Solving the integrated process planning and scheduling problem using an enhanced constraint programming-based approach

Shi, Ganquan; Yang, Zhouwang; Xu, Yang; Quan, Yuchen

doi:10.1080/00207543.2021.1963496

Cited by 5 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each vehicle departs from and returns to a given node, and the optimization objective is a function of the cost incurred by charging the vehicle and the distribution distance. The approaches to the problem are broadly classified into several categories such as heuristic algorithms [3] , operational optimization solvers [4] , and neural network algorithms [5] . The solution result of the heuristic algorithm is only an approximate solution, and its solution speed is significantly faster than that of the traditional algorithm [6] .…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been carried out based on two types of networks, neural networks, and selforganizing mapping networks, which are often limited in solution time [9] . In recent times graphical convolutional neural networks have also been applied to combinatorial optimization problems, and by using image representations, a direct mapping between input and output has been constructed to obtain better feature extraction [10] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving graph convolutional networks with reinforcement learning for pure electric logistics vehicle routing

Zhu,

Yan

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

View full text Add to dashboard Cite

Electric logistics vehicle route planning plays a crucial role in the operations of urban distribution service providers. By optimizing vehicle routes, not only can significant reductions in operating costs be achieved, but also enhanced customer satisfaction. However, the vehicle path problem is a classic NP-hard problem in the field of combinatorial optimization. Therefore, finding an efficient and accurate solution holds great practical significance. In response to the real-time operational requirements, this paper proposes a novel approach that improves the graph convolutional neural network model with reinforcement learning. This approach aims to learn effective strategies for generating vehicle route schedules. Through comprehensive experiments conducted on real geographic data, several key findings have been obtained: 1) Explicitly considering edge features within the model is essential; 2) Employing a joint learning strategy can expedite training convergence and improve solution quality, and 3) Comparative evaluations against existing algorithms in the literature demonstrate the superior performance of our proposed model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving graph convolutional networks with reinforcement learning for pure electric logistics vehicle routing

Zhu,

Yan

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

View full text Add to dashboard Cite

show abstract

“…2021; Shi et al . 2021). Hence, more efficient approaches to approximate good-quality schedules instead of striving for optimal solutions have attracted broad research interest.…”

Section: Introductionmentioning

confidence: 99%

“…In real-world production scheduling, the number of operations to process can easily go into tens of thousands (Da Col and Teppan 2019; Kopp et al 2020;Kovács et al 2021), which exceeds exact optimization capacities even of state-of-the-art solvers for Answer Set Programming (ASP), Mixed Integer Programming (MIP), or Constraint Programming (CP) (Daneshamooz et al 2021;Francescutto et al 2021;Shi et al 2021). Hence, more efficient approaches to approximate good-quality schedules instead of striving for optimal solutions have attracted broad research interest.…”

Section: Introductionmentioning

confidence: 99%

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

El-Kholany

Gebser²,

Schekotihin³

2022

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

Scheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, the Job-shop Scheduling Problem (JSP) is a well known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. Given that already moderately sized JSP instances can be highly combinatorial, and neither optimal schedules nor the runtime to termination of complete optimization methods is known, efficient approaches to approximate good-quality schedules are of interest. In this paper, we propose problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations so that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. Regarding the feasibility and quality of solutions, problem decomposition must respect the precedence of operations within their jobs and partial schedules optimized by time windows should yield better global solutions than obtainable in similar runtime on the entire instance. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract window-wise optimization limitations restricted to partial schedules. Our experiments on JSP benchmark sets of several sizes show that successive optimization by multi-shot ASP solving leads to substantially better schedules within the runtime limit than global optimization on the full problem, where the gap increases with the number of operations to schedule. While the obtained solution quality still remains behind a state-of-the-art Constraint Programming system, our multi-shot solving approach comes closer the larger the instance size, demonstrating good scalability by problem decomposition.

show abstract

A Constrained Programming-Based Algorithm for Optimal Scheduling of Aggregated EVs Power Demand in Smart Buildings

2022

View full text Add to dashboard Cite

In this paper, a novel cooperative charging strategy for electric vehicles tuned by a constraint programming algorithm has been proposed. The implemented model handles heterogeneous and large-scale residential areas by not only reducing the EV peak charging load, but also improving the user satisfaction levels. The evaluation of the capability of the proposed model at both individual and aggregated levels is considered through various scenarios. The simulated results prove the potential of the proposed cooperative EV strategy in outperforming the uncoordinated EV charging model in terms of peak-to-average ratio reduction, user satisfaction level, and load factor improvement in addition to the suppression of the peak load increase. Furthermore, comparative analysis with existing models shows that the proposed algorithm can manage more complex policies and is performed significantly and efficiently.INDEX TERMS EV charging control, Coordinated management, Peak-to-average ratio, User satisfaction.

show abstract

Solving the integrated process planning and scheduling problem using an enhanced constraint programming-based approach

Cited by 5 publications

References 45 publications

Improving graph convolutional networks with reinforcement learning for pure electric logistics vehicle routing

Improving graph convolutional networks with reinforcement learning for pure electric logistics vehicle routing

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

A Constrained Programming-Based Algorithm for Optimal Scheduling of Aggregated EVs Power Demand in Smart Buildings

Contact Info

Product

Resources

About