A bi-objective heuristic approach for green identical parallel machine scheduling

Anghinolfi, Davide; Paolucci, Massimo; Ronco, Roberto

doi:10.1016/j.ejor.2020.07.020

Cited by 56 publications

(26 citation statements)

References 45 publications

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“…(11) Ensure that only one picking stations is responsible for each task; (12) represents the constraint of the starting time of continuous tasks before and after a single picking platform; (13) and (14) indicate that the election platform can only execute one task at most before and after each task, to ensure the relationship between tasks before and after the election platform. (15) represents the constraint between the time when AGV starts to execute the task and the time when the picking station starts to execute the task under the same task; (16) represents the relationship between the time when the picking stage begins to execute the task under the same task and the time when the AGVs end the task; (17) shows the value range of the variable.…”

Section: B Equipment-task Scheduling Modelmentioning

confidence: 99%

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Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

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Section: B Equipment-task Scheduling Modelmentioning

confidence: 99%

“…Scheduling problems are considered as NP-hard problems [16]. Scheduling problems are often solved by intelligent algorithms, a genetic algorithm is widely used in NP-hard problems [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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“…Therefore, the corresponding pure machining scheduling and pure assembly scheduling have been deeply studied. Du et al [1] and B S et al [2] solve scheduling problem with genetic algorithm, Huang et al [3] and Shen et al [4] apply neural network to solve scheduling problem, Ying et al [5] and C S W L A B et al [6] solve scheduling problem with simulated annealing method, Mathlouthi et al [7] and He et al [8] apply abu search method to solve scheduling problem, Wu et al [9] and Anghinol et al [10] solve scheduling problem with heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Scheduling Algorithm for Multi-Device-Processes with the Strategy of Exchanging Adjacent Parallel Processes of The Same Device

Wang

zhang

Cao

2021

Preprint

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The current integrated scheduling algorithm ignores the influence of the pre-process on the post-process when solving the multi-device-process integrated scheduling problem, which leads to the problem of poor tightness between serial processes and low parallelism between parallel processes. This paper points out that there is no restriction of scheduling sequence between adjacent parallel processes on the same processing device, and the scheduling sequence between parallel processes on the same device can be flexibly processed to optimize the scheduling results, on the basis of the current algorithm scheduling scheme, this paper proposes the application of multi-device adjacent parallel process interchange strategy and multi-device adjacent parallel process interchange adjustment strategy, which avoid the influence of the pre-process on the post-process, improves the compactness of the serial process and the parallelism of the parallel process, and optimizes the scheduling results.

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“…Therefore, the corresponding pure machining scheduling and pure assembly scheduling have been deeply studied. Sya et al [4] solved scheduling problem with genetic algorithm, Huang et al[5] and Shen et al [6] applied neural network to solve scheduling problem, Swlab et al[7] solved scheduling problem with simulated annealing method, Mathlouthi et al [8] applied Tabu search method to solve scheduling problem, and Wu et al [9] and Anghinolfi et al [10] solved scheduling problem with heuristic algorithm.…”

mentioning

confidence: 99%

“…[7] solved scheduling problem with simulated annealing method, Mathlouthi et al [8] applied Tabu search method to solve scheduling problem, and Wu et al [9] and Anghinolfi et al [10] solved scheduling problem with heuristic algorithm.…”

mentioning

confidence: 99%

An integrated scheduling algorithm for multi-device-processes with the strategy of exchanging adjacent parallel processes of the same device

Wang

Zhang

Cao

et al. 2021

J Wireless Com Network

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At present, Multi-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence (MISATPS) is an advanced algorithm in the field of integrated scheduling with multi-devices-process problems. This algorithm ignores the influence of the pre-process on the post-process when solving the multi-devices-process integrated scheduling problem, which leads to the problem of poor closeness between serial processes and poor parallelism between parallel processes. This paper points out that there is no restriction of scheduling sequence between parallel processes on the same processing device. It can be scheduled flexibly of the sequence between parallel processes of the same device. Therefore, based on the scheduling scheme of MISATPS, the algorithm is improved by applying the interchange strategy and the interchange adjustment strategy of multi-device adjacent parallel process. In this way, the influence of the pre-process on the post-process is avoided, the compactness of the serial process and the parallelism of the parallel process are improved, and the scheduling result is optimized.

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A bi-objective heuristic approach for green identical parallel machine scheduling

Cited by 56 publications

References 45 publications

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

An Integrated Scheduling Algorithm for Multi-Device-Processes with the Strategy of Exchanging Adjacent Parallel Processes of The Same Device

An integrated scheduling algorithm for multi-device-processes with the strategy of exchanging adjacent parallel processes of the same device

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