Adaptive local search algorithm for solving car sequencing problem

Yilmazlar, I. Ozan; Kurz, Mary E.

doi:10.1016/j.jmsy.2023.05.018

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…Initially, the success rate of each operator is set to zero at the start of every iteration. Then, each time operator n is invoked, its success rate is updated based on the operator's performance [43]. Success is determined when the operator either discovers a better solution or identifies a solution with the same target value but different solution structures.…”

Section: Adaptive Control Mechanismmentioning

confidence: 99%

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Integrated Inbound and Outbound Scheduling for Coal Port: Constraint Programming and Adaptive Local Search

Lu,

Zhang,

Zheng

et al. 2024

JMSE

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The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commodities like coal and iron ore frequently engage in blending operations as a strategic imperative to gain market competitiveness. The process of blending coal and ore entails the timely arrival of the requisite raw materials at predetermined locations. Simultaneously, it necessitates the coordination of the sequencing of goods entering and departing the port to align with the operational demands associated with material stockpiles. This paper describes and analyzes an operational scheduling problem encountered by one of the largest coal blending sea ports in China. Specifically, a rich constraint programming model is presented to define operation sequences integrating daily inbound and outbound services provided by the port, minimizing the overall operation time. In order to enhance the practicality of the method, a CP-based adaptive simulated annealing local search algorithm has been designed and developed for the optimization problem. The empirical validation of the proposed method is conducted using both real production data and generated experimental data adhering to specific rules. The results conclusively demonstrate the efficacy and feasibility of the proposed method. This also substantiates its practicality and effectiveness in real-world applications, facilitating efficient production and energy-saving operations for the coal port.

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Section: Adaptive Control Mechanismmentioning

confidence: 99%

“…Simulated annealing, being one of the predominant single-solution-based meta-heuristic algorithms, plays a crucial role in escaping the local optima by embracing non-improved solutions [43]. It found extensive applications in solving combinatorial optimization problems.…”

Section: Simulated Annealingmentioning

confidence: 99%

Integrated Inbound and Outbound Scheduling for Coal Port: Constraint Programming and Adaptive Local Search

Lu,

Zhang,

Zheng

et al. 2024

JMSE

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“…Tian et al proposed a small-world optimization algorithm and applied it to the resequencing problem between the painting workshop and the assembly workshop [11]. Yilmazlar and Kurz proposed an adaptive local search algorithm for car sequencing problem, and they demonstrated that the algorithm should focus on randomly exploring more non-degenerate solutions rather than spending time accessing degenerate solutions [12]. Thiruvady et al designed a large neighborhood search algorithm to solve the car sequencing problem, and combined Lagrangian relaxation and ant colony optimization to identify promising regions [13].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

Chen,

Liu,

Liu

2023

Processes

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During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid evolutionary algorithm with heuristic rules. In the welding workshop, a genetic algorithm is employed to optimize the vehicle sequencing. Simultaneously, a differential evolution algorithm is used to optimize the inbound sequence of the buffer zone between the welding and painting workshops, as well as the inbound sequence of the buffer zone between the painting and assembly workshops. Heuristic rules are applied to optimize the outbound sequence of the buffer zone between the welding and painting workshops, as well as the outbound sequence of the buffer zone between the painting and assembly workshops. In addition, in order to improve the quality of the initial population, a heuristic method-based initial population construction method is proposed. The optimization objectives are the number of vehicle model changes in the welding workshop, the number of color changes in the painting workshop, and the total number of overloads in the assembly workshop. The experimental results show that the proposed method performs better than the five outstanding evolutionary algorithms.

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