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Laboratory assistant assignment problem using Python programming
Published in Indira Rachmawati, Ratih Hendayani, Managing Learning Organization in Industry 4.0, 2020
Mathematical models can support human resources assignment. One such mathematical model is the liner optimization model. The assignment problem optimizes the allocation of resources to a job or a task so that a minimum cost or maximum payoff is obtained. Previous research on the assignment problem has examined multicore assignment (Sudhakar, Adhikari, & Ramesh, 2016), ergonomics (Gebennini, Zeppetella, & Grassi, 2018), energy sharing (Fu, Moran, Guo, Wong, & Zukerman, 2016), product-to-site (Hillebrand, 2019), manufacturing resources (Na, Woo, & Lee, 2016), using a meta-heuristic algorithm (Peters et al., 2019), the traffic problem (Patriksson, 2015), and many more issues. Other research has used a philosophy of assignment rather than algorithms assigning members of the workforce to industry 4.0 jobs (Aurachman, Model Matematika Dampak Industri 4.0 terhadap Ketenagakerjaan Menggunakan Pendekatan Sistem, 2019) (Aurachman, Perancangan Influence Diagram Perhitungan Dampak Dari Revolusi Industri 4.0 Terhadap Pengangguran Kerja, 2018), assigning a server to work at a highway gate (Aurachman & Ridwan, Perancangan Model Optimasi Alokasi Jumlah Server untuk Meminimalkan Total Antrean pada Sistem Antrean Dua Arah pada Gerbang Tol, 2016), assigning resources to a vehicle route (Desiana, Ridwan, & Aurachman, 2016), or assigning a distribution plan (Muttaqin, Martini, & Aurachman, 2017).
Overview of Cyber-Physical Systems and Cybersecurity
Published in Chong Li, Meikang Qiu, Reinforcement Learning for Cyber-Physical Systems, 2019
Another example of resource allocation for industrial cyber-physical IoT systems based on 5G technologies is presented in [87]. A framework that multiple sensors and actuators establish communication links with a central controller in full-duplex mode with low bandwidth requirement is considered. In [87], the authors segregated the non-convex optimization problem with the objective to maximize the sum energy efficiency of the system into two sub-problems: power allocation and channel allocation. Various techniques are employed to tackle these subproblems, including Dinkelbach’s algorithm, the Hungarian algorithm, and game-theoretic methods. Dinkelbach’s algorithm is a method for solving convex fractional programming [37]. The Hungarian algorithm is a polynomial algorithm for solving the linear assignment problem [72].
Multi-Aerial-Robot Planning
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
The Hungarian algorithm treats the optimal assignment problem as a combinatorial problem in order to efficiently solve an n × n task assignment problem in 0(n3) time. The utility estimates become edge weights in a complete bipartite graph in which each robot and task becomes a vertex. The Hungarian algorithm pseudocode is shown in Algorithm 3; it searches for a perfect matching in a sub-graph of the complete bipartite graph, where the perfect matching is exactly the optimal assignment problem. In step 7, the search process either increases the matching size or the so-called equality graph in which the matching resides.
Survey: mobile sensor networks for target searching and tracking
Published in Cyber-Physical Systems, 2018
Usually, the objective is to minimise energy consumption of the network. Normally, this is done by minimising the movement of the sensors by letting the cost, C(i, j), in Equation () represent distance. This is a different problem than a typical gradient-based control problem, where the sensors are expected to be continuously moving. An advantage over gradient-based approaches is that task allocation typically leads to an optimal solution even with non-convex problems. The assignment problem can be solved in polynomial time with the Hungarian algorithm [71]. Unfortunately, many of the target tracking formulations for MSNs also include non-linear constraints, making the problem NP-hard, with no known solution within polynomial time. Another challenge is to implement assignment problems in a distributed fashion.
Parallel Resource Allocation and Subcarrier Assignment for Downlink OFDMA
Published in IETE Technical Review, 2019
Satyendra Singh Yadav, Paulo Alexandre Crisóstomo Lopes, Sarat Kumar Patra
Example: Consider a system with 2 users and 4 subcarriers. Considering the channels conditions, according to (9) if = [1.5, 1.8, 2.1, 1.2] for first user and = [2.2, 1.6, 1.2, 1.4] for second user. R is denoted as: The rows of the matrix correspond to the users and columns correspond to the subcarriers. Let, the BARE output, be that the first user needs to be assigned only one subcarrier () and the second user need to assigned the remaining 3 subcarriers (). The Hungarian rate matrix , is: Therefore in , the columns represent the subcarriers and the rows represent the users, hence after replicating the rows for user 2 the size of is . Hence, the LAP for Hungarian algorithm can be formulated as: subjected to: The Hungarian algorithm [36] solves the minimum assignment problem. The algorithm can be easily formulated for maximum assignment problem by a change in Hungarian rate matrix as described below in step 1. The following steps describe the Hungarian algorithm stepwise:
State-of-the-Art Auction Algorithms for Multi-depot Bus Scheduling Problem Considering Depot Workload Balancing Constraints
Published in Fuzzy Information and Engineering, 2020
In this method, the depot workload balancing constraints are relaxed. The purpose of the assignment problem is to minimise the cost instead of satisfying depot workload balancing constraints. The following model expresses the assignment problem: where , if path r is assigned to depot k and , otherwise.