Formulation and Solution Approach for Uncertain Multi-Objective Material Requirement Planning Problem with Multi-Mode Demand, Overtime Production, and Outsourcing Possibilities

Abstract

Material requirement planning is a type of production planning problems that is used to plan about a final product, its sub-assemblies, and its raw parts simultaneously by considering time phased demands of the final product. In this study a multi-product material requirement planning problem with limited manufacturing resources is considered. As an important novelty, a multi-mode demand strategy is considered in this problem where the total customers’ satisfaction degrees of the selected demand modes is maximized. Furthermore, three types of capacities such as regular, over time, and outsourcing capacities are considered for such system as another novelty. The problem is formulated as a bi-objective model to maximize total profit and total satisfaction degree of the customers simultaneously. To respect the uncertain nature of the problem, it is formulated in a belief-degree based uncertain form. This is for the first time in the literature of material requirement planning that this type of uncertainty is considered. The uncertain problem is converted to a crisp form using some techniques such as expected value model and chance constrained model. Then, a new hybrid form of the fuzzy programming approach is developed to solve the bi-objective crisp formulations. A case study from the petroleum industries of Iran is used to perform the required computational experiments. The required experiments are done, and possible comparisons are made on the obtained results. Furthermore, some managerial insights are given in order to be used in the production system of the case study. According to the obtained results, the proposed hybrid fuzzy programming approach is superior to existing approaches in at least 38 percent of the experiments.

Keywords

Material requirement planning / multi-mode demand / belief-degree based uncertainty / multi-objective optimization / fuzzy programming approach

References

[1] AbualigahL, Abd ElazizM, SumariP, GeemZ W, GandomiA H. Reptile search algorithm (rsa): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 2022, 191116158

[2] AbualigahL, YousriD, Abd ElazizM, EweesA A, Al-QanessM A, GandomiA H. Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 2021, 157107250

[3] AgushakaJ O, EzugwuA E, AbualigahL. Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 2022, 391114570

[4] AlavidoostM, BabazadehH, SayyariS. An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied Soft Computing, 2016, 40: 221-235

[5] AlfieriA, TolioT, UrgoM. A project scheduling approach to production and material requirement planning in manufacturing-to-order environments. Journal of Intelligent Manufacturing, 2012, 23: 575-585

[6] BadiI, BouraimaM B, StevićŽ, OloketuyiE A, MakindeO O. Optimizing vendor-managed inventory in multi-tier distribution systems. Spectrum of Operational Research, 2024, 1(1): 33-43

[7] BellmanR E, ZadehL A. Decision-making in a fuzzy environment. Management Science, 1970, 174B-141

[8] BiswasS, BiswasB, MitraK. A novel group decision making model to compare online shopping platforms. Spectrum of Decision Making and Applications, 2024, 2(1): 1-27

[9] BiswasS, BožanićD, PamučarD, MarinkovićD. A spherical fuzzy based decision making framework with Einstein aggregation for comparing preparedness of SMEs in quality 4.0. Facta Universitatis, Series: Mechanical Engineering, 2023, 21(3): 453-478

[10] ChenL, PengJ, ZhangB. Uncertain goal programming models for bicriteria solid transportation problem. Applied Soft Computing, 2017, 51: 49-59

[11] DalmanH. Uncertain programming model for multi-item solid transportation problem. International Journal of Machine Learning and Cybernetics, 2018, 9: 559-567

[12] DemirliK, YimerA D. Fuzzy scheduling of a build-to-order supply chain. International Journal of Production Research, 2008, 46(14): 3931-3958

[13] Díaz-MadroñeroM, MulaJ, JiménezM. Material requirement planning under fuzzy lead times. IFAC-PapersOnLine, 2015, 48(3): 242-247

[14] Díaz-MadroñeroM, MulaJ, JiménezM, PeidroD. A rolling horizon approach for material requirement planning under fuzzy lead times. International Journal of Production Research, 2017, 55(8): 2197-2211

[15] DingS. The α-maximum flow model with uncertain capacities. Applied Mathematical Modelling, 2015, 39(7): 2056-2063

[16] DingS, GaoY. The (σ, s) policy for uncertain multi-product newsboy problem. Expert Systems with Applications, 2014, 41(8): 3769-3776

[17] GaoY. Uncertain models for single facility location problems on networks. Applied Mathematical Modelling, 2012, 36(6): 2592-2599

[18] GaoY, KarS. Uncertain solid transportation problem with product blending. International Journal of Fuzzy Systems, 2017, 19: 1916-1926

[19] GhasemiM, BagherifardK, ParvinH, NejatianS, PhoK H. Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators. Applied Intelligence, 2021, 51(8): 5358-5387

[20] GuillaumeR, ThierryC, ZielińskiP. Robust material requirement planning with cumulative demand under uncertainty. International Journal of Production Research, 2017, 55(22): 6824-6845

[21] HasanatiN, PermatasariE, NurhasanahN, HidayatS. Implementation of material requirement planning (mrp) on raw material order planning system for garment industry. IOP Conference Series: Materials Science and Engineering, 2019, 528012064

[22] HnaienF, DolguiA, Ould LoulyM A. Planned lead time optimization in material requirement planning environment for multilevel production systems. Journal of Systems Science and Systems Engineering, 2008, 17: 132-155

[23] HuangX, DiH. Uncertain portfolio selection with background risk. Applied Mathematics and Computation, 2016, 276: 284-296

[24] HussainA, UllahK. An intelligent decision support system for spherical fuzzy Sugeno-Weber aggregation operators and real-life applications. Spectrum of Mechanical Engineering and Operational Research, 2024, 1(1): 177-188

[25] ImranR, UllahK, AliZ, AkramM. A multi-criteria group decision-making approach for robot selection using interval-valued intuitionistic fuzzy information and Aczel-Alsina bonferroni means. Spectrum of Decision Making and Applications, 2024, 1(1): 1-32

[26] JamshidiM, SaneiM, MahmoodiradA. An uncertain allocation model for data envelopment analysis: A case in the iranian stock market. Scientia Iranica, 2022, 29(6): 3434-3454

[27] Kovács G, Vizvári B (2018). A generalization of hunter’s bound to hypergraphs. Annals of Operations Research: 1–7.

[28] LaiY J, HwangC L. A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 1992, 49(2): 121-133

[29] LiR, ChenZ, LiH, TangY. A new distance-based total uncertainty measure in dempster-shafer evidence theory. Applied Intelligence, 2022, 52(2): 1209-1237

[30] LiT, LinP, SunG J, LiuH H. Application of fuzzy programming with recourse in material requirement planning problem. 2009 International Conference on Measuring Technology and Mechatronics Automation, 2009, 2: 546-549

[31] LiuD BUncertainty Theory, 2007

[32] LiuL, ZhangB, MaW. Uncertain programming models for fixed charge multi-item solid transportation problem. Soft Computing, 2018, 22: 5825-5833

[33] MirmohammadiS H, ShadrokhS, KianfarF. An efficient optimal algorithm for the quantity discount problem in material requirement planning. Computers & Operations Research, 2009, 36(6): 1780-1788

[34] MosallaeipourS, MahmoodiradA, NiroomandS, VizvariB. Simultaneous selection of material and supplier under uncertainty in carton box industries: A fuzzy possibilistic multi-criteria approach. Soft Computing, 2018, 22: 2891-2905

[35] MouD, ZhaoW, ChangX. A transportation problem with uncertain truck times and unit costs. Industrial Engineering and Management Systems, 2013, 12(1): 30-35

[36] Mula J, Poler R (2010). Fuzzy material requirement planning. In Production Engineering and Management Under Fuzziness: 39–57.

[37] MulaJ, PolerR, Garcia-SabaterJ P. Material requirement planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 2007, 158(7): 783-793

[38] MulaJ, PolerR, Garcia-SabaterJ P. Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 2008, 46(20): 5589-5606

[39] NiroomandS. A multi-objective based direct solution approach for linear programming with intuitionistic fuzzy parameters. Journal of Intelligent & Fuzzy Systems, 2018, 35(2): 1923-1934

[40] NooriS, FeylizadehM, BagherpourM, ZorriassatineF, ParkinR. Optimization of material requirement planning by fuzzy multi-objective linear programming. Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2008, 222(7): 887-900

[41] NurhajizahN, RakhmawatiF, SutarmanS. Optimization of material requirement planning using a linear program. International Conference on Sciences Development and Technology, 2022, 2: 1-13

[42] Nurprihatin F, Djajasoepena R, Vargas V B, Junior MMC, Vaezi, A (2023). Inventory lot sizing decisions for material requirements planning to minimize inventory costs. JIEMS (Journal of Industrial Engineering and Management Systems) 16(2).

[43] OyeladeO N, EzugwuA E S, MohamedT I, AbualigahL. Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm. IEEE Access, 2022, 10: 16150-16177

[44] RamyaG, ChandrasekaranM, ShankarE. Case study analysis of job shop scheduling and its integration with material requirement planning. Materials Today: Proceedings, 2019, 16: 1034-1042

[45] SahooS K, ChoudhuryB B, DhalP R. A bibliometric analysis of material selection using MCDM methods: Trends and insights. Spectrum of Mechanical Engineering and Operational Research, 2024, 1(1): 189-205

[46] SalehiM, MalekiH R, NiroomandS. A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment. Applied Intelligence, 2018, 48: 2137-2156

[47] SelimH, OzkarahanI. A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 2008, 36: 401-418

[48] SingP, RahamanM, SankarS P M. Solution of fuzzy system of linear equation under different fuzzy difference ideology. Spectrum of Operational Research, 2024, 1(1): 64-74

[49] SukkerdW, WuttipornpunT. Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations. Computers & Industrial Engineering, 2016, 97: 157-169

[50] TorabiS A, HassiniE. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 2008, 159(2): 193-214

[51] WangP, ZhuB, YuY, AliZ, AlmohsenB. Complex intuitionistic fuzzy dombi prioritized aggregation operators and their application for resilient green supplier selection. Facta Universitatis, Series: Mechanical Engineering, 2023, 21(3): 339-357

[52] WenM, GuoL, KangR, YangY. Data envelopment analysis with uncertain inputs and outputs. Journal of Applied Mathematics, 2014, 20141307108

[53] WuttipornpunT, YenradeeP. Development of finite capacity material requirement planning system for assembly operations. Production Planning & Control, 2004, 15(5): 534-549

[54] WuttipornpunT, YenradeeP. Performance of toc based finite capacity material requirement planning system for a multi-stage assembly factory. Production Planning and Control, 2007, 18(8): 703-715

[55] WuttipornpunT, YenradeeP, BeullensP, Van OudheusdenD. Finite capacity material requirement planning system for a multi-stage automotive-part assembly factory. ScienceAsia, 2006, 32: 307-317

[56] Zaedi NIAG, Majid H, Mokhtar A, Samah A A (2020). Material requirement planning using LFL, EOQ and PPB lot sizing technique. Academia of Fundamental Computing Research 1(2).

[57] ZimmermannH J. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1978, 1(1): 45-55

RIGHTS & PERMISSIONS

Systems Engineering Society of China and Springer-Verlag GmbH Germany