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Production scheduling in a reconfigurable manufacturing system benefiting from human-robot collaboration

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第3期62卷 767-783页

作者： Vahedi-Nouri, Behdin Tavakkoli-Moghaddam, Reza Hanzalek, Zdenek Dolgui, Alexandre Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic IMT Atlantique LS2N CNRS Nantes France

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear programming (MILP) model and an efficient constraint programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time.

关键词： Reconfigurable manufacturing system scheduling workforce planning human-robot collaboration constraint programming

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A REACTIVE ITERATED GREEDY ALGORITHM FOR THE NO-WAIT FLOWSHOP TO MINIMIZE TOTAL TARDINESS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第10期20卷 3089-3108页

作者： Prata, Bruno A. Abreu, Levi R. Nagano, Marcelo S. Univ Fed Ceara Dept Ind Engn Fortaleza Brazil Univ Sao Paulo Dept Prod Engn Sao Carlos Brazil

. This paper addresses a no-wait flowshop scheduling problem with total tardiness minimization. We present a mixed-integer linear programming formulation with positional decision variables and a new constraint programming model to solve the problem as exact approaches. As an alternative solution procedure, we develop an innovative Reactive Iterated Greedy (RIG) algorithm that presents a dynamic mechanism to adjust the number of jobs to be removed from the current permutation in the destruction phase. We carried out computational experiments on a set of 800 test instances available in the literature to evaluate the performance of the proposed RIG algorithm in comparison with five solution approaches (Mixed-Integer Linear programming, constraint programming, Block Simulated Annealing, Variable Neighborhood Descent, and Iterated Greedy). The used performance criterion is the Relative Deviation Index (RDI). The RIG algorithm returned an average RDI of 5.0%, representing the best results among the solution procedures under comparison. Computational results pointed out that our proposal outperformed the mathematical model and other metaheuristics available in the literature with statistical significance.

关键词： Production scheduling no-wait flowshop mixed-integer linear pro- gramming constraint programming reactive iterated greedy

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Solving the Minimum Sum Coloring Problem: Alternative Models, Exact Solvers, and Metaheuristics

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INFORMS JOURNAL ON COMPUTING 2025年第2期37卷 197-502, C2页

作者： Du, Yu Glover, Fred Kochenberger, Gary Hennig, Rick Wang, Haibo Hulandageri, Amit Univ Colorado Business Sch Denver CO 80102 USA Entanglement Inc Boulder CO 80305 USA Texas A&M Int Univ Sanchez Sch Business Laredo TX 78040 USA

The minimum sum coloring problem (MSCP), a well-known NP-hard (nondeterministic polynomial time) problem with important practical applications, has been the subject of several papers in recent years. Because of the computational challenge posed by these problems, most solution methods employed are metaheuristics designed to find high-quality solutions with no guarantee of optimality. Exact methods (like Gurobi) and metaheuristic solvers have greatly improved in recent years, enabling high-quality and often optimal solutions to be found to a growing set of MSCPs. Alternative model forms can have a significant impact on the success of exact and heuristic methods in such settings, often providing enhanced performance compared with traditional model forms. In this paper, we introduce several alternative models for MSCP, including the quadratic unconstrained binary problem plus (QUBO-Plus) model for solving problems with constraints that are not folded into the objective function of the basic quadratic unconstrained binary problem (QUBO) model. We provide a computational study using a standard set of test problems from the literature that compares the general purpose exact solver from Gurobi with the leading QUBO metaheuristic solver NGQ and a special solver called Q-Card that belongs to the QUBO-Plus class. Our results highlight the effectiveness of the QUBO and QUBO-Plus models when solved with these metaheuristic solvers on this test bed, showing that the QUBO-Plus solver Q-Card provides the best performance for finding high-quality solutions to these important problems.

关键词： minimum sum coloring problem metaheuristics combinatorial optimization quadratic unconstrained binary optimization constraint programming

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Quadratic horizontally elastic not-first/not-last filtering algorithm for cumulative constraint

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期320卷 505-515页

作者： Kameugne, Roger Betmbe, Severine Fetgo Noulamo, Thierry Univ Maroua Fac Sci Dept Math & Comp Sci POB 814 Maroua Cameroon Univ Dschang Fac Sci Dept Math & Comp Sci POB 67 Dschang Cameroon Univ Dschang Dept Comp Engn UIT Fotso Victor Bandjoun POB 67 Dschang Cameroon

The not-first/not-last rule is a pendant of the edge finding rule, generally embedded in the CUMULATIVE constraint during constraint-based scheduling. It is combined with other filtering rules for more pruning of the tree search. In this paper, the Profile data structure in which tasks are scheduled in a horizontally elastic way is used to strengthen the classic not-first/not-last rule. Potential not-first task intervals are selected using criteria (specified later in the paper), and the Profile data structure is applied to selected task intervals. We prove that this new rule subsumes the classic not-first rule. A quadratic filtering algorithm is proposed for the new rule, thus improving the complexity of the horizontally elastic not-first/not-last algorithm from O(n3) to O(n2). The fixed part of external tasks that overlap with the selected task intervals is considered during the computation of the earliest completion time of task intervals. This improvement increases the filtering power of the algorithm while remaining quadratic. Experimental results, on a well-known suite of benchmark instances of Resource- Constrained Project Scheduling Problems (RCPSPs), show that the propounded algorithms are competitive with the state-of-the-art not-first algorithms in terms of tree search and running time reduction.

关键词： Not-first/not-last algorithm Profile data structure TimeTable data structure Cumulative scheduling constraint programming Horizontally elastic scheduling RCPSP

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A unified solution framework for flexible job shop scheduling problems with multiple resource constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期320卷 479-495页

作者： Kasapidis, Gregory A. Paraskevopoulos, Dimitris C. Mourtos, Ioannis Repoussis, Panagiotis P. Univ Liverpool Management Sch Liverpool England Univ London Bayes Business Sch London England Athens Univ Econ & Business Dept Management Sci & Technol Sch Business Athens Greece Athens Univ Econ & Business Dept Mkt & Commun Sch Business Athens Greece

This paper examines flexible job shop scheduling problems with multiple resource constraints. A unified solution framework is presented for modelling various types of non-renewable, renewable and cumulative resources, such as limited capacity machine buffers, tools, utilities and work in progress buffers. We propose a constraint programming (CP) model and a CP-based Adaptive Large Neighbourhood Search (ALNS-CP) algorithm. The ALNS-CP uses long-term memory structures to store information about the assignment to machines of both individual operations and pairs of operations, as encountered in high-quality and diverse solutions during the search process. This information is used to create additional constraints for the CP solver, which guide the search towards promising regions of the solution space. Numerous experiments are conducted on well-known benchmark sets to assess the performance of ALNS-CP against the current state-of-the-art. Additional experiments are conducted on new instances of various sizes to study the impact of different resource types on the makespan. The computational results show that the proposed solution framework is highly competitive, while it was able to produce 39 new best solutions on well-known problem instances of the literature.

关键词： Scheduling Flexible Job Shop Scheduling constraint programming Adaptive Large Neighbourhood Search Resource constraints

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Joint Routing and Charging Optimization of Electric Passenger Vehicles With Uninterruptible Charging Service

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 18180-18192页

作者： Cao, Yongsheng Yi, Junlin Liu, Yang Zhao, Caiping Li, Demin Zhang, Yihong Han, Zhu East China Univ Polit Sci & Law Shanghai 200042 Peoples R China Shanghai Jiao Tong Univ Artificial Intelligence Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ KoGuan Sch Law Shanghai 200240 Peoples R China Shanghai South City Power Supply Co Shanghai 201802 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

The increasing popularity of electric passenger vehicles (EPVs) has significant implications for transportation networks and power grids. We aim to tackle the routing and charging dispatching problem for EPVs while considering charging station (CS) power limits. We formulate the problem using a clustered rolling framework and introduce an energy criterion to determine EPV availability for shuttle services. The EPV charging dispatching is modeled as a constraint programming problem under CS power limits, with a fixed charging rate assumed at the start of charging. The routing and charging logic-based benders decomposition (RCLBD) algorithm, based on logic-based benders decomposition, effectively handles binary and continuous variables. The EPV routing model serves as the master problem, while the charging model acts as the subproblem. Simulation experiments demonstrate the RCLBD algorithm's performance and efficiency. The algorithm successfully provides efficient pick-up and delivery services, minimizing waiting times for customers. It ensures successful routing and charging solutions for all arriving EPVs. The electricity cost of our proposed RCLBD algorithm is 2.13%, 10.68% lower than that of mixed-integer programming and MIPC method when the number of EPVs is 750. Our proposed routing and charging algorithm shows good performance and efficiency, addressing the challenges posed by the increasing popularity of EPVs.

关键词： Routing Schedules Charging stations Transportation Dispatching Batteries Power system stability constraint programming electric vehicle (EV) pickup and drop-off service routing and charging

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Enhancing constraint Acquisition Through Hybrid Learning: An Integration of Passive and Active Learning Strategies

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2024年第6期33卷

作者： Balafas, Vasileios Tsouros, Dimosthenis C. Ploskas, Nikolaos Stergiou, Kostas Univ Western Macedonia Dept Elect & Comp Engn Campus ZEP Kozani 50100 Greece Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200a B-3001 Leuven Belgium

constraint programming (CP) is a successful methodology for solving combinatorial problems from various domains. Efficiently modeling the problem at hand as a constraint Satisfaction Problem is a crucial, but difficult task in CP. Toward this, a recent approach that is attracting increasing interest is constraint Acquisition, i.e., the (semi)automatic learning of constraints through examples of solutions and non-solutions. This paper introduces a hybrid methodology that combines passive and active learning strategies to acquire both global and fixed arity constraints. This hybrid approach leverages the strengths of both techniques to address their individual limitations. Passive learning rapidly learns constraints from example solutions, while active learning refines and contextualizes constraints through user interaction. The core of the methodology consists of a passive learning module where subsets of variables are compared against global constraints and are validated using a CP solver. constraints consistently present across multiple solution sets are identified as global constraints that belong to the model. Then, fixed arity constraints are refined through an active learning module with user input. Experiments across various problem types, from simple to complex, demonstrate the efficiency of the proposed hybrid methodology.

关键词： constraint programming constraint acquisition modeling

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Logic-based Benders decomposition methods for the distributed permutation flow shop scheduling problem with production and transportation cost

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COMPUTERS & OPERATIONS RESEARCH 2025年 179卷

作者： Xiong, Fuli Shi, Jiangbo Jing, Lin Ping, An Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China

Distributed manufacturing mode can significantly enhance production flexibility and efficiency. Considering that factories and customers in distributed manufacturing environments may be geographically dispersed, address a distributed permutation flow shop scheduling problem (DPFSP) with direct transportation different cost of production and transportation while the goal is to minimize of weighted sum cost makespan (DPFSP-PTM). First, we formulate two mixed-integer linear programming (MILP) models and constraint programming (CP) model to optimize the objective simultaneously. Then, by decomposing DPFSPPTM into an order assignment master problem (AMP) and a series of scheduling subproblems (SSPs), develop two exact methods based on logic-based Benders decomposition (LBBD) and Branch-and-Check (BCH). To accelerate convergence, we propose three strong SSP relaxations based on the single-machine bottleneck enhance the MILP models and AMP. Additionally, we introduce an initial solution generated by the iterated greedy (IG) algorithm to warm-start the LBBD. Finally, we demonstrate the effectiveness of the proposed methods in achieving competitive average optimality gaps and lower bounds across both small-scale large-scale instances.

关键词： Distributed permutation flow shop scheduling problem Logic-based Benders decomposition Branch-and-Check Mixed-integer linear programming constraint programming Iterated greedy algorithm

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Scheduling distributed heterogeneous non-permutation flowshop to minimize the total weighted tardiness

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Xiong, Fuli Chen, Siyuan Xiong, Ningxin Jing, Lin Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China China Univ Petr Beijing Karamay Sch Petr Karamay 834000 Peoples R China

Flowshop scheduling is a critical problem in manufacturing and logistics, where jobs must be processed through a series of machines in a predefined order. In distributed heterogeneous flowshop scheduling, multiple factories with varying processing capacities and resources are involved, making the scheduling problem even more complex. Non-permutation flowshops (NPFS) further complicate this by allowing job sequences to differ across stages, thus significantly expanding the solution space compared to traditional permutation flowshops. Minimizing total weighted tardiness (TWT) is a key objective as it plays a crucial role in avoiding penalties for late deliveries. In this context, this paper addresses a distributed heterogeneous non-permutation flowshop scheduling problem with the objective of minimizing TWT (DHNPFSP_TWT). The problem involves multiple factories operating as NPFS, where job processing times differ across factories for the same production stage. Given the NP-hard nature of the problem, we first proposed a Manne-based mixed-integer linear programming model and a constraint programming (CP) model for small-scale instances. To solve medium- and largescale instances efficiently, we propose a three-phase adaptive evolutionary algorithm (TAE) that combines permutation and non-permutation search strategies, along with a job allocation adjustment phase. The TAE algorithm first finds a permutation solution using NEH3_en and random generation, followed by an adaptive local search and adaptive ruin and recreate algorithm for refinement and mutation. In the non-permutation phase, a greedy insertion strategy and local search techniques explore the solution space. The job allocation adjustment phase reallocates jobs based on the factory with the highest tardiness, and the second and third phases co-evolve to improve solution quality. Additionally, we propose a hybrid algorithm (AE_CP) integrating the strengths of adaptive evolutionary algorithms and CP to further enh

关键词： Distributed heterogeneous non-permutation flowshop Iterated greedy constraint programming Mixed integer linear programming Total weighted tardiness

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Comparison of MILP and CP models for balancing partially automated assembly lines

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年第4期32卷 945-959页

作者： Dimeny, Imre Koltai, Tamas Budapest Univ Technol & Econ Fac Econ & Social Sci Quantitat Social & Management Sci Res Ctr Budapest Hungary Budapest Univ Technol & Econ Dept Management & Business Econ Budapest Hungary

The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.

关键词： Assembly line balancing Mixed integer linear programing constraint programming Human-robot collaboration

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