Optimizing Production Planning and Supplier Selection in Petrochemical Supply Chains
Subject Areas : Operation ResearchReza Babazadeh 1 * , Mohammad Khalili 2 , fatemeh dadmand 3
1 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
2 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Ira
3 - Department of Management, Payame Noor University, Tehran, Iran
Keywords: production planning, supplier selection, Petrochemical industry,
Abstract :
In this research, the intricate world of the petrochemical supply chain was delved into, with a focus on the critical problem of production planning and supplier selection. The aim was to identify effective factors that contribute to the continuous supply chain process of petrochemical production. The study was conducted in two phases, first, the Analytic Hierarchy Process (AHP) method was employed to identify the best suppliers. In the second phase, an innovative model was developed to optimize production planning. The primary objective was to minimize the total cost associated with ordering, holding, and production. To ensure the practicality and relevance of the model, several constraints were incorporated. The results obtained from the AHP method revealed that Shiraz Petrochemical emerged as the optimal supplier for urea, Khorasan Petrochemical for ammonia, and Ilam Petrochemical for sulfur. Additionally, the optimization model provided valuable insights into the optimal production quantities, raw material procurement volumes, and raw material inventory levels for each period.
Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. Or Spectrum, 32(4), 863-904.
Ali, H., Zhang, J., Liu, S., & Shoaib, M. (2023). An integrated decision-making approach for global supplier selection and order allocation to create an environment-friendly supply chain. Kybernetes, 52(8), 2649-2671.
Al-Sharrah, G. K., Hankinson, G., & Elkamel, A. (2006). Decision-making for petrochemical planning using multiobjective and strategic tools. Chemical Engineering Research and Design, 84(11), 1019-1030.
Chauhan, S. S., & Kotecha, P. (2016, November). Single level production planning in petrochemical industries using Moth-flame optimization. In 2016 IEEE region 10 conference (TENCON) (pp. 263-266). IEEE.
Chen, Z., Hammad, A. W., Waller, S. T., & Haddad, A. N. (2023). Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment. Automation in Construction, 150, 104847.
Ding, G., Kaliyaperumal, K., & Wang, X. (2023). A New Mathematical Model for Economic Ordering with Preventive Maintenance and Reworking in a Supply Chain. Discrete Dynamics in Nature and Society, 2023.
Duckwen, A. C., Moreno, M. S., Borio, D. O., & Bandoni, J. A. (2017). Optimal production planning and crude oil blending in a conventional oilfield. In Computer Aided Chemical Engineering (Vol. 40, pp. 1309-1314). Elsevier.
Ghorbani, M., Bahrami, M., & Arabzad, S. M. (2012). An integrated model for supplier selection and order allocation; using Shannon entropy, SWOT and linear programming. Procedia-Social and Behavioral Sciences, 41, 521-527.
Goyal, S. K., & Deshmukh, S. G. (1992). Integrated procurement-production systems: a review. European journal of operational research, 62(1), 1-10.
Hamta, N., Ehsanifar, M., & Biglar, A. (2023). Optimization in Supply Chain Design of Assembled Products: A Case Study of HEPCO Company. Iranian Journal of Management Studies, 16(1), 61-77.
Ishizaka, A., Khan, S. A., Kheybari, S., & Zaman, S. I. (2023). Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework. Annals of Operations Research, 324(1-2), 13-36.
Islam, S., Amin, S. H., & Wardley, L. J. (2021). Machine learning and optimization models for supplier selection and order allocation planning. International Journal of Production Economics, 242, 108315.
Kadambur, R., & Kotecha, P. (2016). Optimal production planning in a petrochemical industry using multiple levels. Computers & Industrial Engineering, 100, 133-143.
Karimi, S. K., Sadjadi, J., & Naini, S. G. J. (2022). A bi-objective production planning for a flexible supply chain solved using NSGA-II and MOPSO. International Journal of Industrial Engineering and Management, 13(1), 18-37.
Kim, J., Tak, K., & Moon, I. (2012). Optimization of procurement and production planning model in refinery processes considering corrosion effect. Industrial & engineering chemistry research, 51(30), 10191-10200.
Kwon, H., Do, T. N., & Kim, J. (2020). Comprehensive decision framework combining price prediction and production-planning models for strategic operation of a petrochemical industry. Industrial & Engineering Chemistry Research, 59(25), 11610-11620.
Kwon, H., Do, T. N., & Kim, J. (2022). Optimization-based integrated decision model for smart resource management in the petrochemical industry. Journal of Industrial and Engineering Chemistry, 113, 232-246.
Liu, Q., Huang, H., Zeng, Z., Chen, L., & Ming, J. (2023). An optimal supplier selection method for uncertain sustainable supply chains. European Journal of Industrial Engineering, 17(3), 431-459.
Meyr, H., Wagner, M., & Rohde, J. (2015). Structure of advanced planning systems. Supply chain management and advanced planning: Concepts, models, software, and case studies, 99-106.
Mina, H., Kannan, D., Gholami-Zanjani, S. M., & Biuki, M. (2021). Transition towards circular supplier selection in petrochemical industry: A hybrid approach to achieve sustainable development goals. Journal of Cleaner Production, 286, 125273.
Razmi, J., & Maghool, E. (2010). Multi-item supplier selection and lot-sizing planning under multiple price discounts using augmented ε-constraint and Tchebycheff method. International Journal of Advanced Manufacturing Technology, 49(1), 379.
Rezaei, A., Aghsami, A., & Rabbani, M. (2021). Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain. International Journal of System Assurance Engineering and Management, 12(6), 1036-1072.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
Shadkam, E. (2023). Investigating the multi-objective optimisation problem of supplier selection using the new COTOP hybrid method. International Journal of Business Performance and Supply Chain Modelling, 14(1), 79-105.
Sung, C., & Maravelias, C. T. (2007). An attainable region approach for production planning of multiproduct processes. AIChE Journal, 53(5), 1298-1315.
Tarei, P. K., Kumar, G., & Ramkumar, M. (2022). A Mean-Variance robust model to minimize operational risk and supply chain cost under aleatory uncertainty: A real-life case application in petroleum supply chain. Computers & Industrial Engineering, 166, 107949.
Ustun, O., & Demı, E. A. (2008). An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection. Omega, 36(4), 509-521.
Ustun, O., & Demı, E. A. (2008). An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection. Omega, 36(4), 509-521.
Varma, V. A., Reklaitis, G. V., Blau, G. E., & Pekny, J. F. (2007). Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities. Computers & Chemical Engineering, 31(5-6), 692-711.
Wang, C. N., Tsai, H. T., Ho, T. P., Nguyen, V. T., & Huang, Y. F. (2020). Multi-criteria decision making (MCDM) model for supplier evaluation and selection for oil production projects in Vietnam. Processes, 8(2), 134.
Zhao, H., Ierapetritou, M. G., Shah, N. K., & Rong, G. (2017). Integrated model of refining and petrochemical plant for enterprise-wide optimization. Computers & Chemical Engineering, 97, 194-207.
Islamic Azad University Rasht Branch ISSN: 2588-5723 E-ISSN:2008-5427
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Optimization Iranian Journal of Optimization Volume 15, Issue 4, 2023, 257-270 Research Paper |
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Online version is available on: https://sanad.iau.ir/journal/ijo
Optimizing Production Planning and Supplier Selection in Petrochemical Supply Chains
Reza Babazadeh1*, Mohammad Khalili 2 and Fatemeh Dadmand 3
1* Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
2 Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
3 Department of Management, Payame Noor University, Tehran, Iran
Revise Date: 23 December 2024 Abstract
Keywords: Production planning Supplier selection Petrochemical industry |
INTRODUCTION
In the petrochemical industry, making strategic decisions and planning for business is a challenging task, particularly in an ever-changing and uncertain market. The industry's sustainable profitability depends on its ability to provide a consistent supply of affordable raw materials, which is becoming increasingly important due to growing competition. To achieve this, companies must use sophisticated business models and precise optimization tools (Varma et al., 2007). The logistics and production planning involved in refinery and petrochemical processes present significant challenges (Kim et al., 2012). In the past, procurement and production activities were performed separately, which could lead to suboptimal performance and overall poor results (Goyal & Deshmukh 1992). To avoid this, it is essential to integrate these two activities since the ordered quantities of raw materials depend on the production quantities of the final product. So, Production planning is a crucial strategy for maintaining profit margins in the industry. It encompasses several decision-making issues, including resource management, product quality management, and inventory management based on market conditions (Kwon et al., 2020). Linear programming is a mathematical optimization technique that can be used to optimize production planning. Linear programming involves finding the best combination of inputs that yield the highest output while considering constraints such as production capacity and available resources (Akkerman, et al., 2010) and transportation (Niu, et al., 2017).
However, relying solely on the opinions of experts to establish the constraints and objectives of the linear programming model can lead to biased or incomplete results. To overcome this limitation, the Analytic Hierarchy Process (AHP) method can be used to incorporate the preferences and judgments of multiple stakeholders in the production planning process. AHP is a decision-making tool that allows decision-makers to evaluate and prioritize multiple criteria in a structured and systematic way (Saaty, 2008). AHP can help in determining the relative importance of each objective and constraint in the production planning process, which can then be used as input parameters in the linear programming model.
The problem of determining optimal production planning targets in process industries such as petrochemicals is challenging because it requires the integration of production planning, scheduling, purchasing and material requirement planning. In this study, the developed optimization model was carried out with the aim of managing production and procurement problems in an integrated manner. Considering the tactical decisions required for the timing and volume of purchases of multiple raw materials, where various products were delivered to customers. An AHP process was also used to evaluate the important criteria related to the selection of different suppliers. Then, the developed model was verified through a case study using the petrochemical industry. Finally, a sensitivity analysis was performed. The need for such study comes from the fact of integrating supplier selection decisions influence production planning decisions in the petrochemical industry. The main contributions of this study that differentiate it from the available works in the literature include:
· Developing an approach based on AHP and mathematical programming methods for the integration purpose
· Applying the proposed approach in the petrochemical industry.
RELATED LITERATURE
Studies in the fields of supplier selection and production planning can be divided into three categories: The first category includes studies that have focused on the issue of supplier selection. For example, Ghorbani et al. (2012) propose a two-phased model for supplier selection and order allocation, which is a multiple criteria decision-making problem affected by conflicting factors. In the first phase, suppliers are evaluated based on qualitative and quantitative criteria obtained from SWOT analysis, and the Shannon entropy method is used to calculate the weight of the criteria. In the second phase, an integer linear programming (ILP) model is used to allocate orders to the selected suppliers based on the results of the first phase, and Razmi and Maghool (2010) proposed a metaheuristic model to select suppliers and determine procurement plans under two types of discount offers. In this paper, a fuzzy bi-objective model is proposed for supplier selection and purchasing problem considering multiple items, multiple periods, capacity constraints, and budget limitations. The model considers different types of discounts, such as all-unit discounts, incremental discounts, and total business volume discounts, and different payment methods proposed by each supplier. The efficiency of each method is tested using an additive utility function offered by the decision maker. Wang et al. (2020) focus on supplier evaluation and selection in the oil industry. It proposes a Multi-Criteria Decision-Making (MCDM) model that integrates the Supply Chain Operation Reference (SCOR) model, Analytic Hierarchy Process (AHP), and the Data Envelopment Analysis (DEA) method. The SCOR model is used to determine evaluation criteria; AHP assigns weights to the criteria, and DEA ranks the suppliers. The study identifies the best suppliers based on the model's implementation and results. Islam et al. (2021) introduce a two-stage solution approach for supplier selection and order allocation planning, considering uncertain demand. It integrates a forecasting procedure with an optimization model. A novel Relational Regress or Chain method is proposed for demand forecasting. In the second stage, a multi-objective programming model is developed to identify suitable suppliers and order quantities. The study compares different forecasting methods and evaluates their impact on supplier selection and order allocation. Mina et al. (2021) focus on the selection of circular suppliers for collaboration in environmentally friendly operations. It integrates Multi-Criteria Decision-Making (MCDM) methods and a fuzzy inference system (FIS) to evaluate and rank suppliers in the circular supply chain. The fuzzy analytic hierarchy process (FAHP) method is used to determine criteria weights, and the fuzzy technique for order of preference by similarity to the ideal solution (FTOPSIS) is used to calculate scores for each supplier. The final ranking is determined using a FIS. Rezaei et al. (2021) address supplier selection and order allocation in a centralized supply chain, considering disruption and environmental risks. The study proposes a mixed-integer nonlinear programming model that incorporates risk reduction strategies and suppliers' reliability. The strategies include protected suppliers, back-up suppliers, additional capacity reservations, emergency stock, and geographical separation. The reliability of suppliers is considered using the failure mode and effects analysis technique. The proposed model is applied to a case study, and the results demonstrate the effectiveness of the risk reduction strategies. Ishizaka et al. (2023) focus on supplier selection in a closed-loop pharmaceutical supply chain. It presents a hybrid framework that combines the best-worst method (BWM) and the geometrical analysis for interactive aid (GAIA) plane. The BWM is used to evaluate supplier performance based on multiple criteria, and the GAIA plane visualizes the results. The study applies this methodology to evaluate the performance of five suppliers in the pharmaceutical industry. Liu et al. (2023) address the supplier selection problem under uncertainty in the public transport production industry. The study proposes a sustainable supplier selection model that incorporates economic, energy, and quality aspects. The multi-objective particle swarm optimization (MOPSO) method is employed to solve the problem. The model helps reduce supply chain risks and incorporates sustainability into supplier selection, considering design uncertainty and the environmental dimension. Ali et al. (2023) focus on global supplier selection and order allocation in an environment-friendly supply chain. The study proposes an integrated approach that combines fuzzy analytical hierarchy process (FAHP), fuzzy technique for order preference by similarity to the ideal solution (FTOPSIS), and multi-choice goal programming (MCGP). FAHP is used to calculate criteria weights, FTOPSIS evaluates supplier performance, and MCGP allocates optimal order quantities. A case study in the automotive industry demonstrates the effectiveness of the proposed approach.
The second category comprises studies that have addressed the problem of production planning. For example, Chauhan and Kotecha (2016) discuss in their article the use of evolutionary computation techniques to solve the combinatorial optimization problem of determining optimal production planning. The authors present a strategy that enables the efficient use of evolutionary algorithms for solving single-level production planning problems. They demonstrate the effectiveness of this strategy using the Moth-flame optimization technique and show that it consistently solves the production planning problem. Duckwen et al. (2017) present a new formulation for optimizing production planning and blending problems in conventional oil reservoirs with fixed topology. The proposed model considers the production of each well and the blending of crude oils to meet client sulfur requirements. It also takes into account the nonlinear behavior of well-flowing pressure over time and uses Generalized Disjunctive Programming and mixed integer nonlinear programming to formulate the problem. The numerical results of this model show that sulfur specification affects well production planning. Kadambur and Kotecha (2016) propose a mathematical formulation for determining more profitable production plans in a petrochemical industry using multiple levels. The benefits of the proposed formulation are demonstrated through eight case studies taken from the literature, which showed an improvement of up to 16.31% in profit. Kwon et al. (2020) present a new decision-making framework for resource and production planning in the petrochemical industry that undervalues uncertainty. Their model is developed using mixed integer linear programming to establish optimal operational strategies for a given inventory capacity. The proposed model is applied to a real petrochemical plant producing six different products, resulting in a 5.50% improvement in total sales and a 13.8% increase in operating profit compared to the Business as Usual (BAU) case. Sung and Maravelias (2007) present a novel approach for solving production planning problems for multiproduct processes using a mixed-integer programming (MIP) scheduling model. This model also presents a rolling horizon algorithm for generating detailed schedules if necessary. Zhao et al. (2017) propose a multi-period enterprise-wide mixed-integer nonlinear programming (MINLP) model to optimize the processing units in the refinery and ethylene plant simultaneously. Results show that the integrated approach improves overall profit compared to the traditional sequential approach, and Al-Sharrah et al. (2006) describe the application of multi-objective optimization tools to plan a mixed-integer model of a petrochemical industry. The main objectives of the optimization were to maximize economic gain while minimizing the risk of plant accidents. The resulting Pareto optimal solutions were analyzed using an economical strategic tool to make the final decision. The proposed procedure was applied to the petrochemical industry in Kuwait and was successful in defining a balanced petrochemical network with acceptable risk. In the study by Kwon et al. (2022), a decision-supporting platform for supply chain management in the petrochemical industry is proposed. The platform integrates various decision-supporting models to address challenges both vertically and horizontally in the supply chain. The platform effectively supports supply chain management, plant planning, and process operation strategy, resulting in improved business profits. Tarei et al. (2022) address the petroleum supply chain network design problem, considering various uncertainties. They propose a Mean-Variance robust optimization model to minimize both operational risk and supply chain cost simultaneously. The results show a trade-off between supply chain cost and risk, and the model provides insights into managing operational risks while considering risk aversion levels. Karimi et al. (2022) focus on the quantification of the positive effects of flexibility dimensions in production planning. They propose a bi-objective mathematical model and employ two metaheuristic algorithms to solve it. The results demonstrate that applying the flexible model leads to a reduction in costs. Hamta et al. (2023) present a case study of HEPCO Company, focusing on the optimization of the supply chain design for assembled products. By using a three-level model and an integer-linear mathematical model, they aim to reduce the total cost of the supply chain. The results demonstrate a significant reduction in costs compared to conventional methods. In the research by Ding et al. (2023), a mathematical model is proposed to optimize economic ordering with preventive maintenance in a supply chain. The model considers production quantity, inventory ordering, and preventive maintenance to minimize costs. The results indicate that longer preventive maintenance periods increase reliability but also lead to higher total costs.
The third category encompasses studies that have simultaneously tackled both supplier selection and production planning. For example, Chen et al. (2023) propose a novel multi-objective mixed integer linear programming model to address the undefined mathematical relations between supplier capacities, material supply shortages, and the impact of material delays on construction projects. The model integrates decisions regarding supplier selection, inventory management, order quantities, and material order splitting. It optimizes the trade-off between overall procurement cost and weighted lateness, considering material prices, supplier capacities, and resulting delays as fuzzy scenario-based parameters. The model's performance is validated through computation experiments on a numerical example. Sensitivity analysis demonstrates that considering high variations in uncertain supplier capacities leads to lower procurement costs and less significant delay impacts. Moreover, greater variations in uncertain material prices increase the total procurement cost by 55%, while greater variations in uncertain delay durations increase the weighted lateness by over 70%. The paper emphasizes the importance of accurate estimates for uncertain parameters and highlights the superiority of the proposed scenario-adjusted model in solving supplier selection and material purchasing problems in construction supply chains. Shadkam (2023) addresses the complex and conflicting objectives involved in selecting the best supplier in the supply chain. The paper introduces a new approach called COTOP, which is a hybrid method combining the cuckoo optimization algorithm (COA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed COTOP method is applied to the evaluation and selection of suppliers. The results demonstrate the efficiency of the algorithm in solving multi-objective problems and its ability to identify the Pareto frontier. The use of the COA enables the method to handle large-scale problems, while the TOPSIS method removes concerns regarding the number of objective functions. Ustun and Demı (2008) propose an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) for supplier selection, which considers both tangible and intangible factors and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP) while minimizing the total cost and total defect rate and balancing the total cost among periods. ANP is used to calculate priorities for each supplier based on 14 criteria involved in four clusters: benefits, opportunities, costs, and risks (BOCR). The priorities of suppliers are used as parameters for the first objective function, and the multi-objective and multi-period problem is solved. The proposed approach is applied to four different plastic molding firms working with a refrigerator plant, and the results show its effectiveness in real-life supplier selection problems.
These studies provide valuable insights and methodologies for addressing the challenges of supplier selection and production planning in various industries. They offer decision-support tools and optimization models that can assist practitioners in making informed decisions and improving operational efficiency. So, the current study uses the AHP process to evaluate the important criteria related to the selection of different suppliers and, based on these results, develops an optimization model for production planning in the petrochemical industry.
PROBLEM STATEMENT AND CASE STUDY
Urmia Petrochemical Plant has been built on a 110-hectare land in the southwestern part of Urmia, located 30 kilometers from the Urmia-Mahabad highway. The plant includes a 50-hectare industrial area, 60 hectares of green space, and wastewater treatment pools. The raw materials of the Urmia petrochemical unit are sulfur, urea and ammonia. These materials are converted into melamine, ammonium sulfate and sulfuric acid products during chemical processes. Urmia's petrochemical production and supply chain are presented in Fig. 1. After sending the orders, the surplus of final products produced is stored in the warehouses of the production unit. The considered planning horizon is one year, and each month is calculated as a time period, so we have 12 time periods. The proposed model is able to optimize the following decision variables: the purchase amount of each raw material, Production quantities of each of the final products, Inventory amount of each raw material, and Amount of each final product.
Fig. 1. Urmia's petrochemical production and supply chain
RESEARCH METHODOLOGY
The current research is a part of applied and developmental research. This research has been done in two phases. In the first phase, experts' opinions have been collected using the field method and questionnaire tool. In the second phase, modeling has been done to provide optimal production planning. The problem-solving approach in this research is presented in Fig. 2.
Fig. 2. Problem-solving approach
Phase 1: Calculate the weight vector of criteria via AHP
The AHP method is in accordance with the opinion of experts, which means that the questionnaire of paired comparisons should be provided to experts and experts specific to the subject. In this research, 7 evaluation criteria were extracted from different articles. These criteria include the following:
• Delivery performance: requires timely delivery of raw materials according to schedule
• How to pay: Pay the amount of the purchased materials in installments
• Payment policies: the possibility of discounts in the type of purchase of raw materials
• Product quality: the degree of quality of purchased materials
• Credibility and work records: commitment to the concluded contracts according to the credit of the supplier
• Geographical location: the time interval depends on the ratio of distance in the delivery of raw materials
• Clean producer: compliance with environmental principles and valid standards
A questionnaire of paired comparisons of evaluation criteria for ranking suppliers was prepared and provided to the managers of the production and purchasing departments and the general manager of Urmia Petrochemical Unit. Analysis and results of the AHP method were obtained using Expert Choice software.
Phase 2: Optimization of production planning
In this section, we have defined the symbols, parameters, and decision variables for an optimization model for production planning and then the objective function and constraints are described mathematically, providing a framework for implementing the model in practice.
Indices
| Index of raw materials | ||||||||||
| Index of suppliers | ||||||||||
| Index of final production materials | ||||||||||
| Index of time period | ||||||||||
| Index of distance discounts | ||||||||||
| Total Cost | ||||||||||
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| The purchase price of raw material i in period t | ||||||||||
| Purchase amount of raw material i in period t | ||||||||||
| The cost of ordering product i in period t | ||||||||||
| Production cost of product k in period t | ||||||||||
| Cost of maintaining product k in period t | ||||||||||
| Inventory amount of product K in period t | ||||||||||
| Production capacity in period t | ||||||||||
| Warehouse capacity for product k in period t | ||||||||||
| The production capacity of the final product k in the period t | ||||||||||
| Unit index of required storage space for raw material i | ||||||||||
| The amount of storage space for the final product k | ||||||||||
| Index of the storage space of the final product k | ||||||||||
| Amount of storage space for raw material i | ||||||||||
| The total demand of raw material i in period t | ||||||||||
| Demand quantity of final product k in period t | ||||||||||
| The minimum order quantity of supplier j in period t | ||||||||||
| The maximum order amount of supplier j in period t | ||||||||||
| Safety stock capacity for product k in period t | ||||||||||
| The amount of raw material i to be purchased in period t | ||||||||||
| The amount of the final product k in the period t | ||||||||||
| The amount of inventory of raw material i in period t | ||||||||||
| The amount of Purchase of raw material i from supplier j in period t | ||||||||||
| The amount of inventory of manufactured product k in period t | ||||||||||
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(10) |
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(11) |
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(12) |
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Table 1: results of AHP method
Cleaner production | Geographical location | Product quality | Payment policies | Payment method | Delivery performance | Criteria | |
0.126 | 0.228 | 0.210 | 0.258 | 0.038 | 0.040 | 0.099 | Weight |
Sulfur | Score | Ammonia | Score | Urea | Score |
Khangiran Refinery | 0.149 | Razi Petrochemical | 0.111 | Shiraz Petrochemical | 0.693 |
Tabriz Refinery | 0.177 | Pardis Petrochemical | 0.146 | Razi Petrochemical | 0.087 |
0.203 | Shiraz Petrochemical | 0.164 | Khorasan Petrochemical | 0.22 | |
Arak Refinery | 0.101 | Marvdashat Petrochemical | 0.123 | ||
Isfahan Refinery | 0.123 | 0.241 |
| ||
Tondguyan Refinery | 0.166 | Kermanshah Petrochemical | 0.215 | ||
Pars Jonoubi | 0.081 | ||||
The results of AHP suggest that the quality of the product and the supplier's location, credibility, and work experience are the most important factors to consider when selecting a supplier for the petrochemical production process. These findings can be used to guide the supplier selection process and ensure that the most important criteria are given the appropriate weight in the decision-making process.
The results of the AHP method indicate that Shiraz Petrochemical has been selected as the best supplier for urea raw material, Khorasan Petrochemical for ammonia, and Ilam Refinery for sulfur. The results of the hierarchical analysis for production planning are used as inputs for our model. In production planning, we encountered various logical constraints and tried to consider most of them. Constraints related to raw materials, demand, production capacity, available space for storing raw materials and final products, constraints on suppliers in supplying raw materials, and the requirement for safety inventory are included in this model. The objective function of the developed model in this study consists of the sum of the costs of raw material ordering, production costs, and material holding costs. The model was coded in GAMS software, and the results are presented in Tables 2 and 3.
Table 2: Results obtained for the objective function and decision variables
Optimal Results | objective function | ||||||||||
2565430000000 | Total cost (Rials) | ||||||||||
Optimum amounts of products | Optimum purchase quantities of each raw material |
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Crystal Melamine | Sulfuric Acid | Ammonium Sulfate | Ammonia | Sulfur | urea | Period | |||||
328400 | 4110200 | 3271340 | 7021080 | 14763080 | 5241740 |
| |||||
313600 | 4166000 | 3332000 | 6977600 | 14996000 | 6213600 |
| |||||
304200 | 4018000 | 3331000 | 6816200 | 15298000 | 4156200 |
| |||||
333000 | 4067000 | 3333000 | 6999000 | 14200000 | 6331000 |
| |||||
333000 | 4125800 | 3136000 | 6605000 | 15123600 | 4134000 |
| |||||
325360 | 4106200 | 3279000 | 7033360 | 14170400 | 6231160 |
| |||||
324380 | 4106200 | 3333000 | 6840380 | 14878400 | 4279280 |
| |||||
315760 | 4096400 | 3333000 | 7131760 | 15458800 | 6227560 |
| |||||
333000 | 3333000 | 3333000 | 6849000 | 14898000 | 4331000 |
| |||||
332420 | 4008200 | 3333000 | 6998420 | 14082400 | 5327520 |
| |||||
333000 | 4047400 | 3234000 | 6801000 | 15162800 | 6232000 |
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327320 | 4037600 | 3273200 | 6873720 | 14021600 | 4237120 |
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Optimum inventory of products | Optimum inventory quantities of each raw material |
| ||||||||
Crystal Melamine | Sulfuric Acid | Ammonium Sulfate | Ammonia | sulfur | urea | Period | ||||
5000 | 33400 | 5000 | 150000 | 0 | 0 |
| ||||
5000 | 5000 | 5000 | 150000 | 0 | 1000000 |
| ||||
15200 | 5000 | 102000 | 0 | 600000 | 0 |
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15000 | 5000 | 5000 | 0 | 0 | 1000000 |
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5000 | 5000 | 5000 | 0 | 600000 | 0 |
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5000 | 5000 | 50000 | 150000 | 0 | 1000000 |
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5000 | 5000 | 100000 | 0 | 0 | 0 |
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6180 | 5000 | 101000 | 150000 | 600000 | 1000000 |
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5000 | 5000 | 53000 | 0 | 600000 | 0 |
| ||||
15000 | 5000 | 5000 | 0 | 0 | 0 |
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5000 | 5000 | 5000 | 0 | 600000 | 1000000 |
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5000 | 5000 | 5000 | 0 | 0 | 0 |
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