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Dempster-shafer theory: How constraint programming can help 18th

Dempster-shafer theory: How constraint programming can help

18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020

作者： Kaltsounidis, Alexandros Karali, Isambo Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9783030501426

Dealing with uncertainty has always been a challenging topic in the area of knowledge representation. Nowadays, as the internet provides a vast platform for knowledge exploitation, the need becomes even more imminent. The kind of uncertainty encountered in most of these cases as well as its distributed nature make Dempster-Shafer (D-S) Theory to be an appropriate framework for its representation. However, we have to face the drawback of the computation burden of Dempster’s rule of combination due to its combinatorial behavior. constraint programming (CP) has proved to be an efficient tool in cases where results have to satisfy some specified properties and pruning of the computation space can be achieved. As D-S theory measures’ computation fulfills this requirement, CP seems a promising framework to employ for this purpose. In this paper, we present our approach to use CP to compute the belief and plausibility measures of D-S Theory and Dempster’s rule of combination as well as the results of the effort. As it was expected, the results are quite promising and in many cases impressive. © Springer Nature Switzerland AG 2020.

关键词： constraint programming

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A resource-constrained optimization model for parallel machine scheduling with constraint programming

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Decision Analytics Journal 2025年 15卷

作者： Mohamed Amine Abdeljaouad Zied Bahroun Nour El Houda Saadani Rahaf Sheiko Karam Al-Assaf ALTEN France Sud-Ouest Rue Alain Fournier 31300 Toulouse France Industrial Engineering ESM Graduate Program College of Engineering American University of Sharjah PO. Box 26666 Sharjah United Arab Emirates Faculté des Sciences de Tunis 2092 Université de Tunis El Manar Tunis Tunisia

This study investigates an NP-hard parallel machine scheduling problem, a critical challenge in manufacturing, healthcare, and logistics industries where efficient resource allocation is essential. The issue involves scheduling operations where each task requires an additional resource, with multiple resource types available, each limited to a single copy. The objective is to minimize the makespan, which is defined as the total completion time of all tasks. A novel constraint programming model is designed to solve the problem to optimality. The proposed model is benchmarked against two existing linear mathematical formulations, achieving up to 95% faster computational times while solving instances with up to 20 machines, 40 resources, and 90 operations per resource—scenarios the linear models failed to handle within reasonable computational limits. Furthermore, the model exhibits excellent scalability, effectively solving more extensive and complex instances. These findings underscore the potential of constraint programming as a powerful tool for tackling complex scheduling problems in resource-constrained environments, with applications in industries where resource-sharing is critical.

关键词： Optimization Parallel machine scheduling Resource allocation constraint programming Makespan minimization Resource sharing

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constraint programming algorithms for route planning exploiting geometrical information

arXiv

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arXiv 2020年

作者： Bertagnon, Alessandro Department of Engineering University of Ferrara Ferrara Italy

Problems affecting the transport of people or goods are plentiful in industry and commerce and they also appear to be at the origin of much more complex problems. In recent years, the logistics and transport sector keeps growing supported by technological progress, i.e. companies to be competitive are resorting to innovative technologies aimed at efficiency and effectiveness. This is why companies are increasingly using technologies such as Artificial Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial intelligence, in particular, is often used to solve optimization problems in order to provide users with the most efficient ways to exploit available resources. In this work we present an overview of our current research activities concerning the development of new algorithms, based on constraint Logic programming (CLP) techniques, for route planning problems exploiting the geometric information intrinsically present in many of them or in some of their variants. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) with the aim to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Routing Problem (Euclidean VRP), in the future. Copyright © 2020, The Authors. All rights reserved.

关键词： constraint programming

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Optimizing Medical Waste Collection in Urban Systems: A constraint programming Approach for Sustainable Public Sector Decision-Making

SSRN

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SSRN 2025年

作者： Pham, Hung Le, Tuan Huynh, Nhi Chu, Thanh Tuan Pham, Hiep Quang, Huy Truong Dao, Son School of Science Engineering and Technology RMIT University Ho Chi Minh City700000 Viet Nam School of Industrial Engineering and Management International University Vietnam National University Ho Chi Minh City700000 Viet Nam The Business School RMIT University Ho Chi Minh City700000 Viet Nam

This study presents a quantitative approach to optimizing the Vehicle Routing Problem (VRP) for medical waste collection (MWC) in Ho Chi Minh City (HCMC), addressing critical challenges in public health and urban sustainability. By employing constraint programming (CP) integrated with chance-constrained programming, the research develops a robust mathematical model to minimize total travel distance while accommodating stochastic waste generation and vehicle capacity constraints. Utilizing empirical data from medical facilities in HCMC, computational analyses validate the model’s effectiveness in enhancing resource utilization, reducing operational costs, and improving the efficiency of waste collection services. The findings contribute to data-driven decision-making in the public sector, offering a scalable solution for urban waste management and reinforcing the role of quantitative modeling in socio-economic planning and sustainable development. © 2025, The Authors. All rights reserved.

关键词： constraint programming

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Production scheduling and lot streaming at flexible job-shops environments using constraint programming

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 136卷 252-264页

作者： Novas, Juan M. Univ Nacl Cordoba CIEM CONICET Medina Allende S-N RA-5000 Cordoba Argentina Univ Tecnol Nacl CIDS Fac Cordoba RA-5000 Cordoba Argentina

In this contribution, the scheduling at flexible job-shops and the lot streaming problem are simultaneously addressed, by means of a novel constraint programming (CP) approach. The proposed CP model can efficiently tackle both the (i) lots splitting, deciding the number of sublots for each lot and the number of parts that belongs to each sublot, and (ii) the scheduling of production tasks, assigning the operations on sublots to machines and defining the start and completion times of those activities. The novelty of the proposal relies on a formulation that (i) can easily be adapted to cope with different operational policies, such as no-wait or wait schedules, idling or intermitted idling, sequence-dependent setup times, among other alternatives, (ii) introduce to new features that impact on the feasibility of the schedules, such as an extension to the inter-operation waiting policy, to consider whether a sublot can wait an unlimited or a limited period of time, as well as an intermediate storage policy, to indicate if the area to hold work-in-progress is constrained or not. Moreover, a set of operational modes found in real industrial settings are addressed. A framework to classify scenarios based on different problem characteristics is also introduced. Different case studies were solved, and good quality solutions were found when minimizing makespan.

关键词： Lot streaming Scheduling Flexible job-shop constraint programming

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Optimization of Next-Day Delivery Coverage using constraint programming and Random Key Optimizers

arXiv

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arXiv 2025年

作者： Brubaker, Kyle Booth, Kyle E.C. Schuetz, Martin J.A. Loick, Philipp Shen, Jian Ramamurthy, Arun Paschos, Georgios Amazon Advanced Solutions Lab SeattleWA98170 United States Amazon EU Science and Technology Luxembourg

We consider the logistics network of an e-commerce retailer, specifically the so-called "middle mile" network, that routes inventory from supply warehouses to distribution stations to be ingested into the terminal ("last mile") delivery network. The speed of packages through this middle mile network is a key determinant for the ultimate delivery speed to the end user. An important target for a retailer is to maximize the fraction of user orders that can be serviced within one day, i.e., next-day delivery. As such, we formulate the maximization of expected next-day delivery coverage within the middle-mile network as an optimization problem, involving a set of temporal and capacity-based constraints on the network and requiring the use of a black-box model to evaluate the objective function. We design both exact constraint programming (CP) and heuristic random-key optimizer (RKO) approaches, the former of which uses a proxy objective function. We perform experiments on large-scale, real-world problem instances and show that both approaches have merit, in that they can match or outperform the baseline solution, a bespoke greedy solver with integrated local search, in expected next-day delivery coverage. Our experiments focus on two high-level problem definitions, starting with a base problem and then adding more complexity, and also explore the generalization of the solvers across a range of problem instance sizes. We find that a hybrid model using RKO and a bespoke local search protocol performs best on the full problem definition with respect to expected next-day delivery (increase of +50 basis points [bps] over baseline) but can take days to run, whereas the hybrid model using CP and local search is slightly less competitive (+20 bps) but takes only hours to run. Copyright © 2025, The Authors. All rights reserved.

关键词： constraint programming

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Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach

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COMPUTERS & CHEMICAL ENGINEERING 2019年 124卷 228-237页

作者： Escobet, T. Puig, V Quevedo, J. Pala-Schonwalder, Pere Romera, J. Adelman, W. Res Ctr Supervis Safety & Automat Control CS2AC Rambla St Nebridi S-N Terrassa 08022 Spain Commun Circuits & Syst Res Grp CIRCUIT Avda Bases Manresa 61-73 Manresa 08242 Spain Schwarzwaldmilch GmbH Freiburg Freiburg Germany

This paper presents the optimal batch scheduling of a multi-product dairy process using an approach that combines optimization and constraint programming techniques. A suitable model describing the subprocesses and production rules is developed allowing to obtain scheduling constraints relating the production process and the machines available together with their relative efficiencies. After the scheduling problem has been formulated, the batch scheduling of a real powder milk/yogurt process is obtained in an optimal manner using the proposed approach with the objective of meeting customers' deadlines considering the efficiencies/costs of available alternative machines. Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided. This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Production batch-scheduling constraint programming Food processing Multi-objective optimization Cost analysis

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A constraint programming approach to type-2 assembly line balancing problem with assignment restrictions

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ASSEMBLY AUTOMATION 2019年第5期39卷 813-826页

作者： Pinarbasi, Mehmet Alakas, Haci Mehmet Yuzukirmizi, Mustafa Kirikkale Univ Dept Ind Engn Kirikkale Turkey

Purpose Main constraints for an assembly line balancing problem (ALBP) are cycle time/number of stations and task precedence relations. However, due to the technological and organizational limitations, several other restrictions can be encountered in real production systems. These restrictions are called as assignment restrictions and can be task assignment, station, resource and distance limitations. The purpose of the study is to evaluate the effects of these restrictions on ALBP using constraint programming (CP) model. Design/methodology/approach A novel CP model is proposed and compared to mixed-integer programming (MIP) as a benchmark. The objective is to minimize the cycle time for a given number of stations. The authors also provide explicit anthology of the assignment restriction effects on line efficiency, the solution quality and the computation time. Findings The proposed approach is verified with the literature test instances and a real-life problem from a furniture manufacturing company. Computational experiments show that, despite the fact that additional assignment restrictions are problematic in mathematical solutions, CP is a versatile exact solution alternative in modelling and the solution quality. Practical implications - Assembly line is a popular manufacturing system in the making of standardized high volume products. The problem of assembly line balancing is a crucial challenge in these settings and consists of assigning tasks to the stations by optimizing one or more objectives. Type-2 AR-ALBP is a specific case with the objective function of minimizing the cycle time for a given number of stations. It further assumes assignment restrictions that can be confronted due to the technological limitations or the strategic decisions of the company management. This is especially encountered in rebalancing lines. Originality/value Several solution approaches such as mathematical modelling, heuristic and meta-heuristic are proposed to solve the ALBP i

关键词： Mixed-integer programming Assembly line balancing Assignment restrictions constraint programming Type-2 problem

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A constraint programming model for food processing industry: a case for an ice cream processing facility

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第21期57卷 6648-6664页

作者： Wari, Ezra Zhu, Weihang Lamar Univ Dept Ind Engn Beaumont TX 77710 USA Univ Houston Dept Engn Technol Houston TX 77004 USA

This paper presents a constraint programming (CP) scheduling model for an ice cream processing facility. CP is a mathematical optimisation tool for solving problems either for optimality (for small-size problems) or good quality solutions (for large-size problems). For practical scheduling problems, a single CP solution model can be used to optimise daily production or production horizon extending for months. The proposed model minimises a makespan objective and consists of various processing interval and sequence variables and a number of production constraints for a case from a food processing industry. Its performance was compared to a Mixed Integer Linear programming (MILP) model from the literature for optimality, speed, and competence using the partial capacity of the production facility of the case study. Furthermore, the model was tested using different product demand sizes for the full capacity of the facility. The results demonstrate both the effectiveness, flexibility, and speed of the CP models, especially for large-scale models. As an alternative to MILP, CP models can provide a reasonable balance between optimality and computation speed for large problems.

关键词： constraint programming scheduling ice cream processing food processing scheduling

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A new constraint programming model and a linear programming-based adaptive large neighborhood search for the vehicle routing problem with synchronization constraints

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COMPUTERS & OPERATIONS RESEARCH 2020年 124卷 105085-105085页

作者： Minh Hoang Ha Tat Dat Nguyen Thinh Nguyen Duy Hoang Giang Pham Thuy Do Rousseau, Louis-Martin Phenikaa Univ Fac Comp Sci ORLab Hanoi Vietnam VNU Univ Engn & Technol Fac Informat Technol ORLab Hanoi Vietnam FPT Univ Dept Comp Sci Hanoi Vietnam Ecole Polytech Montreal Montreal PQ Canada CIRRELT Montreal PQ Canada

We consider a vehicle routing problem which seeks to minimize cost subject to time window and synchronization constraints. In this problem, the fleet of vehicles is categorized into regular and special vehicles. Some customers require both vehicles' services, whose service start times at the customer are synchronized. Despite its important real-world application, this problem has rarely been studied in the literature. To solve the problem, we propose a constraint programming (CP) model and an Adaptive Large Neighborhood Search (ALNS) in which the design of insertion operators is based on solving linear programming (LP) models to check the insertion feasibility. A number of acceleration techniques is also proposed to significantly reduce the computational time. The computational experiments show that our new CP model finds better solutions than an existing CP-based ALNS, when used on small instances with 25 customers and with a much shorter running time. Our LP-based ALNS dominates the CP-based ALNS, in terms of solution quality, when it provides solutions with better objective values, on average, for all instance classes. This demonstrates the advantage of using linear programming instead of constraint programming when dealing with a variant of vehicle routing problems with relatively tight constraints, which is often considered to be more favorable for CP-based methods. We also adapt our algorithm to solve a well-studied variant of the problem, and the obtained results show that the algorithm provides good solutions as state-of-the-art approaches and improves four best known solutions. Crown Copyright (c) 2020 Published by Elsevier Ltd. All rights reserved.

关键词： Vehicle routing problem Time window Synchronization constraint constraint programming Adaptive large neighborhood search

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