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A constraint programming Formulation of the Multi-Mode Resource-Constrained Project Scheduling Problem for the Flexible Job Shop Scheduling Problem

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IEEE ACCESS 2023年 11卷 144928-144938页

作者： Yuraszeck, Francisco Montero, Elizabeth Canut-De-Bon, Dario Cuneo, Nicolas Rojel, Maximiliano Univ Andres Bello Dept Engn Sci Vina Del Mar 2531015 Chile Univ Tecn Federico Santa Maria Dept Comp Sci Valparaiso 2390123 Chile Direcc Programas Invest & Desarrollo Armada Chile Valparaiso 2360035 Chile Univ Andres Bello Fac Engn Vina Del Mar 2531015 Chile

In this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area.

关键词： Task analysis Job shop scheduling Upper bound Metaheuristics Sequential analysis constraint handling Renewable energy sources constraint programming flexible job shop FJSSP job shop JSSP multi-mode resource-constrained project scheduling problem MMRCPSP sequencing flexibility

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Mixed-Integer programming vs. constraint programming for Shop Scheduling Problems: New Results and Outlook

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INFORMS JOURNAL ON COMPUTING 2023年第4期35卷 817-843页

作者： Naderi, Bahman Ruiz, Ruben Roshanaei, Vahid Univ Windsor Fac Engn Dept Mech Automot & Mat Windsor ON N9B 3P4 Canada Univ Politecn Valencia Grp Sistemas Optimizac Aplicada Valencia 46021 Spain Univ Toronto Rotman Sch Management Dept Operat Management & Stat Toronto ON M5S 3E6 Canada

constraint programming (CP) has been recently in the spotlight after new CP-based procedures have been incorporated into state-of-the-art solvers, most notably the CP Optimizer from IBM. Classical CP solvers were only capable of guaranteeing the optimality of a solution, but they could not provide bounds for the integer feasible solutions found if interrupted prematurely due to, say, time limits. New versions, however, provide bounds and optimality guarantees, effectively making CP a viable alternative to more traditional mixed-integer programming (MIP) models and solvers. We capitalize on these developments and conduct a computational evaluation of MIP and CP models on 12 select scheduling problems.1 We carefully chose these 12 problems to represent a wide variety of scheduling problems that occur in different service and manufacturing settings. We also consider basic and well-studied simplified problems. These scheduling settings range from pure sequencing (e.g., flow shop and open shop) or joint assignment-sequencing (e.g., distributed flow shop and hybrid flow shop) to pure assignment (i.e., parallel machine) scheduling problems. We present MIP and CP models for each variant of these problems and evaluate their performance over 17 relevant and standard benchmarks that we identified in the literature. The computational campaign encompasses almost 6,623 experiments and evaluates the MIP and CP models along five dimensions of problem characteristics, objective function, decision variables, input parameters, and quality of bounds. We establish the areas in which each one of these models performs well and recognize their conceivable reasons. The obtained results indicate that CP sets new limits concerning the maximum problem size that can be solved using off-the-shelf exact techniques.

关键词： shop scheduling flow shop job shop flexible job shop open shop parallel machines benchmarks constraint programming mixed integer programming

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Integrating distributed disassembly line balancing and vehicle routing problem in supply chain: Integer programming, constraint programming, and heuristic algorithms

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2023年第1期265卷

作者： Cil, Zeynel Abidin Oztop, Hande Kenger, Zulal Diri Kizilay, Damla Izmir Democracy Univ Dept Ind Engn Izmir Turkiye Hasan Kalyoncu Univ Dept Ind Engn Gaziantep Turkiye

Today, the fast-changing demands of customers force manufacturers to adapt their systems to the variability. As a result of the recent advances in technology and transport systems, most manufacturers have begun to employ more than one distributed facility to respond rapidly to the demands of their customers. In addition, with the emergence of Industry 4.0, information exchange between systems at the same and different levels has become relatively easy. Therefore, effective management of distributed facilities by integrating processes at different levels in the supply chain can provide a significant advantage in adapting the system to customer demand dynamics. Furthermore, recycling waste products and using them in new products have become essential for environmentally friendly production. Therefore, this paper introduces integrated distributed disassembly line balancing and vehicle routing problem first time in the literature. Since the distributed disassembly centers with routing decisions of the vehicles from these centers to the factories have not been considered before, the proposed integrated study will contribute to both industry and the literature. The contribution is not only limited to the proposed integrated problem. Also, novel solution methodologies, mixed-integer linear programming, mixed-integer non-linear programming, and constraint programming models are developed to solve the problem. Besides the mathematical models, a multi-start simulated annealing algorithm is also proposed to overcome the large-size instances due to the complexity of the proposed integrated problem. The comprehensive computational analysis demonstrates that the proposed methods are very competitive in providing good-quality solutions for the problem.

关键词： Disassembly line balancing Vehicle routing Multi-depot constraint programming Simulated annealing Mixed-integer programming

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Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear programming and constraint programming Approaches

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 115卷

作者： Fatemi-Anaraki, Soroush Tavakkoli-Moghaddam, Reza Foumani, Mehdi Vahedi-Nouri, Behdin Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Tech Univ Munich TUM Sch Management Arcisstr 21 D-80333 Munich Germany Univ Sydney Rio Tinto Ctr Mine Automat Australian Ctr Field Robot Sydney NSW Australia

This paper investigates a dynamic scheduling problem within a job shop robotic cell, wherein multiple robotic arms are responsible for material handling in a U-shaped arrangement. Each robotic arm has ac-cess to specific workstations based on their distance in the cell layout. Therefore, a part may need to be exchanged between several robots according to its process plan. For this purpose, intermediate buffers are positioned between each pair of consecutive robots. Due to the dynamic nature of the problem, new jobs arrive at unpredictable times, which in turn necessitates rescheduling taking the system's current state into account. To tackle this problem, firstly, a Mixed-Integer Linear programming (MILP) model is devised. Secondly, three distinct Speed-up constraints (SCs) derived from the problem's inherent charac-teristics are designed and implemented to accelerate the MILP model's solving procedure. Afterward, the problem is formulated using constraint programming (CP) approach. The performance of the CP model and the MILP model in presence of all possible combinations of the SCs are evaluated and compared through solving various random instances. Next, an analysis is performed on the buffers' pick-up crite-rion and how it is affected by the problem's size. Besides, the impact of changes in the robots' speed on the productivity of the cell is assessed. Finally, the extent to which the rescheduling priority affects the output of the model is studied. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Job shop robotic cell Material handling Dynamic scheduling Mixed-integer linear programming 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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A competitive constraint programming approach for the group shop scheduling problem

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Procedia Computer Science 2023年 220卷 946-951页

作者： Francisco Yuraszeck Gonzalo Mejía Dario Canut-de-Bon Universidad Andres Bello Quillota 980 Viña del Mar 2531015 Chile Universidad de La Sabana Campus Universitario Puente del Común Km 7 Autopista Norte de Bogotá Chía 250001 Colombia Dirección de Programas Investigación y Desarrollo de la Armada de Chile General del Canto 398 Valparaíso 2360035 Chile

In this paper, we propose a competitive constraint programming (CP) approach to solve the Group Shop Scheduling Problem (GSSP) under the makespan minimization criteria. Our contribution is two-fold: we provide a flexible mathematical formulation to solve the GSSP that can be used without change to solve other closed-related scheduling problems such as the Open Shop Scheduling Problem (OSSP), Job Shop Scheduling Problem (JSSP), and Mixed Shop Scheduling Problem (MSSP); and we improve several lower bounds and upper bounds from 130 classical GSSP instances from the literature. We evaluate our approach by comparing the performance with competitive methods mainly based on metaheuristics, where we were able to prove optimality in more than 85% of the instances in competitive running time, with a relative percentage deviation lower than 3% on average. In contrast to metaheuristics approaches, our CP method does not require calibrations of multiple parameters, several replicates for each instance, and complex computational coding to be competitive in both, solution quality and computational running times.

关键词： scheduling group shop stage shop mixed shop constraint programming metaheuristics

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Job Shop Scheduling with Limited-Capacity Buffers using constraint programming and Genetic Algorithms

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IFAC-PapersOnLine 2023年第2期56卷 953-958页

作者： Javier Pedrosa Vicenç Puig

This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on constraint programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically.

关键词： Optimization constraint programming Genetic Algorithms JSSP scheduling and planning process industries adaptability limited-buffers storage management

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RETRACTED: constraint programming with Intelligent Backtracking using Artificial Intelligence (Retracted Article)

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第3期48卷 4129-4129页

作者： Ma, Wen Huang, Zuyuan Hsu, Ching-Hsien Enrique Montenegro-Marin, Carlos Yunnan Power Grid Co Ltd Informat Ctr Kunming 650000 Yunnan Peoples R China Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan Univ Dist Francisco Jose de Caldas Fac Ingn Bogota Colombia

Recently, Intelligent Backtracking can be defined as a method used in constrained programming to minimize search results in the resolution of combinatorial problems. The technology utilizes information from limited collections of infeasible restrictions in one search area to discourage searching in other related areas. It will substantially reduce the search space. However, the computing work needed to reduce the search area for many issues. In this research, an Artificial Intelligence-based Branch and Cut Algorithm has been proposed, which uses an intelligent tracking system based on constraint programming. This proposed framework displays an extreme dual ray associated with an unfeasible linear program to automatically deduce minimum infeasible settings. Furthermore, it has reduced space for intelligent monitoring without paying high processing costs. The experimental results demonstrate that the realization of the proposed intellectual monitoring paradigm.

关键词： Intelligent backtracking Artificial intelligence Branch and cut algorithm constraint programming

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MCP-Solver: Integrating Language Models with constraint programming Systems

arXiv

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arXiv 2024年

作者： Szeider, Stefan Algorithms and Complexity Group TU Wien Vienna Austria

While Large Language Models (LLMs) perform exceptionally well at natural language tasks, they often struggle with precise formal reasoning and the rigorous specification of problems. We present MCP-Solver, a prototype implementation of the Model Context Protocol that demonstrates the potential for systematic integration between LLMs and constraint programming systems. Our implementation provides interfaces for the creation, editing, and validation of a constraint model. Through an item-based editing approach with integrated validation, the system ensures model consistency at every modification step and enables structured iterative refinement. The system handles concurrent solving sessions and maintains a persistent knowledge base of modeling insights. Initial experiments suggest that this integration can effectively combine LLMs’ natural language understanding with constraint-solving capabilities. Our open-source implementation is proof of concept for integrating formal reasoning systems with LLMs through standardized protocols. While further research is needed to establish comprehensive formal guarantees, this work takes a first step toward principled integration of natural language processing with constraint-based reasoning. Copyright © 2024, The Authors. All rights reserved.

关键词： constraint programming

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