ORIGINAL_ARTICLE
Simultaneous coordination of review period and order-up-to-level in a manufacturer-retailer chain
In this article, manufacturer-retailer supply chain coordination (SCC) has been developed under periodic review inventory system. In the studied model, the retailer as downstream member uses periodic review inventory policy and decides about review period (T) and order-up-to-level (R). Also, the manufacturer as upstream member faces EPQ system and determines the number of shipments from manufacturer to retailer per production run (n) as a decision variable. Firstly, the problem is investigated in decentralized and centralized models and accordingly, in order to coordinate the mentioned supply chain, a coordination scheme based on quantity discount is proposed. Maximum and minimum discounts, which are acceptable for both members, are determined. Numerical examples and sensitivity analysis indicate that the proposed coordination scheme improves the profitability and performance of both members and entire SC toward decentralized model. Moreover, the developed coordination model can share extra profit between SC members according to their bargaining power fairly.
https://www.jise.ir/article_39090_66bb5c5feaf09f5a41746ce0bbd5f56e.pdf
2017-05-16
1
17
Manufacturer-retailer chain
periodic review inventory system
production
supply chain coordination
Quantity discounts
Maryam
Johari
m_johari@ind.iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Seyyed-Mahdi
Hosseini-Motlagh
motlagh@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Mohammadreza
Nematollahi
nematollahi@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Aljazzara, S. M., Jabera, M. Y.,& Goyal, S.K.(2016). Coordination of a two-level supply chain (manufacturer–retailer) with permissible delay in payments. International Journal of Systems Science: Operations & Logistics, 3(3), 176-188.
1
Annadurai, K., Uthayakumar, R.(2010). Reducing lost-sales rate in (T,R,L) inventory model with controllable lead time. Applied Mathematical Modelling, 34, 3465–3477.
2
Arani, Vafa, H., Rabbani, M., & Rafiei, H. (2016). A revenue-sharing option contract toward coordination of supply chains. International Journal of Production Economics, 178 42-56.
3
Baganha, M.P., Cohen, M.A.(1998). The stabilising effect of inventory in supply. Operations Research, 46 (3), 572–583.
4
Bijvank, M., Johansen, S. G.(2012). Periodic review lost-sales inventory models with compound Poisson demand and constant lead times of any length. European Journal of Operational Research, 220(1), 106–114.
5
Boyacı, T., Gallego, G.(2002).Coordinating pricing and inventory replenishment policies for one wholesaler and one or more geographically dispersed retailers. International Journal of Production Economics, 77(2), 95–111.
6
Braglia, M. , Castellano, D.,& Frosolini, M. (2016).A novel approach to safety stock management in a coordinated supply chain with controllable lead time using present value.Applied Stochastic Models in Business and Industry 32(1), 99-112.
7
Cachon, G. P., Zipkin, P.H.(1999).Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain. Management science, 45(7), 936-953.
8
Chaharsooghi, S. K., Heydari, J. (2010a). Optimum coverage of uncertainties in a supply chain with an order size constraint. The International Journal of Advanced Manufacturing Technology, 47(1-4),
9
Chaharsooghi, S. K., Heydari, J.(2010b).Supply chain coordination for the joint determination of order quantity and reorder point using credit option. European Journal of Operational Research, 204(1), 86–95.
10
Chiu, C-H, Choi, T-M,& Li, X. (2011). Supply chain coordination with risk sensitive retailer under target sales rebate. Automatica, 47(8), 1617–1625.
11
Cobb, B. R. (2016). Lead time uncertainty and supply chain coordination in lost sales inventory models. International Journal of Inventory Research, 3(1), 5-30.
12
Duan, Y., Luo, J. & Huo, J.(2010). Buyer–vendor inventory coordination with quantity discount incentive for fixed life time product. International Journal of Production Economics, 128(1), 351–357.
13
Dutta, P., Das, D. & Schultma, F.(2016). Design and planning of a closed-loop supply chain with three way recovery and buy-back offer. Journal of Cleaner Production, 135 , 604e619.
14
Feng, X., Moon, I.,& Ryu, K.(2014). Revenue-sharing contracts in an N-stage supply chain with reliability considerations. International Journal of Production Economics,147, 20–29.
15
FerhanÇebi, I.(2016). A two stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Information Sciences, 339, 143–157.
16
Govindan, k., Diabat, A.,& Popiuc, M. N. (2012). Contract analysis: A performance measures and profit evaluation within two-echelon supply chains. Computers & Industrial Engineering, 63(1), 58-74.
17
Glock, Ch. H. (2012). The joint economic lot size problem: A reviewInternational Journal of Production Economics, 135(2), 671–686.
18
Heydari, J. (2013). Coordinating replenishment decisions in a two-stage supply chain by considering truckload limitation based on delay in payments. International Journal of Systems Science, 46(10), 1897-1908.
19
Heydari, J. (2014a). Coordinating supplier׳ s reorder point: A coordination mechanism for supply chains with long supplier lead time. Computers & Operations Research, 48, 89-101.
20
Heydari, J.(2014b).Lead time variation control using reliable shipment equipment: An incentive scheme for supply chain coordination. Transportation Research Part E, 63,44–58.
21
Heydari, J.(2014c).Supply chain coordination using time-based temporary price discounts. Computers & Industrial Engineering, 75, 96–101.
22
Heydari, J., Norouzinasab, Y. (2016). Coordination of pricing, ordering, and lead time decisions in a manufacturing supply chain. .Journal of Industrial and Systems Engineering, 9, 1-16.
23
Hou, J., Zeng, A. Z., Zhao, L.(2010). Coordination with a backup supplier through buy-back contract under supply disruption. Transportation Research Part E, 46, 881–895.
24
Hsueh, C-F.(2014). Improving corporate social responsibility in a supply chain through a new revenue sharing contract.International Journal of Production Economics, 151, 214-222.
25
Hsu, S-L., & Lee Ch. Ch.(2009).Replenishment and lead time decisions in manufacturer–retailer chains. .Transportation Research Part E, 45, 398–408.
26
Jeuland, A. P., Shugan, S. M. (1983). Managing Channel Profits. Marketing science, 2(3), 239-272.
27
Joglekar, P. (1988). Comments on “A quantity discount pricing model to increase vendor profits”. Management science, 34(11), 1391-1398.
28
Kanchana, K., Anulark, T.(2006). An approximate periodic model for fixed-life perishable products in a two-echelon inventory distribution system. International Journal of Production Economics, 100, 101–115.
29
Kouki, Ch., Jouini, O. (2015). On the effect of life time variability on the performance of inventory systems.International Journal of Production Economics, 167, 23–34.
30
Kreng, V. B., Tan, Sh-J. (2011). Optimal replenishment decision in an EPQ model with defective items under supply chain trade credit policy. Expert Systems with Applications, 38, 9888–9899.
31
Li, J., Liu, L. (2006). Supply chain coordination with quantity discount policy. International Journal of Production Economics, 101(1), 89-98.
32
Li, L., Wang, Y., & Dai, W.(2016). Coordinating supplier retailer and carrier with price discount policy. Applied Mathematical Modelling, 40,646–657.
33
Lin, Y-J. (2010).A stochastic periodic review integrated inventory model involving defective items, backorder price discount,and variable lead time. 4OR, 8(3), 281–297.
34
Liu, Zh.,et al.(2014). Risk hedging in a supplyc hain: Option vs. price discount. International Journal of Production Economics, 151, 112–120.
35
Masihabadi, S., Eshghi, K. (2011). Coordinating a Seller-Buyer Supply Chain with a Proper Allocation of Chain’s Surplus Profit Using a General Side-Payment Contract. Journal of Industrial and Systems Engineering, 5, 63-79.
36
Mokhlesian, M., ZEGORDI, S. H.(2015).Coordination of pricing and cooperative advertising for perishable products in a two-echelon supply chain: A bi-level programming approach. Journal of Industrial and Systems Engineering, 8(4).
37
Muniappana, P., Uthayakumarb, R.,& Ganesha, S. (2015). A production inventory model for vendor–buyer coordination with quantity discount, backordering and rework for fixed life time products. Journal of Industrial and Production Engineering, 1-8.
38
Nematollahi, M., Hosseini-Motlagh, S. M., & Heydari, J. (2016a). Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. Journal of Cleaner Production. http://dx.doi.org/10.1016/j.jclepro.2016.10.062
39
Nematollahi, M., Hosseini-Motlagh, S. M.,& Heydari, J. (2016b). Coordination of social responsibility and order quantity in a two echelon a collaborative decision making perspective. International Journal of Production Economics.http://dx.doi.org/10.1016/j.ijpe.2016.11.017
40
Rosenblatt, M. J., Lee, H. L.(1985). Improving profitability with quantity discounts under fixed demand. IIE Transactions, 17, 388–395.
41
Silver, E.A, Pyke, D.F.,& Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (Vol. 3). New York: Wiley.
42
Song, D-P, Dong, j.-X., & Xu, J.(2014). Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties. European Journal of Operational Research, 232, 522–536.
43
Soni, H. N., Joshi, M.(2015). A periodic review inventory model with controllable lead time and back order rate in fuzzy-stochastic environment.Fuzzy Information and Engineering 7 (1),101-114.
44
Wong, W. k., Qi, J., & Leung, S.Y.S.(2009). Coordinating supply chains with sales rebate contracts and vendor-managedinventory. International Journal of Production Economics, 120(1), 151–161.
45
ORIGINAL_ARTICLE
Optimal production and marketing planning with geometric programming approach
One of the primary assumptions in most optimal pricing methods is that the production cost is a non-increasing function of lot-size. This assumption does not hold for many real-world applications since the cost of unit production may have non-increasing trend up to a certain level and then it starts to increase for many reasons such as an increase in wages, depreciation, etc. Moreover, the production cost will eventually have a declining trend. This trend curve can be demonstrated in terms of cubic function and the resulted optimal pricing model can be modeled in Geometric Programming (GP). In this paper, we present a new optimal pricing model where the cost of production has different trends depending on the production size. The resulted problem is formulated as a parametric GP with five degrees of difficulty and it is solved using the recent advances of optimization techniques. The paper is supported with various numerical examples and the results are analyzed under different scenarios.
https://www.jise.ir/article_39088_77aa9e4e52e0c0596a7c50cc51b7516f.pdf
2017-05-17
18
29
Geometric programming
Nonlinear Model
Production and Operations Management
Optimal pricing
Marketing planning
Seyed Reza
Moosavi Tabatabaei
sr_tabatabaei@iust.ac.ir
1
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran, Center of Excellence in Optimization and Manufacturing
AUTHOR
Seyed Jafar
Sadjadi
sjsadjadi@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Abuo-El-Ata, M.O., Fergany, H.A. and El-Wakeel, M.F., 2003. Probabilistic multi-item inventory model with varying order cost under two restrictions: a geometric programming approach. International Journal of Production Economics, 83(3), pp.223-231.
1
Chen, C.K., 2000. Optimal determination of quality level, selling quantity and purchasing price for intermediate firms. Production Planning & Control, 11(7), pp.706-712.
2
Cheng, T.C.E., 1989. An economic order quantity model with demand-dependent unit cost. European Journal of Operational Research, 40(2), pp.252-256.
3
Duffin, R.J., Peterson, E.L. and Zener, C.M., 1967. Geometric programming: theory and application.
4
Fathian, M., Sadjadi, S.J. and Sajadi, S., 2009. Optimal pricing model for electronic products. Computers & Industrial Engineering, 56(1), pp.255-259.
5
Ghosh, P. and Roy, T.K., 2013. A goal geometric programming problem (G 2 P 2) with logarithmic deviational variables and its applications on two industrial problems. Journal of Industrial Engineering International, 9(1), pp.1-9.
6
Grant, M.C. and Boyd, S.P., 2011. CVX Research, Inc. CVX: Matlab software for disciplined convex programming, cvxr. com/cvx.
7
Jung, H. and Klein, C.M., 2005. Optimal inventory policies for an economic order quantity model with decreasing cost functions. European Journal of Operational Research, 165(1), pp.108-126.
8
Kim, D. and Lee, W.J., 1998. Optimal joint pricing and lot sizing with fixed and variable capacity. European Journal of Operational Research, 109(1), pp.212-227.
9
Kochenberger, G.A., 1971. Inventory models: Optimization by geometric programming. Decision sciences, 2(2), pp.193-205.
10
Lee, W.J., 1993. Determining order quantity and selling price by geometric programming: optimal solution, bounds, and sensitivity. Decision Sciences,24(1), pp.76-87.
11
Lee, W.J. and Kim, D., 1993. Optimal and heuristic decision strategies for integrated production and marketing planning. Decision Sciences, 24(6), pp.1203-1214.
12
Lilien, G.L., Kotler, P. and Moorthy, K.S., 1992. Marketing modelsPrentice-Hall. Englewood Cliffs, NJ.
13
Liu, S.T., 2007. Profit maximization with quantity discount: An application of geometric programming. Applied mathematics and computation, 190(2), pp.1723-1729.
14
Mandal, N.K., Roy, T.K. and Maiti, M., 2005. Multi-objective fuzzy inventory model with three constraints: a geometric programming approach. Fuzzy Sets and Systems, 150(1), pp.87-106.
15
Parlar, M. and Weng, Z.K., 2006. Coordinating pricing and production decisions in the presence of price competition. European journal of operational research, 170(1), pp.211-227.
16
Sadjadi, S.J., Aryanezhad, M.B. and Jabbarzadeh, A., 2010. Optimal marketing and production planning with reliability consideration. African Journal of Business Management, 4(17), p.3632.
17
Sadjadi, S.J., Oroujee, M. and Aryanezhad, M.B., 2005. Optimal production and marketing planning. Computational Optimization and Applications, 30(2), pp.195-203.
18
ORIGINAL_ARTICLE
Enhanced chromosome repairing mechanism based genetic algorithm approach for the multi-period perishable production inventory-routing problem
One of the important aspects of distribution optimization problems is simultaneously, controlling the inventory while devising the best vehicle routing, which is a famous problem, called inventory-routing problem (IRP). When the lot-sizing decisions are jointed with IRP, the problem will get more complicated called production inventory-routing problem (PIRP). To become closer to the real life problems that includes products that have a limited life time like foods, it seems reasonable to narrow down the PIRP problem to the perishable products, which is perishable-production inventory-routing problem (P-PIRP). This paper addresses a P-PIRP in a two echelon supply chain system where the vendor must decide when and how much to produce and deliver products to the customer’s warehouse. Here, the general model of PIRP as mixed integer programming (MIP)is adopted and the perishability constraint are added in order to solve the P-PIRP problems. Due to the complexity of problem, providing solution for the medium to large instances cannot be easily achieved by business applications, and then using the meta-heuristics is unavoidable. The novelty of this research is devising an enhanced genetic algorithm (GA) using multiple repairing mechanisms, which because of its computationally cumbersomeness have absorbed less attention in the literature. The problem runs through some generated instances and shows superiority in comparison to the business application.
https://www.jise.ir/article_39089_ca47d471dc7a254610cc58c7f880a9cf.pdf
2017-05-19
30
56
Production inventory routing problem
IRP
mixed integer-programming
perishable
Genetic algorithm
Parviz
Fattahi
p.fattahi@alzahra.ac.ir
1
Department of Industrial Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Mehdi
Tanhatalab
me.tanhatalab@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Mahdi
Bashiri
bashiri@shahed.ac.ir
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
Abdelmaguid, T. F. & M. M. Dessouky (2006) A genetic algorithm approach to the integrated inventory-distribution problem. International Journal of Production Research, 44, 4445-4464.
1
Adulyasak, Y., J.-F. Cordeau & R. Jans (2015) The production routing problem: A review of formulations and solution algorithms. Computers & Operations Research, 55, 141-152.
2
Al Shamsi, A., A. Al Raisi & M. Aftab. (2014) Pollution-inventory routing problem with perishable goods. In Logistics Operations, Supply Chain Management and Sustainability, 585-596. Springer.
3
Andersson, H., A. Hoff, M. Christiansen, G. Hasle & A. Løkketangen (2010) Industrial aspects and literature survey: Combined inventory management and routing. Computers & Operations Research, 37, 1515-1536.
4
Bell, W. J., L. M. Dalberto, M. L. Fisher, A. J. Greenfield, R. Jaikumar, P. Kedia, R. G. Mack & P. J. Prutzman (1983) Improving the Distribution of Industrial Gases with an On-Line Computerized Routing and Scheduling Optimizer. Interfaces, 13, 4-23.
5
Campbell, A. M. & M. W. P. Savelsbergh (2004) A Decomposition Approach for the Inventory-Routing Problem. Transportation Science, 38, 488-502.
6
Clarke, G. & J. W. Wright (1964) Scheduling of vehicles from a central depot to a number of delivery points. Operations research, 12, 568-581.
7
Coelho, L. C., J.-F. Cordeau & G. Laporte (2014) Thirty Years of Inventory Routing. Transportation Science, 48, 1-19.
8
Coelho, L. C. & G. Laporte (2014) Optimal joint replenishment, delivery and inventory management policies for perishable products. Computers & Operations Research, 47, 42-52.
9
Devapriya, P., W. Ferrell & N. Geismar (2016) Integrated Production and Distribution Scheduling with a Perishable Product. European Journal of Operational Research.
10
Dror, M. & P. Trudeau (1989) Savings by split delivery routing. Transportation Science, 23, 141-145.
11
Federgruen, A., G. Prastacos & P. H. Zipkin (1986) An allocation and distribution model for perishable products. Operations Research, 34, 75-82.
12
Kleywegt, A. J., V. S. Nori & M. W. P. Savelsbergh (2002) The Stochastic Inventory Routing Problem with Direct Deliveries. Transportation Science, 36, 94-118.
13
Le, T., A. Diabat, J.-P. Richard & Y. Yih (2013) A column generation-based heuristic algorithm for an inventory routing problem with perishable goods. Optimization Letters, 7, 1481-1502.
14
Mester, D., O. Bräysy & W. Dullaert (2007) A multi-parametric evolution strategies algorithm for vehicle routing problems. Expert Systems with Applications, 32, 508-517.
15
Mirzaei, S. & A. Seifi (2015) Considering lost sale in inventory routing problems for perishable goods. Computers & Industrial Engineering, 87, 213-227.
16
Moin, N. H., S. Salhi & N. Aziz (2011) An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. International Journal of Production Economics, 133, 334-343.
17
Rahimi, M., A. Baboli & Y. Rekik (2016) Sustainable Inventory Routing Problem for Perishable Products by Considering Reverse Logistic. IFAC-PapersOnLine, 49, 949-954.
18
Savaşaneril, S. & N. Erkip (2010) An analysis of manufacturer benefits under vendor-managed systems. IIE Transactions, 42, 455-477.
19
Shaabani, H. & I. N. Kamalabadi (2016) An efficient population-based simulated annealing algorithm for the multi-product multi-retailer perishable inventory routing problem. Computers & Industrial Engineering, 99, 189-201.
20
Sindhuchao, S., H. E. Romeijn, E. Akçali & R. Boondiskulchok (2005) An integrated inventory-routing system for multi-item joint replenishment with limited vehicle capacity. Journal of Global Optimization, 32, 93-118.
21
Soysal, M., J. M. Bloemhof-Ruwaard, R. Haijema & J. G. van der Vorst (2015) Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty. International Journal of Production Economics, 164, 118-133.
22
ORIGINAL_ARTICLE
An improved memetic algorithm to minimize earliness–tardiness on a single batch processing machine
In this research, a single batch processing machine scheduling problem with minimization of total earliness and tardiness as the objective function is investigated.We first formulate the problem as a mixed integer linear programming model. Since the research problem is shown to be NP-hard, an improved memetic algorithmis proposed to efficiently solve the problem. To further enhance the memetic algorithm and avoid premature convergence, we hybridize it with a variable neighborhood search procedureas its local search engine. A dynamic programming approach is also proposed to find optimal schedule for a given set of batches. Wedesign a Taguchi experiment to evaluate the effects of different parameters on the performance of the proposed algorithm. The results of an extensive computational study demonstrate the efficacy of the proposed algorithm.
https://www.jise.ir/article_39091_542e8d4a895809ec22257a245966d266.pdf
2017-05-24
57
72
Batch processing machine
Total earliness and tardiness
Memetic algorithm
Variable neighborhood search
Dynamic programming
Neda
Rafiee Parsa
nedarafiee@aut.ac.ir
1
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Behrooz
Karimi
b.karimi@aut.ac.ir
2
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Seyed Mohammad
Moattar Husseini
moattarh@aut.ac.ir
3
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Al-Salamah, M. (2015). Constrained binary artificial bee colony to minimize the makespan for single machine batch processing with non-identical job sizes. Applied Soft Computing, 29, 379-385.
1
Brucker, P., Gladky, A., Hoogeveen, H., Kovalyov, M.Y., Potts, C., Tautenhahn, T. & Van De Velde, S. (1998). Scheduling a batch machine. Journal of Scheduling, 1, 31–54.
2
Cabo, M., Possani, E., Potts, C.N. & Song, X. (2015). Split–merge: Using exponential neighborhood search for scheduling a batching machine. Computers & Operations Research, 63, 125-135.
3
Chen, H., Du, B. & Huang, G.Q. (2011). Scheduling a batch processing machine with non-identical job sizes: a clustering perspective. International Journal of Production Research, 49(19), 5755-5778.
4
Cheng, B., Li, K. & Chen, B. (2010). Scheduling a single batch-processing machine with non-identical job sizes in fuzzy environment using an improved ant colony optimization. Journal of Manufacturing Systems, 29(1), 29-34.
5
Coffman, E.G., Garey, M.R. & Johnson, D.S. (1997). Approximation algorithms for bin packing: a survey. In: S.H. Dorit, Approximation algorithms for NP-hard problems (pp. 46-93): PWS Publishing Co.
6
Damodaran, P., Ghrayeb, O. & Guttikonda, M.C. (2013). GRASP to minimize makespan for a capacitated batch-processing machine. The International Journal of Advanced Manufacturing Technology, 68(1-4), 407-414.
7
Dupont, L. & Dhaenens-Flipo, C. (2002). Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure. Computers & Operations Research, 29(7), 807-819.
8
Dupont, L. & Jolai, F. (1998). Minimizing makespan on a single batch processing machine with non-identical job sizes. European Journal of Automation Systems, 32, 431-440.
9
Graham, R.L., Lawler, E.L., Lenstra, J.K. & Kan, A. (1979). Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of discrete Mathematics, 5, 287-326.
10
Hart, W.E., Krasnogor, N. & Smith, J.E. (2005). Recent advances in memetic algorithms (Vol. 166): Springer Verlag.
11
Jia, Z.-h. & Leung, J.Y.T. (2014). An improved meta-heuristic for makespan minimization of a single batch machine with non-identical job sizes. Computers & Operations Research, 46(0), 49-58.
12
Jolai, F. & Dupont, L. (1998). Minimizing mean flow times criteria on a single batch processing machine with non-identical jobs sizes. International Journal of Production Economics, 55(3), 273-280.
13
Li, Z., Chen, H., Xu, R. & Li, X. (2015). Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes. Computers & Industrial Engineering, 87, 590-599.
14
Malapert, A., Gueret, C. & Rousseau, L.-M. (2012). A constraint programming approach for a batch processing problem with non-identical job sizes. European Journal of Operational Research, 221(3), 533-545.
15
Mathirajan, M. & Sivakumar, A.I. (2006). A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. The International Journal of Advanced Manufacturing Technology, 29(9-10), 990-1001.
16
Mönch, L., Fowler, J.W., Dauzère-Pérès, S., Mason, S.J. & Rose, O. (2011). A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations. Journal of Scheduling, 14(6), 583-599.
17
Mönch, L. & Unbehaun, R. (2007). Decomposition heuristics for minimizing earliness–tardiness on parallel burn-in ovens with a common due date. Computers & Operations Research, 34(11), 3380-3396.
18
Mönch, L., Unbehaun, R. & Choung, Y.I. (2006). Minimizing earliness–tardiness on a single burn-in oven with a common due date and maximum allowable tardiness constraint. OR Spectrum, 28(2), 177-198.
19
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program, C3P Report, 826.
20
Potts, C.N. & Kovalyov, M.Y. (2000). Scheduling with batching: a review. European Journal of Operational Research, 120(2), 228-249.
21
Qi, X. & Tu, F. (1999). Earliness and tardiness scheduling problems on a batch processor. Discrete Applied Mathematics, 98(1), 131-145.
22
Rafiee Parsa, N., Karimi, B. & Husseinzadeh Kashan, A. (2010). A branch and price algorithm to minimize makespan on a single batch processing machine with non-identical job sizes. Computers & Operations Research, 37(10), 1720-1730.
23
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes: Asian productivity organization.
24
Uzsoy, R. (1994). Scheduling a single batch processing machine with non-identical job sizes. International Journal of Production Research, 32(7), 1615-1635.
25
Wang, H.-M. (2011). Solving single batch-processing machine problems using an iterated heuristic. International Journal of Production Research, 49(14), 4245-4261.
26
Xu, R., Chen, H. & Li, X. (2012). Makespan minimization on single batch-processing machine via ant colony optimization. Computers & Operations Research, 39(3), 582-593.
27
Zhao, H., Hu, F. & Li, G. (2006). Batch scheduling with a common due window on a single machine. In: Fuzzy Systems and Knowledge Discovery (pp. 641-645): Springer.
28
Zhou, S., Chen, H., Xu, R. & Li, X. (2014). Minimising makespan on a single batch processing machine with dynamic job arrivals and non-identical job sizes. International Journal of Production Research, 52(8), 2258-2274.
29
ORIGINAL_ARTICLE
Evaluating alternative MPS development methods using MCDM and numerical simulation
One of the key elements in production planning hierarchy is master production scheduling. The aim of this study is to evaluate and compare thirteen alternative MPS development methods, including multi-objective optimization as well as twelve heuristics, in different operating conditions for multi-product single-level capacity-constrained production systems. We extract six critical criteria from the previous related researches and employ them in a MCDM framework. The Shannon entropy is used to weight the criterion and TOPSIS is proposed for ranking the alternative methods. To be able to generalize the results, 324 cases considering different operating conditions are simulated. The results show that the most important criteria are instability and inventory/setup costs, respectively. A performance analysis of MPS development methods is reported that the heuristics provides better results than multi-objective optimization in many conditions. A sensitivity analysis for critical parameters is also provided. Finally, the proposed methodology is implemented in a wire & cable company.
https://www.jise.ir/article_39499_154a965f2249e452d68022e9e32123e2.pdf
2017-05-27
73
90
Master production scheduling
Multi-criteria decision making
heuristics
TOPSIS
Shannon entropy, Numerical simulation
Mohammad Mehdi
Lotfi
lotfi@yazd.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Negin
Najafian
negin_69_n@yahoo.com
2
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
Akhoondi, F., Lotfi, M.M. (2016). A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands, International Journal of Production Research, 54(12): 3659-3676.
1
Dixon, P.S., Silver, E.A. (1981). A heuristic solution procedure for the multi-item, single level, limited capacity, lot sizing problem, Journal of Operations Management, 2 (1): 23-40.
2
Eisenhut, P.S. (1975). A dynamic lot sizing algorithm with capacity constraints, IIE Transactions, 7 (2): 170-176.
3
Gahm, C., Dünnwald, B., Sahamie, R. (2014). A multi-criteria master production scheduling approach for special purpose machinery, International Journal of Production Economics,149: 89-101.
4
Gunther, H.O. (1987). Planning lot sizes and capacity requirements in a single stage production systems, European Journal of Operational Research, 31 (2): 223-231.
5
Hajipour, V., Fattahi, P., Nobari, A. (2014). A hybrid ant colony optimization algorithm to optimize capacitated lot-sizing problem, Journal of Industrial and Systems Engineering, 7(1):1-20.
6
Heemsbergen, B., Malstrom, E.M. (1994). A simulation of single-level MRP lot sizing heuristics: an
7
analysis of performance by rule, Production Planning & Control, 5(4): 381-391.
8
Herrera, C., Belmokhtar-Berraf, S., Thomas, A., Parada, V. (2015). A reactive decision-making approach to reduce instability in a master production schedule, International Journal of Production Research, 54(8): 2394-2404.
9
Jeunet, J., Jonard, N. (2000). Measuring the performance of lot-sizing technique in uncertain environment, International Journal of Production Economics, 64 (1-3): 197-208.
10
Jonsson, P., Kjellsdotter, L. (2015). Improving performance with sophisticated master production scheduling, International Journal of Production Economics, 168, 118-130.
11
Karimi, B., Ghomi Fatemi, S.M.T., Wilson, J.M. (2003). The capacitated lot sizing problem: a review of models and algorithms, Omega, The international Journal of management science, 31: 365-378.
12
Kirca, O., Kokten, M. (1994). A new heuristic approach for the multi-item dynamic lot sizing problem, European Journal of Operational Research, 75 (2): 332-3241.
13
Lambrecht, M.R., Vanderveken, H. (1979). Heuristic procedures for the single operation, multi-item loading problem, IIE Transactions, 11 (4): 319-325.
14
Maes, J., Van Wassenhove, L.N. (1986). A simple heuristic for the multi-item single level capacitated lot sizing problem, Operations Research Letters, 4 (6): 265-273.
15
Ponsignon, T., Mönch, L. (2014). Simulation-based performance assessment of master planning approaches in semiconductor manufacturing, Omega, The international Journal of management science, 46: 21-35.
16
Razmi, J., Lotfi, M.M. (2011). Principles of production planning and inventory control, Tehran University Publications.
17
Selen, W.J., Heuts, R.M. (1989). A modified priority index for Gunther’s lot-sizing heuristic under capacitated single stage production, European Journal of Operational Research, 41 (2): 181-185.
18
Soares, M.M., Vieira, G.E. (2009). A new multi-objective optimization method for master production scheduling problems based on genetic algorithm, International Journal of Advanced Manufacturing Technology, 41 (5): 549-567.
19
Sridharan, S., Beny, W., Udaya Bhanu, V. (1988). Measuring master production schedule stability under rolling planning horizon, Decision Sciences, 19 (1): 147-166.
20
Supriyanto, I., Noche, B. (2011). Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule, Logistics Journal, 7: 1-14.
21
Unahabhokha, C., Schuster, E.W., Allen, S.J., Finch, B.J. (2003). Master production schedule stability under conditions of finite capacity, Working Paper, Massachusetts Institute of Technology.
22
Vieira, G.E., Favaretto, F. (2006). A new and practical heuristic for master production scheduling creation, International Journal of Production Research, 44(18-19): 3607-3625.
23
Xie, J., Zhao, X., Lee, T.S. (2003). Freezing the master production schedule under single resource constraint and demand uncertainty, International Journal of Production Economics, 83 (1): 65-84.
24
Xie, J., Zhao, X., Lee, T.S., Zhao, X. (2004). Impact of forecasting error on the performance of capacitated multi-item production systems, Computers & Industrial Engineering, 46 (2): 205-219.
25
Zhao, X., Lam, K. (1997). Lot-sizing rules and freezing the master production schedule in material requirements planning system, International Journal of Production Economics, 53 (3): 281-305.
26
Zhao, X., Xie, J. (1998). Multilevel lot-sizing heuristics and freezing the master production schedule in material requirements planning systems, Production Planning & Control, 9(4): 371-384.
27
Zhao, X., Xie, J., Jiang, G.S. (2001). Lot-sizing rule and freezing the master production schedule under capacity constraint and deterministic demand, Production and operations management, 10 (1): 45-67.
28
ORIGINAL_ARTICLE
An integrated production-marketing planning model With Cubic production cost function and imperfect production process
The basic assumption in the traditional inventory model is that all outputs are perfect items. However, this assumption is too simplistic in the most real-life situations due to a natural phenomenon in a production process. From this it is deduced that the system produces non-perfects items which can be classified into four groups of perfect, imperfect, reworkable defective and non-reworkable defective items. In this paper, compared with classic model, a new integrated imperfect quality economic production quantity problem is proposed where demand can be determined as a power function of selling price, advertising intensity, and customer services volume. Furthermore, as novelty way the unit cost is defined as a cubic function of outputs which is similar to real world. Also, a geometric programming modeling procedure is employed to formulate the problem. Finally, a numerical example is illustrated to study and analysis the behavior and application of the model.
https://www.jise.ir/article_43331_3da8dce3869a5b98ac36ff5d4bd958d2.pdf
2017-05-29
91
108
Geometric programming
Inventory
Comprehensive demand function
Cubic production cost function
Non-perfect production process
َAghil
Hamidi
aghil_hamidi@ind.iust.ac.ir
1
Department of industrial Engineering, Iran University of science and Technology, Narmak, Tehran, Iran
AUTHOR
Seyed Jafar
Sadjadi
sjsadjadi@iust.ac.ir
2
Department of industrial Engineering, Iran University of science and Technology, Narmak, Tehran, Iran
AUTHOR
Ali
Bonyadi Naeinib
bonyadi@iust.ac.ir
3
Department of progress Engineering, Iran University of science and Technology, Narmak, Tehran, Iran
LEAD_AUTHOR
Seyed Reza
Moosavi Tabatabaeia
r_tabatabae@iust.ac.ir
4
Department of industrial Engineering, Iran University of science and Technology, Narmak, Tehran, Iran
AUTHOR
Abuo-El-Ata, M., Fergany, H. A., & El-Wakeel, M. F. (2003). Probabilistic multi-item inventory model with varying order cost under two restrictions: a geometric programming approach. International Journal of Production Economics, 83(3), 223-231.
1
Baker, R., & Urban, T. L. (1988). A deterministic inventory system with an inventory-level-dependent demand rate. Journal of the operational Research Society, 823-831.
2
Beightler, C. S., & Phillips, D. T. (1976). Applied geometric programming (Vol. 150): Wiley New York.
3
Boyd, S., Kim, S.-J., Vandenberghe, L., & Hassibi, A. (2007). A tutorial on geometric programming. Optimization and engineering, 8(1), 67-127.
4
Brown, R. M., Conine, T. E., & Tamarkin, M. (1986). a note on holding costs and lot-size errors. Decision Sciences, 17(4), 603-610.
5
Cárdenas-Barrón, L. E. (2008). Optimal manufacturing batch size with rework in a single-stage production system–A simple derivation. Computers & Industrial Engineering, 55(4), 758-765.
6
Chan, W., Ibrahim, R., & Lochert, P. (2003). A new EPQ model: integrating lower pricing, rework and reject situations. Production Planning & Control, 14(7), 588-595.
7
Chen, C.-K. (2000). Optimal determination of quality level, selling quantity and purchasing price for intermediate firms. Production Planning & Control, 11(7), 706-712.
8
Cheng, T. (1989). An economic order quantity model with demand-dependent unit cost. European Journal of Operational Research, 40(2), 252-256.
9
Chiang, M. (2005). Geometric programming for communication systems: Now Publishers Inc.
10
Duffin, R. J., Peterson, E. L., & Zener, C. (1967). Geometric programming: theory and application: Wiley New York.
11
F. Ghazi Nezami , S. J. S., Mir B. Aryanezhad. (2009). A Geometric Programming Approach for a Nonlinear Joint Production- Marketing Problem. International Association of Computer Science and Information Technology - Spring Conference
12
Fathian, M., Sadjadi, S. J., & Sajadi, S. (2009). Optimal pricing model for electronic products. Computers & Industrial Engineering, 56(1), 255-259.
13
Groenevelt, H., Pintelon, L., & Seidmann, A. (1992). Production lot sizing with machine breakdowns. Management Science, 38(1), 104-123.
14
Gujarati, D. N. (2003). Basic Econometrics. 4th: New York: McGraw-Hill.
15
Hayek, P. A., & Salameh, M. K. (2001). Production lot sizing with the reworking of imperfect quality items produced. Production Planning & Control, 12(6), 584-590.
16
Islam, S. (2008). Multi-objective marketing planning inventory model: A geometric programming approach. Applied Mathematics and Computation, 205(1), 238-246.
17
Jamal, A., Sarker, B. R., & Mondal, S. (2004). Optimal manufacturing batch size with rework process at a single-stage production system. Computers & Industrial Engineering, 47(1), 77-89.
18
Jung, H., & Klein, C. M. (2001). Optimal inventory policies under decreasing cost functions via geometric programming. European Journal of Operational Research, 132(3), 628-642.
19
Jung, H., & Klein, C. M. (2006). Optimal inventory policies for profit maximizing EOQ models under various cost functions. European Journal of Operational Research, 174(2), 689-705.
20
Khanra, S., & Chaudhuri, K. (2003). A note on an order-level inventory model for a deteriorating item with time-dependent quadratic demand. Computers & operations research, 30(12), 1901-1916.
21
Kim, D., & Lee, W. J. (1998). Optimal joint pricing and lot sizing with fixed and variable capacity. European Journal of Operational Research, 109(1), 212-227.
22
Kotb, K. A., & Fergany, H. A. (2011). Multi-item EOQ model with both demand-dependent unit cost and varying leading time via geometric programming. Applied Mathematics, 2(5), 551-555.
23
Lee, J. S., & Park, K. S. (1991). Joint determination of production cycle and inspection intervals in a deteriorating production system. Journal of the operational Research Society, 775-783.
24
Lee, W. J. (1993a). Determining Order Quantity and Selling Price by Geometric Programming: Optimal Solution, Bounds, and Sensitivity. Decision Sciences, 24(1), 76-87.
25
Lee, W. J. (1993b). Optimal order quantities and prices with storage space and inventory investment limitations. Computers & Industrial Engineering, 26(3), 481-488.
26
Lee, W. J., & Kim, D. (1993). Optimal and heuristic decision strategies for integrated production and marketing planning. Decision Sciences, 24(6), 1203-1214.
27
Lin, C.-S. (1999). Integrated production-inventory models with imperfect production processes and a limited capacity for raw materials. Mathematical and Computer Modelling, 29(2), 81-89.
28
Liu, J. J., & Yang, P. (1996). Optimal lot-sizing in an imperfect production system with homogeneous reworkable jobs. European Journal of Operational Research, 91(3), 517-527.
29
Mandal, N. K., Roy, T. K., & Maiti, M. (2006). Inventory model of deteriorated items with a constraint: a geometric programming approach. European Journal of Operational Research, 173(1), 199-210.
30
Panda, D., & Maiti, M. (2009). Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: A geometric programming approach. Mathematical and Computer Modelling, 49(9), 1733-1749.
31
Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycles with imperfect production processes. IIE transactions, 18(1), 48-55.
32
Sadjadi, S. J., Aryanezhad, M.-B., & Jabbarzadeh, A. (2009). An integrated pricing and lot sizing model with reliability consideration. Paper presented at the Computers & Industrial Engineering, 2009. CIE 2009. International Conference on.
33
Sadjadi, S. J., Oroujee, M., & Aryanezhad, M. B. (2005). Optimal production and marketing planning. Computational Optimization and Applications, 30(2), 195-203.
34
Salameh, M., & Jaber, M. (2000). Economic production quantity model for items with imperfect quality. International Journal of Production Economics, 64(1), 59-64.
35
Sana, S., Goyal, S., & Chaudhuri, K. (2004). A production–inventory model for a deteriorating item with trended demand and shortages. European Journal of Operational Research, 157(2), 357-371.
36
Sana, S. S., & Chaudhuri, K. (2008). A deterministic EOQ model with delays in payments and price-discount offers. European Journal of Operational Research, 184(2), 509-533.
37
Subramanyam, E. S., & Kumaraswamy, S. (1981). EOQ formula under varying marketing policies and conditions. AIIE Transactions, 13(4), 312-314.
38
You, P.-S. (2005). Inventory policy for products with price and time-dependent demands. Journal of the Operational Research Society, 56(7), 870-873.
39
You, P.-S., & Hsieh, Y.-C. (2007). An EOQ model with stock and price sensitive demand. Mathematical and Computer Modelling, 45(7), 933-942.
40
Yum, B. J., & Mcdowellj, E. d. (1987). Optimal inspection policies in a serial production system including scrap rework and repair: an MILP approach. International Journal of Production Research, 25(10), 1451-1464.
41
ORIGINAL_ARTICLE
Coordination and profit sharing in a two-level supply chain under periodic review inventory policy with delay in payments contract
In this paper,a coordination model has been investigated for a two-level supply chain (SC) consisting of one retailer and one supplier under periodic review inventory system. The review period and the retailer’s safety factorare assumed to be decision variables. The retailer faces stochastic demand following a normal distribution with known mean and variance. Moreover, it is assumed that unmet demand will be backordered. Firstly, the investigated SC is modeled under the decentralized and centralized decision-making structures, afterwards, a coordination mechanism based on delay in payments is proposed for transition from the decentralized to centralized model. To fairly share the surplus profit obtained by coordination, a profit sharing strategy is developed which is based on the bargaining power of the two SC members. Finally, a set of numerical experiments and sensitivity analysis are carried out. Numerical examples indicate that the proposed delay in payments contract can achieve channel coordination and the whole SC cost will decrease under the coordination model while the costs of neither retailer nor the supplier will increase.
https://www.jise.ir/article_43418_491f18c97b57c0d3d512fe440735cd12.pdf
2017-05-31
109
131
supply chain coordination
periodic review inventory system
delay in payments
profit sharing
Sara
Hojati
s_hojati@ind.iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Seyed Mohammad
Seyedhosseini
seyedhosseini@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Seyyed-Mahdi
Hosseini-Motlagh
motlagh@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Mohammadreza
Nematollahi
nematollahi@ind.iust.ac.ir
4
School of Industrial Engineering, Iran University of Science & Technology,
AUTHOR
Axsäter, S., 1993. Optimization of order‐up‐to‐s policies in two‐echelon inventory systems with periodic review. Naval Research Logistics (NRL), 40(2), pp.245-253.
1
Annadurai, K. and Uthayakumar, R., 2010. Reducing lost-sales rate in (T, R, L) inventory model with controllable lead time. Applied Mathematical Modelling, 34(11), pp.3465-3477.
2
Cachon, G.P. and Lariviere, M.A., 2005. Supply chain coordination with revenue-sharing contracts: strengths and limitations. Management science, 51(1), pp.30-44.
3
Chaharsooghi, S.K. and Heydari, J., 2009. Supply chain coordination for the joint determination of order quantity and reorder point using credit option. European Journal of Operational Research, 204(1), pp.86-95.
4
Chaharsooghi, S.K., Heydari, J. and Kamalabadi, I.N., 2011. Simultaneous coordination of order quantity and reorder point in a two-stage supply chain. Computers & Operations Research, 38(12), pp.1667-1677.
5
Chung, W., Talluri, S. and Narasimhan, R., 2014. Quantity flexibility contract in the presence of discount incentive. Decision Sciences, 45(1), pp.49-79.
6
Ding, D. and Chen, J., 2008. Coordinating a three level supply chain with flexible return policies. Omega, 36(5), pp.865-876.
7
Duan, Y., Huo, J., Zhang, Y. and Zhang, J., 2012. Two level supply chain coordination with delay in payments for fixed lifetime products. Computers & Industrial Engineering, 63(2), pp.456-463.
8
Gao, D., Zhao, X. and Geng, W., 2014. A delay-in-payment contract for Pareto improvement of a supply chain with stochastic demand. Omega, 49, pp.60-68.
9
Giannoccaro, I. and Pontrandolfo, P., 2004. Supply chain coordination by revenue sharing contracts. International journal of production economics, 89(2), pp.131-139.
10
Heydari, J. and Norouzinasab, Y., 2016. Coordination of pricing, ordering, and lead time decisions in a manufacturing supply chain. Journal of Industrial and Systems Engineering, 9, pp.1-16.
11
Heydari, J., 2014. Lead time variation control using reliable shipment equipment: An incentive scheme for supply chain coordination. Transportation Research Part E: Logistics and Transportation Review, 63, pp.44-58.
12
Heydari, J., 2014. Coordinating replenishment decisions in a two-stage supply chain by considering truckload limitation based on delay in payments. International Journal of Systems Science, 46(10), pp.1897-1908.
13
Heydari, J., 2014. Supply chain coordination using time-based temporary price discounts. Computers & Industrial Engineering, 75, pp.96-101.
14
Heydari, J., Choi, T.M. and Radkhah, S., 2016. Pareto improving supply chain coordination under a money-back guarantee service program. Service Sci.
15
Heydari, J. and Asl-Najafi, J., 2016. Coordinating inventory decisions in a two-echelon supply chain through the target sales rebate contract. International Journal of Inventory Research, 3(1), pp.49-69.
16
Hsu, S.L. and Lee, C.C., 2009. Replenishment and lead time decisions in manufacturer–retailer chains. Transportation Research Part E: Logistics and Transportation Review, 45(3), pp.398-408.
17
Jaber, M.Y. and Osman, I.H., 2006. Coordinating a two-level supply chain with s and profit sharing. Computers & Industrial Engineering, 50(4), pp.385-400.
18
Johari, M., Hosseini-Motlagh, S.M., Nematollahi, M.R., 2016. Simultaneous coordination of review period and safety stock in a manufacturer-retailer chain. Journal of Industrial and Systems Engineering, in press.
19
Kanchanasuntorn, K. and Techanitisawad, A., 2006. An approximate periodic model for fixed-life perishable products in a two-echelon inventory–distribution system. International Journal of Production Economics, 100(1), pp.101-115.
20
Kanda, A. and Deshmukh, S.G., 2009. A framework for evaluation of coordination by contracts: A case of two-level supply chains. Computers & Industrial Engineering, 56(4), pp.1177-1191.
21
Lin, Y.J., 2010. A stochastic periodic review integrated inventory model involving defective items, backorder price discount, and variable lead time. 4OR, 8(3), pp.281-297.
22
Li, J. and Liu, L., 2006. Supply chain coordination with quantity discount policy. International Journal of Production Economics, 101(1), pp.89-98.
23
Linh, C.T. and Hong, Y., 2009. Channel coordination through a revenue sharing contract in a two-period newsboy problem. European Journal of Operational Research, 198(3), pp.822-829.
24
Mallidis, I., Vlachos, D., Iakovou, E. and Dekker, R., 2014. Design and planning for green global supply chains under periodic review replenishment policies. Transportation Research Part E: Logistics and Transportation Review, 72, pp.210-235.
25
Mokhlesian, M. and Zeghrdi, S.H., 2015. Coordination of pricing and cooperative advertising for perishable products in a two-echelon supply chain: A bi-level programming approach. Journal of Industrial and Systems Engineering, 8(4), pp.38-58.
26
Munson, C.L. and Rosenblatt, M.J., 2001. Coordinating a three-level supply chain with quantity discounts. IIE transactions, 33(5), pp.371-384.
27
Nematollahi, M., Hosseini-Motlagh, S.M. and Heydari, J., 2016. Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. Journal of Cleaner Production.
28
Nematollahi, M., Hosseini-Motlagh, S.M. and Heydari, J., 2016. Coordination of social responsibility and order quantity in a two-echelon supply chain: A collaborative decision-making perspective. International Journal of Production Economics.
29
Ouyang, L.Y. and Chuang, B.R., 2000. A periodic review inventory model involving variable lead time with a service level constraint. International Journal of Systems Science, 31(10), pp.1209-1215.
30
Ouyang, L.Y., Chuang, B.R. and Lin, Y.J., 2007. Effective investment to reduce lost-sales rate in a periodic review inventory model. OR Spectrum, 29(4), pp.681-697.
31
Silver, E.A. and Bischak, D.P., 2011. The exact fill rate in a periodic review base stock system under normally distributed demand. Omega, 39(3), pp.346-349.
32
Sinha, D. and Matta, K.F., 1991. Multi-echelon (R, S) inventory model. Decision Sciences, 22(3), pp.484-499.
33
Tsay, A.A., 1999. The quantity flexibility contract and supplier-customer incentives. Management science, 45(10), pp.1339-1358.
34
Taylor, T.A., 2002. Supply chain coordination under channel rebates with sales effort effects. Management science, 48(8), pp.992-1007.
35
Wu, C. and Zhao, Q., 2014. Supplier–buyer deterministic inventory coordination with trade credit and shelf-life constraint. International Journal of Systems Science: Operations & Logistics, 1(1), pp.36-46.
36
Xiong, H., Chen, B. and Xie, J., 2011. A composite contract based on buy back and quantity flexibility contracts. European Journal of Operational Research, 210(3), pp.559-567.
37
ORIGINAL_ARTICLE
A multi-objective genetic algorithm (MOGA) for hybrid flow shop scheduling problem with assembly operation
Scheduling for a two-stage production system is one of the most common problems in production management. In this production system, a number of products are produced and each product is assembled from a set of parts. The parts are produced in the first stage that is a fabrication stage and then they are assembled in the second stage that usually is an assembly stage. In this article, the first stage assumed as a hybrid flow shop with identical parallel machines and the second stage will be an assembling work station. Two objective functions are considered that are minimizing the makespan and minimizing the sum of earliness and tardiness of products. At first, the problem is defined and its mathematical model is presented. Since the considered problem is NP-hard, the multi-objective genetic algorithm (MOGA) is used to solve this problem in two phases. In the first phase the sequence of the products assembly is determined and in the second phase, the parts of each product are scheduled to be fabricated. In each iteration of the proposed algorithm, the new population is selected based on the non-dominance rule and fitness value. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are designed and the reliability of the proposed algorithm is compared with two prominent multi-objective genetic algorithms, i.e. WBGA, and NSGA-II. The computational results show that the performance of the proposed algorithms is good in both efficiency and effectiveness criteria. In small-sized problems, the number of non-dominance solution come out from the two algorithms N-WBGA (the proposed algorithm) and NSGA-II is approximately equal. Also, more than 90% solution of algorithms N-WBGA and NSGA-II are identical to the Pareto-optimal result. Also in medium problems, two algorithms N-WBGA and NSGA-II have approximately an equal performance and both of them are better than WBGA. But in large-sized problems, N-WBGA presents the best results in all indicators.
https://www.jise.ir/article_46250_d424c0fba47242c277d94a333ec129e7.pdf
2017-06-02
132
154
multi-objective genetic algorithm
Two-stage production system
Assembly
Seyed Mohammad
Hosseini
sh.hosseini@shahroodut.ac.ir
1
Department of Industrial Engineering and management, Shahrood University of technology, Shahrood, Iran
LEAD_AUTHOR
Allahverdi A, Al-Anzi FS., (2009). The two-stage assembly scheduling problem to minimize total completion time with setup times. Computers & Operations Research 36: 2740-2747
1
Allahverdi, A., Aydilek, H., (2015). The two stage assembly flowshop scheduling problem to minimize total tardiness. Journal of Intelligent Manufacturing 26: 225-237.
2
Allahverdi, A., Aydilek H., Aydilek A., (2016). Two-Stage Assembly Scheduling Problem to Minimize Total Tardiness with Setup Times. Applied Mathematical Modelling 40: 7796-7815
3
Arroyo JAC, Armentano VA., (2005). Genetic local search for multi-objective flowshop scheduling problems. European Journal of Operational Research 167: 717–738
4
Cheng TCE, Wang G., (1999). Scheduling the fabrication and assembly of components in a two-machine flow shop. IIE Transactions 31: 135-143
5
Coello CA, Lamont GB, Veldhuizen DAV., (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Second edition, Springer.
6
Ehrgott M., (2005). Multicriteria Optimization. Springer, Berlin, second edition, ISBN
7
Fattahi P, Hosseini SMH, Jolai F., (2012). A mathematical model and extension algorithm for assembly flexible flow shop scheduling problem. International Journal of Advance Manufacture Technology 65:787–802. DOI 10.1007/s00170-012-4217-x.
8
Fattahi, P., Hosseini, S.M.H., Jolai, F. and Tavakoli-Moghadam, R. (2014). A branch and bound algorithm for hybrid flow shop scheduling problem with setup time and assembly operations. Applied Mathematical Modelling. 38 :119-134.
9
Fattahi, P., Hosseini, S.M.H., Jolai, F. and Safi-Samghabadi, A. (2014). Multi-objective scheduling problem in a threestage production system. International Journal of Industrial Engineering & Production Research. 25 :1-12.
10
Hariri AMA, Potts CN (1997). A branch and bound algorithm for the two-stage assembly scheduling problem. European Journal of Operational Research 103: 547-556
11
Jung s., Woo yb., Soo Kim B., (2017). Two-stage assembly scheduling problem for processing products with dynamic component-sizes and a setup time. Computers & Industrial Engineering 104: 98–113
12
Karimi N, Zandieh M, Karamooz HR (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications 37: 4024–4032
13
Konak A, Coit DW, Smith AE (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety 91: 992–1007
14
Koulamas Ch, Kyparisis GJ (2007). A note on the two-stage assembly flow shop scheduling problem with uniform parallel machines. European Journal of Operational Research 182: 945–951
15
Lee CY, Cheng TCE, Lin BMT (1993). Minimizing the makespan in the 3-machine assembly-type flowshop scheduling problem. Management Science 39: 616-625
16
Lin R, Liao ChJ (2012). A case study of batch scheduling for an assembly shop. International Journal of Production Economics 139: 473–483
17
Loukil T, Teghem J, Tuyttens D (2005). Solving multi-objective production scheduling problems using metaheuristics. European Journal of Operational Research 161: 42–61
18
Moslehi G, Mirzaee M, Vasei M, Modarres M, Azaron A (2009). Two-machine flow shop scheduling to minimize the sum of maximum earliness and tardiness. International Journal of Production Economics 122: 763–773
19
Potts CN, Sevast'Janov SV, Strusevich VA, Van Wassenhove LN, Zwaneveld CM (1995). The two-stage assembly scheduling problem: Complexity and approximation. Operations Research 43: 346-355
20
Rahimi-Vahed AR, Rabbani M, Tavakkoli-Moghaddam R, Torabi SA, Jolai F., (2007). A multi-objective scatter search for a mixed-model assembly line sequencing problem. Advanced Engineering Informatics 21: 85–99
21
Sukkerd, W. Wuttipornpun T., (2016). Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations. Computers & Industrial Engineering, 97: p. 157-169.
22
Sun Y, Zhang Ch, Gao L, Wang X., (2010). Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int J Adv Manuf Technol DOI 10.1007/s00170-010-3094-4
23
Sung CS, Kim Hah., (2008). A two-stage multiple-machine assembly scheduling problem for minimizing sum of completion times. International Journal of Production Economics 113: 1038-1048
24
Sup Sung Ch, Juhn J (2009). Makespan minimization for a 2-stage assembly scheduling problem subject to component available time constraint. International Journal of Production Economics 119: 392–401
25
Yokoyama M., (2001). Hybrid flow-shop scheduling with assembly operations. International Journal of Production Economics 73: 103-116
26
Yokoyama M, Santos DL (2005). Three-stage flow-shop scheduling with assembly operations to minimize the weighted sum of product completion times. European Journal of Operational Research 161: 754-770
27
Yokoyama M., (2008). Flow-shop scheduling with setup and assembly operations. European Journal of Operational Research 187: 1184–1195
28
ORIGINAL_ARTICLE
Solving a multi-objective mixed-model assembly line balancing and sequencing problem
This research addresses the mixed-model assembly line (MMAL) by considering various constraints. In MMALs, several types of products which their similarity is so high are made on an assembly line. As a consequence, it is possible to assemble and make several types of products simultaneously without spending any additional time. The proposed multi-objective model considers the balancing and sequencing problems, simultaneously. Based on the assembly problem, the various tasks of models are assigned to the workstations, while in the sequencing problem, a sequence of models for production is determined. The two meta-heuristic algorithms, namely MOPSO and NSGA-II are used to solve the developed model and different comparison metrics are applied to compare these two proposed meta-heuristics. Several test problems based on empirical data is used to illustrate the performance of our proposed model. The results show that NSGA-II outperforms the MOPSO algorithm in most metrics used in this paper. Moreover, the results indicate that our proposed model is more effective and efficient to assignment of tasks and sequencing models than manual strategy. Finally, conclusion remarks and future research are provided.
https://www.jise.ir/article_46950_77b2ad78d7092848ce6c8c5a59d4bbc0.pdf
2017-06-17
155
170
mixed-model assembly line, sequencing
balancing, mixed-integer linear programming, meta-heuristic algorithms
Masoud
Rabani
mrabani@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mehdi
Yazdanbakhsh
mehdi.yazdan14@ut.ac.ir
2
College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Hamed
Farrokhi-Asl
hamed.farrokhi@alumni.ut.ac.ir
3
School of Industrial Engineering, Iran University of Science &Technology, Tehran, Iran
AUTHOR
Asefi, H., F. Jolai, M. Rabiee, and M. T. Araghi. 2014. “A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem.” The International Journal of Advanced Manufacturing Technology 75: 1017-1033.
1
Becker, C. and Scholl, A., 2006. A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168 (3), 694–715.
2
Behnamian, J., S. F. Ghomi, and M. Zandieh. 2009. “A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic.” Expert Systems with Applications 36: 11057-11069.
3
Bock, S., Rosenberg, O., and Brackel, T.V., 2006. Controlling mixed-model assembly lines in real-time by using distributed systems. European Journal of Operational Research, 168 (3), 880–904.
4
Boysen, N., Fliedner, M., and Scholl, A., 2007. A classification of assembly line balancing problems. European Journal of Operational Research, 183 (2), 674–693.
5
Boysen, N., Fliedner, M., and Scholl, A., 2009. Sequencing mixed-model assembly lines: survey, classification and model critique. European Journal of Operational Research, 192 (2), 349–373.
6
Bukchin, J., Dar-El, E.M., and Rubinovitz, J., 2002. Mixed model assembly line design in a make-to-order environment. Computers & Industrial Engineering, 41 (4), 405–421.
7
Bukchin, Y. and Rabinowitch, I., 2006. A branch-and-bound based solution approach for the mixed-model assembly linebalancing problem for minimizing stations and task duplication costs. European Journal of Operational Research, 174 (1), 492–508. International Journal of Production Research 5013
8
Cochran, W.G. and Cox, G.M., 1992. Experimental designs. 2nd ed. New York: Wiley.
9
Coello, C. A. C, Pulido, G. T, & Lechuga, M. S. handling multiple objectives with particle swarm optimization. Evolutionary Computation, IEEE Transactions on, 8(3), 256-279 (2004)
10
Dar-El, E.M. and Nadivi, A., 1981. A mixed–model sequencing application. International Journal of Production Research, 19 (1), 69–84.
11
Deb K, Pratap A, Agarwal S, and Meyarivan T. A. M. T, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182-197 (2002)
12
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lecture notes in computer science, 1917, 849-858.
13
Erel, E., Gocgun, Y., and Sabuncuog˘ lu, _I., 2007. Mixed-model assembly line sequencing using beam search. International Journal of Production Research, 45 (22), 5265 – 5284.
14
Gutjahr, A.L. and Nemhauser, G.L., 1964. An algorithm for the line balancing problem. Management Science, 11 (2), 308–315.
15
Holland, J, Adaptation in Natural and Artificial Systems. Ann Harbor: University of Michigan (1975)
16
Hwang, R. and Katayama, H., 2010. Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multiobjective evolutionary approach. International Journal of Production Research, 48 (21), 6417 – 6441.
17
Hyun, C.J., Kim, Y., and Kim, Y.K., 1998. A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Computers & Operations Research, 25 (7–8), 675–690.
18
Karimi, N., M. Zandieh, and H. R. Karamooz. 2010. “Bi-objective group scheduling in hybrid flexible flow shop: A multi-phase approach.” Expert Systems with Applications 37: 4024-4032.
19
Kim, Y.K., Hyun, C.J, and Kim, Y., 1996. Sequencing in mixed model assembly lines: a genetic algorithm approach. Computers & Operations Research, 23 (12), 1131–1145.
20
Kim, Y.K., Kim, J.Y., and Kim, Y., 2000. A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Applied Intelligence, 13 (3), 247–258.
21
Kim, Y.K., Kim, J.Y., and Kim, Y., 2006. An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model u-lines. European Journal of Operational Research, 168 (3), 838–852.
22
Mansouri, S.A., 2005. A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. European Journal of Operational Research, 167 (3), 696–716.
23
Miltenburg, J., 2002. Balancing and scheduling mixed-model u-shaped production lines. International Journal of Flexible Manufacturing Systems, 14 (2), 119–151.
24
Montgomery, D.C., 2000. Design and analysis of experiments. 5th ed. New York: Wiley.
25
Moore J, Chapman R, Application of particle swarm to multiobjective optimization. Department of Computer Science and Software Engineering, Auburn University (1999(
26
Naderi, B., Zandieh, M., and Fatemi Ghomi, S.M.T., 2009. Scheduling job shop problems with sequence-dependent setup times. International Journal of Production Research, 47 (21), 5959 – 5976.
27
Rabbani, M., Farrokhi-Asl, H., & Ameli, M. (2016). Solving a fuzzy multi-objective products and time planning using hybrid meta-heuristic algorithm: Gas refinery case study. Uncertain Supply Chain Management, 4(2), 93-106.
28
Rabbani, M., Mousavi, Z., & Farrokhi-Asl, H. (2016). Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem. Journal of Industrial and Production Engineering, 1-13.
29
Rabbani, M., Siadatian, R., Farrokhi-Asl, H., & Manavizadeh, N. (2016). Multi-objective optimization algorithms for mixed model assembly line balancing problem with parallel workstations. Cogent Engineering, (just-accepted), 1158903.
30
Rekiek, B. and Delchambre, A., 2006. Assembly line design: the balancing of mixed-model hybrid assembly lines with genetic algorithms. 1st ed. London: Springer.
31
Roberts, S.D. and Villa, C.D., 1970. On a multiproduct assembly line-balancing problem. AIIE Transactions, 2 (4), 361–364.
32
Sawik, T., 2002. Monolithic vs. hierarchical balancing and scheduling of a flexible assembly line. European Journal of Operational Research, 143 (1), 115–124.
33
Scholl, A., 2010. Homepage for assembly line optimization research. Available from:5http://www.assembly–line–balancing.de/4 [Accessed 10 July 2010].
34
Simaria, A.S. and Vilarinho, P.M., 2004. A genetic algorithm based approach to the mixed-model assembly line balancing problem of type ii. Computers & Industrial Engineering, 47 (4), 391–407.
35
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
36
Taguchi, G., 1986. Introduction to quality engineering: designing quality into products and processes. 1st ed. White Plains, NY: Asian Productivity Organization.
37
Tavakkoli-Moghaddam, R. and Rahimi-Vahed, A.R., 2006. Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system. Applied Mathematics and Computation, 181 (2), 1471–1481.
38
Thomopoulos, N.T., 1967. Line balancing-sequencing for mixed-model assembly. Management Science, 14 (2), 59–75.
39
Tsai, L.H., 1995. Mixed-model sequencing to minimize utility work and the risk of conveyor stoppage. Management Science, 41 (3), 485–495.
40