Journal articles: 'Production-Inventory Planning Problem'

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Lin, Jennifer, Henry C. J. Chao, and Peterson Julian. "Planning Horizon for Production Inventory Models with Production Rate Dependent on Demand and Inventory Level." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/961258.

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This paper discusses why the selection of a finite planning horizon is preferable to an infinite one for a replenishment policy of production inventory models. In a production inventory model, the production rate is dependent on both the demand rate and the inventory level. When there is an exponentially decreasing demand, the application of an infinite planning horizon model is not suitable. The emphasis of this paper is threefold. First, while pointing out questionable results from a previous study, we propose a corrected infinite planning horizon inventory model for the first replenishment cycle. Second, while investigating the optimal solution for the minimization problem, we found that the infinite planning horizon should not be applied when dealing with an exponentially decreasing demand. Third, we developed a new production inventory model under a finite planning horizon for practitioners. Numerical examples are provided to support our findings.

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Silva Filho, Oscar S. "Production Planning Problem with Inventory Boundary Affected by Demand Uncertainty." IFAC Proceedings Volumes 33, no. 17 (July 2000): 651–56. http://dx.doi.org/10.1016/s1474-6670(17)39480-6.

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Filho, Oscar S. Silva. "A CONSTRAINED STOCHASTIC PRODUCTION PLANNING PROBLEM WITH IMPERFECT INFORMATION OF INVENTORY." IFAC Proceedings Volumes 38, no. 1 (2005): 121–26. http://dx.doi.org/10.3182/20050703-6-cz-1902.01504.

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Gang, Duan, Chen Li, Li Yin-Zhen, Song Jie-Yan, and Akhtar Tanweer. "Optimization on Production-Inventory Problem with Multistage and Varying Demand." Journal of Applied Mathematics 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/648262.

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This paper addresses production-inventory problem for the manufacturer by explicitly taking into account multistage and varying demand. A nonlinear hybrid integer constrained optimization is modeled to minimize the total cost including setup cost and holding cost in the planning horizon. A genetic algorithm is developed for the problem. A series of computational experiments with different sizes is used to demonstrate the efficiency and universality of the genetic algorithm in terms of the running time and solution quality. At last the combination of crossover probability and mutation probability is tested for all problems and a law is found for large size.

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Liu, Guo Li, Jun Zhao, and Wei Wang. "Optimal Planning for Product Blending." Advanced Materials Research 339 (September 2011): 358–61. http://dx.doi.org/10.4028/www.scientific.net/amr.339.358.

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This paper deals with the product blending problem originating from the production system of a large typical oil refinery. A deterministic mixed integer programming model is proposed. The objective is to make an effective production-inventory plan for product blending unit (PBU) in order to meet the demand of product oil with no backlogging allowed and minimize the total costs, that is, the sum of purchasing, production, inventory and setup costs. The constraints related to material balance, different capacities and different production schemes are considered. A numerical example is subsequently provided to illustrate the broad applicability of the proposed model.

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Gligoric, Zoran, Cedomir Beljic, Branko Gluscevic, and Cedomir Cvijovic. "Underground Lead-Zinc Mine Production Planning Using Fuzzy Stochastic Inventory Policy / Planowanie Wydobycia Cynku I Ołowiu W Kopalniach Podziemnych Z Wykorzystaniem Podejścia Stochastycznego Z Elementami Logiki Rozmytej Do Określania Niezbędnego Poziomu Zapasów." Archives of Mining Sciences 60, no. 1 (March 1, 2015): 73–92. http://dx.doi.org/10.1515/amsc-2015-0006.

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Abstract Methodology for long-term underground lead-zinc mine planning based on fuzzy inventory theory is presented in this paper. We developed a fuzzy stochastic model of inventory control problem for planning lead-zinc ore production under uncertainty. The final purpose of this article is to find the optimal quantity of mined ore that should be stockpiled, in order to enable “feeding” of mineral processing plant in cases when the production in underground mine is interrupted, by using Possibilistic mean value of fuzzy number for defuzzing the fuzzy total annual inventory costs, and by using Extension of the Lagrangean method for solving inequality constrain problem. The different types of costs involved in mined ore inventory problems affect the efficiency of production scheduling. Dynamic nature of lead and zinc metal price is described by Ornstein-Uhlenbeck stochastic mean reverting process. The model is illustrated with a numerical example.

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Adhi Wicaksana, Bagus Ismail, and Erni Suparti. "Optimisasi Jumlah Produksi Menggunakan Model Newsboy dan Perencanaan Pengendalian Bahan Baku Menggunakan Material Requirement Planning (MRP)." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 2, no. 2 (June 25, 2019): 88. http://dx.doi.org/10.21070/prozima.v2i1.1918.

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A company have to do inventory control for available guarantying of material, component or item at the time to fulfill production schedule, and available guarantying of product become to consumer and take care of inventory at minimum condition. As object of research is CV. Cita Nasional located in Salatiga. Problems that exist in the CV. Cita Nasional often experience shortages of raw materials caused by internal and external factors. The approach taken to solve this problem by making a production plan using the Newsboy Problem because milk products including perishable product and raw material inventory planning (MRP). From the calculation results obtained the company must produce optimal demand every Monday to Sunday. While the raw material inventory plan using Economic Order Quantity (EOQ) method. Company decreased cost for fresh milk amounting to Rp.35.526.780 and decreased raw material expense cost for whey powder amounting to Rp. 22.573.650.

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Ogunwolu, Ladi, O. A. Alli, Chidi Onyedikam, and A. A. Sosimi. "Multi-Item Multi-Period Dynamic Capacity-Constrained Lot-Sizing Model with Parallel Machines and Fuzzy Demand." Advanced Materials Research 367 (October 2011): 627–38. http://dx.doi.org/10.4028/www.scientific.net/amr.367.627.

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Multi-item, multi-period production systems are prevalent in traditional production and distribution settings. A dynamic lot size production scheduling model (DLSPM) for multi-Production/inventory item multi-period production system with parallel machines is proposed in this paper. A mathematical framework that extends the DLSPM to multi-Production/inventory item-multi-period production planning constrained by storage space was built. The criteria of DLSPM explore optimal production schedule with the constraints of inventory, backlogs, production and demand to minimize the total inventory costs over finite planning horizon. Demand analogous to a typical production environment considered includes dynamic deterministic and fuzzy demand. The model was tested with both deterministic and fuzzy demand spread over ten years, for five equal planning periods, with a two Production/inventory item and two parallel machine test bed. From the various demand types, several iterations (sub problems) were generated and optimality condition was then verified. To capture the imprecision that is often inherent in the estimated future demand, demand was specified by fuzzy numbers and modeled using the triangular membership function distribution. Centre of gravity defuzzification scheme was used within finite intervals to obtain defuzzified demand. Tora Operations Research software was used to run the model using a test problem. Computational results vindicate the robustness and flexibility of the approach based on the quality of the solutions obtained.

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Wang, Neng Min, Zheng Wen He, Qiu Shuang Zhang, and Lin Yan Sun. "Single Item Lot Sizing Models with Bounded Inventory and Remanufacturing." Advanced Materials Research 102-104 (March 2010): 791–95. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.791.

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Dynamic lot sizing problem for systems with bounded inventory and remanufacturing was addressed. The demand and return amounts are deterministic over the finite planning horizon. Demands can be satisfied by manufactured new items, but also by remanufactured returned items. In production planning, there can be situations where the ability to meet customer demands is constrained by inventory capacity rather than production capacity. Two different limited inventory capacities are considered; there is either bounded serviceables inventory or bounded returns inventory. For the two inventory case, we present exact, polynomial time dynamic programming algorithm based on the idea of Teunter R, et al. (2006).

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Wu, Jing, Dan Zhang, Yang Yang, Gongshu Wang, and Lijie Su. "Multi-Stage Multi-Product Production and Inventory Planning for Cold Rolling under Random Yield." Mathematics 10, no. 4 (February 15, 2022): 597. http://dx.doi.org/10.3390/math10040597.

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This paper studies a multi-stage multi-product production and inventory planning problem with random yield derived from the cold rolling process in the steel industry. The cold rolling process has multiple stages, and intermediate inventory buffers are kept between stages to ensure continuous operation. Switching products during the cold rolling process is typically very costly. Backorder costs are incurred for unsatisfied demand while inventory holding costs are incurred for excess inventory. The process also experiences random yield. The objective of the production and inventory planning problem is to minimize the total cost including the switching costs, inventory holding costs, and backorder costs. We propose a stochastic formulation with a nonlinear objective function. Two lower bounds are proposed, which are based on full information relaxation and Jensen’s inequality, respectively. Then, we develop two heuristics from the proposed lower bounds. In addition, we propose a two-stage procedure motivated by newsvendor logic. To verify the performance of the proposed bounds and heuristics, computational tests are conducted on synthetic instances. The results show the efficiency of the proposed bounds and heuristics.

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Benkherouf, Lakdere, and Dalal Boushehri. "Optimal Policies for a Finite-Horizon Production Inventory Model." Advances in Operations Research 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/768929.

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This paper is concerned with the problem of finding the optimal production schedule for an inventory model with time-varying demand and deteriorating items over a finite planning horizon. This problem is formulated as a mixed-integer nonlinear program with one integer variable. The optimal schedule is shown to exist uniquely under some technical conditions. It is also shown that the objective function of the nonlinear obtained from fixing the integrality constraint is convex as a function of the integer variable. This in turn leads to a simple procedure for finding the optimal production plan.

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Salviano, Oscar, and Frederic Andres. "Chance-constrained LQG production planning problem under partially observed forward-backward inventory systems." IFAC-PapersOnLine 53, no. 2 (2020): 10828–35. http://dx.doi.org/10.1016/j.ifacol.2020.12.2869.

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Popescu, Liviu. "Applications of Optimal Control to Production Planning." Information Technology And Control 49, no. 1 (March 25, 2020): 89–99. http://dx.doi.org/10.5755/j01.itc.49.1.23891.

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In this paper we solve a problem of optimization and production planning using the optimal control methods and Pontryagin Maximum Principle. We propose an economic model and find an optimal plan of production for n products, to ensure the required quantity at specified delivery data with minimum cost of inventory and production. We prove that the economic system is not controllable, in the sense that we cannot reach any final stock quantity. Finally, we justify this construction with a numerical example.

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Smith, Neale R., Jorge Limón Robles, and Leopoldo Eduardo Cárdenas-Barrón. "Optimal Pricing and Production Master Planning in a Multiperiod Horizon Considering Capacity and Inventory Constraints." Mathematical Problems in Engineering 2009 (2009): 1–15. http://dx.doi.org/10.1155/2009/932676.

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We formulate and solve a single-item joint pricing and master planning optimization problem with capacity and inventory constrains. The objective is to maximize profits over a discrete-time multiperiod horizon. The solution process consists of two steps. First, we solve the single-period problem exactly. Second, using the exact solution of the single-period problem, we solve the multiperiod problem using a dynamic programming approach. The solution process and the importance of considering both capacity and inventory constraints are illustrated with numerical examples.

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Seyedhosseini, S. M., and S. M. Ghoreyshi. "An Integrated Model for Production and Distribution Planning of Perishable Products with Inventory and Routing Considerations." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/475606.

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In many conventional supply chains, production planning and distribution planning are treated separately. However, it is now demonstrated that they are mutually related problems that must be tackled in an integrated way. Hence, in this paper a new integrated production and distribution planning model for perishable products is formulated. The proposed model considers a supply chain network consisting of a production facility and multiple distribution centers. The facility produces a single perishable product that is storable only for predetermined periods. A homogenous fleet of vehicles is responsible for delivering the product from facility to distribution centers. The decisions to be made are the production quantities, the distribution centers that must be visited, and the quantities to be delivered to them. The objective is to minimize the total cost, where the trip minimization is considered simultaneously. As the proposed formulation is computationally complex, a heuristic method is developed to tackle the problem. In the developed method, the problem is divided into production submodel and distribution submodel. The production submodel is solved using LINGO, and a particle swarm heuristic is developed to tackle distribution submodel. Efficiency of the algorithm is proved through a number of randomly generated test problems.

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Zeddam, Besma, Fayçal Belkaid, and Mohammed Bennekrouf. "An Efficient Approach for Solving Integrated Production and Distribution Planning Problems." International Journal of Applied Logistics 10, no. 2 (July 2020): 25–44. http://dx.doi.org/10.4018/ijal.2020070102.

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The increasing customer expectations for customized products of high quality in short delays and the worldwide competition in terms of quality and costs have pushed industries to implement new strategies to manage their supply chain decisions. In this context, the integrated planning is becoming the most dominant over the operational research field because of its efficiency and its ability to cover the different aspects of the problem. Production routing problem is one of the problems of the integrated planning that is of interest in optimizing simultaneously production, inventory, and distribution planning. This paper has the purpose of developing two mono-objective models for the production-routing problem; one of them minimizes the total costs, while the other one minimizes the energy consumed by the production system. Finally, a bi-objective model is proposed to combine the two objectives mentioned previously using the LP-metric method in the context of a sustainable supply chain. Experimental results are also presented and discussed through the different scenarios.

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Khanra, S., and K. S. Chaudhuri. "A production-inventory model for a deteriorating item with shortage and time dependent demand." Yugoslav Journal of Operations Research 21, no. 1 (2011): 29–45. http://dx.doi.org/10.2298/yjor1101029k.

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In the present article, a production-inventory model is developed over a finite planning horizon where the demand varies linearly with time. The machine production rate is assumed to be finite and constant. Shortages in inventory are allowed and are completely backlogged. The associated constrained minimization problem is numerically solved. Sensitivity analysis is also presented for the given model.

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Wei, Mingyuan, Hao Guan, Yunhan Liu, Benhe Gao, and Canrong Zhang. "Production, Replenishment and Inventory Policies for Perishable Products in a Two-Echelon Distribution Network." Sustainability 12, no. 11 (June 10, 2020): 4735. http://dx.doi.org/10.3390/su12114735.

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The research on production, delivery and inventory strategies for perishable products in a two-echelon distribution network integrates the production routing problem (PRP) and two-echelon vehicle routing problem (2E-VRP), which mainly considers the inventory and delivery sustainability of perishable products. The problem investigated in this study is an extension of the basic problems, and it simultaneously optimizes production, replenishment, inventory, and routing decisions for perishable products that will deteriorate over the planning horizon. Additionally, the lead time has been considered in the replenishment echelon, and the unit inventory cost varying with the inventory time is considered in the inventory management. Based on a newly designed model, different inventory strategies are discussed in this study: old first (OF) and fresh first (FF) strategies both for the first echelon and second echelon, for which four propositions to model them are proposed. Then, four valid inequalities, including logical inequalities, a ( ℓ , S , W W ) inequality, and a replenishment-related inequality, are proposed to construct a branch-and-cut algorithm. The computational experiments are conducted to test the efficiency of valid inequalities, branch-and-cut, and policies. Experimental results show that the valid inequalities can effectively increase the relaxed lower bound by 4.80% on average and the branch-and-cut algorithm can significantly reduce the computational time by 58.18% on average when compared to CPLEX in small and medium-sized cases. For the selection of strategy combinations, OF–FF is suggested to be used in priority.

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Wahyuni, Wahyuni, Muhammad Latiful Fatih, Raissa Muthia Syahrani Hsb, Sakina Sakina, and Suhairi Suhairi. "Analisis Studi Kelayakan Bisnis Dalam Aspek Produksi." VISA: Journal of Vision and Ideas 2, no. 1 (February 8, 2022): 126–34. http://dx.doi.org/10.47467/visa.v2i1.960.

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This article aims to determine the feasibility of a business in Production analysis. In this case the author uses a business feasibility analysis through the production aspect, which means the activities that arise when an idea in a planned business has shown opportunities and illustrates the advantages in terms of marketing. The problem of the production process and the operation process consists of the selection of production strategies, product selection and planning, quality planning, technology selection, production capacity planning, factory layout planning, layout planning, production quantity planning, inventory management and product quality control. Keywords: Business Feasibility Study, Production, Production Aspects,

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Wahyuni, Wahyuni, Muhammad Latiful Fatih, Raissa Muthia Syahrani Hsb, Sakina Sakina, and Suhairi Suhairi. "Analisis Studi Kelayakan Bisnis Dalam Aspek Produksi." VISA: Journal of Vision and Ideas 2, no. 2 (February 8, 2022): 126–34. http://dx.doi.org/10.47467/visa.v2i2.960.

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This article aims to determine the feasibility of a business in Production analysis. In this case the author uses a business feasibility analysis through the production aspect, which means the activities that arise when an idea in a planned business has shown opportunities and illustrates the advantages in terms of marketing. The problem of the production process and the operation process consists of the selection of production strategies, product selection and planning, quality planning, technology selection, production capacity planning, factory layout planning, layout planning, production quantity planning, inventory management and product quality control. Keywords: Business Feasibility Study, Production, Production Aspects,

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Chekoubi, Zakaria, Wajdi Trabelsi, Nathalie Sauer, and Ilias Majdouline. "The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic." Sustainability 14, no. 20 (October 20, 2022): 13563. http://dx.doi.org/10.3390/su142013563.

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Sustainable supply chains depend on three critical decisions: production, inventory management, and distribution with reverse flows. To achieve an effective level of operational performance, policymakers must consider all these decisions, especially in Closed-Loop Supply Chains (CLSCs) with remanufacturing option. In this research paper, we address the Integrated Production-Inventory-Routing Problem with Remanufacturing (IPIRP-R) of returned End-Of-Life (EOL) products. The aim behind solving this optimization problem is to minimize conjointly the total manufacturing, remanufacturing, setup, inventory, and routing costs over the planning horizon. A two-phase decomposition heuristic is developed to solve the model iteratively. Our study finds its originality in the fact of jointly optimizing the Capacitated Lot-Sizing Problem with Remanufacturing (CLSP-R) option and the Vehicle Routing Problem with Simultaneous Pick-up and Delivery (VRPSPD) in a single framework. Numerical results showed that our solution approach provides good solutions regarding small and medium-scale size instances under acceptable computational time, especially for problems occurring with significant manufacturing and remanufacturing costs under relatively low pickup requests.

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Sun, Xiaochen, Mengmeng Wu, and Fei Hu. "Two-Period Inventory Control with Manufacturing and Remanufacturing under Return Compensation Policy." Discrete Dynamics in Nature and Society 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/871286.

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As an effective way of decreasing production cost, remanufacturing has attracted more and more attention from firms. However, it also brings many difficulties to firms, especial when firms remanufacture products which they produce. A primary problem for the case is how to acquire the used product sold by the firm itself. In this paper, we consider a return compensation policy for acquiring used product from customers. Under this policy, the return quantity of used product is a proportion of demand. We study an inventory replenishment and production planning problem for a two-period inventory system with dependent return and demand. We formulate the problem into a three-stage stochastic programming problem, where the firm needs to make decisions on the replenishment quantity of new raw material inventory in each period and the production quantities of manufacturing and remanufacturing ways. We give the optimal production policy of manufacturing and remanufacturing ways for the realized demand and prove the objective function for each stage to be concave in the inventory replenishment quantity. Moreover, we prove that the basic inventory policy is still optimal for each period and give the analytical conditions of the optimal inventory levels which are unrelated to acquisition price. Finally, we investigate numerical studies to analyze managerial insights.

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Biswas, Pablo, and Bhaba Sarker. "Operational planning of supply chains in a production and distribution center with just-in-time delivery." Journal of Industrial Engineering and Management 13, no. 2 (June 17, 2020): 332. http://dx.doi.org/10.3926/jiem.3046.

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Purpose: A supply chain consists of raw material suppliers, manufacturers and retailers where inventory of raw materials and finished goods are involved, respectively. Therefore, it is important to find optimal solutions, which are beneficial for both supplier, manufacturer and retailer.Design/methodology/approach: This research focuses on a semi-continuous manufacturing facility by assuming that the production of succeeding cycle starts immediately after the production of preceding cycle. In reality, the inventory of a supply chain system may not be completely empty. A number of products may be left over after the deliveries are made. These leftover inventories are added to the next shipment after the production of required amount to makeup a complete batch for shipment. Therefore, it is extremely important to search for an optimal strategies for these types production facilities where leftover finished goods inventory remains after the final shipment in a production cycle. Considering these scenarios, an inventory model is developed for an imperfect matching condition where some finished goods remains after the shipments.Findings: Based on the previous observation, this research also considers a single facility that follows JIT delivery and produces multiple products to satisfy customers’ demand. For this problem a rotational cycle model is developed to optimize the facility operations. Both problems are categorized as mixed integer non-linear programming problems which are to be solved to find optimum number of orders, shipments and rotational cycle policy for multiple products. Also, this solution will lead to estimate the optimum production quantity and minimum total system cost.Research limitations: This research considers the supply chain based on manufacturers point of view and it does not consider the transportation cost associated with supply chain. Next study will be focused on issues with joint decision making, information sharing, and transportation decision.Practical implications: This study will help the managers of refinery and paper industries in making their operation smooth by applying optimizing techniques and robust decision making.Originality/value: Based on the literature, no research was found on continuous production system supply chain and its optimization with JIT delivery. This research will definitely provide a direction for such problem to the researchers.

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Moin, Noor Hasnah, and Titi Yuliana. "Three-Phase Methodology Incorporating Scatter Search for Integrated Production, Inventory, and Distribution Routing Problem." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/304981.

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This paper proposes the use of scatter search metaheuristic to solve an integrated production, inventory, and distribution routing problem. The problem is based on a single production plant that produces a single product that is delivered toNgeographically dispersed customers by a set of homogenous fleet of vehicles. The objective is to construct a production plan and delivery schedule to minimize the total costs and ensuring each customer’s demand is met over the planning horizon. We assumed that excess production can be stored at the plant or at customer’s sites within some limits, but stockouts due to backordering or backlogging are not allowed. Further testing on a set of benchmark problems to assess the effectiveness of our method is also carried out. We compare our results to the existing metaheuristic algorithms proposed in the literature.

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Aziz, Ridwan Al, Himangshu Kumar Paul, Touseef Mashrurul Karim, Imtiaz Ahmed, and Abdullahil Azeem. "Modeling and optimization of multi-layer aggregate production planning." Journal of Operations and Supply Chain Management 11, no. 2 (November 17, 2018): 1. http://dx.doi.org/10.12660/joscmv11n2p1-15.

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Aggregate production planning has attracted the attention of researchers for quite a long time now; and the continued researches depict the significance and scope for improvement in this arena. Here, a multi-product, multi-level and multi-period model has been formulated to identify the required aggregate plan for meeting the forecast demand, by regulating production rates, inventory, workforce, various production costs, and other controllable variables. Several new contributing factors, such as costs related to material handling, raw material inventory and worker training have been included in the objective function and constraint equations to make the model more realistic. A case study has been presented for a cosmetics and toiletries manufacturer in Bangladesh. Eventually, the problem has been solved using Genetic Algorithm and Particle Swarm Optimization approach. The solution illustrates that the model can be applied in a real world scenario to enhance productivity and profitability.

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Hidayah, Nur, and Asnil Aidah. "Application of Optimal Control Theory to Inventory Problems That Are Increasing at PT. Canang Indah." ZERO: Jurnal Sains, Matematika dan Terapan 6, no. 1 (September 23, 2022): 16. http://dx.doi.org/10.30829/zero.v6i1.12453.

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This journal discusses optimal control of inventory problems that are increasing. The inventory in the company is needed, to meet every incoming demand. Inventory in minimum quantity can result in shortage of inventory. But inventory quantity maximum can result in losses, due to the minimum demand. The purpose of this research is to determine the level of optimal inventory in PT. Canang Indah. Using the optimal control theory model and analyzing the stability of the dynamic differential equation, to find the optimal inventory level. Obtained optimal inventory levels achieve stability at the time . For the planning length of 12 months includes: raw material inventory (logs sengon and rambung), production (finished materials in process) and finished particle board products that are in the warehouse. From this research optimal control theory can be applied in PT. Canang Indah to optimize inventory on the problem of increasing inventory.

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Pratiwi, Annisa Indah, Afif Hakim, and Ragil Yuli Santosa. "DEVELOPMENT OF OPERATION PROCESS MAP AND ANALYSIS OF INVENTORY CONTROL BASED ON MATERIAL REQUIREMENT PLANNING IN ASSEMBLY LINE." Journal of Industrial Engineering and Halal Industries 1, no. 1 (June 1, 2020): 30–38. http://dx.doi.org/10.14421/jiehis.1800.

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Production planning and control is a theory which discusses all the activities that take place in a production process which includes product planning, costs, production processes, scheduling, forecasting demand and many other products. Planning the amount of inventory that will be owned company is one of the problems often faced by companies. Especially when the inventory is one important factor that can support the company's production processes and help meet customer demand. For companies that have strategies make to stock, inventory can have a major impact on the pricing of the product or the company's finances. Appropriate inventory management can be one of the keys to minimize and optimize the company's costs to be incurred. Determination lot size in MRP is a complex and difficult problem. Lot size is defined as a quantity stated in the order acceptance and delivery of orders in the MRP. Decisions about the size of the lot and when production is very important because it involves the use of labor and equipment are economical. Based on observations there are 13 components which are arranged in the trolley assembly process and are divided into 4 work stations in the production process. After making a map process operations per each work station, the cycle time for 1800 seconds is obtained. Based on data analysis about inventory costs for each component, it can be seen that the calculation using the EOQ method is the method that produces the calculation results with the largest value of Rp. 111,000,500, - then for the L4L and POQ methods produce the calculation results that have the same value of Rp. 16,200,000. Then the decision taken is to choose the L4L method because with this technique can produce exactly how many raw materials needed.

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Niknamfar, Amir Hossein. "Multi-objective production-distribution planning based on vendor-managed inventory strategy in a supply chain." Industrial Management & Data Systems 115, no. 6 (July 13, 2015): 1086–112. http://dx.doi.org/10.1108/imds-03-2015-0073.

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Purpose – The production-distribution (P-D) problems are two critical problems in many industries, in particular, in manufacturing systems and the supply chain management. In previous researches on P-D planning, the demands of the retailers and their inventory levels have less been controlled. This may lead into huge challenges for a P-D plan such as the bullwhip effects. Therefore, to remove this challenge, the purpose of this paper is to integrate a P-D planning and the vendor-managed inventory (VMI) as a strong strategy to manage the bullwhip effects in supply chains. The proposed P-D-VMI aims to minimize the total cost of the manufacturer, the total cost of the retailers, and the total distribution time simultaneously. Design/methodology/approach – This paper presents a multi-objective non-linear model for a P-D planning in a three-level supply chain including several external suppliers at the first level, a single manufacturer at the second level, and multi-retailer at the third level. A non-dominated sorting genetic algorithm and a non-dominated ranking genetic algorithm are designed and tuned to solve the proposed problem. Then, their performances are statistically analyzed and ranked by the TOPSIS method. Findings – The applicability of the proposed model and solution methodologies are demonstrated under several problems. A sensitivity analysis indicates the market scale and demand elasticity have a substantial impact on the total cost of the manufacturer in the proposed P-D-VMI. Originality/value – Although the P-D planning is a popular approach, there has been little discussion about the P-D planning based on VMI so far. The novelty comes from developing a practical and new approach that integrates the P-D planning and VMI.

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He, Longfei, Huangli Peng, Zhanwen Niu, Haili Lu, and Xiangli Xie. "Optimal Production Planning for Manufacturing Systems with Instantaneous Stock-Dependent Demand and Imperfect Yields." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/271902.

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We consider an EPL model like manufacturing system in presence of production imperfectness and stock-demand dependence simultaneously. During the production process, the system can evolve from in-control state into out-of-control state at any random time, after which the defective items will be generated likely causing quantity loss. Meanwhile, the market demand rate is instantaneously dependent on the timely holding inventory. The manufacturer has to determine his production run length and cycle time by taking into account possible imperfect production, stock-dependent demand, and inventory holding capacity bound. We empolder a model to capture this problem and develop computational algorithm to solve it. We further conduct numerical studies to validate our model and solving method. Sensitivity analyses are reported to show the effect of parameters on the system performance.

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Malindzakova, Marcela, Patrik Garaj, Jarmila Trpčevská, and Dusan Malindzak. "Setting MRP Parameters and Optimizing the Production Planning Process." Processes 10, no. 4 (April 1, 2022): 690. http://dx.doi.org/10.3390/pr10040690.

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This article describes a methodical framework that combines two specific methods of Lean management, namely the ABC method and the MRP planning method. The article further argues that combining the ABC inventory method with subsequent MRP planning is beneficial if the combination is implemented in practice. To demonstrate the benefits, the framework is tested using a case study company. The presented case-study problem is to reduce the number of changeover downtimes in the environment of an engineering production company. The researched company deals with the problem of setting up production lines in a way to minimize the number of downtimes within one work shift. Within the solution, four possible variants of the production plan are presented. By combining the ABC and MRP methods, up to four changeovers can be saved, which in financial terms represents a saving of about EUR 450,000 per year.

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Rina Handayani and Manaek Hamonangan Sihombing. "Planning of Optimal Raw Materials Inventory Using the Marcov Chain Method." Journal of Science Technology (JoSTec) 4, no. 1 (December 10, 2022): 151–61. http://dx.doi.org/10.55299/jostec.v4i1.157.

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Inventory is an important issue to control. Both finished goods and raw material inventories are the same with ingredient raw for the production process . no seldom our find problem where company no To do good control _ in management inventory that results in needs customer no fulfilled . Ray Traso Jaya is company that manufactures and sells paving block products , bataco , riol concrete and pits wind where in the production process experience disturbance in carry out the production process caused level management supply ingredient raw sand and cement that are not optimal as a result company difficulty for Fulfill needs customers who are fluctuating . Formulas problem in study this is " How " determine supply ingredient raw materials and costs supply ingredient optimal raw material at Sinar Traso Jaya?”. Study this aim for determine supply ingredient optimal raw material and cost supply ingredient optimal raw . Method analysis used _ in research this is method Analysis Calculation With Markov Chain. Where is the result study this company can adapt costs required at the time level booking with ingredient fluctuating standard _ with notice stock ingredient available raw . _ That is if ingredient raw sand initial 0 or stock empty on level ordering 170.638 kg the optimal cost is at Rp. 1300,000 , if ingredient cement raw 0 or stock empty on level booking as much as 57,617 kg with the optimal cost is IDR 1,300,000.

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Sadiq, Shireen S., Adnan Mohsin Abdulazeez, and Habibollah Haron. "Solving multi-objective master production schedule problem using memetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 938. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp938-945.

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A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service.

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Kibouka, Guy-Richard, Donatien Nganga-Kouya, Jean-Pierre Kenne, Victor Songmene, and Vladimir Polotski. "Production Planning of a Failure-Prone Manufacturing System under Different Setup Scenarios." Journal of Applied Mathematics 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4930817.

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This paper presents a control problem for the optimization of the production and setup activities of an industrial system operating in an uncertain environment. This system is subject to random disturbances (breakdowns and repairs). These disturbances can engender stock shortages. The considered industrial system represents a well-known production context in industry and consists of a machine producing two types of products. In order to switch production from one product type to another, a time factor and a reconfiguration cost for the machine are associated with the setup activities. The parts production rates and the setup strategies are the decision variables which influence the inventory and the capacity of the system. The objective of the study is to find the production and setup policies which minimize the setup and inventory costs, as well as those associated with shortages. A modeling approach based on stochastic optimal control theory and a numerical algorithm used to solve the obtained optimality conditions are presented. The contribution of the paper, for industrial systems not studied in the literature, is illustrated through a numerical example and a comparative study.

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Wang, Neng Min, Zheng Wen He, and Lin Yan Sun. "Study on the Bounded Inventory Model with Returning Items and Disposals." Advanced Materials Research 102-104 (March 2010): 920–25. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.920.

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This paper addresses a dynamic lot sizing problem with mixed returning items and disposals and bounded inventory. The returning items mean that returns are in good enough condition to re-enter the inventory supply stream. The producing, the holding, backlogging and disposals cost functions are concave cost functions. Furthermore, backlogging level and inventory level at each period is limited. The goal is to minimize the total cost of production, inventory holding/backlogging and disposal. A dynamic programming algorithm with complexity O(T3) is developed to solve this model, where T is the length of the planning horizon.

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Sukmawati, Cici Emilia, and Ayu Ratna Juwita. "Forecasting Model Number Production of Car Spare Parts at PT. Showa Katou Indonesia with Arima Method." JURNAL SISFOTEK GLOBAL 12, no. 1 (March 30, 2022): 65. http://dx.doi.org/10.38101/sisfotek.v12i1.478.

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In the case of single part production planning at PT Showa Katou Indonesia The problem is the production plan is only a production schedule, the schedule is made only two times (morning and evening) in a day. The schedule is created after the Production Planning Inventory Control (PPIC) contacts the customer to ascertain what the customer needs. After knowing what the customer needs, a production schedule and planning are made. The impact of this erratic production schedule causes loss of production time because if there is no demand then nothing is done by workers and the machine stops production because they have to wait for an erratic production schedule. Another impact is the absence of stock in the warehouse and delays in delivery because they are only waiting for the production schedule from PPIC and waiting for finished goods to be produced. To reduce the bad impact, it is necessary to forecast production planning with data mining methods to help these problems. The method used is the ARIMA method with the model (p,d,q) (1,1,1). The results of testing using tools and manual testing showed significant values with MAD = 52.45, MSE = 3917.84, MAPE = 0.05.

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Ben Abid, Taycir, Omar Ayadi, and Faouzi Masmoudi. "An Integrated Production-Distribution Planning Problem under Demand and Production Capacity Uncertainties: New Formulation and Case Study." Mathematical Problems in Engineering 2020 (April 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/1520764.

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In this study, we propose to solve a biobjective tactical integrated production-distribution planning problem for a multisite, multiperiod, multiproduct, sea-air intermodal supply chain network under uncertainties. Two random parameters are considered simultaneously: product replenishment orders and production capacity, which are modelled via a finite set of scenarios, using a two-stage stochastic approach. A corresponding mathematical model is developed, coded, and solved using the LINGO 18.0 software optimisation tool. This model aims to simultaneously minimise the total costs of production in both regular and overtime, inventory, distribution, and backordering activities and maximise the customer satisfaction level over the tactical planning horizon. The AUGMECON technique is applied to handle with the multiobjective optimisation. The applicability and the performance of the proposed model are tested through a real-life case study inspired from a medium-sized Tunisian textile and apparel company. Sensitivity analysis on stochastic parameters and managerial insights for the studied supply chain network are argued based on the empirical findings.

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KUMAR, G. MOHAN, and A. NOORUL HAQ. "HYBRID GENETIC — ANT COLONY ALGORITHMS FOR SOLVING AGGREGATE PRODUCTION PLAN." Journal of Advanced Manufacturing Systems 04, no. 01 (June 2005): 103–11. http://dx.doi.org/10.1142/s021968670500059x.

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It is necessary for the management of any industry to workout an intermediate range plan also known as aggregate production plan, consistently with the long range policies and resources allocated by long range decisions. It is a procedure of translating the expected demand and production capacity of the available facilities into future manufacturing plans for a family of products. It includes decisions on production quantity, work force and inventory to workout a low cost product and timely delivery. Ant colony optimization algorithm finds its extensive application in solving job shop scheduling, assignment problems, transportation problems, etc. Genetic algorithms are proposed to solve the problem, already by the authors. In this paper, an attempt is made to solve an aggregate production-planning problem for obtaining an effective solution using ant colony algorithm. Also a hybrid algorithm that combines genetic algorithm and ant colony algorithm is proposed and its effectiveness over the models developed using genetic algorithms and ant colony algorithm is also analyzed.

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Fatma, Erika. "MINIMALISASI BIAYA SIMPAN DAN BIAYA SETUP PADA MULTIPLE PRODUK: SIMULASI DENGAN CAPACITATED LOT SIZING PROBLEM." Jurnal Riset Manajemen dan Bisnis (JRMB) Fakultas Ekonomi UNIAT 4, no. 2 (June 12, 2019): 205–14. http://dx.doi.org/10.36226/jrmb.v4i2.260.

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Lot sizing problem in production planning aims to optimize production costs (processing, setup and holding cost) by fulfilling demand and resources capacity costraint. The Capacitated Lot sizing Problem (CLSP) model aims to balance the setup costs and inventory costs to obtain optimal total costs. The object of this study was a plastic component manufacturing company. This study use CLSP model, considering process costs, holding costs and setup costs, by calculating product cycle and setup time. The constraint of this model is the production time capacity and the storage capacity of the finished product. CLSP can reduce the total production cost by 4.05% and can reduce setup time by 46.75%. Keyword: Lot size, CLSP, Total production cost.

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Zhou, Yancong, Xudong Guo, and Xiaochen Sun. "Acquisition Pricing and Inventory Decisions on Dual-Source Spare-Part System with Final Production and Remanufacturing." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8038045.

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The life spans of durable goods are longer than their warranty periods. To satisfy the service demand of spare parts and keep the market competition advantage, enterprises have to maintain the longer inventory planning of spare parts. However, how to obtain a valid number of spare parts is difficult for those enterprises. In this paper, we consider a spare-part inventory problem, where the inventory can be replenished by two ways including the final production order and the remanufacturing way. Especially for the remanufacturing way, we consider the acquisition management problem of used products concerning an acquisition pricing decision. In a multiperiod setting, we formulate the problem into a dynamic optimization problem, where the system decisions include the final production order and acquisition price of used products at each period. By stochastic dynamic programming, we obtain the optimal policy of the acquisition pricing at each period and give the optimal policy structure of the optimization problem at the first period. Then, a recursion algorithm is designed to calculate the optimal decisions and the critical points in the policy. Finally, the numerical analyses show the effects of demand information and customer’s sensitive degree on the related decisions and the optimal cost.

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Biazzi, Jorge Luiz. "Aggregate planning for probabilistic demand with internal and external storage." Journal of Operations and Supply Chain Management 11, no. 1 (June 15, 2018): 37. http://dx.doi.org/10.12660/joscmv11n1p37-52.

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This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits.

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Xi, Jia, and Ping Ba Sha. "Research on Optimization of Inventory Management Based on Demand Forecasting." Applied Mechanics and Materials 687-691 (November 2014): 4828–31. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4828.

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Demand forecasting is the basis of the inventory management. Aiming at the problem of subjective forecasting method, we use quadratic exponential smoothing method to establish the mathematical model, to forecast sales volume of product A in every month in 2013. And based on demand forecasting, we put forward ABC classification management method to solve the inventory management issues. The research result of this paper has important implications in improving the inventory management level for many enterprises.Demand Forecasting and Inventory ManagementInventory management is an important part of enterprise management, and it directly affects the business situation of enterprises. A reasonable inventory can significantly enhance the comprehensive competitiveness of enterprises; too much or too little inventory settings would have a bad impact on the business, and some company even bog down because of inventory problems companies bogged down because of inventory problems [1-2]. To do inventory management, what should we do in the first step. The answer is demand forecast. When business scale reaches a certain level, it would need strict, systematic demand forecasting. The more accurate the demand forecasting is, the more accurate inventory planning would be, and more favorable for business enterprises.Few companies are able to be completely in accordance with the order production, and the vast majority of businesses are not waiting for orders after arrival, then determine how much raw material and manpower needed, and how to arrange production. Because it often takes a long production cycle, and no one is willing to wait a month to buy a bag of washing powder. Successful companies always make accurate predictions for product demand, and then put into production according to forecasting [3]. Due to their more accurate predictions, they can often carry out a reasonable plan and inventory management. Inventory forecasting, its essence is demand forecasting [4]. Demand forecasting provides important information for inventory management such as inventory amount, lead time, inventory turns. Demand forecasting is based on research and statistics, to make a scientific and reasonable inference for product demand. Product demand generally is within a certain period, certain market range, the number of consumers’ demand for a product. Demand forecasting results can help companies determine the amount of raw material inventory and products, and provide enterprise continuous production of raw materials needed, save liquidity and reduce inventory costs, improving the comprehensive competitiveness of enterprises.Product Demand Forecasting Model

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Suparno, Suparno, and Anik Rufaidah. "Analisis Perbandingan Metode Moving Average dan Exponential Smoothing untuk Meramalkan Permintaan Produk Turning Pada CV. Gavra Perkasa." Jurnal Optimalisasi 7, no. 2 (October 26, 2021): 201. http://dx.doi.org/10.35308/jopt.v7i2.4311.

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CV. Gavra Perkasa is a manufacturing industry with the finished product in the form of turning. The number of sales transactions will affect the inventory of raw materials. The problem with the company is that the company's ability to predict the amount of raw materials that must be available in the following month is not optimal due to fluctuating demand. This problem will have an impact on planning inventory inventory during the production process and recording inventory usage to optimize storage costs. This study aims to plan inventory demand in the future and to plan inventory use using the Moving average and Exponential smoothing methods. These two methods are used because the results of the demand data plot show a stationary data pattern. The accuracy of the forecasting results was analyzed with the smallest MSE, MAD, and MAPE results. Based on the analysis results, the 5-month MA method is the most accurate with prediction results in 2017 of 57000 units with MAD, MSE, and MAPE values of 3684.21, 24345260, and 0.07.

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Mirzapour Al-e-Hashem, S. M. J., A. Baboli, S. J. Sadjadi, and M. B. Aryanezhad. "A Multiobjective Stochastic Production-Distribution Planning Problem in an Uncertain Environment Considering Risk and Workers Productivity." Mathematical Problems in Engineering 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/406398.

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A multi-objective two stage stochastic programming model is proposed to deal with a multi-period multi-product multi-site production-distribution planning problem for a midterm planning horizon. The presented model involves majority of supply chain cost parameters such as transportation cost, inventory holding cost, shortage cost, production cost. Moreover some respects as lead time, outsourcing, employment, dismissal, workers productivity and training are considered. Due to the uncertain nature of the supply chain, it is assumed that cost parameters and demand fluctuations are random variables and follow from a pre-defined probability distribution. To develop a robust stochastic model, an additional objective functions is added to the traditional production-distribution-planning problem. So, our multi-objective model includes (i) the minimization of the expected total cost of supply chain, (ii) the minimization of the variance of the total cost of supply chain and (iii) the maximization of the workers productivity through training courses that could be held during the planning horizon. Then, the proposed model is solved applying a hybrid algorithm that is a combination of Monte Carlo sampling method, modified -constraint method and L-shaped method. Finally, a numerical example is solved to demonstrate the validity of the model as well as the efficiency of the hybrid algorithm.

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Kulyk, Anatolii. "APPLICATION OF THE (R, S) MODEL WHEN PLANNING STOCKS IN AN INDUSTRIAL ENTERPRISE." Scientific Notes of Ostroh Academy National University, "Economics" Series 1, no. 26(54) (September 29, 2022): 37–42. http://dx.doi.org/10.25264/2311-5149-2022-26(54)-37-42.

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One of the current problems in the enterprise management system is the creation and replenishment of stocks, the organization of continuous monitoring and prompt supply of raw materials. Using mathematical models, you can solve the problem of forming the optimal production program of the enterprise, investing in production, as well as to carry out strategic planning of enterprise development. In each case, it is important to build a model that describes the system under study, and on its basis to find the optimal ratio between costs and benefits of the selected level of stocks and determine what size stocks are sufficient. Correct inventory management allows you to maintain the production process and meet the needs of consumers in a timely manner. The purpose of this study is to build a model with a system with fully backordering, which is the optimal value of the stock level and the amount of average quarterly costs, which corresponds to the optimal value. Research methods are based on a probabilistic model of management approach for the enterprise. The method of building a model of inventory management system with periodic inspections was used, which found the average values of the intensity of demand for raw materials, unit costs and the cost of order verification. Depending on the initial values of the parameters, the optimal reserve of the quarterly stock is calculated, which corresponds to the given demand intensity. The study concludes on the value of the optimal level of stocks, as well as the average cost for the period corresponding to the regulatory level of stock.

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LEE, SHINE-DER, SHU-CHUAN LAN, and CHIN-MING YANG. "ECONOMIC PRODUCTION LOT SIZING MODEL WITH STOCHASTIC DEMAND." Asia-Pacific Journal of Operational Research 31, no. 03 (June 2014): 1450015. http://dx.doi.org/10.1142/s0217595914500158.

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We consider the extended economic production quantity (EPQ) problem when demand follows a Poisson process in a production system. A fixed lot sizing policy is implemented to minimize fluctuation of workload, and to smooth production planning and inventory control. The considered costs include setup cost, inventory carrying cost, and shortage cost when demand cannot be satisfied from stock. The main contributions of this paper are two folds. We develop and analyze the extended EPQ model. Under some mild conditions, the expected cost per unit time can be shown to be convex. Via computational experiments, we demonstrate that, in comparison with classical EPQ model, the average reduction of expected cost is significant when demand is random and the proposed model is used to determine lot sizing policy. Our computational tests have also illustrated the impact of various parameters on the expected cost model and the lot sizing policy.

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Badiane, Abdoulaye, Sylvie Nadeau, Jean-Pierre Kenné, and Vladimir Polotski. "Optimizing production while reducing machinery lockout/tagout circumvention possibilities." Journal of Quality in Maintenance Engineering 22, no. 2 (May 9, 2016): 188–201. http://dx.doi.org/10.1108/jqme-04-2014-0015.

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Purpose – The optimization of production imposes a review of facility maintenance policies. Accidents during maintenance activities are frequent, sometimes fatal and often associated with deficient or absent machinery lockout/tagout. Lockout/tagout is often circumvented in order to avoid what may be viewed as unnecessary delays and increased production costs. To reduce the dangers inherent in such practice, the purpose of this paper is to propose a production strategy that provides for machinery lockout/tagout while maximizing manufacturing system availability and minimizing costs. Design/methodology/approach – The joint optimization problem of production planning, maintenance and safety planning is formulated and studied using a stochastic optimal control methodology. Hamilton-Jacobi-Bellman equations are developed and studied numerically using the Kushner approach based on finite difference approximation and an iterative policy improvement technique. Findings – The analysis leads to a solution that suggests increasing the “comfortable” inventory level in order to provide the time required for lockout/tagout activities. It is also demonstrated that the optimization of lockout/tagout procedures is particularly important when the equipment is relatively new and the inventory level is minimal. Research limitations/implications – This paper demonstrates that it is possible to integrate production, maintenance and lockout/tagout procedures into production planning while keeping manufacturing system cost objectives attainable as well as ensuring worker safety. Originality/value – This integrated production and maintenance policy is unique and complements existing procedures by explicitly accounting for safety measures.

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Mifdal, Lahcen, Zied Hajej, and Sofiene Dellagi. "Joint Optimization Approach of Maintenance and Production Planning for a Multiple-Product Manufacturing System." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/769723.

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This paper deals with the problem of maintenance and production planning for randomly failing multiple-product manufacturing system. The latter consists of one machine which produces several types of products in order to satisfy random demands corresponding to every type of product. At any given time, the machine can only produce one type of product and then switches to another one. The purpose of this study is to establish sequentially an economical production plan and an optimal maintenance strategy, taking into account the influence of the production rate on the system’s degradation. Analytical models are developed in order to find the production plan and the preventive maintenance strategy which minimizes sequentially the total production/inventory cost and then the total maintenance cost. Finally, a numerical example is presented to illustrate the usefulness of the proposed approach.

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Sohrabi, Babak, and MohammadReza Sadeghi Moghadam. "Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm." International Journal of Applied Industrial Engineering 1, no. 1 (January 2012): 38–54. http://dx.doi.org/10.4018/ijaie.2012010104.

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The present study, using genetic algorithm, tries to improve material flow management in supply chain. Consequently, in this paper, an integrated supply-production and distribution planning (SPDP) is considered despite the fact that in most of the Iranian industrial firms, SPDP is done independently. The effective use of integrated SPDP not only enhances the performance rather decreases inventory cost, holding cost, shortage cost and overall supply chain costs. A quantitative mathematical model is used to the problem articulation, and then it is solved by applying heuristic genetic algorithm (GA) method. The proposed model with genetic algorithm could provide the best satisfactory result with the minimum cost. The reliability test was carried by comparing the model results with that of the amount of variables.

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Tshinangi, Kapya, Olufemi Adetunji, and V. S. S. Yadavalli. "A Lot-Sizing Model for a Multi-State System with Deteriorating Items, Variable Production Rate, and Imperfect Quality." International Journal of Mathematical, Engineering and Management Sciences 7, no. 5 (October 1, 2022): 730–48. http://dx.doi.org/10.33889/ijmems.2022.7.5.048.

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Conventional production systems assume that during the manufacturing processes, machines operate without breakdown over an infinite planning horizon and manufacture only products of good quality. Imperfect production processes as a result of machine degradation are common in manufacturing. This paper deals with a problem that concerns the modelling and evaluation of the performance of a multi-state production system that is subject to degradation and its effect on lot sizing. Here, we consider that the cycle starts with a particular production rate until a point when the inventory reaches a certain level after which the failure mode is activated due to the deterioration of certain components, leading to a reduction in the production rate in order to ensure the continuity of supply until the maximum inventory level is reached. Production then stops to restore the machine and the cycle starts again. We have assumed that the rate at which inventory deteriorates is exponential and that demand is constant. A numerical example is used to illustrate the model application, followed by sensitivity analysis. This paper contributes to lot sizing in the area of machine reliability by considering a production system in a degraded state with a non-increasing production rate for deteriorating items with imperfect quality and partial backlogging.

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Firanda, Gusti, and Rika Rosnelly. "THE APPLICATION OF THE TRIPLE EXPONENTIAL SMOOTHING METHOD IN THE PREDICTION OF WAREHOUSE INVENTORY." Jurnal Informatika Kaputama (JIK) 7, no. 1 (January 1, 2023): 47–56. http://dx.doi.org/10.59697/jik.v7i1.6.

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Prediction is an attempt to predict or predict something that will happen in the future by utilizing various relevant information in previous (historical) times through a scientific method. One of the applications of prediction in the business world is to predict future warehouse inventory based on the company's usage in the previous period. Prediction (Forecasting) is very helpful in planning and making decisions in an activity. Analysis is very important in learning, because research becomes more precise and focused. The problem faced by the company at this time is that the company has difficulty predicting warehouse inventory in the next period because there is no system that can be used by the company to predict. This predictive information will be very useful for companies in the process of planning clean water production to customers. In overcoming the problems mentioned above, a system is needed that can process data on goods that go in and out of the warehouse every period to get a prediction of warehouse inventory for the next period. To get optimal forecasting results, one method that can be used is the Triple Exponential Smoothing method. Triple Exponential Smoothing Method This method is a forecast method proposed by Brown, using quadratic equations .

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Journal articles on the topic 'Production-Inventory Planning Problem'

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