eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
193
201
10.22034/2017.3.01
2736
The Use of Metaheuristics for a Stochastic Supply Chain Design Problem’s Resolution –A Comparison Study–
Fouad Maliki
mafouad11@gmail.com
1
Mehdi Souier
souier.mehdi@gmail.com
2
Mohammed Dahane
mohammed.dahane@univ-lorraine.fr
3
Zaki Sari
zaki_sari@yahoo.com
4
Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria
Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria
Laboratory of Industrial Engineering of Production and Maintenance, Metz, France
Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria
In a competitive and maintainability context, each company finds to optimize her supply chain in order to maintain her customers by providing the best quality of products in the best delays and with the lost costs. In this sense, we are interested to a single commodity stochastic supply chain design problem. Our supply chain is composed of suppliers and retailers; the objective is to find the best location of distribution centres (DCs) and to serve retailers from suppliers trough DCs in a random supply lead time. We presented a non-linear optimization model integrated selection of suppliers, the location of DCs, and retailers allocation decisions with an oriented cost function to minimize. Note that the determination of exact solutions for this problem is a NP-hard problem. Accordingly, we propose an optimization approach using three different metaheuristics: genetic algorithm, simulated annealing and taboo search to solve this problem in order to find the best supply chain structure (location of DCs, allocation of suppliers to DCs and DCs to retailers). Computational results are presented and compared to evaluate the efficiency of the proposed approaches.
http://www.ijsom.com/article_2736_9b46635d9fec65755119970fb6abcd53.pdf
Distribution network
Suppliers selection
Metaheustics
Optimization
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
202
215
10.22034/2017.3.02
2740
مقاله پژوهشی
Cooperative Grey Games: Grey Solutions and an Optimization Algorithm
Osman Palanci
osmanpalanci@sdu.edu.tr
1
Mehmet Onur Olgun
onurolgun@sdu.edu.tr
2
Serap Ergun
serapbakioglu@sdu.edu.tr
3
Sırma Zeynep Alparslan Gok
zeynepalparslan@yahoo.com
4
Gerhard Wilhelm Weber
gerhard.weber@put.poznan.pl
5
Suleyman Demirel University, Isparta, Turkey
Suleyman Demirel University, Isparta, Turkey
Suleyman Demirel University, Isparta, Turkey
Suleyman Demirel University, Isparta, Turkey
Institute of Applied Mathematics, Middle East Technical University and Poznan University of Technology, Poznan, Poland
In this paper, some set-valued solutions using grey payoffs, namely, the grey core, the grey dominance core and the grey stable sets for cooperative grey games, are introduced and studied. Our main results contained are relations between the grey core, the grey dominance core and the grey stable sets of such a game. Moreover, we present a linear programming (LP) problem for the grey core. On the other hand, we suggest a corresponding optimization-basedalgorithm finding the grey core element of a cooperative grey game. Finally, we give an application how cooperative grey game theory can be used to model users' behaviors in various multimedia social networks. The paper ends with aconclusion and an outlook to future investigations.
http://www.ijsom.com/article_2740_12fe9a6d0b0b0be196b8b20a249e2624.pdf
Cooperative grey games
Grey core
Grey dominance core
Grey stable sets
Linear optimization
Social networks
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
215
228
10.22034/2017.3.03
2733
مقاله پژوهشی
A Markov Chain Analysis of the Effectiveness of Kanban Card with Dynamic Information
Fahimeh Tanhaie
fahimeh.tanhaie@ut.ac.ir
1
Fariborz Jolai
fjolai@ut.ac.ir
2
Masoud Rabbani
mrabani@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
The pull system produces products based on customer demands. Each station is isolated until a customer order is placed; then a signal or Kanban is sent from downstream station to upstream station and continues until the first station. Most of the papers studied pull system in deterministic environment while many real production lines are subjected to different types of uncertainties. The objective of this paper is to apply a dynamic Kanban system that changes the information on the Kanban cards based on the remained inventory in the buffer. The proposed approach uses a Markov chain analysis to compare effectiveness of the Kanban card with dynamic information with the Kanban card with static information. In this paper the production line of two work stations and two inventory buffer is modeled. Throughput, shortage, work-in-process and cycle time are the model measurement parameters and the results show the advantages of the proposed approach.
http://www.ijsom.com/article_2733_de18891b7d836ce4315273208a61ff26.pdf
Pull system
Kanban
Buffer
Dynamic environment
Markov chain
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
229
247
10.22034/2017.3.04
2732
مقاله پژوهشی
Optimization of a Multi-product Three-echelon Supply Chain
Mehrnaz Najafi
mehr.n.25@gmail.com
1
Ali Ghodratnama
ghodratn@ut.ac.ir
2
Hamid Reza Pasandideh
pasandid@yahoo.com
3
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
This paper aims at single-objective optimization of multi-product for three-echelon supply chain architecture consisting of production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the quantity of products to be shipped from plants to DCs, from DCs to CZs , cycle length, and production quantity so as to minimize the total cost .To optimize the objective, three-echelon network model is mathematically represented considering the associated constraints, production, capacityand shipment costs and solved using genetic algorithm (GA) and Simulated Annealing (SA).Some numerical illustrations are provided at the end to not only show the applicability of the proposed methodology, butalso to select the best method using a t-test along with the simple additive weighting (SAW) method.
http://www.ijsom.com/article_2732_fb3dcbadbfa254ea41640d7d563bb726.pdf
Three echelon supply chain
Genetic Algorithm
Simulated annealing algorithm
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
248
262
10.22034/2017.3.05
2735
مقاله پژوهشی
A New Robust Mathematical Model for the Multi-product Capacitated Single Allocation Hub Location Problem with Maximum Covering Radius
Mahdi Alinaghian
alinaghian@cc.iut.ac.ir
1
S.Reza Madani
r.madani@in.iut.ac.ir
2
Hossain Moradi
hossein.moradi1@in.iut.ac.ir
3
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
This paper presents a new robust mathematical model for the multi-product capacitated single allocation hub location problem with maximum covering radius. The objective function of the proposed model minimizes the cost of establishing hubs, the expected cost of preparing hubs for handling products, shipping and transportation in all scenarios, and the cost variations over different scenarios. In the proposed model, a single product of a single node cannot be allocated to more than one hub, but different products of one node can be allocated to different hubs. Also, a product can be allocated to a hub only if equipment related to that product is installed on that hub. Considering the NP-Hard complexity of this problem, a GA-based meta-heuristic algorithm is developed to solve the large scale variants of the problem. To evaluate the performance of the proposed algorithm, its results are compared with the results of exact method and simulated annealing algorithm. These results show the good performance of the proposed algorithm.
http://www.ijsom.com/article_2735_19e97962fca611266cb9476232d54c41.pdf
Multi-product
Hub location
Single allocation
Robust optimization
Genetic Algorithm
Simulated annealing algorithm
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
263
273
10.22034/2017.3.06
2734
مقاله پژوهشی
Price Discount Determination in Pricing and Inventory Control of Perishable Good with Time and Price Demand
Fatemeh Zabihi
fatemehzabihy@yahoo.com
1
Morteza Khakzar Bafruei
khakzar@jdsharif.ac.ir
2
Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran
Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran
Determining appropriate inventory control policies and product price are important aspects in the competitive markets of perishable products. Customers’ willing to pay for perishable product is declining when approaching to the end of product’s expiry date. In this paper, we consider price discount in pricing model as an alternative approach to influence on consumers’ purchase decision. The model determines the optimal values of selling price, discount time and replenishment schedule simultaneously such that the total profit is maximized. However, because of demand increase during the discount interval, different demand rate function which is a function of price and time is used in the model. In this regard, at first, we model the problem without regarding discount that its solution shows an impossible result in reality which the replenishment time is very short. But then with regarding discount in the model, more products are sold and thus the profit increases. Finally, we solve two numerical examples used an iterative algorithm by performing a sensitivity analysis of the model parameters and also discuss about specific managerial insights.
http://www.ijsom.com/article_2734_4e7d4b46111ec33f2419a642518b4b68.pdf
Pricing
Price discount
Inventory Control
Demand rate function
eng
Kharazmi University
International Journal of Supply and Operations Management
23831359
23832525
2017-08-01
4
3
274
278
10.22034/2017.3.07
2738
Public Procurement Process Design and Small and Medium Enterprises Access to Contracts in Uganda
Peter Obanda
pobanda2001@yahoo.com
1
Kyambogo University, Kampala, Uganda
Small and Medium Enterprises are often excluded from public procurement contracts due to several factors. We need effective public procurement policies, systems and personnel to ensure that the opportunities are scaled up rapidly, transparently and fairly so that SMEs can get access to public procurement contracts. The advocacy for SMEs access to public procurement contracts is largely driven by status discrimination, equality and sustainable development. In this paper, we analytically design a procurement process that can enable SME’s access public procurement contracts within the Public Procurement and Disposal Authority legal framework. We take cognizance of the public procurement environment and develop a supportive management framework.
http://www.ijsom.com/article_2738_87dc6688b2388d9258794179f9140006.pdf
Small enterprises
Procurement process
Management framework