Seyyed Esmaeil Najafi; Dragan Marinkovic; Nenad Komazec
Abstract
This study investigates how the current academic literature discusses Wireless Sensor Network (WSN) applications in agriculture. The WSN is widely used to build decision support systems to overcome many problems in the real world. Using the basic principles of Internet and WSN technology, precision agriculture ...
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This study investigates how the current academic literature discusses Wireless Sensor Network (WSN) applications in agriculture. The WSN is widely used to build decision support systems to overcome many problems in the real world. Using the basic principles of Internet and WSN technology, precision agriculture systems based on the Internet of Things (IoT) technology are explained in detail, especially on the hardware architecture, network architecture, and software process control of the precision agriculture system. The software monitors data from the wireless sensors, but implementing a WSN will optimize the usage of water fertilizers and maximize crop yield. Nowadays, the climatic conditions are not the same and predictable. There are many ways to cultivate healthy crops in a year. But it requires a lot of human resources, which is a burden nowadays. We are designing a WSN for smart agriculture to make it smart and straightforward and give correct input to the corp.
Payam Chiniforooshan; Dragan Marinkovic
Abstract
This paper deals with the single machine scheduling problem with sequence-dependent setup time and learning effect on processing time, where the objective is to minimize total earliness and tardiness of the jobs. A Mixed Integer Linear Programming (MILP) model capable of solving small-sized problems ...
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This paper deals with the single machine scheduling problem with sequence-dependent setup time and learning effect on processing time, where the objective is to minimize total earliness and tardiness of the jobs. A Mixed Integer Linear Programming (MILP) model capable of solving small-sized problems is proposed to formulate this problem. In view of the NP-hard nature of the problem, the Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed to solve the large-sized problems. In order to utilize Particle Swarm Optimization (PSO) to solve the scheduling problems, the proposed HPSO approach uses a random key representation to encode solutions, which can convert the job sequences to continuous position values. Also, the local search procedure is included within the HPSO to enhance the exploitation of the algorithm. The performance of the proposed HPSO is verified for small and medium-sized problems by comparing its results with the best solution obtained by the LINGO. In order to test the applicability of the proposed algorithm to solve large-sized problems, 120 instances are generated, and the results are compared with a Random Key Genetic Algorithm (RKGA). The results show the effectiveness of the proposed model and algorithm.
