A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

Min Tang; Fuying Jing; Xiangrui Chao

doi:10.3934/jimo.2019059

Article Contents

2020, Volume 16, Issue 5: 2389-2406. Doi: 10.3934/jimo.2019059

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A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

1.
School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
College of Communication Science and Art, Chengdu University of Technology, Chengdu 610059, China

^* Corresponding author: chaoxr@uestc.edu.cn
^* Corresponding author: chaoxr@uestc.edu.cn

Received: September 2018

Revised: February 2019

Early access: May 2019

Published: August 2020

Abstract / Introduction Full Text(HTML) Figure(8) / Table(4) Related Papers Cited by

Abstract

In the real-world production process, the firms need to determine the optimal production planning under minimum production quantity constraint in order to achieve economies of scale. However, the inventory cost will hugely increase when there is a very large amount of production in a period and also a large amount of total demands for the next few periods. This paper considers a single-item dynamic lot sizing problem with production-or-outsourcing decisions. In each period, the production level cannot be lower than a given quantity in order to make full use of resources, but the outsourcing is unrestricted. The demands in a period can be backlogged. The production and outsourcing costs are fixed-plus-linear, and the inventory and backlogging costs are linear. We establish a mathematical programming model according to the real problem in the firm. We explore some structural properties of the optimal solution and use them to develop a dynamic programming algorithm to solve the proposed problem. We further present a special case with stationary production and outsourcing costs which can be solved with reduced computational complexities. In the end, we use three numerical instances to show how to obtain the optimal solutions by using the dynamic programming algorithm. Furthermore, we show that the policy of backlogging or outsourcing can reduce the total cost.

Keywords:

Mathematics Subject Classification: Primary: 90C39, 90C90; Secondary: 90B05.

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Figure 1. The sketch for production and outsourcing decisions

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Figure 2. The sketch for $ F(i,j,t) $

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Figure 3. The sketch for $ F(i,j,j_1 ,j_1 ',g_2 ,j_2 ,...,g_n ,j_n ,t) $

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Figure 4. The sketch for $ F(i,j,g_1 ,j_1 ,...,g_n ,j_n ,t) $

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Figure 5. The sketch for $ F(e,i,t) $

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Figure 6. The sketch for $ F(e,i,j_1 ,j_1 ',g_2 ,j_2 ,...,g_n ,j_n ,t) $

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Figure 7. The sketch for $ F(e,i,g_1 ,j_1 ,...,g_n ,j_n ,t) $

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Figure 8. The sketch for $ F(g_n ,j_n ,t) $

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Table 1. The basic notation

$ K_t : $ fixed (setup) costs for production in period $ t $, $ t=1,2,..,T $;
$ c_t : $ per unit production cost in period $ t $, $ t=1,2,..,T $;
$ k_t : $ fixed (setup) costs for outsourcing in period $ t $, $ t=1,2,..,T $;
$ o_t : $ per unit outsourcing cost in period $ t $, $ t=1,2,..,T $;
$ h_t : $ per unit holding cost in period $ t $, $ t=1,2,..,T $;
$ b_t : $ per unit backlogging cost in period $ t $, $ t=1,2,..,T $

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Table 2. Summary of Computations of Example 1

$ t $	1	2	3	4	5	6	7	8	9	10
$ d_t $	8	6	9	29	5	6	5	15	5	6
$ X_t^\ast $	30
$ X_t^\ast $	30	0
$ X_t^\ast $	30	0	0
$ X_t^\ast $	30	0	0	30
$ X_t^\ast $	30	0	0	30	0
$ X_t^\ast $	30	0	0	33	0	0
$ X_t^\ast $	30	0	0	38	0	0	0
$ X_t^\ast $	30	0	0	53	0	0	0	0
$ X_t^\ast $	30	0	0	58	0	0	0	0	0
$ X_t^\ast $	30	0	0	38	0	0	0	30	0	0
$ F(t) $	254	286	300	526	532	556	606	786	856	874

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Table 3. Summary of Computations of Example 2

$ t $	1	2	3	4	5	6	7	8	9	10
$ d_t $	8	6	9	29	5	6	5	15	5	6
$ X_t^\ast $	30
$ X_t^\ast $	0	30
$ X_t^\ast $	0	0	30
$ X_t^\ast $	0	0	0	52
$ X_t^\ast $	0	0	0	57	0
$ X_t^\ast $	0	0	0	63	0	0
$ X_t^\ast $	0	0	0	68	0	0	0
$ X_t^\ast $	0	0	0	57	0	0	0	30
$ X_t^\ast $	0	0	0	57	0	0	0	31	0
$ X_t^\ast $	0	0	0	57	0	0	0	37	0	0
$ F(t) $	254	258	268	388	418	466	546	682	688	748

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Table 4. Summary of Computations of Example 3

$ t $	1	2	3	4	5	6	7	8	9	10
$ d_t $	8	6	9	29	5	6	5	15	5	6
$ X_t^\ast ,O_t^\ast $	0, 8
$ X_t^\ast ,O_t^\ast $	0, 14	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	30, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	34, 0	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	40, 0	0, 0	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	45, 0	0, 0	0, 0	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	45, 0	0, 0	0, 0	0, 0	0, 15
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	45, 0	0, 0	0, 0	0, 0	0, 20	0, 0
$ X_t^\ast ,O_t^\ast $	0, 23	0, 0	0, 0	45, 0	0, 0	0, 0	0, 0	0, 26	0, 0	0, 0
$ F(t) $	98	146	236	448	472	532	582	722	762	822

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