Showing 28 results for Production
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.
Mohsen Khezeli, Esmaeil Najafi, Mohammad Haji Molana, Masoud Seidi,
Volume 33, Issue 2 (6-2022)
Abstract
Nowadays, supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, a logistics network design is considered to optimize the total cost and increase the network stability and resiliency. First, a mixed integer nonlinear programming model (MINLP) is formulated to minimize the transportation time and transportation cost of products. The proposed model consists of two main stages.
One is a normal stage that minimizes the transportation and holding costs, all manufacturers are also assumed to be healthy and in service. In this stage, the quantity of customer demand met by each manufacturer is eventually determined.
The second is the resilience stage. A method is presented by creating an information network in this supply chain for achieving the resilient and sustainable production and distribution chain that, if some manufacturers break down or stop production, Using the Restarting and load sharing scenarios in the reactive approach to increase resilience with accepting the costs associated with it in the supply network and return to the original state in the shortest possible time, the consequences of accidental failure and shutdown of production units are managed.
Two capacities are also provided for each manufacturer
- Normal capacity to meet the producer's own demand
- Load sharing capacity, Determine the empty capacity and increase the capacity of alternative units to meet the out-of-service units demand
In order to solve the model, we used GAMS & Matlab software to find the optimal solutions. A hybrid priority-based Non-dominated Sorting Genetic Algorithms (NSGA-II) and Sub-population Genetic Algorithm (SPGA- II) is provided in two phases to find the optimal solutions. The solutions are represented with a priority matrix and an Allocated vector. To compare the efficiency of two algorithms several criteria are used such as NPS, CS and HV. Several Sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit.
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.
Sofia Kassami, Abdelah Zamma, Souad Ben Souda,
Volume 33, Issue 3 (9-2022)
Abstract
Modeling supply chain planning problems is considered one of the most critical planning issues in Supply Chain Management (SCM). Nowadays, decisions making must be sufficiently sustainable to operate appropriately in a complex and uncertain environment of the market for many years to beyond the next decade. Therefore, making these decisions in the presence of uncertainty is a critical issue,as highlighted in a large number of relevant publications over the past two decades.The purpose of this investigation is to model a multilevel supply chain problem and determine the constraints that prevent the flow from performing properly, subject to various sources and types of uncertainty that characterize the flow. Therefore, it attempts to establish a generic model that relies on the stochastic approach. Several studies have been conducted on uncertainty in order to propose an optimal solution to this type of problem. Thus, in this study, we will use the method of "Mixed integer optimization program" which is the basis of the algorithm that will be employed. This inaccuracy of the supply chain is handled by the fuzzy sets. In this paper, we intend to provide a new model for determining optimal planning of tactical and strategical decision-making levels, by building a conceptual model. Therefore, it enables us to model the mathematical programming problem. We investigate in this attempt, attention to solving the mathematical model. So in the resolution we are going through the algorithm in machine learning, therefore providing as in the end an optimal solution for the planning of production.
Fatemeh Hajisoltani, Mehdi Seifbarghy, Davar Pishva,
Volume 34, Issue 1 (3-2023)
Abstract
The main objective of this research is effective planning as well as greener production and distribution of mineral products in supply chain network. Through a case study in cement industry, it considers the design of the mining supply chain network including several factories with a number of production lines and multiple distribution centers. It leaves part of the transportation operation to contractor companies so as to enable the core company to better focus on its products’ quality and also create job opportunities to local people. It employs a multi-period and multi-product mixed integer linear programming model to both maximize the profit of the factory as well as minimize its carbon dioxide gas emissions which are released during cement production and transportation process. Due to the uncertainty of its cost parameters, fuzzy logic has been used for the modeling and solved via a novel fuzzy multi-choice goal programming approach. Sensitivity analysis has also been done on some key parameters. Comparing results of the model with those from the single-objective models, shows that the model has good efficiency and can be used by managers of mining industries such as cement. Although leaving part of the transportation operations to contractor companies increases the number of vehicles used by the contractor companies, its associated decrease in the number of required factory vehicles, improves both objectives of the model. This should be considered by the managers since on top of profit maximization, it can help them build an eco-friendly image. Mining industries generally generate significant amount of pollutions and companies that pay attention to different dimensions of their social responsibilities can remain stable in the competitive market.
Iffan Maflahah, Wila Wirvikananda, Hamzah Fansuri, Dian Farida Asfan, Raden Faridz,
Volume 35, Issue 3 (9-2024)
Abstract
Seablite salt (Suaeda maritima) was a unique product currently under development. Seablite is a low-sodium salt essential for modern society, particularly for those who prioritize their health. This investigation aims to employ a dynamic system approach to evaluate the revenue and profit generated by the salt production system. The dynamic systems approach steps: the construction of the causal loop diagram, the development of the stock-and-flow model, the parameterization of the model, the simulation to analyze the system's behavior under various conditions, the verification and validation, the development of policy recommendations, and the conclusion with a summary of the core findings. The model was developed using four submodels: (1) demand, (2) supply, (3) production cost, and (4) revenue. The moderate scenario demonstrates that the salt flow requirements can be satisfied by utilizing the dynamic system to protect the revenue and production costs. It was consistent with the escalating production expenses. According to the optimistic scenario, the salt demand can be satisfied until 2026. The company's revenue is insufficient to cover production costs due to the rise in raw material prices. Farmers begin to reap the rewards in this scenario. It's because the overall revenue exceeds the production costs.
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.
Weny Findiastuti, Fitri Agustina, Rullie Annisa, Ach Dafid, Iffan Maflahah, Ananda Rafli Siswanto,
Volume 36, Issue 2 (6-2025)
Abstract
Indonesia faces environmental challenges due to the increasing exploitation of natural resources and industrial emissions. This study aims to design an environmental impact mitigation strategy in the furniture industry using the Life Cycle Assessment (LCA) method, with a case study of UD Putra Bali. The analysis includes the Life Cycle Inventory (LCI), Life Cycle Impact Assessment (LCIA), and Life Cycle Interpretation to identify the greatest impacts and develop recommendations for improvement. The results of the study indicate that the life cycle of wooden door products produces an environmental impact of 13.1 kPt. The stage with the greatest impact is the finishing process, especially in the human toxicity water category of 11.3 kPt, due to thinner-based paint. In addition, the delivery of finished products contributes to the global warming category of 0.0539 kPt, which is caused by the use of vehicles with high emission specifications and inefficient delivery routes. Recommendations for improvement include the implementation of cleaner production, namely replacing thinner-based paint with more environmentally friendly water-based paint and optimizing delivery routes using the saving matrix nearest insert method to reduce the total distance traveled and transportation emissions. After the implementation of the mitigation strategy, the environmental impact of the finishing process decreased to 10.3 kPt, while the impact of the finished product delivery decreased to 0.0526 kPt. This study shows that the application of LCA can identify the main sources of environmental impacts and generate data-based improvement strategies. The implementation of this strategy is expected to enhance the sustainability of the furniture industry and reduce the production process's environmental footprint.