production scheduling

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.

The main goal of the research is production timing with the approach of meta-heuristic algorithms. First, the mathematical model of the production schedule was presented, and then the model was solved with the genetic algorithm. All types of genetic operators were considered at this stage, and an attempt was made to achieve better answers by choosing appropriate methods. The result of applying these targeted selection methods was the rapid convergence of the population. However, this fast convergence did not provide an optimal solution because it quickly converged all the people of the population to a local optimal solution and did not allow the algorithm to search more of the solution space. Therefore, contrary to expectations, the targeted selection methods without a suitable generation method did not improve the algorithm's efficiency. At this stage, the generation methods were considered; the optimal solution for big problems was also obtained by implementing the selection methods. By finding the appropriate generation method, it was observed that even the operators who did not have much ability to see close to optimal solutions succeeded in finding optimal solutions.

This paper examines the joint optimization of production and maintenance planning for a single-machine deteriorating system. To achieve optimal performance and meet customer demand at the lowest cost, manufacturing companies need to carefully plan production and maintenance, considering various factors such as time, cost, output levels in any period and its impact on machine deterioration. In this research, we attempt to plan the production and maintenance process for a single-machine single-product system over multi-period. The machine has two operational states during production and gradually deteriorates as it ages. Maintenance operations restore the machine to healthy state and reduce the probability of producing defective products. We model the problem using the Markov decision process and employ the value iteration algorithm to determine the optimal policy, i.e., the best actions to take at each decision epoch. We evaluate the model's effectiveness by solving a numerical example and analyzing how changes in different parameters affect the results. The findings reveal the relationship between various parameters and the average cost rate. Changes in the mentioned rate due to changes in setup cost and the probability of producing conforming products are almost uniform without any drastic fluctuations. If the production cost of each item exceeds a certain threshold, the company's obligations are not enforceable.

This paper investigates steel-making continuous casting (SCC) scheduling problem. SCC is a high temperature and large-scale logistics machining process with batch production at the last stage that was identified as the key process of modern iron and steel enterprises. This paper presents a mathematical model for scheduling SCC process. The model is developed as a Mixed Zero- One Linear programming (MZOLP) based on actual production situations of SCC. The objective is to schedule a set of charges (jobs) to minimize the earliness and tardiness penalty costs as well as the charge waiting time cost. The solution methodology is developed based on a branch-and-bound algorithm. A heuristic method is presented at the beginning of the search in order to compute an initial upper bound. A lower bound and an upper bound are developed and a method for reducing branches is established based on the batch production in the continuous casting (CC) stage. Moreover, branching schemes are proposed. The branch- and- bound algorithm incorporating the initial upper bound, the lower and upper bound, the method for reducing branches, and branching schemes is tested on a set of instances. The analysis shows the efficiency of the proposed features for the algorithm.

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in recent years the pressures of global environmental organizations have prompted private and public organizations to take action to reduce environmental pollutants. This paper presents a nonlinear mixed integer model for the production and distribution of goods with specified shipping capacity and specific delivery time for customers. The proposed model is applicable to flexible production systems; it also provides routing for the means of transportation of products, as well as the reduction of emissions from production and distribution. The model is presented, and then by mathematical linearization is transformed into a mixed integer linear model. The data of a furniture company is used to solve the linear model, and then the linear model with the company data is solved by CPLEX software. The numerical results show that as costs increase, delays are reduced and consequently, customer satisfaction increases, and as costs increase the air pollution decreases.

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in recent years the pressures of global environmental organizations have prompted private and public organizations to take action to reduce environmental pollutants. This paper presents a nonlinear mixed integer model for the production and distribution of goods with specified shipping capacity and specific delivery time for customers. The proposed model is applicable to flexible production systems; it also provides routing for the means of transportation of products, as well as the reduction of emissions from production and distribution. The model is presented, and then by mathematical linearization is transformed into a mixed integer linear model. The data of a furniture company is used to solve the linear model, and then the linear model with the company data is solved by CPLEX software. The numerical results show that as costs increase, delays are reduced and consequently, customer satisfaction increases, and as costs increase the air pollution decreases.

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.

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.

Planning and scheduling are among the most important parts of the management’s duties. Development of an efficient scheduling method can results in productivity improvement of an organization. Given the importance of production scheduling in an organization, this research seeks to propose a solution for one of the important problems for the production managers. This problem occurs if a considerable percentage of available production times is allocated to machine setup times. The objective of this research is to find a scheduling method to reach minimum of total production time, earliness and tardiness times. In previous researches not all effective factors on this scheduling method such as machine idle times and machine setup costs have been studied simultaneously. A mathematical model for the optimization of multi-product single-machine scheduling problem have been developed which considered sequence dependent setup costs, costs due to delay in delivery, holding costs, and costs related to machine idle time. Comparative results for the random small size test cases show that the proposed mathematical model can obtained an optimal solution in a relatively low computation time, however, for the large-scale cases this model is not efficient and an approximate method is required for these cases.

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of Decision Making Units (DMUs) which was first introduced by Charnes, Cooper and Rhodes in1978 as the CCR model .One of the most important topics in management science is determining the efficiency of DMUs. DEA technique is employed for this purpose. In many DEA models, the best performance of a DMU is indicated by an efficiency score of one. There is often more than one DMU with this efficiency score. To rank and compare efficient units, many methods have been introduced. Moreover, the main assumption in all DEA models was that all input and output values are positive, but practically, we encounter many cases that violate this term and we ultimately have negative inputs and outputs.

Shop floor performance has great influence on performance of a manufacturing system. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. Anyway, these aspects of operations planning have an interaction effect on each other significantly and hereupon for improving the system performance; they need to be considered commonly. The main objective of this study is to provide a new approach in the quality control process. x control chart has been used in previous models, but in this study to improve the quality and increase the accuracy, cumulative sum control chart which is useful for detecting small changes by considering previous information of process is used and causes 5% improvement in objective function in the case that x control chart was used.