Production planning and efficiency evaluation of a three-stage network

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of industrial engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Mathematics, Shahr.e Qods Branch, Islamic Azad University, Tehran, Iran

Abstract

In this paper, we investigate the production planning and warehouse layout for an authentic case, in which, a factory usually faces a challenge in the quest for sufficient space for produce and the management of warehouse items. We consider a network comprising of a production area, a warehouse area and a delivery point area and to solve this problem an integrated model for the produce and warehouse management has been rendered. We contemplate on a mixed integer, nonlinear model programming, targeting at minimizing total production costs, set-up costs, warehouse reservation and storage costs, transportation and delay penalty costs for this problem; besides which, the  issue of perishable goods is also under consideration. Morever, we utilized the data envelopment analysis (DEA) to measure the efficiency of the model results. This factory is taken into consideration as a dynamic network and a multiplicative DEA approaches are utilized to measure efficiency. Given the non-linearity of the models, a heuristic method is used to linearize the models. The ranking results of the dynamic network under study, demonstrated that, the time periods namely, (24) and (1) were the best and the poorest periods, respectively, in terms of efficiency. 

Keywords

Main Subjects


Afzalinejad, M., & Abbasi, Z. (2018). A slacks-based model for dynamic data envelopment analysis. Journal of Industrial & Management Optimization, 429-444.
Alvim, A.C.F., & Taillard, E.D. (2009). Popmusic for the point feature label placement problem. European Journal of Operational Research, 192, 396-413.
Aviles-Sacoto, S. V., Cook, W. D., Güemes-Castorena, D., Benita, F., Ceballos, H., & Zhu, J. (2018). Evaluating the Efficiencies of Academic Research Groups: A Problem of Shared Outputs. Asia-Pacific Journal of Operational Research, 1850042.
Badiezadeh, T., & Farzipoor, R. (2014). Efficiency evaluation of production lines using maximal balance index, International Journal Management and Decision Making, Vol. 13, No. 3, pp. 302-317.
Bakker, M., Riezebos, J., & Teunter, R.H. (2012). Review of inventory systems with deterioration since 2001, European Journal of Operational Research, 221: 275–28.
Banker, R.D., Charnes, A., & Cooper W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30:1078-1092.
Buschkühl, L., Sahling, F., Helber, S., & Tempelmeier, H. (2010). Dynamic capacitated lot-sizing problems: a classification and review of solution approaches. OR Spectrum, 32, 231-261.
Çelk, M., & Süral, H. (2014). Order picking under random and turnover-based storage policies in fishbone aisle warehouses. IIE transactions, 46(3), 283 -300.
Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units European Journal of Operational Research, 2(6), 429-444.
Chen, H. (2015). Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems. Omega, 56, 25-36.
Chopra, S., & Meindle, P. (2004). Supply chain management- strategy, planning and operation. 2nd Ed. Prentic Hall.
Cook, W.D., & Zhu, J. (2014). Data Envelopment Analysis - A Handbook of Modeling. Internal Structure and Network. Springer, New York.
Cunasekaran, A. Goyal (1993). Multi level lot sizing in a Rayon Yarn company: a case study , European Journal of Operational Research, 2, 159-174.
De Koster, R., Le-Duc, T., & Roodbergen, K.J. (2007). Design and control of warehouse order picking: a literature review. European Journal of Operational Research, 182, 481-501.
Ebrahimnejad, A., Tavana, M., & Mansourzadeh, S. M. (2015). An interactive MOLP method for solving outputoriented DEA problems with undesirable factors. Journal of Industrial & Management Optimization, 11(4), 1089-1110.
Fare, R., & Grosskopf, S. (2000). Network DEA, Socio Economics Planning Science, Vol. 4, No. 1, pp. 35–49.
Fare, R., Grosskopf, S., Lovell, K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach, Review of Economics and Statistics, Vol. 71, No. 1, pp. 90–98.
Farrell, M.J. (1957). The Measurement of Productive Efficiency Journal of the Royal Statistical Society Series A (General) 120:253-290.
Gelders, L.F., & Wassenhove. L.N (1981). product planning a review, European hournal of OR, 2,101-110.
Gu, J., Goetschalckx, M., & McGinnis, L.F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177, 1-21.
Hwang, H., & Kang, J. (2016). Two-phase algorithm for the lot-sizing problem with backlogging for stepwise transportation cost without speculative motives. Omega, 59, 238-250.
Jahanshahloo, G.R., HosseinzadehLotfi, F., Shoja, N., Tohidi, G., & Razavyan, S. (2005). Undesirable inputs and outputs in DEA models, Applied Mathematics and Computation, Vol. 169, No. 2, pp. 917–925.
Jamalnia, A., & Soukhakian, M. A. (2009). A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning. Computers & Industrial Engineering, 56(4), 1474-1486.
Jans, R., & Degraeve, Z. (2008). Modeling industrial lot-sizing problems: a review. International Journal of Production Research, 46, 1619-1643.
Jianfeng, M., Linan, Q., & Lizhi, D. (2017). Efficiency measurement and decomposition in hybrid two-stage DEA with additional inputs. Expert Systems with Applications, 79, 348-357.
Jolai, F., Gheisariha, E., & Nojavan, F. (2011). Inventory Control of Perishable Items in a Two-Echelon Supply Chain. Journal of Industrial Engineering, University of Tehran, Special Issue, PP. 69-77.
Kao, C. (2009). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196(3), 1107-1112.
Kao, C. (2014). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research, 232, 117-124.
Kao, C., & Hwang, S.N. (2008). Efficiency de composition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185 (1): 418-429.
Karimi, B., Ghomi, S. F., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378.
Kawaguchi, H., Tone, K., & Tsutsui, M., (2014). Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model. Health Care Management Science,17, 101-112.
Korhonen, P.J., & Luptacik, M. (2004). Eco-efficiency analysis of power plants: an extension of data envelopment analysis, European Journal of Operational Research, 154(2): 437–446.
Kou, M., Chen, K., Wang, S., & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-divisionsystems associated with DEA: An application to OECD countries‟ national innovation systems, Expert Systems with Applications, 46, 494-510.
Kritikos, M.N. (2017). A full ranking methodology in data envelopment analysis based on a set of dummy decision making units, Expert Systems with Applications, 77, 211-225.
Lee, T., Zhang, Y., & Jeong, B.H. (2016). A multi-period output DEA model with consistent time lag effects.Computers & Industrial Engineering, 93, 267-274.
Lewis, H.F., & Sexton, T.R. (2004). Network DEA: Efficiency analysis of organization with complex internal structure. Computers & Operations Research, 31, 1365-1410.
Lieberopoulos, G., & Dallery, Y. (2003). comparative modeling of multi production-inventory control policies with lot sizing. International Journal of production research, Vol. 41, No.6, 1273-1298.
Liu, X., & Tu, Y. (2008). Production planning with limited inventory capacity and allowed stockout. Int. J. Production Economics, 111, 180-191.
Lu, W.M., & Lo, S.F. (2007). A closer look at the economic– environmental disparities for regional development in China, European Journal of Operational Research, 183(2): 882–894.
Mahdavi, M. (2013). Development of certain inventory control models for deterioration items by considering backlog shortage and discounts, Journal of Industrial Engineering, Vol. 47, No. 1, PP. 69-80
Makui, A., Heydari, M., Aazami, A., & Dehghani, E. (2016). Accelerating Benders decomposition approach for robust aggregate production planning of products with a very limited expiration date, Computers & Industrial Engineering, 100: 34-51.
Malmborg, C.J., & Altassan, K.M. (1988). Analysis of storage assignment policies in less than unit load warehousing systems. International Journal of Production Research, 36, 3459–3475.
Mirzazadeh, A., Seyed Esfehani, M., & Fatemi, M. (2006), Determining economic order policy for deteriorating items with time-dependent inflation. Journal of University College Of Engineering, University of Tehran, Vol. 40, PP. 585-595.
Mula. J., & Poler, R. (2006). Models for production planning under uncertainty : a review, International journal of Production Economic,103, 271-285.
Page, E., & Paul, R.J. (1976). Multi-product inventory situations with one restriction. Operational Research Quarterly, 27, 815-834.
Sarkar, B., Cárdenas-Barrón, L. E., Sarkar, M., & Singgih, M. L. (2014). An economic production quantity model with random defective rate, rework process and backorders for a single stage production system. Journal of Manufacturing Systems, 33(3), 423-435.
Seiford, L.M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research, Vol. 142, No. 1, pp. 16–20.
Sengupta, J.K. (1995). Dynamic of Data Envelopment Analysis: Theory of Systems Efficiency. Springer Science & Business Media, Netherlands.
Simpson, N. C. (2001). Questioning the relative virtues of dynamic lot sizing rules. Computers & Operations Research, 28(9), 899-914.
Wang, W., Lu, W., & Liu, P. A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding companies. Expert Systems with Applications, 41, (2014), 4290-4297.
Wanke, P., & Barros, C. Two-Stage DEA: An application to major Brazilian banks, Expert Systems with Applications, 41, (2014), 2337-2344.
Wee, H.M. Economic production lot size model for deteriorating items with partial back-ordering. Computers & Industrial Engineering, 24, (1993), 449-458
Wu, M. Y., & Wee, H. M. Buyer-seller joint cost model for deteriorating items with multiple lot-size deliveries. Journal of the Chinese institute of industrial engineers, 18(1), (2001), 109-119.
Yang, P. C., & Wee, H. M.  Economic ordering policy of deteriorated item for vendor and buyer: an integrated approach. Production Planning & Control, 11(5), (2000), 474-480.
Yu, M.M., & Lin, E.T.J. Efficiency and effectiveness in railway performance using a multi-activity network DEA model, Omega, Vol. 36, No. 6, pp, (2008), 1005–1017.
Zhang, G., & Lai, K.K. Tabu search approach for multi-level warehouse layout problem with adjacent constraints, Engineering Optimization, 42, (2010), 775-790.
Zhang, G., Nishi, T., Turner, S. D., Oga, K., & Li, X. An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches. Omega, 68, (2017), 85-94.
Zeng, A. Z., Mahan, M., & Fluet, N. Designing an efficient warehouse layout to facilitate the order-filling process: An industrial distributor's experience. Production and Inventory Management Journal, 43(3/4), (2002), 83.
Zhou, Y. W., & Wang, S. D. Optimal production and shipment models for a single-vendor–single-buyer integrated system. European Journal of Operational Research, 180(1), (2007), 309-328.