Multi-objective optimization of the order scheduling problem in mail-order pharmacy automation systems

Dauod, Husam; Li, Debiao; Yoon, Sang Won; Srihari, K.

doi:10.1007/s00170-016-9123-1

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…In 2013, considering multiple production departments and multiple production processes, an NSGA-II-based Pareto optimization model was developed to handle this problem [15]. In a study by Debiao Li, the multi-objective optimization problem (MOP) for minimizing collation delays and makespan was presented for the order scheduling problem in a mailorder pharmacy automation systems with a min-max Pareto objective function [16].…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Order Scheduling via Reinforcement Learning

Chen,

Tian,

2023

Algorithms

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Order scheduling is of a great significance in the internet and communication industries. With the rapid development of the communication industry and the increasing variety of user demands, the number of work orders for communication operators has grown exponentially. Most of the research that tries to solve the order scheduling problem has focused on improving assignment rules based on real-time performance. However, these traditional methods face challenges such as poor real-time performance, high human resource consumption, and low efficiency. Therefore, it is crucial to solve multi-objective problems in order to obtain a robust order scheduling policy to meet the multiple requirements of order scheduling in real problems. The priority dispatching rule (PDR) is a heuristic method that is widely used in real-world scheduling systems In this paper, we propose an approach to automatically optimize the Priority Dispatching Rule (PDR) using a deep multiple-objective reinforcement learning agent and to optimize the weighted vector with a convex hull to obtain the most objective and efficient weights. The convex hull method is employed to calculate the maximal linearly scalarized value, enabling us to determine the optimal weight vector objectively and achieve a balanced optimization of each objective rather than relying on subjective weight settings based on personal experience. Experimental results on multiple datasets demonstrate that our proposed algorithm achieves competitive performance compared to existing state-of-the-art order scheduling algorithms.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Order Scheduling via Reinforcement Learning

Chen,

Tian,

2023

Algorithms

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“…In this applied case, an ant colony optimisation based approach is proposed to address flexible job shop scheduling with routing flexibility and setup times problems. Recent publications are found in [8]. This research group employs a genetic algorithm with the Pareto front technique.…”

Section: Solving Combinatorial Problems Through Evolutionary Algorithmsmentioning

confidence: 99%

“…Constraints (6) and (7) determine the completion time for each task. Constraint (8) defines the completion time of each quay crane. Constraint (9) ensures that the first task of a crane is not started before the crane is ready.…”

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confidence: 99%

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Pérez-Rodríguez

2021

MCA

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The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.

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“…UPMSPs are employed in various applications, such as various manufacturing industries, including food processing plants, and car factories [10], semiconductor [11,12], tobacco [13], textile [14], petroleum [15], and tire [16]. Additionally, they have been applied in multiprocessor computer [17][18][19], for multithreading [20], in hospital operating rooms [21], human resources management [22], mail facilities [23], printing [24], pharmacy automation [25], vehicular networks [26], and heterogeneous systems that include GPU and CPU [27].…”

Section: Introductionmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

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Multi-objective optimization of the order scheduling problem in mail-order pharmacy automation systems

Cited by 14 publications

References 21 publications

Multi-Objective Order Scheduling via Reinforcement Learning

Multi-Objective Order Scheduling via Reinforcement Learning

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

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