# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 2 Literature Review
[33] proposed and evaluated the application of the Expected Externality Mechanism as an approach to solving the problem of efficiently and fairly allocating resources in a number of different computational settings based on economic principles. Tests indicated that the mechanism meets its theoretical predictions in practice and can be implemented in a computationally tractable manner. [PERSON] et al. [34] proposed two task scheduling algorithms for heterogeneous systems. Their offline and online scheduling algorithms aimed at reducing the overall makespan of task allocation in cloud computing environments. The algorithms' simulation proved that they outperformed standard algorithms in terms of makespan and cloud utilization. Minimizing makespan was the objective of the research conducted by [PERSON] [35] which tackled single and parallel batch processing machine scheduling. The author presented a reformulation for parallel batch processing machines and proposed an exact algorithm to solve this problem. Also, [PERSON] et al. [36] developed a mathematical model and a fuzzy ant colony optimization (FACO) algorithm to schedule parallel non-identical size jobs with fuzzy processing times. The batch processing utilized machines with different capacities and aimed at minimizing the makespan. Additionally, [PERSON] [37] proposed two fast algorithms and a polynomial time approximation scheme (PTAS) to tackle the problem of scheduling n jobs on m parallel batching machines with inclusive processing set restrictions and non-identical capacities. The research aimed at finding a non-preemptive schedule to minimize makespan. Another study by [PERSON] and [PERSON] [38] aimed at creating a task scheduling process by examining the various real times scheduling algorithm. The study presented a new algorithm for task scheduling in a multiprocessor environment. The authors used TORSCHE toolbox for developing real-time scheduling in addition to utilizing features of particle swarm optimization. The proposed algorithm succeeded in executing a maximum number of the process in a minimum time. Also, [PERSON] et al. [39] investigated the Distributed No-idle Permutation Flowshop Scheduling Problem (DNIPFSP) with the objective of minimizing the makespan. The authors proposed an Iterated Reference Greedy (IRG) algorithm that was compared with a state-of-the-art iterated greedy (IG) algorithm, as well as the Mixed Integer Linear Programming (MILP) model on two benchmark problems showing promising results. An energy-efficient flexible job shop scheduling problem (EFJSP) with transportation was the core of research by [PERSON] and [PERSON] [40]. The authors developed an imperialist competitive algorithm with feedback to minimize makespan, total tardiness, and total energy consumption. The conducted experiments provided promising computational results, which proved the effectiveness of the proposed algorithm. Furthermore, a study addressing distributed unrelated parallel machines scheduling problem aiming at minimizing makespan in the heterogeneous production network was proposed by [PERSON] et al. [41]. A novel imperialist competitive algorithm with memory was developed by authors and experiments were conducted to test the performance of where the computational results proved the effectiveness of the algorithm. A study aimed at optimizing the trade-off between the total cost of tardiness and batch delivery was conducted by [PERSON] et al. [42]. To achieve this goal, the authors proposed three new metaheuristic algorithms which are the Differential Evolution with different mutation strategy variation, a Moth Flame Optimization, and Levy-Flight Moth Flame Optimization algorithm. The algorithms were validated through an industrial case study. [PERSON] [43] tackled the dynamic flexible job shop scheduling problem under new job insertions. The goal of the research was to minimize the total tardiness; therefore, the authors proposed a deep Q-network (DQN).