Univ Melbourne Sch Math & Stat Melbourne Australia Santa Catarina State Univ UDESC Sao Bento Do Sul Brazil

This paper proposes an exact constraint programming (CP) method with an extensive focus on real-world constraints for the Multi-manned Assembly Line Balancing Problem with Assignment Restrictions (MALBPAR). We perform an in-depth literature review to gather examples from real assembly lines and organize the AR regarding tasks, stations, workers, and mounting positions. Our study classifies the AR related to transformed resources and provides a general and unified model. We explore the concept of variable workplaces to dynamically assign workers to mounting positions and aggregate no overlap restrictions to avoid interference between workers. The classic MALBP model is extended by gradually incrementing the number of restrictions. The model variant found in the literature is herein called Partial MALBP-AR. Compared to the previous state-of-the-art Tabu Search Algorithm (TSA) for this problem, the Partial MALBP-AR found twelve additional optimality proofs. Besides the relevant results regarding solution quality, the CP method also has a satisfactory CPU performance. We also propose an entirely new set of AR and test these practical conditions with the so-called Extended MALBP-AR. Such an extended model, which covers all the AR presented here, reached optimality within the computational time limit for 36 out of 38 instances. The worst-case gap for an open instance is 8.20%. The results show a trade-off between the number of deemed restrictions and the computational performance. However, considering the detailed set of AR, we can obtain more representative solutions regarding the final balancing implementation compared to theoretical cases. The method can be used to design experiments, turning certain constraints on and off and allowing managers to evaluate different resource allocation scenarios.

关键词： Combinatorial optimization Scheduling Assembly line balancing Multi-manned station Assignment restrictions constraint programming

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A hybrid constraint programming and cross-entropy approach for balancing U-Shaped disassembly line with flexible workstations and spatial constraints

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2025年 45卷

作者： Zhang, Yu Zhang, Zeqiang Chu, Feng Mammar, Said Southwest Jiaotong Univ Sch Mech Engn Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China Univ Paris Saclay Univ Evry Blvd Francois Mitterrand F-91000 Evry France

Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and production efficiency. This study addresses the U-shaped disassembly line balancing problem, considering the coexistence of separate stations and spatial limitations within workstations. A mixed-integer nonlinear programming model and a constraint programming model are developed to accurately capture this complex problem. Additionally, a novel hybrid constraint programming with a goal-driven cross-entropy optimization algorithm (CP-GDCE) is introduced. This algorithm combines a multi-objective cross-entropy grouping framework, a constraint programming-based heuristic initialization, a multi-point crossover recombination mechanism, and large neighborhood search techniques, significantly enhancing solution efficiency and accuracy. Extensive benchmarking and experimental validation indicate that the CP-GDCE not only excels in addressing the specific problem of this study but also demonstrates superiority in classic disassembly line balancing issues. In 21 test cases, the CP-GDCE achieved superior hypervolume and inverted generational distance values compared to 11 benchmark algorithms. A practical application using a printer disassembly example shows that the proposed U-shaped configuration is highly flexible and efficient, compatible with both traditional U-shaped and straight disassembly lines. This configuration significantly reduces the total length of the disassembly line, improving space utilization and highlighting its practical potential and advantages.

关键词： U-shaped disassembly line Flexible workstation Spatial limitation Mixed-integer programming constraint programming Cross-entropy method

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A constraint programming-based lower bounding procedure for the job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2025年 177卷

作者： Yuraszeck, Francisco Mejia, Gonzalo Rossit, Daniel Alejandro Luer-Villagra, Armin Univ Andres Bello Dept Engn Sci Quillota 980 Vina Del Mar 2531015 Chile Univ La Sabana Fac Engn Campus Puente Comun Km 7 Autopista Norte Bogota Chia 250001 Colombia Univ Nacl UNS Dept Engn Bahia Blanca Argentina Univ Nacl Sur UNS CONICET INMABB Bahia Blanca Argentina Univ Andres Bello Dept Engn Sci Autopista Concepc Talcahuano 7100 Talcahuano Chile

This paper presents a novel constraint programming (CP) approach to obtain strong lower bounds for the Job Shop Scheduling Problem (JSSP) under the makespan criterion. Our approach comprises two phases. In the first phase, a relaxation of the original problem is solved, while in the second phase, this relaxation is iteratively tightened until a time limit is reached or no better bounds are found. We tested our procedure with 80 JSSP open instances, and the results validated our approach as we were able to find 7 new lower bounds and prove optimality in one instance.

关键词： Scheduling Job shop Lower bound Resource-constrained project scheduling problem constraint programming

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Combining constraint programming and a participatory approach to design agroecological cropping systems

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AGRICULTURAL SYSTEMS 2025年 222卷

作者： Challand, Margot Vismara, Philippe de Tourdonnet, Stephane Univ Montpellier Inst Agro Montpellier ABSys CIHEMCIRADINRAE Montpellier France Univ Montpellier Inst Agro Montpellier MISTEA INRAE Montpellier France Univ Montpellier LIRMM CNRS 161 Rue Ada Montpellier France

Context: Agroecology implementation around the world have shown that increasing the complexity of the agroecosystem leads to increased resilience, lower dependence on synthetic inputs, the provision of ecosystem services and improved performance. However, designing diversified agroecosystems is particularly complex because of the diverse factors to take into account for each specific local context and the range of possible spatiotemporal crop combinations. Objective: Here we propose an iterative agroecological design approach combining artificial intelligence with constraint programming and co-design workshops with farmers to explore and optimize spatiotemporal cropping arrangements in diversified cropping systems. Methods: Our iterative approach comprises a three-step loop for designing new cropping systems: 1) identifying problem data and spatiotemporal constraints;2) applying a flexible constraint programming model, and refining/removing constraints iteratively with farmers' input until a solution is found;and 3) evaluating solutions through model assessment and workshops with farmers, leading to the design of a new scenario if necessary (repeating step 2). We applied our approach to a case study involving diversified mixed fruit tree-vegetable cropping systems in southern France, whereby farmers were involved in co-design workshops with an agronomist. Results and conclusions: The constraint programming model simulated most important farmers' constraints while adapting to the input of new information during the design process. The workshops facilitated knowledge elicitation, with progressive questioning of farming practices, while fostering a learning process through farmer- agronomist discussions. Meanwhile, the scope of the problem was iteratively outlined during the process, driven by the need to seek trade-offs between all of the constraints, and informed by model feedback. This approach allowed farmers to explore and assess disruptive scenarios, in turn faci

关键词： Agroecological design Spatiotemporal arrangement constraint programming Participatory approach Mixed horticultural systems

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A Flexible Scheduling Framework for Repetitive Construction Projects Based on constraint programming

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Journal of Construction Engineering and Management 2025年第7期151卷

作者： Cao, Qiangnan Zou, Xin Zhang, Lihui School of Economics and Management North China Electric Power Univ. Beijing102206 China Dept. of Economic Management North China Electric Power Univ. Hebei071003 China

Scheduling repetitive construction projects (RCPs) is a challenging task due to the nature of the activities involved. It requires careful consideration of both flexibility and computational performance. This paper develops a versatile RCP scheduling framework applicable to multiple construction scenarios, providing greater flexibility than existing models. The framework incorporates various scheduling features for repetitive activities, such as soft logic, multiskilled crews, multimode execution, crew transfers, and continuous or noncontinuous execution. It also offers optional optimization objectives, such as minimizing the overall project duration, total cost, crew interruption time, and number of employed crews. Planners can combine the features of repetitive activities and choose one or more objectives to meet their needs and preferences. The proposed framework is based on constraint programming, which balances computational efficiency and solution quality while remaining user-friendly. The practicality and effectiveness of the framework are verified through three case studies involving five real-life projects. The results demonstrate that the proposed framework can produce RCP schedules with shorter duration, lower costs, or reduced resource utilization compared with those generated by existing models. © 2025 American Society of Civil Engineers.

关键词： constraint programming

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A constraint programming Model For Serial Batch Scheduling With Minimum Batch Size

arXiv

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arXiv 2025年

作者： Huertas, Jorge A. Van Hentenryck, Pascal Georgia Institute of Technology AtlantaGA United States

In serial batch (s-batch) scheduling, jobs are grouped in batches and processed sequentially within their batch. This paper considers multiple parallel machines, nonidentical job weights and release times, and sequence-dependent setup times between batches of different families. Although s-batch has been widely studied in the literature, very few papers have taken into account a minimum batch size, typical in practical settings such as semiconductor manufacturing and the metal industry. The problem with this minimum batch size requirement has been mostly tackled with dynamic programming and meta-heuristics, and no article has ever used constraint programming (CP) to do so. This paper fills this gap by proposing, for the first time, a CP model for s-batching with minimum batch size. The computational experiments on standard cases compare the CP model with two existing mixed-integer programming (MIP) models from the literature. The results demonstrate the versatility of the proposed CP model to handle multiple variations of s-batching;and its ability to produce, in large instances, better solutions than the MIP models faster. Copyright © 2025, The Authors. All rights reserved.

关键词： constraint programming

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An adapted constraint-programming formulation of the resource-constrained project scheduling problem applied to the identical parallel machines group shop and mixed shop scheduling problems

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第3期32卷 1422-1441页

作者： Yuraszeck, Francisco Mejia, Gonzalo Luer-Villagra, Armin Univ Andres Bello Dept Engn Sci Quillota 980 Vina Del Mar 2531015 Chile Univ La Sabana Campus Puente Comun Km 7 Autopista Norte Bogota Chia 250001 Colombia Univ Andres Bello Dept Engn Sci Autopista Concepc Talcahuano 7100 Vina Del Mar Chile

In this paper, we study the group shop and the mixed shop scheduling problems with single and identical parallel machines at each workstation with the makespan criterion. We adapted a constraint programming formulation previously presented for the classical resource-constrained project scheduling problem. The effectiveness of our approach is evident in the fact that it achieved optimality in 107 out of 130 classical group shop scheduling problem instances and in 320 classical mixed shop scheduling problem instances. In the last set, we obtained 13 new optimal solutions.

关键词： resource-constrained project scheduling group shop stage shop mixed shop parallel machines constraint programming job shop open shop

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A constraint-programming approach for the storage space allocation problem in a distribution center

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第0期

作者： Telha, Claudio Gonzalez-Ramirez, Rosa G. Univ Andes Fac Engn & Appl Sci Monsenor Alvaro Portillo 12455 Las Condes Santiago De Chi Chile

In the storage location assignment problem under a picker-to-parts system (SLAP-FP), we assign warehouse space to products based on customer orders. The distance we have to travel to pick up customer orders and the frequency of restocking in the warehouse depend on the location of the products and their allocated space. The goal of SLAP-FP is to minimize the costs of restocking the products and picking the orders. This study introduces a novel constraint programming formulation to solve the SLAP-FP. The model uses logical rules and rational expressions to describe the problem concisely. Numerical results with standard solvers show that the proposed model significantly outperforms the previously known integer programming approach. Specifically, the constraint programming model is especially good at finding better solutions in large instances with many different products.

关键词： storage space allocation slotting constraint programming warehousing order picking

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