ORIGINAL_ARTICLE
Coordinating a Seller-Buyer Supply Chain with a Proper Allocation of Chain’s Surplus Profit Using a General Side-Payment Contract
In this paper, seller-buyer supply chain coordination with general side-payment contracts is introduced to gain the maximum possible chain profit. In our model, the logistics costs for both buyer and seller are considered and the final demand is also supposed to be a decreasing function of the retail price. Since parties aim to maximize their individual profits, the contractual parameters are set in a way that these decisions become aligned with system optimal decisions. Therefore, a side payment contract is suggested in our model to assign the chain surplus profit to the chain members such that they have no intention to leave the coalition. Then, we change the contract into a quantity discount-like contract which makes the contract much easier to be implemented in a real situation. The model will also be extended to include two buyers and a single seller. Finally, by numerical analysis, we show that by using this kind of contract, a significant improvement in the chain members’ profits and the total chain revenue will be achieved.
http://www.jise.ir/article_4042_73ee422836fa3fa0fe0320dcb22f4705.pdf
2011-07-01T11:23:20
2020-08-09T11:23:20
63
79
Seller-Buyer Supply Chain
supply chain coordination
Supply Chain Contracts
game theory
Sina
Masihabadi (1985-2011)
true
1
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
AUTHOR
Kourosh
Eshghi
eshghi@sharif.edu
true
2
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Zip code 14588-89694, Tehran, Iran
AUTHOR
[1] Breiter A., Hegmanns T., Hellingrath B., Spinler S. (2009), Coordination in Supply Chain
1
Management-Review and Identification of Directions for Future Research, In: Voß S. et al. (eds.);
2
Logistik Management: Systeme, Methoden, Integration, first edition, Physica-Verlag; 1-36.
3
[2] Cachon G.P. (2003), Supply chain coordination with contracts, In: de Kok A.G., Graves S.C. (Eds.);
4
Supply Chain Management: Design, Coordination and Operation, Elsevier, Amsterdam; 229–340.
5
[3] Carter J.R., Ferrin B.G. (1995), The impact of transportation costs on supply chain management;
6
Journal of Business Logistics 16 (1); 189–212.
7
[4] Esmaeili M., Aryanezhad M.B., Zeephongseku P. (2009), A game theory approach in seller–buyer
8
supply chain; European Journal of Operational Research 195; 442-448.
9
[5] Hezarkhani B., Kubiak W. (2010), Coordinating Contracts in SCM: A Review of Methods and
10
Literature; Decision Making in Manufacturing and Services 4 (1–2); 5–28.
11
[6] Kanda A., Deshmukh S.G. (2008), Supply chain coordination: perspectives, empirical studies and
12
research directions; International Journal of Production Economics115; 316-335.
13
[7] Lai G., Debo L.G., Sycara K. (2009), Sharing inventory risk in supply chain: The implication of
14
financial constraint; Omega 37; 811-825.
15
[8] Lee W.J., Kim D., Cabot A.V. (1996), Optimal demand rate, lot sizing, and process reliability
16
improvement decisions; IIE Transactions 28; 941–952.
17
[9] Leng M., Parlar M. (2010), Game-theoretic analyses of decentralized assembly supply chains: Noncooperative
18
equilibria vs. coordination with cost-sharing contracts; European Journal of Operational
19
Research 204; 96-104.
20
[10] Leng M., Zhu A. (2009), Side-payment contracts in two-person nonzero-sum supply chain games:
21
Review, discussion and applications; European Journal of Operational Research 196; 600-618.
22
[11] Lemaire J. (2008), Cooperative game theory and its insurance applications; ASTIN Bulletin 21 (1);17–
23
[12] Nagarajan M., Sosic G. (2008), Game-theoretic analysis of cooperation among supply chain agents:
24
Review and extensions; European Journal of Operational Research 187; 719-745.
25
[13] Prakash L. Abad (1988), Determining optimal selling price and the lot size when the supplier offers
26
all-unit quantity discounts; Decision Sciences 3 (19); 622–634.
27
[14] Rubin P.A., Carter J.R. (1990), Joint optimality in buyer–supplier negotiations, Journal of Purchasing
28
and Materials Management 26(2); 20–26.
29
[15] Sarmah S.P., Acharya D., Goyal S.K. (2006), Buyer vendor coordination models in supply chain
30
management; European Journal of Operational Research 175; 1-15.
31
ORIGINAL_ARTICLE
Continuous Review Perishable Inventory System with OneSupplier, One Retailer andPositive Lead Time
We consider a two-echelon single commodity inventory system with one warehouse(supplier) at the higher echelon and one retailer at the lower echelon. The retailer stocks individual items of a commodity and satisfies unit demands which occur according to a Poisson process. The supplier stocks these items in packets and uses these packetsto: (i) satisfy the demands that occur for single packet and that form an independent Poisson process and (ii) replenish the retailer's stock. The supplier implements (s,S)ordering policy to replenish the stock of packets and the lead time is assumed to have exponential distribution. Though the retailer's stock is replenished instantaneously by the supplier if packets are available, and a random stock out period may occur at the retailer node when the supplier has zero stock. It is assumed that because of better stocking facility that is usually available at the warehouse, the items do not perish at the warehouse, but they do perish at the retailer node. It is also assumed that the items have exponential life time distribution at the lower echelon. The joint probability distribution of the inventory levels on both nodes is obtained in the steady state. Various system performance measures are calculated. The long run total expected cost per unit time is derived. These results are illustrated with numerical examples. Some special cases are discussed in detail.
http://www.jise.ir/article_4043_4b414cacc3b5400d549b30749693fd3a.pdf
2011-07-01T11:23:20
2020-08-09T11:23:20
80
106
Stochastic inventory
Supply chain management
Poisson demand
Perishable
items
Positive lead time
M.
Rajkumar
true
1
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
AUTHOR
B.
Sivakumar
true
2
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
Department of Applied Mathematics and Statistics,Madurai Kamaraj University,Madurai, India.
AUTHOR
G.
Arivarignan
true
3
Department of Applied Mathematics and Statistics, Madurai Kamaraj University, Madurai, India.
Department of Applied Mathematics and Statistics, Madurai Kamaraj University, Madurai, India.
Department of Applied Mathematics and Statistics, Madurai Kamaraj University, Madurai, India.
AUTHOR
[1] Andersson J., Melchiors P. (2001), A two-echelon inventory model with lost sales;International
1
Journal of Production Economics 69(3); 307-315.
2
[2] Axsäter S. (1990), Simple solution procedures for a class of two-echelon inventory problems;
3
Operations research38(1); 64-69.
4
[3] Axsäter S., Marklund J. (2008), Optimal position-based warehouse ordering in divergent two-echelon
5
inventory systems;Operations research56(4); 976-991.
6
[4] BeamonB.M. (1998), Supply chain design and analysis;International Journal of Production
7
Economics55(3); 281-294.
8
[5] Bykadorov I., Ellero A., Moretti E., Vianello S. (2009), The role of retailer's performance in optimal
9
wholesale price discount policies;European Journal of Operational Research 194(2); 538-550.
10
[6] Clark A.J., Scarf H. (1960), Optimal policies for a multi-echelon inventory problem;Management
11
Science 6(4); 475-490.
12
[7] Clark A.J. (1972), An informal survey of multi-echelon inventory theory;Naval Research Logistics
13
Quarterly 19(4); 621-650.
14
[8] Duc T.T.H., Luong H.T., Kim Y.D. (2008), A measure of bull-whip effect in supply chains with a
15
mixed autoregressive-moving average demand process;European Journal of Operational Research
16
187(1); 243-256.
17
[9] Goyal S.K., Tao C.C. (2009), Optimal ordering and transfer policy for an inventory with stock
18
dependent demand;European Journal of Operational Research 196(1); 177-185.
19
[10] Graves S.C. (1985), A multi-echelon inventory model for a repairable item with one-for-one
20
replenishment;Management Science 31(10), 1247-1256.
21
[11] Hill R.M., Seifbarghy M., Smith D.K. (2007), A two-echelon inventory model with lost sales;
22
European Journal of Operational Research 181(2); 753-766.
23
[12] Kalpakam S.,Arivarignan G. (1988), A continuous review perishable inventory model;Statistics 19(3);
24
[13] Kogan K.,Herbon A. (2008), A supply chain under limited-time promotion: The effect of customer
25
sensitivity;European Journal of Operational Research 188(1); 273-292.
26
[14] Ming T.C., Li D., Yan H. (2008), Mean-variance analysis of a single supplier and retailer supply chain
27
under a returns policy;European Journal of Operational Research184(1); 356-376.
28
[15] Olsson R.J.,Hill R.M. (2007), A two-echelon base-stock inventory model with Poisson demand and the
29
sequential processing of orders at the upper echelon;European Journal of Operational Research
30
177(1); 310-324.
31
[16] Powell S.G., Pyke D.F. (1998), Buffering unbalanced assembly systems;IIE Transactions 30(1); 55-
32
[17] Roundy R.O. (1985),98%-Effective integer-ratio lot-sizing for one-warehouse multi-retailer
33
systems;Management Science 31(11); 1416-1430.
34
[18] Simon R.M.(1971), Stationary properties of a two echelon inventory model for low demand items;
35
Operations Research19; 761-777.
36
[19] Sivazilian S.D. (1974), A continuous review (s,S) inventory system with arbitrary inter arrival
37
distribution between unit demands;Operations Research 22; 65-71.
38
[20] Szmerekovsky J.G., Zhang J. (2009), Pricing and two-tier advertising with one manufacturer and one
39
retailer;European Journal of Operational Research 192(3); 904-917.
40
[21] Wong H., Kranenburg B., Houtum G.J. van,Cattrysse D. (2007), Efficient heuristics for two-echelon
41
spare parts inventory systems with an aggregate mean waiting time constraint per local warehouse;OR
42
Spectrum 29; 699-722.
43
ORIGINAL_ARTICLE
Simultaneous Lot Sizing and Scheduling in a Flexible Flow Line
This paper breaks new ground by modelling lot sizing and scheduling in a flexible flow line (FFL) simultaneously instead of separately. This problem, called the ‘General Lot sizing and Scheduling Problem in a Flexible Flow Line’ (GLSP-FFL), optimizes the lot sizing and scheduling of multiple products at multiple stages, each stage having multiple machines in parallel. The objective is to satisfy varying demand over a finite planning horizon with minimal inventory, backorder and production setup costs. The problem is complex as any product can be processed on any machine but with different process rates and sequence-dependent setup times & costs. The efficiency of two alternative models is assessed and evaluated using numerical tests.
http://www.jise.ir/article_4044_b674194cc350a8d18a5ba34919e423d5.pdf
2011-07-01T11:23:20
2020-08-09T11:23:20
107
119
Lot sizing
scheduling
Flexible Flow Line
Mathematical Modelling
Masoumeh
Mahdieh
true
1
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
AUTHOR
Mehdi
Bijari
bijari@cc.iut.ac.ir
true
2
Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Alistair
Clark
true
3
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
Department of Mathematics and Statistics, Bristol Institute of Technology, University of the West of
England, Bristol, England
AUTHOR
[1] Almada-lobo B., Klabjan D., Carravilla M.A., Oliveira J. (2007), Single machine multi-product
1
capacitated lot sizing with sequence-dependent setups; International Journal of Production Research
2
45; 4873-4894.
3
[2] Bitran G.R., Yanasse H.H. (1982), Computational complexity of the capacitated lot size problem;
4
Management Science 28; 1174-1186.
5
[3] Clark A.R., Clark S.J. (2000), Rolling-horizon lot-sizing when set-up times are sequence-dependent;
6
International Journal of Production Research 38; 2287-2307.
7
[4] Clark A.R., Neto R.M., Toso E.A.V. (2006), Multi-period production setup-sequencing and lot-sizing
8
through ATSP subtour elimination and patching; In: Proceedings of the 25th workshop of the UK
9
planning and scheduling special interest group. University of Nottingham; 80–87.
10
[5] Fandel G., Stammen-Hegene C. (2006), Simultaneous lot sizing and scheduling for multi-product
11
multi-level production; International Journal of Production Economics 104; 308-316.
12
[6] Fleischmann B., Meyr H. (1997), The general lotsizing and scheduling problem; OR Spectrum 19; 11-
13
[7] Linn R., Zhang W. (1999), Hybrid flow shop scheduling: a survey; Computers & Industrial
14
Engineering 37; 57-61.
15
[8] Meyr H. (2000), Simultaneous lotsizing and scheduling by combining local search with dual
16
reoptimization; European Journal of Operational Research 120; 311-326.
17
[9] Meyr H. (2002), Simultaneous lotsizing and scheduling on parallel machines; European Journal of
18
Operational Research 139; 277-292.
19
[10] Özdamar L., Barbaroso lu G. (1999), Hybrid heuristics for the multi-stage capacitated lot sizing and
20
loading problem; Journal of the Operational Research Society 50; 810-825.
21
[11] Pinedo M. (1995), Scheduling: Theory, Algorithms and Systems; Englewood Cliffs, NJ; Prentice Hall.
22
[12] Quadt D. (2004), Lot-sizing and scheduling for flexible flow lines; Springer Verlag.
23
[13] Quadt D., Kuhn H. (2005), Conceptual framework for lot-sizing and scheduling of flexible flow lines;
24
International Journal of Production Research 43; 2291-2308.
25
[14] Quadt D., Kuhn H. (2007), A taxonomy of flexible flow line scheduling procedures; European Journal
26
of Operational Research 178; 686-698.
27
[15] Quadt D., Kuhn H. (2007), Batch scheduling of jobs with identical process times on flexible flow lines;
28
International Journal of Production Economics 105; 385-401.
29
ORIGINAL_ARTICLE
Estimating Process Capability Indices Using Univariate g and h Distribution
Process capability of a process is defined as inherent variability of a process which is running under chance cause of variation only. Process capability index is measuring the ability of a process to meet the product specification limit. Generally process capability is measured by 6 assuming that the product characteristic follows Normal distribution. In many practical situations the product characteristics do not follow normal distribution. In this paper, we describe an approach of estimating process capability assuming generalised g and h distribution proposed by Tukey.
http://www.jise.ir/article_4045_568a690a5dced6be0141b50f9eed83a4.pdf
2011-07-01T11:23:20
2020-08-09T11:23:20
120
127
Process Capability
Tukey Univariate g and h Distribution
Nandini
Das
true
1
SQC&OR unit, Indian Statistical Institute, Kolkata, India
SQC&OR unit, Indian Statistical Institute, Kolkata, India
SQC&OR unit, Indian Statistical Institute, Kolkata, India
AUTHOR
[1] Castagliola P. (1996), Evaluation of nonnormal capability indices using Burr’s distributions; Quality
1
Engineering 8(4); 587–593.
2
[2] Clements J.A. (September 1989), Process capability calculations for non-normal distributions; Quality
3
Progress; 95–100.
4
[3] Farnum N.R. (1997), Using Johnson curves to describe nonnormal process data; Quality Engineering
5
9(2); 329–336.
6
[4] Hoaglin D.C. (1985), Summarizing shape numerically: The g-andh distributions. In: Hoaglin D.C.,
7
Mosteller F., and Tukey J.W. (Eds.), Exploring Data Tables, Trends and Shapes. Wiley, New York.
8
[5] Johnson N.L., Kotz S. (1993), Process Capability Indices; John Wiley & Sons.
9
[6] Kaminsky F.C., Dovich R.A., Burke R.J. (1998), Process capability indices: now and in the future;
10
Quality Progress; 10, 445-453.
11
[7] Kane V.E. (1986), Process capability indices; Journal of Quality Technology 18; 41-52.
12
[8] Kendal M.G., Stuart A. (1963), The advanced theory of statistics; vol-I, 2nd ed. London: Charles
13
Griffin and Company Limited.
14
[9] Kocherlakota S., Kocherlakota K., Kirmani S.N.A.U. (1992), Process capability indices under
15
nonnormality; Int. J. Math. Statist. Sci.1 (2); 175–210.
16
[10] Kotz S., Johnson N.L. (2002), Process capability indices - a review; Journal of Quality Technology
17
34(1); 2-19.
18
[11] Kotz S., Lovelace C. (1998), Introduction to Process Capability Indices; Arnold, London, UK.
19
[12] MacGillivray H.L., Cannon W.H. (2000), Generalizations of the g and-h distributions and their uses;
20
Unpublished thesis.
21
[13] Majumder M.m.A, Ali M.M. (2008), a comparison of methods of estimation of parametersof Tukey’s
22
gh family of distributions; Pak. J. Statist. Vol. 24(2); 135-144.
23
[14] Martinez J., Iglewicz B. (1984), Some properties of the Tukey g-andh family of distributions;
24
Communications in Statistics—Theory and Methods 13(3); 353–369.
25
[15] Mukherjee S.P., Singh N.K. (1998), Sampling properties of an estimator of a new process capability
26
index for weibull distributed quality characteristics; Quality Engineering 10; 291- 294.
27
[16] Munechika M. (1992), Studies on process capability in machining processes; Reports of Statistical
28
Applied research JUSE 39; 14–29.
29
[17] Pal S. (2004), Evaluation of normal process capability indices using generalized lambda Distribution;
30
Quality Engineering, 17; 77-85.
31
[18] Polansky A.M., Chou Y.M., Mason R.L. (1998), Estimating process capability indices for a truncated
32
distribution; Quality Engineering 11(2); 257–265.
33
[19] Pyzdek T. (1995), Why normal distributions aren’t [all that normal]; Quality Engineering 7; 769–777.
34
[20] Rayner G.D, MaCgillivray H.L. (2002), Numerical maximum likelihood estimation for the g-and-k
35
and generalized g-and-h distributions; Statistics and Computing 12; 57–75.
36
[21] Rodriguez R.N. (1992), Recent developments in process capability analysis; Journal of Quality
37
Technology 24; 176–186.
38
[22] Somerville S.E., Montgomery D.C. (1997), Process capability indices and non-normal distributions;
39
Quality Engineering 9(2); 305–316.
40
[23] Spiring, F., Leung B., Cheng S., Yeung A. (2003), A bibliography of process capability papers;
41
Quality and Reliability Engineering International 19(5); 445-460.
42
[24] Sundaraiyer V.H. (1996), Estimation of a process capability index for inverse Gaussian distribution;
43
Communications in Statistics—Theory and Methods 25; 2381–2398.
44
[25] Tukey J.W. (1977), Modern techniques in data analysis; NSF Sponsored Regional Research
45
Conference, Southeastern Massachusetts University, North Dartmouth, Massachusetts.
46