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Showing 3 results for Production Scheduling
Routing and Production Scheduling for a Mobile Factory
Adeleh Behzad, Mohammadali Pirayesh, Mohammad Ranjbar,
Volume 28, Issue 3 (9-2017)
Abstract

In last decades, mobile factories have been used due to their high production capability, carrying their equipment and covering rough and uneven routes. Nowadays, more companies use mobile factories with the aim of reducing the transportation and manufacturing costs. The mobile factory must travel between the suppliers, visit all of them in each time period and return to the initial location of the mobile factory. In this paper, we present an integer nonlinear programming model for production scheduling and routing of mobile factory with the aim of maximization of profit. This problem is similar to the well-known Traveling Salesman Problem (TSP) which is an NP-hard problem. Also at each supplier, the scheduling problem for production is NP-hard. After linearization, we proposed a heuristic greedy algorithm. The efficiency of this heuristic algorithm is analyzed using the computational studies on 540 randomly generated test instances. Finally, the sensitivity analysis of the production cost, transportation cost and relocation cost was conducted.


Integrated capacitated transportation and production scheduling problem in a fuzzy environment
Amir Mohammad Fathollahi Fard, Mostafa Hajiaghaei-Keshteli,
Volume 29, Issue 2 (6-2018)
Abstract

Nowadays, several methods in production management mainly focus on the different partners of supply chain management. In real world, the capacity of planes is limited. In addition, the recent decade has seen the rapid development of controlling the uncertainty in the production scheduling configurations along with proposing novel solution approaches. This paper proposes a new mathematical model via strong recent meta-heuristics planning. This study firstly develops and coordinates the integrated air transportation and production scheduling problem with time windows and due date time in Fuzzy environment to minimize the total cost. Since the problem is NP-hard, we use four meta-heuristics along with some new procedures and operators to solve the problem. The algorithms are divided into two groups: traditional and recent ones. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as traditional algorithms, also Keshtel Algorithm (KA) and Virus Colony Search (VCS) as the recent ones are utilized in this study. In addition, by using Taguchi experimental design, the algorithm parameters are tuned. Besides, to study the behavior of the algorithms, different problem sizes are generated and the results are compared and discussed.


Real-Time Scheduling Approach in Internet of Things-Enabled Production Monitoring Systems
Nur Islahudin, Dony Satriyo Nugroho, Zaenal Arifin, Helmy Rahadian, Herwin Suprijono,
Volume 35, Issue 4 (12-2024)
Abstract
The Internet of Things (IoT) emerged as a pivotal catalyst in shaping the landscape of Industrial Revolution 4.0. Its integration within the manufacturing sector holds transformative potential for enhancing productivity on the production shop floor. Real-time monitoring of production processes becomes feasible through the implementation of IoT. Allows companies to promptly assess whether production outcomes align with predetermined plans, facilitating agile adjustments for swift improvements. In the face of volatile consumer demand, the company can efficiently strategize planned production approaches in response to significant shifts in consumer needs. This study endeavours to design a robust real-time production monitoring system employing the Internet of Things paradigm. The system's architecture emphasizes embedding sensors within the production floor processes to discern product types. Subsequently, a web platform enables seamless dissemination of production data to all relevant components. By leveraging real-time monitoring capabilities through IoT, the company gains the agility to swiftly decide and adapt production strategies, especially amid dynamic shifts in consumer demand.
 
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