When to use Integer Programming Software to solve large multi-demand multidimensional knapsack problems: a guide for operations research practitioners

AbstractIn maximizing the profits to be obtained the company needs optimal production planning. The plan considers the resources of the company. PT XYZ is a furniture company. This research focuses on optimizing production planning on the manufacture of door products at PT. XYZ. There are several types of products issued in: D1 type door, D2 type door, D3 type door, and D4 type door. Production planning at PT. XYZ can be seen as an integer program model, which is a method related to optimizing resources to increase profits. Optimization is done by determining the amount of production for each type and each calculating existing resources. The solution search for this model is done by the Branch and Bound algorithm. Based on the calculation results using QM software for Windows, the amount corresponding to production is obtained by using Branches and Bound giving an increase of 36.5% compared to the acquisition of PT. XYZ before. Keywords: Branch and Bound Algorithms, Integer Programming,Optimization AbstrakDalam memaksimalkan keuntungan yang akan diperoleh perusahaan perlu adanya perencanaan produksi yang optimal. Perencanaan tersebut mempertimbangkan ketersediaan sumber daya pada perusahaan. PT XYZ merupakan perusahaan yang bergerak di bidang furniture. Penelitian ini fokus kepada pengoptimalan perencanaan produksi pada pembuatan produk pintu di PT.XYZ. Terdapat beberapa jenis produk yang diproduksi di antaranya: Pintu tipe D1, Pintu tipe D2, Pintu tipe D3, dan Pintu tipe D4. Perencanaan produksi di PT.XYZ ini dapat dikatakan sebagai model program integer, karena semua variabel menghendaki hasilnya berupa bilangan bulat. Program tersebut berhubungan dengan pengoptimalan ketersediaan sumber daya bertujuan untuk memaksimalkan keuntungan. Pengoptimalan yang dilakukan yaitu dengan menentukan jumlah produksi untuk masing-masing tipe serta mempertimbangkan semua ketersediaan sumber daya yang ada. Pencarian solusi untuk model ini dilakukan dengan algoritma Branch and Bound. Berdasarkan hasil perhitungan menggunakan software QM for Windows, diketahui bahwa penentuan jumlah produksi dengan menggunakan algoritma Branch and Bound memberikan peningkatan keuntungan sebesar 36.5% dibandingkan dengan keuntungan PT.XYZ sebelumnya. Kata kunci: Optimasi, program integer, algoritma Branch and BoundReferensi[1] Sofyan Assauri. Manajemen Produksi dan Operasi. Lembaga Penerbit FakultasEkonomi Universitas Indonesia. Jakarta. 2008.[2] Winston, W. L. Operations Research: Applications and Algorithms. Edisi Keempat.Canada: Brooks/Cole-Thomson Learning. 2004.[3] Akram, S. A., dan Jaya, A. I. Optimalisasi Produksi Roti dengan Menggunakan Metode Branch and Bound (Studi Kasus Pada Pabrik Roti Syariah Bakery, Jl. Maleo, Lrg.VIII No. 68 Palu). Jurnal Ilmiah Matematika dan Terapan, 13(2): 98-107. 2016.[4] Jiao, H. W., dkk. An Effective Branch and Bound Algorithm for MinimaxLinear Fractional Programming. Journal of Applied Mathematics, Volume 2014: 8. 2014.[5] Williams, H. P. The Problem with Integer Programming. Journal of Management Mathematics, 22(3): 213-230. 2011.[6] Falani, I. Penentuan Nilai Parameter Metode Exponential Smoothing dengan Algoritma Genetik dalam Meningkatkan Akurasi Forecasting. Journal of Computer Engineering System and Science, 3(1): 14–16. 2018.[7] Mehdizadeh, E., dan Jalili, S. An Algorithm Based on Theory of Constraints and Branch and Bound for Solving Integrated Product-Mix-Outsourcing Problem. Journal of Optimization in Industrial Engineering, 12(1): 167-172. 2019.[8] Taylor, B. W. Introduction to Management Science. Edisi ke-11. United States of America: Prentice-Hall International, INC. 2013[9] Puryani., dan Ristono, A. Penelitian Operasional. Yogyakarta: Graha Ilmu. 2012.[10] Yusrah N. dkk. Implementasi Algoritma Branch and Bound Dalam Penentuan Jumlah Produksi Untuk Memaksimalkan Keuntungan. Jurnal String Vol. 3 No. 1 Agustus 2018. ISSN: 2527-9661[11] Taha, H. A. Operations Research: An Introduction. Edisi ke-8. United States of America: Prentice-Hall International, INC. 2007.

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A Novel Artificial Bee Colony Algorithm Based on Attraction Pheromone for the Multidimensional Knapsack Problems

Artificial Intelligence and Computational Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-23887-1_1 ◽

2011 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Hongkai Wei ◽

Junzhong Ji ◽

Yufang Qin ◽

Yamin Wang ◽

Chunnian Liu

Keyword(s):

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Knapsack Problems ◽

Bee Colony ◽

Multidimensional Knapsack

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Imposing Contiguity Constraints in Political Districting Models

Operations Research ◽

10.1287/opre.2021.2141 ◽

2021 ◽

Author(s):

Hamidreza Validi ◽

Austin Buchanan ◽

Eugene Lykhovyd

Keyword(s):

Integer Programming ◽

Operations Research ◽

Programming Model ◽

A Priori ◽

Source Code ◽

Branch And Cut ◽

Computer Implementation ◽

Census Tracts ◽

Integer Programming Model ◽

Political Districting

For nearly 60 years, operations research techniques have assisted in the creation of political districting plans, beginning with an integer programming model. This model, which seeks compactness as its objective, tends to generate districts that are contiguous, or nearly so, but provides no guarantee of contiguity. In the paper “Imposing contiguity constraints in political districting models” by Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd, the authors consider and analyze four different contiguity models (two old and two new). Their computer implementation can handle redistricting instances as large as Indiana (1,511 census tracts). Their fastest approach uses a branch-and-cut algorithm, where contiguity constraints are added in a callback. Critically, many variables can be fixed to zero a priori by Lagrangian arguments. All test instances and source code are publicly available.

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