Optimasi Produksi dan Distribusi di Perusahaan Gas Cair dengan Menggunakan Linear Programming dan Algoritma Cross Entropy

Banyak industri telah mengalami peningkatan atau perbaikan pada sistem produksi dan distribusi melalui global supply chain management. Sampai saat ini pun, masalah integrasi optimasi sistem produksi dan distribusi demi perbaikan kedua sistem tersebut masih menjadi tantangan bagi dunia industri, termasuk industri kimia. Oleh sebab itu, dilakukan penelitian optimasi kedua sistem tersebut. Untuk pengoptimalan sistem produksi, dilakukan pendekatan dengan linear programming. Sedangkan untuk pengoptimalan sistem distribusi menggunakan algoritma cross entropy. Pengoptimalan sistem distribusi dilakukan dengan mempertimbangkan jumlah permintaan dan jumlah produk yang dihasilkan oleh pihak produksi. Hasil yang didapatkan setelah penggunaan pendekatan ini adalah terdapat peningkatan profit yang cukup signifika

Building Decision Support Systems in Excel for Production and Distribution Planning: A Case Study

We develop a decision support system in Microsoft Excel that integrates production and distribution for a manufacturer of natural fiber-based products in North America. The production and distribution of the company’s products were optimized using a linear programming model, implemented in Excel. The spreadsheet dynamically adjusts the formulation to reflect the user’s current requirements, solves the optimization model in the background, and generates detailed managerial reports. In addition, it allows users to conduct what-if analyses by varying the number of plants and warehouses. It demonstrates the ability of a Linear Programming Model run on an Excel platform to provide the firm with an optimized production plan resulting in significant, cost savings since implementation

An Integrated Strategy for a Production Planning and Warehouse Layout Problem: Modeling and Solution Approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for their products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported

Truckload Shipment Planning and Procurement

This dissertation presents three issues encountered by a shipper in the context of truckload transportation. In all of the studies, we utilize optimization techniques to model and solve the problems. Each study is inspired from the real world and much of the data used in the experiments is real data or representative of real data. The first topic is about the freight consolidation in truckload transportation. We integrate it with a purchase incentive program to increase truckload utilization and maximize profit. The second topic is about supporting decision making collaboration among departments of a manufacturer. It is a bi-objective optimization model. The third topic is about procurement in an adverse market. We study a modification of the existing procurement process to consider the market stochastic into marking decisions. In all three studies, our target is to develop effectively methodologies to seek optimal answers within a reasonable amount of time

A decentralized production and distribution planning model in an uncertain environment

[EN] Distributed Decision Making (DDM) is a discipline of decision theory in which decision making power is distributed among several decision making units. Supply Chain planning problems usually involve multiple decision makers, making DDM highly suitable for realistic modelling. Furthermore, due to the complexity and dynamism of supply chain environments, accounting for uncertainty is important when modelling a supply chain planning problem. This chapter contributes to existing knowledge on the one hand with a brief literature review of DDM systems developed in the recent past. On the other hand, it contributes a proposed DDM coordination mechanism for a supply chain planning problem with two distributed decision makers, in a multi-echelon context, with multiple product levels. The DDM system’s performance is evaluated under demand uncertainty by applying a fuzzy approach. Computational results show that the proposed distributed model closely approximated the optimal solutions generated by the centralised model, strengthening the evidence for DDM’s applicability to real problems. Finally, the fuzzy approach is shown to be a useful tool for decision makers in evaluating risk in their supply chain planning decisionsThis work has been funded by the Universitat Politècnica de València project: ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12)Hegeman, J.; Peidro Payá, D.; Alemany Díaz, MDM.; Díaz-Madroñero Boluda, FM. (2014). A decentralized production and distribution planning model in an uncertain environment. Studies in Fuzziness and Soft Computing. 313:317-353. https://doi.org/10.1007/978-3-642-53939-8_14S317353313Cao, D., Chen, M.: Capacitated plant selection in a decentralized manufacturing environment: A bilevel optimization approach. Eur. J. Oper. Res. 169, 97–110 (2006)Davis, T.: Effective supply chain management. Sloan Manag. Rev. 34, 35–46 (1993)Frayret, J.: A multidisciplinary review of collaborative supply chain planning. In: Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4414–4421, San Antonio, TX, USA (2009)Gaudreault, J., Frayret, J., Pesant, G.: Distributed search for supply chain coordination. Comput. Ind. 60, 441–451 (2009)Jiménez, M., Arenas, M., Bilbao, A., Rodríguez, M.: Linear programming with fuzzy parameters: an interactive method resolution. Eur. J. Oper. Res. 177, 1599–1609 (2007)Jung, H., Chen, F., Jeong, B.: Decentralized supply chain planning framework for third party logistics partnership. Comput. Ind. Eng. 55, 348–364 (2008)Lu, S., Lau, H., Yiu, C.: A hybrid solution to collaborative decision-making in a decentralized supply chain. J. Eng. Technol. Manag. 29, 95–111 (2012)Nishi, T., Konishi, M., Ago, M.: A distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line. Comput. Chem. Eng. 31, 1205–1221 (2007)Park, Y.: An integrated approach for production and distribution planning in supply chain management. Int. J. Prod. Res. 43(6), 1205–1224 (2005)Peidro, D., Mula, J., Poler, R., Verdegay, J.: Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets Syst. 160, 2640–2657 (2009)Schneeweiss, C.: Invited review: distributed decision making-a unified approach. Eur. J. Oper. Res. 150, 237–252 (2003)Stadtler, H.: A framework for collaborative planning and state-of-the-art. OR Spectr. 31, 5–30 (2009)Walther, G., Schmid, E., Spengler, T.: Negotiation-based coordination in product recovery networks. Int. J. Prod. Econ. 111, 334–350 (2008)Wernz, C., Deshmukh, A.: Multiscale decision-making: bridging organizational scales in systems with distributed decision-makers. Eur. J. Oper. Res. 202, 828–840 (2010)Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965