Parviz Fattahi, Mehdi Tanhatalab, Joerin Motavallian, Mehdi Karimi,
Volume 31, Issue 2 (6-2020)
Abstract
The present work addresses inventory-routing rescheduling problem (IRRP) that is needed when some minor changes happen in the time of execution of pre-planned scheduling of an inventory-routing problem (IRP). Due to the complexity of the process of departing from one pre-planned scheduling IRP to a rescheduling IRP, here a decision-support tool is devised to help the decision-maker. This complexity comes from the issue that changes in an agreed schedule including the used capacity of the vehicle, total distance and other factors that need a re-agreements negotiation which directly relates to the agreed costs especially when a carrier contractor is responsible for the distribution of goods between customers. From one side he wants to stick to the pre-planned scheduling and from the other side, changes in predicted data of problem at the time of execution need a new optimized solution. The proposed approached applies mathematical modeling for optimizing the rescheduled problem and offers a sensitivity analysis to study the influence of the different adjustment of variables (carried load, distance, …).
Hadi Mokhtari, Ali Salmasnia, Ali Fallahi,
Volume 33, Issue 1 (3-2022)
Abstract
This paper designs a Scenario analysis approach to determine the joint production policy for two products under possible substitution. The Scenario analysis is designed to improve decision making by considering possible outcomes and their implications. The traditional multi-products production models assume that there is no possible substitution between products. However, in real-world cases, there are many substitutable products where substitution may occur in the event of a product stock-out. The proposed model optimizes production quantities for two products under substitution with the aim of minimizing the total cost of inventory system, including setup and holding costs, subject to a resource constraint. To analyze the problem, four special Scenarios are derived and discussed in detail. Furthermore, the total cost functions are derived for each Scenario separately, and then a solution procedure is suggested based on the Scenarios developed. The numerical examples are implemented, and the results are discussed in detail.
Vahid Razmjoei, Iraj Mahdavi, Nezam Mahdavi-Amiri, Mohammad Mahdi Paydar,
Volume 33, Issue 2 (6-2022)
Abstract
Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.
Adnan Ali Hassan Alhosani, Fadillah Ismail,
Volume 33, Issue 3 (9-2022)
Abstract
Dubai has witnessed the growth of numbers in population and global visitors, which makes it necessary for the city to have an excellent police department to secure all citizens, residents and visitors. This is necessary for improving Dubai's security and financial condition and cementing the city's importance in the world. The main objective of this study is to examine the relationship between the delegation of authority, organizational functionality and decision-making process among the employees in Dubai police department UAE. A total of 380 employees were selected as the study sample using a multistage sampling method. Questionnaires were used in data collection and responses were analysed using partial least squares structural equation modelling (PLS-SEM) for data analysis. The results showed that the delegation of authority affects decision-making among the target population. Moreover, delegation of authority helps the organisation in achieving the objectives with accordance to the imperative’s factors of organizational functionality of the organisation. The results of this research contributed substantially to the current body of knowledge in the domain of delegation of authority in Arab context. The novelty of this study stem from the reality that the issues and problems of power delegation in Dubai police department was assessed in terms of decision-making process. From these results some recommendations are also suggested which are quite helpful especially, with regards to the latest global models of contemporary leadership and the latest approaches and methods of modern decision-making.
Motahare Gitinavard, Parviz Fattahi, Seyed Mohammad Hassan Hosseini, Mahsa Babaei,
Volume 33, Issue 4 (12-2022)
Abstract
This paper aims to introduce a joint optimization approach for maintenance, quality, and buffer stock policies in single machine production systems based on a P control chart. The main idea is to find the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control limits simultaneously, such that the expected total cost per time unit is minimized. In the considered system, we have a fixed rate of production and stochastic machine breakdowns which directly affect the quality of the product. Periodic preventive maintenance (PM) is performed to reduce out-of-control states. In addition, corrective maintenance is required after finding each out-of-control state. A buffer is used to reduce production disturbances caused by machine stops. To ensure that demand is met during a preventive and corrective maintenance operation. All features of three sub-optimization problems including maintenance, quality control, and buffer stock policies are formulated and the proposed integrated approach is defined and modeled mathematically. In addition, an iterative numerical optimization procedure is developed to provide the optimal values for the decision variables. The proposed procedure provides the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control chart limits simultaneously, in a way that the total cost per time unit is minimized. Moreover, some sensitivity analyses are carried out to identify the key effective parameters.
Amir Nayeb, Esmaeil Mehdizadeh, Seyed Habib A. Rahmati,
Volume 34, Issue 2 (6-2023)
Abstract
In the field of scheduling and sequence of operations, one of the common assumptions is the availability of machines and workers on the planning horizon. In the real world, a machine may be temporarily unavailable for a variety of reasons, including maintenance activities, and the full capacity of human resources cannot be used due to their limited number and/or different skill levels. Therefore, this paper examines the Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP) considering the limit of preventive maintenance (PM). Due to various variables and constraints, the goal is to minimize the maximum completion time. In this regard, Mixed Integer Linear Programming (MILP) model is presented for the mentioned problem. To evaluate and validate the presented mathematical model, several small and medium-sized problems are randomly generated and solved using CPLEX solver in GAMS software. Because the solving of this problem on a large scale is complex and time-consuming, two metaheuristic algorithms called Genetic Algorithm (GA) and Vibration Damping Optimization Algorithm (VDO) are used. The computational results show that GAMS software can solve small problems in an acceptable time and achieve an accurate answer, and also meta-heuristic algorithms can reach appropriate answers. The efficiency of the two proposed algorithms is also compared in terms of computational time and the value obtained for the objective function.
Hamza Samouche, Abdellah El Barkany, Ahmed Elkhalfi,
Volume 34, Issue 2 (6-2023)
Abstract
Sales and operations planning (S&OP) is considered as an important tool at the planning strategic level. Its models vary depending on industries. The Asian model is known to be very developed. Having several parameters, the Asian model proves to be an effective tool, precisely for the study of capacity. However, after several searches made in various databases, we did not find any concrete model actually used in industry and whose parameters are presented and which defines the analysis logic to better align supply and demand. In this article, we will carry out various simulations on the basis of the data of a model of sales and operations planning used in a wire harnesses factory, in order to explain the decision-making process during S&OP meetings. The parameters of the model and the various constraints that were facing the sales and operations planning team are presented and discussed as well as the financial consequences of certain decisions. As a result of this study, we can notice that S&OP is indeed a powerful tool that makes it possible to detect in advance the various constraints whose resolution concludes in an optimal alignment between customer demand and factory capacity.
Ali Qorbani, Yousef Rabbani, Reza Kamranrad,
Volume 34, Issue 4 (12-2023)
Abstract
Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions.
Hamed Nozari, Maryam Rahmaty,
Volume 34, Issue 4 (12-2023)
Abstract
In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with the League Championship Algorithm (LCA), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA) methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased, and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. By reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the Objective Function Value (OBF) averages among the solution methods, they were prioritized, and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the make-to-order supply chain problem.
Iwa Kustiyawan, Mas Rahman Roestan, Catur Riani,
Volume 34, Issue 4 (12-2023)
Abstract
This research aims to identify the initial OEE (Overall Equipment Efficiency) values on automated packaging machines with a 2d barcode track and trace system. Quantitative research methods used to obtain the OEE value, analysis of factors affecting the OEE values, developing a strategy to make improvements, and evaluate these strategies on the level of machine productivity. The importance of the subject lies in the need to improve the efficiency and productivity of pharmaceutical packaging processes. The pharmaceutical industry is facing increasing pressure to optimize operations and reduce waste. Implementing effective performance measurement tools such as Overall Equipment Effectiveness (OEE) can help identify areas for improvement and enhance productivity. This study found that the track-and-trace system was below the company's standard, indicating room for improvement. Then, countermeasures were implemented to increase productivity and machine effectiveness, and the initial OEE value of the automated packaging machine with 2D barcodes improved. Thus, this study demonstrated the effectiveness of the proposed framework in evaluating and improving OEE in pharmaceutical packaging processes, highlighting the significance of digitalization and automation technologies in enhancing productivity.
