ORIGINAL_ARTICLE
Optimizing hospital facility layout planning through process mining of clinical pathways
During the recent years, demand for healthcare services has dramatically increased. As the demand for healthcare services increases, so does the necessity of constructing new healthcare buildings and redesigning and renovating existing ones. Increasing demands necessitate the use of optimization techniques to improve the overall service efficiency in healthcare settings. However, high complexity of care processes remains the major challenge to accomplish this goal. This study proposes a method based on process mining results to address the high complexity of care processes and to find the optimal layout of the various medical centers in an emergency department. ProM framework is used to discover clinical pathway patterns and relationship between activities. Sequence clustering plug-in is used to remove infrequent events and to derive the process model in the form of Markov chain. The process mining results served as an input for the next phase which consists of the development of the optimization model. Comparison of the current ED design with the one obtained from the proposed method indicated that a carefully designed layout can significantly decrease the distances that patients must travel.
https://aotp.fabad-ihe.ac.ir/article_57299_d474f8f017ce791a350cbf8e1b416ff8.pdf
2018-02-01
1
9
10.22121/aotp.2018.114464.1008
Healthcare processes
Process mining
optimization
Facility layout planning
Young Hoon
Lee
young@yonsei.ac.kr
1
System Optimization Lab, Department of Information and Industrial Engineering, Yonsei University, Seoul, South Korea
AUTHOR
Farhood
Rismanchian
rismanchian.farhood@gmail.com
2
System Optimization Lab, Department of Information and Industrial Engineering, Yonsei University, Seoul, South Korea
LEAD_AUTHOR
Veiga, G. (2009). Developing Process Mining Tools. An Implementation of Sequence Clustering for ProM, Master's Thesis, IST–Technical University of Lisbon.
1
Kotzer, A. M., Zacharakis, S. K., Raynolds, M., & Buenning, F. (2011). Evaluation of the built environment: staff and family satisfaction pre-and post-occupancy of the Children's Hospital. HERD: Health Environments Research & Design Journal, 4(4), 60-78.
2
Lang, M., Bürkle, T., Laumann, S., & Prokosch, H.-U. (2008). Process mining for clinical workflows: challenges and current limitations. Studies in Health Technology and Informatics, 136, 229–34.
3
Mans, R. S., Schonenberg, M. H., Song, M., van der Aalst, W. M. P., & Bakker, P. J. M. (2009). Application of Process Mining in Healthcare -- A Case Study in a Dutch Hospital. In A. Fred, J. Filipe, & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies: International Joint Conference, BIOSTEC 2008 Funchal, Madeira, Portugal, January 28-31, 2008 Revised Selected Papers (pp. 425–438). Berlin, Heidelberg: Springer Berlin Heidelberg.
4
Poelmans, J., Dedene, G., Verheyden, G., Van Der Mussele, H., Viaene, S., & Peters, E. (2010). Combining business process and data discovery techniques for analyzing and improving integrated care pathways. Advances in Data Mining. Applications and Theoretical Aspects, 505-517.
5
Rais, A., & Viana, A. (2011). Operations research in healthcare: a survey. International transactions in operational research, 18(1), 1-31.
6
Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information systems, 37(2), 99-116.
7
Ulrich, R. S., & Zhu, X. (2007). Medical complications of intra-hospital patient transports: Implications for architectural design and research. HERD: Health Environments Research & Design Journal, 1(1), 31-43.
8
Van Dongen, B. F., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A. J. M. M., & Van Der Aalst, W. M. (2005, June). The prom framework: A new era in process mining tool support. In ICATPN (Vol. 3536, pp. 444-454).
9
Zhou, Z., Wang, Y., & Li, L. (2014, April). Process mining based modeling and analysis of workflows in clinical care-a case study in a chicago outpatient clinic. In Networking, Sensing and Control (ICNSC), 2014 IEEE 11th International Conference on (pp. 590-595). IEEE.
10
ORIGINAL_ARTICLE
A cost based mathematical formulation for U-type assembly line balancing problem
This paper focuses on formulating a typical U-type assembly line balancing problem. A cost based objective function including equipment cost, worker time related cost, and station opening cost is introduced to be minimized in existence of a constant cycle time. Finally, efficiency of the proposed formulation of the introduced problem is studied and tested over some benchmarks.
https://aotp.fabad-ihe.ac.ir/article_55450_3de2b8585c360e5d77ed9fa4b7b24843.pdf
2018-02-01
11
21
10.22121/aotp.2018.109416.1005
U-type assembly line balancing problem
Mathematical Modeling
Cost based objective function
Morteza
Khorram
m.khorram@sutech.ac.ir
1
Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Fars, Iran
LEAD_AUTHOR
Aydemir-Karadag, A., & Turkbey, O. (2013). Multi-objective optimization of stochastic disassembly line balancing with station paralleling. Computers and Industrial Engineering, 65, 413–425.
1
Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32, 909–932.
2
Baykasoglu, A. (2006). Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing, 17, 217–232.
3
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168, 694–715.
4
Chica, M., Cordón, Ó., & Damas, S. (2011). An advanced multi objective genetic algorithm design for the time and space assembly line balancing problem. Computers and Industrial Engineering, 61, 103–117.
5
Hamta, N., Fatemi Ghomi, S. M. T., Jolai, F., & Akbarpour Shirazi, M. (2013). A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141, 99–111.
6
Nourmohammadi, A., & Zandieh, M. (2011). Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS. International Journal of Production Research, 49, 2833–2855.
7
Ogan, D., & Azizoglu, M. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. Journal of Manufacturing Systems, 36, 46–54.
8
Ponnambalam, S. G., Aravindan, P., & Mogileeswar Naidu, G. (2000). A multiobjective genetic algorithm for solving assembly line balancing problem. International Journal of Advanced Manufacturing Technology, 16(5), 341–352.
9
Zhang, W., & Gen, M. (2011). An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time. Intelligent Manufacturing, 22, 367–378.
10
ORIGINAL_ARTICLE
On the control of a dynamical system defined by a decreasing one-dimensional set-valued function
Agricultural production can be described by discrete time as there is harvest in every year only once. The agricultural production is uncertain because of the weather and the ever changing technology. At the same time, the sector prefers stability which is reflected in the small changes in the prices. The uncertainty of the price may be modeled by a set-valued function in a single product market. The independent variable is the price expectation of the producer which is the future value of the price estimated by the producer. It can be assumed that the set-valued function is decreasing because in the case of higher price expectation, greater quantity appears on the market and thus the real market price becomes the lower. The stability of the market may require some control. In this paper the existence of an appropriate control to reach a target interval and to keep the trajectory in the interval is investigated from mathematical point of view. Necessary and sufficient conditions are given for the existence of the viable solution. The “striped structure” of the dynamical system is explored as well.
https://aotp.fabad-ihe.ac.ir/article_55631_17d79afed67a9f3abb33a4da02809774.pdf
2018-02-01
23
33
10.22121/aotp.2018.110970.1007
Set-valued function
Dynamical system
Control
Target interval
Viable solution
Béla
Vizári
vizvaribela@gmail.com
1
Department of Industrial Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
LEAD_AUTHOR
Gergely
Kovács
kovacs.gergely@edutus.hu
2
Department of Economics, Edutus College, Tatabánya, Hungary
AUTHOR
Ahmad, H. F. (2015). Endogenous price expectations as reference points in auctions. Journal of Economic Behavior & Organization, 112, 46-63.
1
Bacsi, Z., & Vizvári, B. (1999). Modelling chaotic behaviour in agricultural pricesusing a discrete deterministic nonlinear price model. Annals of Operations Research, 89, 125-148
2
Flåm, S. D., & Kaniovski, Y. M. (2002). Price expectations and cobwebs under uncertainty. Annals of Operations Research, 114(1), 167-181.
3
Freeland, C. (2009). The credit crunch according to Soros. Financial Times, 30(01).
4
Gennaioli, N., Ma, Y., Shleifer, A. (2015). Expectations and Investment. National Bureau of Economics, NBER Working Paper No. 21260.
5
Dr. Agrarwissenschaften. Thesis, Zentrum für Entwicklungsforschung, Landwirtschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität, Bonn. Mekbib Gebretsadik Haile, Volatility of International Food Prices (Impacts on Resource Allocation and on Food Supply Response), 2015.
6
Kenyon, D. E. (2001). Producer ability to forecast harvest corn and soybean prices. Review of Agricultural Economics, 23(1), 151-162.
7
Keynes, J.M. The General Theory of Employment, Interest and Money, Macmillen, London, on page 156. 1963.
8
Imre, K., & Tibor, K. (1982). On the possibility of the realization of farming advantages in the Hungarian society (in Hungarian). Valóság, 6, 45-55.
9
Kovács, G., Mureşan, M., & Vizvári, B. (2001). On a new approach of the price expectations of producers. Szigma, No. 1-2, 1-11.
10
Kovács, G., Mureşan, M., & Vizvári, B. (2002). Remarks on multifunction-based dynamical systems. Dynamical Systems and Applications, 11, 325-332.
11
Nerlove, M. (1958). Adaptive expectations and cobweb phenomena. The Quarterly Journal of Economics, 72(2), 227-240.
12
Masuku, M. B., Sukati, M. C., & Rugambisa, J. I. (2017). Supply Response of Milk Producers to Economic and Non-Economic Factors in Swaziland. Journal of Agricultural Studies, 5(4), 14-34.
13
Szidarovszky, F., & Molnár, S. (1994). Adaptive and extrapolative estimations in a special discrete dynamic producer-consumer model. Szigma, 25, 221-227.
14
ORIGINAL_ARTICLE
Optimal system and approximate solutions of the nonlinear filtration equation
In this paper, the problem of determining the most general Lie point approximate symmetries group for the nonlinear filtration equation with a small parameter is analyzed. By applying the basic Lie approximate symmetry method for the nonlinear filtration equation with a small parameter, the classical Lie point approximate symmetry operators are obtained. Also, the algebraic structure of the Lie algebra of approximate symmetries is discussed and an optimal system of one-dimensional subalgebras of the nonlinear filtration equation with a small parameter, symmetry algebra which creates the preliminary classification of group invariant solutions is constructed. Particularly, the Lie invariants as well as similarity reduced equations corresponding to infinitesimal symmetries and group invariant solutions associated to the symmetries are obtained.
https://aotp.fabad-ihe.ac.ir/article_57300_02707fb4fef3391a9dc75c130f6ce0a9.pdf
2018-02-01
35
42
10.22121/aotp.2018.115787.1009
Lie group analysis
Approximate symmetry
Optimal system
Invariant solution
Filtration equation
Mohammad
Rahimian
m.rahimian@kiau.ac.ir
1
Department of Mathematics, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman , Iran
LEAD_AUTHOR
Alexandrova, A. A., Ibragimov, N. H., & Lukashchuk, V. O. (2014). Group classification and conservation laws of nonlinear filtration equation with a small parameter. Communications in Nonlinear Science and Numerical Simulation, 19(2), 364-376.
1
Bluman, G. W., & Kumei, S. (1989). Symmetries and Differential Equations. New York: Springer.
2
Bluman, G. W., Cheviakov, A. F., & Anco, S. C. (2010). Applications of symmetry methods to partial differential equations (Vol. 168, pp. xx+-398). New York: Springer.
3
Baikov, V. A., Gazizov, R. K., & Ibragimov, N. K. (1989). Approximate symmetries. Sbornik: Mathematics, 64, 427-441.
4
Camacho, J. C., Bruzón, M. S., Ramírez, J., & Gandarias, M. L. (2011). Exact travelling wave solutions of a beam equation. Journal of Nonlinear Mathematical Physics, 18(supp01), 33-49.
5
Euler, N., Shul'ga, M. W., & Steeb, W. H. (1992). Approximate symmetries and approximate solutions for a multidimensional Landau-Ginzburg equation. Journal of Physics A: Mathematical and General, 25(18), 1095-1103.
6
Fushchich, W. I., & Shtelen, W. M. (1989). On approximate symmetry and approximate solutions of the nonlinear wave equation with a small parameter. Journal of Physics A, 22, 887-890.
7
Gazizov, R. K. (1996). Lie algebras of approximate symmetries. Journal of Nonlinear Mathematical Physics, 3(1-2), 96-101.
8
Ibragimov, N. H., & Kovalev, V. F. (2009). Approximate and Renormgroup Symmetries. Beijing, China, Non-linear Physical Science: Higher Education Press.
9
Pakdemirli, M., Yürüsoy, M., & Dolapçı, İ. T. (2004). Comparison of approximate symmetry methods for differential equations. Acta Applicandae Mathematica, 80(3), 243-271.
10
Olver, P. J. (1993). Applications of Lie groups to differential equations. New York, in Graduate Texts in Mathematics: Springer.
11
Ovsiannikov, L. V. (1982). Group Analysis of Differential Equations. New York: Academic.
12
ORIGINAL_ARTICLE
Emergency response time minimization by incorporating ground and aerial transportation
In real life, many events may have severe effects on human being lives. These events can happen casually such as accident, heart attack or another severe disease, and deliberately like fights among people. From the engineering point of view, it does not matter what the reason of happening such events is, but the important thing is to rescue the affected people as much as possible in a short time and based on a scheduling point of view. In this study, we consider a real-life medical emergency service problem for a city with its known hospitals or medical care center locations. A limited number of ground and aerial vehicles, like ambulance and helicopter, are given to be assigned to these sites in which at most one vehicle from each type can be assigned. The aim is minimizing the total travel distances which are a function of the response time to the patients. To solve the problem, a mathematical formulation is proposed, and a metaheuristic solution method based on the genetic algorithm is developed, since the problem belongs to the NP-hard family of problems.
https://aotp.fabad-ihe.ac.ir/article_57301_fe9e7250d07ff399673d2c609ade1c73.pdf
2018-02-01
43
57
10.22121/aotp.2018.108905.1004
Response time
Aerial transportation
Emergency Service
Genetic algorithm
Mazyar
Ghadiri Nejad
mazyar.ghadirinejad@gmail.com
1
Production Department, Technology, University of Vaasa, Vaasa, Finland
LEAD_AUTHOR
Mahdi
Banar
mahdi_banar@yahoo.com
2
Industrial Engineering department, Engineering Faculty, Eastern Mediterranean University, Famagusta, TRNC, Turkey
AUTHOR
Abe, T., Takahashi, O., Saitoh, D., & Tokuda, Y. (2014). Association between helicopter with physician versus ground emergency medical services and survival of adults with major trauma in Japan. Critical care, 18(4), R146.
1
Adenso-Diaz, B., & Rodriguez, F. (1997). A simple search heuristic for the MCLP: Application to the location of ambulance bases in a rural region. Omega, 25(2), 181-187.
2
Ahmadi-Javid, A., Seyedi, P., & Syam, S. S. (2017). A survey of healthcare facility location. Computers & Operations Research, 79, 223-263.
3
Andruszkow, H., Schweigkofler, U., Lefering, R., Frey, M., Horst, K., Pfeifer, R., ... & Hildebrand, F. (2016). Impact of helicopter emergency medical service in traumatized patients: which patient benefits most? PloS one, 11(1), e0146897.
4
Ardekani, L. H., Haight, D., Ingolfsson, A., Salama, M., & Stanton, M. (2014). Scheduling and routing ambulances that provide inter-facility patient transfers. working paper.
5
Aringhieri, R., Bruni, M. E., Khodaparasti, S., & van Essen, J. T. (2017). Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research, 78, 349-368.
6
Başar, A., Çatay, B., & Ünlüyurt, T. (2012). A taxonomy for emergency service station location problem. Optimization letters, 6(6), 1147-1160.
7
Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European journal of operational research, 147(3), 451-463.
8
Brown, B. S., Pogue, K. A., Williams, E., Hatfield, J., Thomas, M., Arthur, A., & Thomas, S. H. (2012). Helicopter EMS transport outcomes literature: annotated review of articles published 2007–2011. Emergency medicine international, 2012.
9
Chin, S. N., Cheah, P. K., Arifin, M. Y., Wong, B. L., Omar, Z., Yassin, F. M., & Gabda, D. (2017, April). Determinants of ambulance response time: A study in Sabah, Malaysia. In AIP Conference Proceedings (Vol. 1830, No. 1, p. 080003). AIP Publishing.
10
Diaz, M. A., Hendey, G. W., & Bivins, H. G. (2005). When is the helicopter faster? A comparison of helicopter and ground ambulance transport times. Journal of Trauma and Acute Care Surgery, 58(1), 148-153.
11
Galvagno, S. M. (2013). Comparative effectiveness of helicopter emergency medical services compared to ground emergency medical services. Critical Care, 17(4), 169.
12
Gendreau, M., Laporte, G., & Semet, F. (2001). A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel computing, 27(12), 1641-1653.
13
Ghadiri Nejad, M., Güden, H., Vizvári1, B., & Vatankhah Barenji, R. (2017). A Mathematical Model and Simulated Annealing Algorithm for Solving the Cyclic Scheduling Problem of a Flexible Robotic Cell. Advances in Mechanical Engineering. In press.
14
Ghadiri Nejad M, Kovács G, Vizvári B, Barenji RV. (2017). An optimization model for cyclic scheduling problem in flexible robotic cells. International Journal of Advanced Manufacturing Technology.
15
Ghadiri Nejad, M., Shavarani, S. M., Vizvári, B., & Vatankhah Barenji, R. (2017). Trade-off between process scheduling and production cost in cyclic flexible robotic cell. International Journal of Advanced Manufacturing Technology. In press.
16
Golabi, M., Shavarani, S. M., & Izbirak, G. (2017). An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake. Natural Hazards, 87(3), 1545-1565.
17
Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88-105.
18
Knyazkov, K., Derevitsky, I., Mednikov, L., & Yakovlev, A. (2015). Evaluation of Dynamic Ambulance Routing for the Transportation of Patients with Acute Coronary Syndrome in Saint-petersburg. Procedia Computer Science, 66, 419-428.
19
Lam, S. S. W., Nguyen, F. N. H. L., Ng, Y. Y., Lee, V. P. X., Wong, T. H., Fook-Chong, S. M. C., & Ong, M. E. H. (2015). Factors affecting the ambulance response times of trauma incidents in Singapore. Accident Analysis & Prevention, 82, 27-35.
20
Li, X., Zhao, Z., Zhu, X., & Wyatt, T. (2011). Covering models and optimization techniques for emergency response facility location and planning: a review. Mathematical Methods of Operations Research, 74(3), 281-310.
21
McCormack, R., & Coates, G. (2015). A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247(1), 294-309.
22
Mosallaeipour, S., Nejad, M. G., Shavarani, S. M., & Nazerian, R. (2017). Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study. Production Engineering, 1-12.
23
Overstreet, R. E., Hall, D., Hanna, J. B., & Kelly Rainer Jr, R. (2011). Research in humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 114-131.
24
Pinto, L. R., Silva, P. M. S., & Young, T. P. (2015). A generic method to develop simulation models for ambulance systems. Simulation Modelling Practice and Theory, 51, 170-183.
25
Rahmaniani, R., & Shafia, M. A. (2013). A study on maximum covering transportation network design with facility location under uncertainty. Journal of Industrial and Production Engineering, 30(2), 78-93.
26
Schmid, V., & Doerner, K. F. (2010). Ambulance location and relocation problems with time-dependent travel times. European journal of operational research, 207(3), 1293-1303.
27
Shavarani, S. M., Ghadiri Nejad, M., Rismanchian, F., & Izbirak, G. (2017). Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon prime air in the city of San Francisco. The International Journal of Advanced Manufacturing Technology, 1-13.
28
Sullivent, E. E., Faul, M., & Wald, M. M. (2011). Reduced mortality in injured adults transported by helicopter emergency medical services. Prehospital Emergency Care, 15(3), 295-302.
29
Thomas, S. H. (2007). Helicopter EMS transport outcomes literature: annotated review of articles published 2004-2006. Prehospital Emergency Care, 11(4), 477-488.
30
Wisborg, T., & Bjerkan, B. (2014). Air ambulance nurses as expert supplement to local emergency services. Air medical journal, 33(1), 40-43.
31
ORIGINAL_ARTICLE
A polynomial-time algorithm to determine BCC efficient frontier without solving a mathematical programming problem
In this paper, we restrict our attention to the efficient frontier of the BCC model, where the BCC model is a well-known basic model in Data Envelopment Analysis (DEA). We here assume that each Decision Making Unit (DMU) has one input and one output. In order to obtain BCC efficient frontier, the paper proposes a polynomial-time algorithm of complexity bonded by to produce well-behaved affine functions. The produced functions are then used to determine a point-wise minimum of a finite number of affine functions. It will be shown that by finding this function, we in fact also determine the efficient frontier of the BCC model. The main advantage of this approach is ability to achieve the efficient frontier, without solving a mathematical programming problem. Also, all of the Pareto efficient DMUs, as BCC-efficient DMUs, can be easily obtained using the proposed algorithm. A numerical example is presented to explain the use and effectiveness of the proposed algorithm.
https://aotp.fabad-ihe.ac.ir/article_57302_5c8dd6ec55093c2a57f7701b57732648.pdf
2018-02-01
59
68
10.22121/aotp.2018.106001.1002
Data Envelopment Analysis
BCC model
Efficient frontier
Point-wise minimum
Pareto efficient DMUs
Masoud
Sanei
masoudsanei49@yahoo.com
1
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Hamid
Hassasi
hamid.mesho@gmail.com
2
Faculty of Management Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Amirteimoori, A., & Kordrostami, S. (2012). Generating strong defining hyperplanes of the production possibility set in data envelopment analysis. Applied Mathematics Letters, 25(3), 605-609.
1
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
2
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
3
Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to Data Envelopment Analysis, Applications References and DEA-Solver Software. Kluwer Academic Publishers.
4
Lotfi, F. H., Jahanshahloo, G. R., Mozaffari, M. R., & Gerami, J. (2011). Finding DEA-efficient hyperplanes using MOLP efficient faces. Journal of Computational and Applied Mathematics, 235(5), 1227-1231.
5
Jahanshahloo, G. R., Lotfi, F. H., & Zohrehbandian, M. (2005). Finding the piecewise linear frontier production function in data envelopment analysis. Applied Mathematics and Computation, 163(1), 483-488.
6
Jahanshahloo, G. R., Lotfi, F. H., Rezai, H. Z., & Balf, F. R. (2007). Finding strong defining hyperplanes of production possibility set. European Journal of Operational Research, 177(1), 42-54. G.R.
7
Jahanshahloo, G. R., Shirzadi, A., & Mirdehghan, S. M. (2009). Finding strong defining hyperplanes of PPS using multiplier form. European Journal of Operational Research, 194(3), 933-938. G.R.
8
Korhonen, P. (1997). Searching the efficient frontier in data envelopment analysis. IIASA. IR-97-79. P.
9
Murty, K. G. Linear programming. john Wily 1983.
10
Yu, G., Wei, Q., Brockett, P., & Zhou, L. (1996). Construction of all DEA efficient surfaces of the production possibility set under the generalized data envelopment analysis model. European Journal of Operational Research, 95(3), 491-510.
11
ORIGINAL_ARTICLE
A credibility-constrained programming for closed-loop supply chain network design problem under uncertainty
A closed-loop supply chain network (CLSCN) is consisted of both forward and reverse supply chains. In this paper, a CLSCN is including multiple plants, collection centers, demand markets, products and disposal centers. The plants manufacture the new products, then the new products are distributed to the demand market locations and the returned products are collected for sending to the collection centers. Collection centers have important role in recognizing the returned products conditions and the next action of supply chain as follows: inspection and/or separation of the collected products to check whether they are recoverable for sending to remanufacturing plants or unrecoverable ones to be sent to the disposal centers. A mixed-integer linear programming model is proposed to minimize the total cost. Since the uncertain parameters including cost, capacity, demand and the returned products influence the proposed CLSCN, a trapezoidal fuzzy model has been proposed to cope with the vagueness. The expected value is applied to the objective function and the chance constrained programming approach is used to model the uncertain constraint with fuzzy parameters. The numerical examples are coded and solved by GAMZ software. The computational results demonstrate the applicability of the proposed model and solution approach.
https://aotp.fabad-ihe.ac.ir/article_57303_66f1311485d82097f0bbd5e42c5720e7.pdf
2018-02-01
69
83
10.22121/aotp.2018.109753.1006
Closed-loop supply chain (CLSC)
Mixed-integer linear programming (MILP)
Possibilistic programming
Fuzzy mathematical programming
Credibility theory
Sara
Baranifar
sarabaranifar@gmail.com
1
Department of Industrial Engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
LEAD_AUTHOR
Al-Salem, M., Diabat, A., Dalalah, D., & Alrefaei, M. (2016). A closed-loop supply chain management problem: Reformulation and piecewise linearization. Journal of Manufacturing Systems, 40, 1-8
1
Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications, 73, 82-91.
2
Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
3
Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93-103.
4
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
5
Lai, Y. J., & Hwang, C. L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146.
6
Li, X., & Liu, B. (2006). A sufficient and necessary condition for credibility measures. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(05), 527-535.
7
Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.
8
Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10(4), 445-450.
9
Mahmoodirad, A., & Sanei, M. (2016). Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics. Scientia Iranica. Transaction E, Industrial Engineering, 23(3), 1429.
10
Özceylan, E., Demirel, N., Çetinkaya, C., & Demirel, E. (2017). A closed-loop supplychain network design for automotive industry in Turkey. Computers & Industrial Engineering In press.
11
Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80.
12
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
13
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14-38.
14
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
15
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.
16
Sanei, M., Mahmoodirad, A., & Niroomand, S. (2016). Two-Stage Supply Chain Network Design Problem with Interval Data. International Journal of e-Navigation and Maritime Economy, 5, 074 – 084.
17
Soto, A. V., Chowdhury, N. T., Allahyari, M. Z., Azab, A., & Baki, M. F. (2017). Mathematical modeling and hybridized evolutionary LP local search method for lot-sizing with supplier selection, inventory shortage, and quantity discounts. Computers & Industrial Engineering, 109, 96-112.
18
Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International journal of management reviews, 9(1), 53-80.
19
Tavana, M., Santos-Arteaga, F. J., Mahmoodirad, A., Niroomand, S., & Sanei, M. (2017). Multi-stage supply chain network solution methods: hybrid metaheuristics and performance measurement. International Journal of Systems Science: Operations & Logistics, 1-18.
20
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
21
Talaei, M., Farhang, B., Pishvaee, M.S., Bozorgi-Amiri, A., & Gholamnejad, S., (2015). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. J. Clean.Prod. 15, 959–6526.
22
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3-28.
23