ORIGINAL_ARTICLE
Designing Humanitarian Relief Supply Chains by Considering the Reliability of Route, Repair Groups and Monitoring Route
Most humanitarian relief items' investigations try to satisfy demands in disaster areas in an appropriate time and reduce the rate of causality. Time is an essential element in humanitarian relief items; the quietest response time, the more rescued people. Reducing response time with high reliability is the main objective of this research. In our investigation, monitoring the route’s situation after occurrence disaster with drones and motorcycles is planned for collecting information about routes and demand points in the first stage. The collected information is analyzed by the disaster management to determine the probability of each scenario. By evaluating collected data, the route repair groups are sent to increase the route’s reliability. In the final step, the relief items operation allocates the relief items to demand points. All in all, this research tries to present a practical model and real situation to survive more people after occurrence disaster. An exact solver solves the evolutionary model in small and medium scales; the developed model in big scale is solved by Grasshopper Optimization Algorithm (GOA), and then results are evaluated. The evaluation results indicate the positive effect of valid initial information on the humanitarian supply chain’s performance.
https://aie.ut.ac.ir/article_80317_bf28347198108009546aed216e230938.pdf
2019-10-01
93
126
10.22059/jieng.2021.306436.1733
humanitarian relief supply chain
Monitoring Routes
Repairing groups
Reliability of routes
Grasshopper Optimization Algorithm
Bahman
Momeni
behnammomeni@ut.ac.ir
1
School of Industrial & Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Amir
Aghsami
a.aghsami@ut.ac.ir
2
School of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
Masoud
Rabbani
mrabani@ut.ac.ir
3
School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
[1] Noham, R., & Tzur, M. (2018). Designing humanitarian supply chains by incorporating actual post-disaster decisions. European Journal of Operational Research, 265(3), 1064-1077.
1
[2] Oruc, B. E., & Kara, B. Y. (2018). Post-disaster assessment routing problem. Transportation research part B: methodological, 116, 76-102.
2
[3] Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30(3), 835-854.
3
[4] Rennemo, S. J., Rø, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation research part E: logistics and transportation review, 62, 116-135.
4
[5] Edrissi, A., Nourinejad, M., & Roorda, M. J. (2015). Transportation network reliability in emergency response. Transportation research part E: logistics and transportation review, 80, 56-73.
5
[6] Huang, K., Jiang, Y., Yuan, Y., & Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 75, 1-17.
6
[7] Torabi, S.A., Doodman, M. and Bozorgi Amiri, A., (2018). Integrating Pre-and Post-Disaster Operations Considering the Restoration of Disrupted Routes and Warehouses. Advances in Industrial Engineering, 52(2), pp.179-192.
7
[8] Danesh Alagheh Band, T.S., Aghsami, A. and Rabbani, M., 2020. A Post-disaster Assessment Routing Multi-Objective Problem under Uncertain Parameters. International Journal of Engineering, 33(12), pp.2503-2508.
8
[9] Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum, 35(4), 905-933.
9
[10] Döyen, A., Aras, N., & Barbarosoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123-1145.
10
[11] Galindo, G., & Batta, R. (2013). Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sciences, 47(1), 20-37.
11
[12] Chang, F. S., Wu, J. S., Lee, C. N., & Shen, H. C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Systems with Applications, 41(6), 2947-2956.
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[13] Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28.
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[14] Sheu, J. B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation research part B: methodological, 67, 284-305.
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[15] Kabra, G., & Ramesh, A. (2015). Analyzing ICT issues in humanitarian supply chain management: A SAP-LAP linkages framework. Global Journal of Flexible Systems Management, 16(2), 157-171.
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[16] Khayal, D., Pradhananga, R., Pokharel, S., & Mutlu, F. (2015). A model for planning locations of temporary distribution facilities for emergency response. Socio-Economic Planning Sciences, 52, 22-30.
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[17] Ruan, J., Shi, P., Lim, C. C., & Wang, X. (2015). Relief supplies allocation and optimization by interval and fuzzy number approaches. Information Sciences, 303, 15-32.
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[18] Tofighi, S., Torabi, S. A., & Mansouri, S. A. (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250(1), 239-250.
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[19] Yadav, D. K., & Barve, A. (2016). Modeling post-disaster challenges of humanitarian supply chains: A TISM approach. Global Journal of Flexible Systems Management, 17(3), 321-340.
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[20] Cantillo, V., Serrano, I., Macea, L. F., & Holguín-Veras, J. (2018). Discrete choice approach for assessing deprivation cost in humanitarian relief operations. Socio-Economic Planning Sciences, 63, 33-46.
20
[21] Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., & Taheri-Moghaddam, A. (2016). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation research part E: logistics and transportation review, 93, 485-509.
21
[22] Shamsi Gamchi, N. and Torabi, A., (2018). Application of option contract in Epidemic control using vaccination. Advances in Industrial Engineering, 52(4), pp.609-620.
22
[23] Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37.
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[24] Cotes, N., & Cantillo, V. (2019). Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89-100.
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30
ORIGINAL_ARTICLE
Production Scheduling Optimization Algorithm for the Steel-Making Continuous Casting Processes
This paper investigates steel-making continuous casting (SCC) scheduling problem. SCC is a high temperature and large-scale logistics machining process with batch production at the last stage that was identified as the key process of modern iron and steel enterprises. This paper presents a mathematical model for scheduling SCC process. The model is developed as a Mixed Zero- One Linear programming (MZOLP) based on actual production situations of SCC. The objective is to schedule a set of charges (jobs) to minimize the earliness and tardiness penalty costs as well as the charge waiting time cost. The solution methodology is developed based on a branch-and-bound algorithm. A heuristic method is presented at the beginning of the search in order to compute an initial upper bound. A lower bound and an upper bound are developed and a method for reducing branches is established based on the batch production in the continuous casting (CC) stage. Moreover, branching schemes are proposed. The branch- and- bound algorithm incorporating the initial upper bound, the lower and upper bound, the method for reducing branches, and branching schemes is tested on a set of instances. The analysis shows the efficiency of the proposed features for the algorithm.
https://aie.ut.ac.ir/article_80318_4b0d869a834d1a996b0b02969e442b3e.pdf
2019-10-01
127
147
10.22059/jieng.2021.306226.1732
Steel making
continuous casting
Production Scheduling
Branch and Bound Algorithm
Mahdi
Nakhaeinejad
m.nakhaeinejad@yazd.ac.ir
1
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
[1] Tang, L., Liu, J., Rong, A., and Yang, Z. “A review of planning and scheduling systems and methods for integrated steel production”, European Journal of operational research, 133, 1- 20 (2001).
1
[2] Tang, L., Liu, J., Rong, A., and Yang Z. “A mathematical programming model for scheduling steelmaking- continuous casting production”, European Journal of Operatoinal Research, 120, 423- 435 (2000).
2
[3] Atighehchian, A., Bijari, M., and Tarkesh, H. “A novel hybrid algorithm for scheduling steel-making continuous casting production”, Computers & Operations Research, 36, 2450- 2461 (2009).
3
[4] Tang, L., Luh, P. B., Liu, J., and Fang, L. “Steel-making process scheduling using Lagrangian relaxation”, International Journal of Production Research, 40 (1), 55- 70 (2002).
4
[5] Xuan, H., and Tang L. “Scheduling a hybrid flow shop with batch production at the last stage”, Computers & Operations Research, 34, 2718- 2733 (2007).
5
[6] Harjunkoski, I., and Grossmann, I. E. “A decomposition approach for the scheduling of a steel plant production”, Computers and Chemical Engineering, 25, 1647- 1660 (2001).
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[7] Gupta, J.N.D., Hariri, A.M.A., and Potts, C.N. “Scheduling a two-stage hybrid flow shop with parallel machines at the first stage”, Annals of Operations Research, 69, 171- 191 (1997).
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[8] Missbauera, H., Hauberb, W., and Stadler, W. “A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry”, International Journal of Production Research, 47, 4147- 4172 (2009).
8
[9] Sun, L. “Scheduling of Steel-making and Continuous Casting System Using the Surrogate Subgradient Algorithm for Lagrangian Relaxation”, 6th annual IEEE Conference on Automation Science and Engineering, Toronto, Ontario, Canada, August 21- 24 (2010).
9
[10] Dao-Iei, Z., Zhong, Z., and Xiao-qiang, G. “Intelligent Optimization-Based Production Planning and Simulation Analysis for Steelmaking and Continuous Casting Process”, Journal of Iron and Steel Research, International, 17(9), 19- 24, 30 (2010).
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[11] Witt, A., and Voss, S. “Application of a mathematical model to an intermediate- to long-term real-world steel production planning problem based on standard software”, European Journal of Industrial Engineering, 5 (1), 81- 100 (2011).
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[12] Wei, L., and Liang-liang, S. “Steel-Making and Continuous/Ingot Casting Scheduling of Mixed Charging Plan Based on Batch Splitting Policy”, Journal of iron and steel research, international, 19 (2), 17- 21 (2012).
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[13] Gui-rong Wang, Qi-qiang Li, Lu-hao Wang “An improved cross entropy algorithm for steelmaking- continuous casting production scheduling with complicated technological routes”, Journal of Central South University. 22 (8), 2998-3007 (2015).
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[14] Touil Achraf, Echchtabi Abdelwahed, Bellabdaoui Adil “A simulated annealing method to optimize the order of the sequences in continuous- casting”, International Journal of Mathematics and Computational Science. Vol. 1, No. 5, pp. 282- 287 (2015).
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[15] Haderaa H., Harjunkoskia I., Sanda G., Grossmannc I. E., Engell S. “Optimization of steel production scheduling with complex time-sensitive electricity cost”, Computers and Chemical Engineering 76, 117–136 (2015).
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[16] Nastasi G., Colla V. and Seppia M. D. “A Multi-Objective Coil Route Planning System for the Steelmaking Industry Based on Evolutionary Algorithms”, International Journal of Simulation Systems Science & Technologies 16, 61- 68 (2015).
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[17] Armellini D., Borzonea P., Ceschiab S., Gasperob L. D., and Schaerfb A. “Modeling and solving the steelmaking and casting scheduling problem”, International Transaction in Operation Research. 1–34 (2018).
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[19] Peng K., Pan Q., Gao L., Zhang B., Pang X. “An Improved Artificial Bee Colony Algorithm for Real-World Hybrid Flowshop Rescheduling in Steelmaking-Refining Continuous Casting Process”. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2018.05.056, (2018).
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[20] Jiang S., Zheng Z., Liu M. “A Preference-Inspired Multi-Objective Soft Scheduling Algorithm for the Practical Steelmaking-Continuous Casting Production”. Computers and Industrial Engineering, https://doi.org/10.1016/j.cie.2017.10.028, (2018).
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[21] Fazel Zarandi, M. H., Dorry F. “A Hybrid Fuzzy PSO Algorithm for Solving Steelmaking- Continuous Casting Scheduling Problem”, International Journal of Fuzzy System, 20(1):219– 235 (2018).
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[22] Rahal Said, Li Zukui, Papageorgiou Dimitri J. “Proactive and Reactive Scheduling of the Steelmaking and Continuous Casting Process through Adaptive Robust Optimization”, Computers and Chemical Engineering, https://doi.org/10.1016/j.compchemeng.2019.106658, (2019).
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[23] Cui Haijuan, Luo Xiaochuan, Wang Yuan “Scheduling of steelmaking-continuous casting process using deflected surrogate Lagrangian relaxation approach and DC algorithm”, Computers & Industrial Engineering, 140, 106271 (2020).
23
[24] Long Jianyu, Sun Zhenzhong, Pardalos Panos M., Bai Yun, Zhang Shaohui, Li Chuan “A robust dynamic scheduling approach based on release time series forecasting for the steelmaking-continuous casting production”, Applied Soft Computing Journal, https://doi.org/10.1016/j.asoc.2020.106271, (2020).
24
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[28] Haouari, M., Hidri, L., and Gharbi, A. “Optimal scheduling of a two-stage hybrid flow shop”, Mathematical Methods of Operations Research. 64, 107- 124 (2006).
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[29] Ng, C.T., Wang, J.-B., Cheng, T.C.E., and Liu, L.L. “A Branch and Bound algorithm for solving a two-machine flow shop problem with deteriorating jobs”, Computers & Operations Research, 37, 83- 90 (2010).
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[30] Ranjbar, M., Davari, M., and Leus, R. “Two Branch and Bound algorithms for the robust parallel machine scheduling problem”, Computers & Operations Research, 39, 1652- 1660 (2012).
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[31] Khoudi A., Berrichi A. “Minimize total tardiness and machine unavailability on single machine scheduling problem: bi-objective Branch and Bound algorithm”, Operational Research, https://doi.org/10.1007/s12351-018-0384-3, (2018).
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[32] Tanaka Shunji, Tierney Kevin, Parreño-Torres Consuelo, Alvarez-Valdes Ramon, Ruiz Rubén “A Branch and Bound approach for large pre-marshalling problems”, European Journal of Operational Research, 278, 211– 225 (2019).
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[33] Bunel Rudy, Turkaslan Ilker, Torr Philip H.S., Kumar M. Pawan “Branch and Bound for Piecewise Linear Neural Network Verification”. Journal of Machine Learning Research, 21, 1-39 (2020).
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34
ORIGINAL_ARTICLE
Designing a Multi-Objective Three-Stage Location-Routing Model for Humanitarian Logistic Planning under Uncertainty
Natural and technological disasters threaten human life all around the world significantly and impose many damages and losses on them. The current study introduces a multi-objective three-stage location-routing problem in designing an efficient and timely distribution plan in the response phase of a possible earthquake. This problem considers uncertainty in parameters such as demands, access to routes, time and cost of travels, and the number of available vehicles. Accordingly, a three-stage stochastic programming approach is applied to deal with the uncertainties. The objective functions of the proposed problem include minimizing the unsatisfied demands, minimizing the arriving times, and minimizing the relief operations costs. A modified algorithm of the improved version of the augmented ε-constraint method, which finds Pareto-optimal solutions in less computational time, is presented to solve the proposed multi-objective mixed-integer linear programming model. To validate the model and evaluate the performance of the methods several test problems are generated and solved by them. The computational results show the satisfactory performance of the proposed methods and effectiveness of the proposed model for delivery of relief commodities in the affected areas.
https://aie.ut.ac.ir/article_80319_b5abce702792889b3085117a5977b5fa.pdf
2019-10-01
149
167
10.22059/jieng.2021.313355.1744
Humanitarian Logistics
Location-routing problem
Disaster management
Multi-objective optimization
Stochastic Programming
Fatemeh
Zafari
fatemehzafari1992@gmail.com
1
Industrial Engineering Department, Yazd University, Yazd, Iran.
AUTHOR
Davood
Shishebori
shishebori@yazd.ac.ir
2
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
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[11] Ukkusuri, S.V. and W.F. Yushimito, Location routing approach for the humanitarian prepositioning problem. Transportation research record, 2008. 2089(1): p. 18-25.
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[13] Wang, H., L. Du, and S. Ma, Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review, 2014. 69: p. 160-179.
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[15] Vahdani, B., et al., Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International journal of disaster risk reduction, 2018. 27: p. 290-306.
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[20] Abounacer, R., M. Rekik, and J. Renaud, An exact solution approach for multi-objective location–transportation problem for disaster response. Computers & Operations Research, 2014. 41: p. 83-93.
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[21] Rezaei-Malek, M., et al., An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation research part E: logistics and transportation review, 2016. 93: p. 485-509.
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[28] Döyen, A., N. Aras, and G. Barbarosoğlu, A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 2012. 6(6): p. 1123-1145.
28
[29] Rennemo, S.J., et al., A three-stage stochastic facility routing model for disaster response planning. Transportation research part E: logistics and transportation review, 2014. 62: p. 116-135.
29
[30] Rath, S., M. Gendreau, and W.J. Gutjahr, Bi‐objective stochastic programming models for determining depot locations in disaster relief operations. International Transactions in Operational Research, 2016. 23(6): p. 997-1023.
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[37] Abdolazimi O, Esfandarani MS, Shishebori D. Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Computing and Applications. 2020 Oct 20:1-6.
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[41] Abdolazimi O, Esfandarani MS, Salehi M, Shishebori D. Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production. 2020 Aug 10;264:121566.
41
ORIGINAL_ARTICLE
A Novel Approach for Multi Product Demand Forecast Using Data Mining Techniques (Empirical Study: Carpet Industry)
Accurate demand forecasting plays an important role in meeting customers’ expectations and satisfaction that strengthen the enterprise's competitive position. In this research, time series and artificial neural networks methods compete to provide more precise demand estimation while having a large variety of products. After obtaining the initial results, suggestions have been implemented to improve forecasting accuracy. As a direct result of that, the average of mean absolute percentage error (MAPE) of all products' demand forecast reduces significantly. To improve the quality of historical records, association rules and substitution ratio have been applied . This method plays a significant role to detect the existing pattern in historical data and MAPE reduction. The satisfactory and applicable results provide the company with more accurate forecast. Moreover, the issue of precepting confusing historical data which caused unforecastable trends has been solved. The R language and “neuralnet”, “nnfor”, “forecast”, and “arules” packages have been applied in programming.
https://aie.ut.ac.ir/article_80320_dde46b307de7536717baf09a84a0fd51.pdf
2019-10-01
169
184
10.22059/jieng.2021.316849.1746
Artificial Neural Network
Association Rules
Demand Forecasting
Data Mining
time series
Sayedmohammadreza
Vaghefinezhad
vaghefinezhad@ut.ac.ir
1
Industrial Engineering, Kish International Campus, University of Tehran
AUTHOR
Jafar
Razmib
jrazmi@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
LEAD_AUTHOR
Fariborz
Jolai
fjolai@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
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34
ORIGINAL_ARTICLE
A Mathematical Model for Solving Location-Routing Problem with Simultaneous Pickup and Delivery Using a Robust Optimization Approach
In this study, a robust optimization model is introduced, we propose a location-routing problem with simultaneous pickup and delivery under a hard time window that has a heterogeneous and limited depot and vehicle capacities and multi-variety of products and uncertain traveling time that considering all of these constraints together make the problem closer to real practical world’s problems, that not been studied in previous papers. For this purpose, a mixed-integer linear programming (MILP) model is proposed for locating depots and scheduling vehicle routing with multiple depots. Then, the robust counterpart of the proposed MILP model is proposed. The results show that the GA performs much better than the exact algorithm concerning time. GAMS software fails to solve the large-size problem, and the time to find a solution grows exponentially with increasing the size of the problem. However, the GA quite efficient for problems of large sizes, and can nearly find the optimal solution in a much shorter amount of time. Also, results in the Robust model show that increasing the confidence level has led to an increase in the value of the objective function of the robust counterpart model, this increase does not exhibit linear behavior. At 80% confidence level, the minimum changes in the objective function are observed, if we want to obtain a 90% confidence level, it requires more cost, but increasing the confidence level from 70% to 80% does not need more cost, so an 80% confidence level can be considered as an ideal solution for decision-makers.
https://aie.ut.ac.ir/article_80321_81aad25605b01b5996009bab252b1750.pdf
2019-10-01
185
208
10.22059/jieng.2021.313677.1745
Supply Chain
Location-Routing Problem (LRP)
Simultaneous pickup and delivery
Time window
Genetic Algorithm (GA)
Robust Optimization (RO) Approach
Mostafa
Bakhtiari
mst.bakhtiari@ut.ac.ir
1
Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran
AUTHOR
Sadoullah
Ebrahimnejad
ibrahimnejad@kiau.ac.ir
2
Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
LEAD_AUTHOR
Mina
Yavari-Moghaddam
mina.yavari@bamilo.com
3
Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
AUTHOR
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81
ORIGINAL_ARTICLE
Home Health Care Scheduling and Routing with Temporal Dependencies and Continuity of Care
Due to facing an acute shortage of beds in hospitals, the danger of getting involved in hospital infections and high-cost hospitals care, the Home Health Care industry has encountered high demands in recent years. Different stakeholders with various interests are involved in home health care that makes the process of planning and scheduling of nurses, who offered services, challenging. This paper, therefore, focuses on scheduling and routing nurses traveled to the patient’s home by considering the main features of the problem such as Continuity of Care and temporal dependencies. A new formulation for adjusting the time distance between two consecutive jobs performed by a nurse is presented. A feasible solution has to consider nurse and patient’s preferences, time windows for jobs, nurse’s qualification, and waiting time. A genetic algorithm is proposed to solve the problem. The computational results show the efficiency of the proposed algorithm, especially for large-size instances. Finally, the effect of the nurse’s dispatching policy on the objective function, waiting, and traveling times is examined.
https://aie.ut.ac.ir/article_80322_5089c0393fce4fe82d2c2ab610f5320e.pdf
2019-10-01
209
228
10.22059/jieng.2021.317379.1748
Continuity of Care
Genetic Algorithm
Home Health Care
Mathematical Programming
Temporal Interdependency
Mahyar
Mirabnejad
mahyar.mirabnejad@ut.ac.ir
1
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Fariborz
Jolai
fjolai@ut.ac.ir
2
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Zeinab
Sazvar
sazvar@ut.ac.ir
3
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mehrdad
Mirzabaghi
m.mirzabaghi@ut.ac.ir
4
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
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2
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3
[4] Bertels, S., & Fahle, T. (2006). A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem. Computers & Operations Research, 33(10), 2866-2890.
4
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5
[6] Trautsamwieser, A., Gronalt, M., & Hirsch, P. (2011). Securing home health care in times of natural disasters. OR spectrum, 33(3), 787-813.
6
[7] Rasmussen, M. S., Justesen, T., Dohn, A., & Larsen, J. (2012). The home care crew scheduling problem: Preference-based visit clustering and temporal dependencies. European Journal of Operational Research, 219(3), 598-610.
7
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8
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