Introduce a new mathematical approach to inventory management in production processes under constrained conditions

• Autorzy: Darmawan Dani , Kurniady Dedy Achmad , Komariah Aan , Tamam Badrud , Muda Iskandar , Pallathadka Harikumar

Identyfikatory

DOI

10.2478/fcds-2022-0023

• Języki publikacji: EN

Abstrakty

EN

Nowadays, some manufacturing organizations may well face production restrictions. For example, in case the number of products goes up, the company might not be capable of producing all products. As a consequence, the company may face backlogging. In the meanwhile, in case the demand for products rises, the given company may experience a restricted capacity to react to that kind of demand properly; thus, it will suffer backlogging. Over the course of this study, that kind of company facing the mentioned circumstances is considered. To meet those exceeded demands, companies would be forced to purchase some products from outside. Thus, the study's primary aim is to define and calculate the optimum make and buy a number of products so that overall inventory cost is reduced and optimized. To do so, a model is proposed referred to as the make-with-buy model. This model is designed and solved by exact solution software in the based branch and bound method. The results of the study confirm the feasibility and efficiency of this method and demonstrate that this model can be applied to lessen the overall inventory costs, including maintenance, order, setup, and purchasing costs, and also the total costs of products.

Słowa kluczowe

EN

production capacity; make-with-buy; inventory cost; production restrictions

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2022
• Tom: Vol. 47, No. 4
• Strony: 421--431
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Darmawan Dani

• Direktorat Jenderal Pendidikan Vokasi, Ministry of Education Culture Research and Technology. Indonesia

• https://orcid.org/0000-0002-8374-9169

autor

Kurniady Dedy Achmad

• Educational Administration Program, Universitas Pendidikan Indonesia. Indonesia

• https://orcid.org/0000-0003-1120-8133

autor

Komariah Aan

• Educational administration Program, Universitas Pendidikan Indonesia. Indonesia

• https://orcid.org/0000-0003-2269-3592

autor

Tamam Badrud

• Pascasarjana Universitas Wiralodra, Indonesia

• https://orcid.org/0000-0003-2269-3592

autor

Muda Iskandar

• Department of Doctoral Program, Faculty Economic and Business, Universitas Sumatera Utara, Medan, Indonesia

• https://orcid.org/0000-0001-6478-9934

autor

Pallathadka Harikumar

• Manipur International University, Imphal, Manipur, India

• https://orcid.org/0000-0002-0705-9035

Bibliografia

• [1] Baruffaldi G., Accorsi R., Manzini R., Warehouse management system customization and information availability in 3pl companies: a decision-support tool, Industrial Management & Data Systems, 119, 2, 2018, 251-273.
• [2] Ben-Daya M., The economic production lot-sizing problem with imperfect production processes and imperfect maintenance, International Journal of Production Economics, 76, 3, 2002, 257-264.
• [3] Cárdenas-Barrón L.E., Economic production quantity with rework process at a single-stage manufacturing system with planned backorders, Computers & Industrial Engineering, 57, 3, 2009, 1105-1113.
• [4] Cherchata A., Popovychenko I., Andrusiv U., Gryn V., Shevchenko N., Shkuropatskyi O., Innovations in logistics management as a direction for improving the logistics activities of enterprises, Management Systems in Production Engineering, 30, 1, 2022, 9-17.
• [5] Chiu Y.S.P., Chiu S.W., Determining the materials procurement policy based on the economic order/production models with backlogging permitted, The International Journal of Advanced Manufacturing Technology, 30, 1, 2006, 156-165.
• [6] Dolgui A., Ivanov D., Sethi S P., Sokolov B., Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications, International Journal of Production Research, 57, 2, 2019, 411-432.
• [7] Gharaei A., Karimi M., Shekarabi S.A.H., An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: the outer approximation with equality relaxation and augmented penalty algorithm, Applied Mathematical Modelling, 69, 2019, 223-254.
• [8] Goli A., Malmir B., A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18, 1, 2020, 140-152.
• [9] Goli A., Khademi-Zare H., Tavakkoli-Moghaddam R., Sadeghieh A., Sasanian M., Malekalipour Kordestanizadeh R., An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study, Network: computation in neural systems, 32, 1, 2021, 1-35.
• [10] Hariga M.A., Economic production-ordering quantity models with limited production capacity, Production Planning & Control, 9, 7, 1998, 671-674.
• [11] Goyal S.K., Gopalakrishnan M., Production lot sizing model with insufficient production capacity, Production Planning & Control, 7, 2, 1996, 222-224.
• [12] Mishra U., Wu J.Z., Sarka B., Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions, Journal of Cleaner Production, 279, 2021, 123699.
• [13] Mahapatra A.S., N Soni H., Mahapatra M.S., Sarkar B., Majumder S., A continuous review production-inventory system with a variable preparation time in a fuzzy random environment, Mathematics, 9, 7, 2021, 747.
• [14] Pahlevan S.M., Hosseini S.M.S., Goli A., Sustainable supply chain network design using products’ life cycle in the aluminum industry, Environmental Science and Pollution Research, 2021, 1-25.
• [15] Pentico D.W., Drake M.J., A survey of deterministic models for the EOQ and EPQ with partial backordering, European journal of operational research, 214, 2, 2011, 179-198.
• [16] Pasandideh S.H.R., Niaki S.T.A., A genetic algorithm approach to optimize a multi-products EPQ model with discrete delivery orders and constrained space, Applied Mathematics and Computation, 195, 2, 2008, 506-514.
• [17] Paul S.K., Chowdhury P., A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19, International Journal of Physical Distribution & Logistics Management, 51, 2, 2020, 104-125.
• [18] Saha E., Ray P.K., Modelling and analysis of inventory management systems in healthcare: A review and reflections, Computers & Industrial Engineering, 137, 2019, 106051.
• [19] Soleymanfar V.R., Makui A., Taleizadeh A.A., Tavakkoli-Moghaddam R., Sustainable EOQ and EPQ models for a two-echelon multi-product supply chain with return policy, Environment, Development and Sustainability, 24, 4, 2022, 5317-5343.
• [20] Taleizadeh A.A., Askari R., Konstantaras I., An optimization model for a manufacturing-inventory system with rework process based on failure severity under multiple constraints, Neural Computing and Applications, 34, 6) 2022, 4221-4264.
• [21] Taleizadeh A.A., Niaki S.T.A., Najafi A.A., Multi-product single-machine production system with stochastic scrapped production rate, partial backordering and service level constraint, Journal of Computational and Applied Mathematics, 233, 8, 20101834-1849.
• [22] Teerasoponpong S., Sopadang A., Decision support system for adaptive sourcing and inventory management in small-and medium-sized enterprises, Robotics and Computer-Integrated Manufacturing, 73, 2022, 102226.