Dynamic Programming to Solve Picking Schedule at the Tea Plantation
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2018-11-30 https://doi.org/10.14419/ijet.v7i4.30.22286 -
Dynamic Programming, Minimizes Cost, Picking Schedule. -
Abstract
The tea picking schedule at PT Perkebunan Ciater is set to be the same for all plantation blocks. In fact, the altitude from sea level and the pruning age of each plantation block is different, this results in the difference of buds’ growth. The implementation of the same picking schedule causes the quality and quantity of tea buds often could not be fulfilled. This research is to determine the precise picking schedule by considering the buds’ growth of each plantation block. Two steps are implemented to solve the problem. The first step is to look for picking period and the pattern of buds’ quality for each plantation block, which corresponds to the altitude of the location and the pruning age. The regression method is applied in this first step. The buds’ quality pattern is then used to determine the cost of decreasing buds’ quality and the costs of the buds that left in the plantation. The second step is to develop the picking schedule using dynamic programming, which minimizes the total cost of picking. In addition to this, we also develop a rolling schedule, which schedule time interval is three days. The model results show that the proposed schedule gives a better total cost than the current schedule and the buds’ quality target is easier to achieve.
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References
[1] Liu F, Wang J, Chen H & Yang D (2014), Machine scheduling with outsourcing: coping with supply chain uncertainty with a second supplying source. The International Journal of Logistics Management 25(1), 133–159.
[2] Astaraky D & Patrick J (2015), A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling. European Journal of Operational Research 245(1), 309–319.
[3] Rasti-Barzoki M & Hejazi SR (2015), Pseudo-polynomial dynamic programming for an integrated due date assignment, resource allocation, production, and distribution scheduling model in supply chain scheduling. Applied Mathematical Modelling 39, 3280–3289.
[4] Bennell JA, Mesgarpour M & Potts CN (2017), Dynamic scheduling of aircraft landings. European Journal of Operational Research 258(1), 315–327.
[5] Gromicho JAS, van Hoorn JJ, Saldanha-da-Gama F &Timmer GT (2012), Solving the job-shop scheduling problem optimally by dynamic programming. Computers & Operations Research 39, 2968–2977.
[6] Hsu CI, Li HC, Liu SM & Chao CC (2011), Aircraft replacement scheduling: A dynamic programming approach. Transportation Research Part E 47, 41–60.
[7] Li H & Womer NK (2015), Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming. European Journal of Operational Research 246(1), 20–33.
[8] Wang J & Fung RY (2015), Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences. Artificial intelligence in medicine 63(1), 33–40.
[9] Tuong NH, Soukhal A & Billaut JC (2010), A new dynamic programming formulation for scheduling independent tasks with common due date on parallel machines. European Journal of Operational Research 202(3), 646–653.
[10] Wang KJ & Nguyen PH (2017), Capacity planning with technology replacement by stochastic dynamic programming. European Journal of Operational Research 260, 739–750.
[11] Yang X & Strauss AK (2017), An approximate dynamic programming approach to attended home delivery management. European Journal of Operational Research 263(3), 935–945.
[12] Yu S, Gao S & Sun H (2016), A dynamic programming model for environmental investment decision-making in coal mining. Applied Energy 166, 273–281.
[13] Tripathy PK, Dash RK &Tripathy CR (2015), A dynamic programming approach for layout optimization of interconnection networks. Engineering Science and Technology, an International Journal 18(3), 374–384.
[14] Quyen NTP, Kuo RJ, Chen JC & Yang CL (2017), Dynamic programming to solve resource constrained assembly line balancing problem in footwear manufacturing. Proceedings of the 4th International Conference on Industrial Engineering and Applications (ICIEA), IEEE 66, 66–70.
[15] Lekkakos SD & Serrano A (2016), Supply chain finance for small and medium sized enterprises: the case of reverse factoring. International Journal of Physical Distribution & Logistics Management 46(4), 367–392.
[16] Meissner J & Senicheva OV (2018), Approximate Dynamic Programming for lateral transshipment problems in multi-location inventory systems. European Journal of Operational Research 265(1), 49–64.
[17] Qiu R, Sun M & Lim YF (2017), Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches. European Journal of Operational Research 261(3), 880–892.
[18] Rivotti P & Pistikopoulos EN (2014), Constrained dynamic programming of mixed-integer linear problems by multi-parametric programming. Computers & Chemical Engineering 70, 172–179.
[19] Chebil K & Khemakhem M (2015), A dynamic programming algorithm for the knapsack problem with setup. Computers & Operations Research 64, 40–50.
[20] Claßen G, Koster AM & Schmeink A (2015), The multi-band robust knapsack problem-A dynamic programming approach. Discrete Optimization 18, 123–149.
[21] Furini F, Ljubić I & Sinnl M (2017), An effective dynamic programming algorithm for the minimum-cost maximal knapsack packing problem. European Journal of Operational Research 262(2), 438–448.
[22] Hao C & Yue Y (2016), Optimization on Combination of Transport Routes and Modes on Dynamic Programming for a Container Multimodal Transport System. Procedia Engineering 137, 382–390.
[23] Liu J & Xie K (2017), Emergency materials transportation model in disasters based on dynamic programming and ant colony optimization. Kybernetes 46(4), 656–671.
[24] Yuan Y &Tang L (2017), Novel time-space network flow formulation and approximate dynamic programming approach for the crane scheduling in a coil warehouse. European Journal of Operational Research 262(2), 424–437.
[25] Çimen M & Soysal M (2017), Time-dependent green vehicle routing problem with stochastic vehicle speeds: An approximate dynamic programming algorithm. Transportation Research Part D: Transport and Environment 54, 82–98.
[26] Xiao Y & Konak A (2017), A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem. Journal of Cleaner Production 167, 1450–1463.
[27] Diban P, Aziz MKA, Foo DCY, Jia X, Li Z & Tan RR (2016), Optimal biomass plantation replanting policy using dynamic programming. Journal of Cleaner Production 126, 409–418.
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How to Cite
Budijati, S. M., & Iskandar, B. P. (2018). Dynamic Programming to Solve Picking Schedule at the Tea Plantation. International Journal of Engineering & Technology, 7(4.30), 285-290. https://doi.org/10.14419/ijet.v7i4.30.22286Received date: 2018-11-29
Accepted date: 2018-11-29
Published date: 2018-11-30