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1 - The Use of Metaheuristics for a Stochastic Supply Chain Design Problem’s Resolution –A Comparison Study– http://www.ijsom.com/article_2736.html 10.22034/2017.3.01 1 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. 0 - 193 201 - - Fouad Maliki Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria Algeria mafouad11@gmail.com - - Mehdi Souier Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria Algeria souier.mehdi@gmail.com - - Mohammed Dahane Laboratory of Industrial Engineering of Production and Maintenance, Metz, France France mohammed.dahane@univ-lorraine.fr - - Zaki Sari Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria Algeria zaki_sari@yahoo.com Distribution network Suppliers selection Metaheustics Optimization Benyoucef, L., Xie X., and Tanonkou G.A. (2013). Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach. International Journal of Production Research, Vol. 51 (21), pp. 6435-6454.##Bischoff M. and Kerstin D. (2009). Allocation search methods for a generalized class of location–allocation problems. European Journal of Operational Research, Vol. 192, pp. 793-807.##Daskin M.S., Coullard C. and Shen, Z.J.M. (2006). An inventory-Location Model: Formulation, Solution Algorithms and Computational results. Annals of Operations Research, Vol. 110, pp. 83-106.##De Boer L., Labro E. and Morlacchi P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing and Supply Management, Vol. 7, pp. 75-89.##Felfel H., Ayadi O. and Masmoudi F. (2015). A stochastic programming approach for a multi-site supply chain planning in textile and apparel industry under demand uncertainty. International Journal of Supply and Operations Management, Vol. 2 (3), pp. 925-946.##Gebennini E., Gamberini R. and Manzini R. (2009). An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization. The international journal of production and economics, Vol. 122, pp. 286-304.##Inemek A. and Tuna O. (2009). Global supplier selection strategies and implications for supplier performance: Turkish suppliers' perception. International Journal of Logistics Research and Applications, Vol. 12, pp. 381-406.##Jain V., Benyoucef L. and Deshmukh S.G. (2009). Strategic supplier selection: some emerging issues and challenges. Int. J. Logistics Systems and Management, Vol. 5 (1/2), 61-88.##Maliki F., Benyoucef L. and Sari Z. (2011). Sensitivity analysis for a stochastic multi modal location-allocation integrated suppliers selection problem. The international conference on Industrial Engineering and System Management (IESM 2011), Metz, France.##Maliki F. and Sari Z. (2012). Etude comparative des politiques de gestion de stock lors de la conception des chaînes logistiques. 9e conférence internationale de Modélisation, Optimisation et Simulation (MOSIM’12), Bordeaux, France.##Maliki F., Sari Z. and Souier M. (2013). Resolution of stochastic supply chain design problem by metaheuristic. The international conference Control, Decision and Information Technologies (CODIT’13), proceedings, Hammamet, Tunisia, pp. 366-371.##Maliki F., Brahami M.A., Dahane M. and Sari Z. (2014). A location-allocation problem design with unvailabilities management. 44th international conference on computers & industrial engineering (CIE44&IMSS’14) proceedings, Istanbul, Turkey, pp. 669-679.##Maliki F., Brahami M.A., Dahane M. and Sari Z. (2016). Facility unvailabilities management and supply chains design. Journal européen des systèmes automatisés, Vol. 49 (4-5), pp. 471-485.##Melo M.T., Nickel S. and Saldanha-da-gama F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, Vol. 196(2), pp. 401-412.##Owen S. H. and Daskin M.S. (1998). Strategic facility location: A review. European Journal of Operational Research, Vol. 111 (3), pp. 423-447.##Rezaei A. H. and Adressi A. (2015). Supply chain performance evaluation using data envelopment analysis. International Journal of Supply and Operations Management, Vol. 2 (2), pp. 748-758.##Shen Z.J.M., Coullard C. and Daskin M.S. (2003). A Joint Location-Inventory Model. Transportation Science, 37(1), pp. 40-55.##Shishebori D. and Ghaderi A. (2015). An integrated approach for reliable facility location/network design problem with link disruption. International Journal of Supply and Operations Management, Vol. 2 (1), pp. 640-661.##Snyder L.V. and Daskin M.S. (2005). Reliability Models for Facility Location: the expected failure cost case. Transportation Science, Vol. 39 (3), pp. 400-416.##Tanonkou G.A., Benyoucef L. and Xie X. (2006). A Two-Period Stochastic programming Model for Distribution network Design. Proceedings of the 12th IFAC Symposium on Information Control Problems in Manufacturing, St Etienne, France, pp. 377-382.##Tanonkou G.A., Benyoucef L. and Xie X. (2007). Joint Facility Location and Supplier Selection Decisions of distribution Networks with Random Supply Lead Time. International Conference on Industrial Engineering and Systems Management (IESM 2007), Pékin, Chine, pp. 1-10.##Tanonkou G.A. (2007). Une approche par relaxation lagrangienne pour l’optimisation d’un réseau de distribution : modèles stochastiques et fiables, université Paul Verlaine de Metz.##Vijayashree M., and Uthayakumar R. (2015). Integrated inventory model with controllable lead time involving investment for quality improvement in supply chain system. 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1 - Cooperative Grey Games: Grey Solutions and an Optimization Algorithm http://www.ijsom.com/article_2740.html 10.22034/2017.3.02 1 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. 0 - 202 215 - - Osman Palanci Suleyman Demirel University, Isparta, Turkey Turkey osmanpalanci@sdu.edu.tr - - Mehmet Onur Olgun Suleyman Demirel University, Isparta, Turkey Turkey onurolgun@sdu.edu.tr - - Serap Ergun Suleyman Demirel University, Isparta, Turkey Turkey serapbakioglu@sdu.edu.tr - - Sırma Zeynep Alparslan Gok Suleyman Demirel University, Isparta, Turkey Turkey zeynepalparslan@yahoo.com - - Gerhard Wilhelm Weber Institute of Applied Mathematics, Middle East Technical University and Poznan University of Technology, Poznan, Poland Turkey gerhard.weber@put.poznan.pl Cooperative grey games Grey core Grey dominance core Grey stable sets Linear optimization Social networks Bockarjova Z.M., Sauhats A., Vempers G. and Tereskina I., (2010). On application of the cooperative game theory to energy supply system planning. 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1 - A Markov Chain Analysis of the Effectiveness of Kanban Card with Dynamic Information http://www.ijsom.com/article_2733.html 10.22034/2017.3.03 1 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. 0 - 215 228 - - Fahimeh Tanhaie School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran fahimeh.tanhaie@ut.ac.ir - - Fariborz Jolai School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran fjolai@ut.ac.ir - - Masoud Rabbani School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran mrabani@ut.ac.ir Pull system Kanban Buffer Dynamic environment Markov chain Bitran, G., and Chang, L. (1987). A Mathematical Programming Approach to a Deterministic Kanban System. Management Science, Vol. 33(4), pp. 427-441.##Framinan, J., González, P., and Ruiz-Usano, R. (2003). The CONWIP production control system: Review and research issues. Production Planning & Control, Vol. 14(3), pp. 255-265.##Framinan, J., González, P., and Ruiz-Usano, R. (2006). Dynamic card controlling in a Conwip system. International Journal of Production Economics, Vol. 99(1-2), pp. 102-116.##Gong, Q., Yang, Y., and Wang, S. (2014). Information and decision-making delays in MRP, KANBAN, and CONWIP. International Journal of Production Economics, Vol. 156, pp. 208-213.##González-R, P., Framinan, J., and Pierreval, H. (2011). Token-based pull production control systems: an introductory overview. Journal of Intelligent Manufacturing, Vol. 23(1), pp. 5-22.##Gupta, S., and Al-Turki, Y. (1997). An algorithm to dynamically adjust the number of Kanbans in stochastic processing times and variable demand environment. Production Planning & Control, Vol. 8(2), pp. 133-141.##Gupta, M. (2003). Constraints management--recent advances and practices. International Journal of Production Research, Vol. 41(4), pp. 647-659.##Hopp, W., and Roof, M. (1998). 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1 - Optimization of a Multi-product Three-echelon Supply Chain http://www.ijsom.com/article_2732.html 10.22034/2017.3.04 1 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. 0 - 229 247 - - Mehrnaz Najafi Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Iran mehr.n.25@gmail.com - - Ali Ghodratnama Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Iran ghodratn@ut.ac.ir - - Hamid Reza Pasandideh Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Iran pasandid@yahoo.com Three echelon supply chain Genetic Algorithm Simulated annealing algorithm Alimardani, M., Jolai, F., and Rafiei, H. 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Strategic facility location: A review. European Journal of Operational Research ,Vol. 111, pp. 423-47.##Park Y. A. (2001). hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines. International Journal of Production Economics, Vol. 73, pp.175-188.##Panda, S., Modak, N.M., and Cárdenas-Barrón, L.E. (2017). Coordination and benefit sharing in a three-echelon distribution channel with deteriorating product. Computers & Industrial Engineering ,Vol. 113, pp. 630-645.##Pasandideh, S.H.R., Niaki, S.T.A., and  Aryan Yeganeh, J. A. (2010). Parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages. Advances in Engineering Software,Vol. 41, pp. 306-314.##Pasandideh, S.H.R., and Niaki, S.T.A. (2008). 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1 - A New Robust Mathematical Model for the Multi-product Capacitated Single Allocation Hub Location Problem with Maximum Covering Radius http://www.ijsom.com/article_2735.html 10.22034/2017.3.05 1 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. 0 - 248 262 - - Mahdi Alinaghian Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran Iran alinaghian@cc.iut.ac.ir - - S.Reza Madani Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran Iran r.madani@in.iut.ac.ir - - Hossain Moradi Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran Iran hossein.moradi1@in.iut.ac.ir Multi-product Hub location Single allocation Robust optimization Genetic Algorithm Simulated annealing algorithm Alinaghian, M., Ghazanfari, M., Salamatbakhsh, A. and Norouzi, N. 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1 - Price Discount Determination in Pricing and Inventory Control of Perishable Good with Time and Price Demand http://www.ijsom.com/article_2734.html 10.22034/2017.3.06 1 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. 0 - 263 273 - - Fatemeh Zabihi Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran Iran fatemehzabihy@yahoo.com - - Morteza Khakzar Bafruei Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran Iran khakzar@jdsharif.ac.ir Pricing Price discount Inventory Control Demand rate function Chang, H., Teng, J., Ouyang, and Dye, C. (2006). Retailer’s optimal pricing and lotsizing policies for deteriorating items with partial backlogging. European Journal of Operational Research ,Vol. 168, pp. 51-64.##Díaz, I. M. (2006). Demand restrictions in price-based decisions: manager versus? consumers. Journal of Product & Demand Management , Vol. 15 (3), pp. 214-224.##Dye, C. (2007). Joint pricing and ordering policy for a deteriorating inventory with partial backlogging. Omega, Vol. 35, pp. 184-189.##Eilon, S., and Mallaya, R. V. (1966). Issuing and pricing policy of semi-perishables. In Proceedings of the 4th International Conference on Operational Research, Wiley-Interscience. New York, NY, USA.##Ghare, P. M., and Schrader, G. H. (1963). A model for an exponentially decaying inventory. Journal of Industrial Engineering , Vol. 14, pp. 238-243.##Karaesmen, I., Scheller–Wolf, A., and Deniz, B. (2011). Managing perishable and aging inventories: review and future research directions. Planning Production and Inventories in the Extended Enterprise: International Series in Operations Research and Management Science , Vol. 151, pp. 393-436.##Maihami, R., and Karimi, B. (2014). Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts. Computers & Operations Research , Vol. 51, pp. 302-312.##Maihami, R., and Nakhai Kamalabadi, I. (2012). Joint pricing and inventory control for non-instantaneous deteriorating items with partial backlogging and time and price dependent demand. International Journal Production Economics, Vol. 136, pp. 116-122.##Nahmias, S. (1982). Perishable inventory theory: A review. Operations Research , demonstrate Vol. 30 (3), pp. 680-708.##Raafat, F. (1991). Survey of literature on continuously deteriorating inventory model. Journal of the Operational Research Society , Vol. 42, pp. 27-37.##Tajbakhsh, M., Lee, C., and Zolfaghari, S. (2011). An inventory model with random discount offerings. Omega, Vol. 39 (6), pp. 710–718.##Wee, H. M. (1995). Joint pricing and replenishment policy for deteriorating inventory with declining market. International Journal of Production Economics , Vol. 40, pp. 163–171.##Yang, C., Quyang, L., and Wu, H. (2009). Retailers optimal pricing and ordering policies for Non-instantaneous deteriorating items with price-dependentdemand and partial backlogging. Mathematical Problems in Engineering . Article ID 198305, 18 pages##Yu-Chung, T., and Ji, S. G. (2008). Dynamic pricing, promotion and replenishment policies for a deteriorating item under permissible delay in payments. Computers & Operation Research , Vol. 35, pp. 3562-3580.##
1 - Public Procurement Process Design and Small and Medium Enterprises Access to Contracts in Uganda http://www.ijsom.com/article_2738.html 10.22034/2017.3.07 1 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. 0 - 274 278 - - Peter Obanda Kyambogo University, Kampala, Uganda Uganda pobanda2001@yahoo.com Small enterprises Procurement process Management framework Choi Jeong-Wook. (2010). A study on the role of public procurement. Can public procurement make society better? In proceedings of The 4th International Public Procurement Conference. Seoul: South Korea.##Commission Staff Working Document. (2008). European code of best practices facilitating access by SMEs to public procurement contracts.##Commission of the European Communities Commonwealth Secretariat. (2010). Improving SMEs access to the public market. Trade, Enterprise & Agricultural Department, October, Draft report, Republic of Uganda.##De Boer Luitzen, Linthorst Merijn, Schotanus Fredo and Telgen Jan. (2006). An analysis of some mistakes, miracles and myths in supplier selection.##De Boer and Van Stekelenborg, H. A. Rob. (1995). Design support for purchasing. 4th International IPSERA Conference. Birmingham.##Linthorst M. Merijn and Telgen Jan. (2006). Public Purchasing future: Buying from multiple suppliers.##McCrudden Christopher. (2004). Using public procurement to achieve social outcomes. Natural Resources Forum 28, 257-267.##McKevitt Davis and David Paul. (2013). Microenterprises: how they interact with public procurement processes. International Journal of Public Sector Management, Vol.26 (6), pp. 469-480.##Monczka Robert, Trent Robert and Handfield Robert (2005). Purchasing and supply chain management. South Western. Thomson.##Mont, O. and Leire, C. (2008). Socially responsible purchasing in the supply Chain: drivers and barriers in Sweden. Social Responsibility Journal, Vol. 5 (3), pp. 388-407.##Obanda, W. Peter. (2012). SMEs and public procurement contracts in developing countries. In proceedings of The 5th International Public Procurement Conference, Seattle: Washington, USA.##Obanda, W. Peter. (2010). Fighting corruption in tactical procurement. PhD Thesis.##Okello-Obura C. and Matovu James. (2011). SMEs and Business information provision strategies: Analytical perspective. Library Philosophy and Practice.##Procurement Innovation Group. (2010). Using public procurement to stimulate innovation and SME access to public contracts. Department of Enterprise, Trade and Employment. July.##Telgen Jan. (2006). Public procurement in perspective in International Public procurement, Cases and Commetaries,##Knight L.A., Harland C.M., Telgen J., Thai K.V. and McKen K.E. (Eds). Routledge##Kisamba Mugerwa (2017). Middle Income status far fetched, The New Vision, Friday, September 8##The Organization of Government Commerce (2010). Small supplier big opportunity: Flagging your contracts to SMEs. www.ogc.gov.uk##The Public Procurement and Disposal of Public Assets Act. No.1 of 2003. Prime Concepts Limited.##The Public Procurement and Disposal of Public Assets Regulations (2014).Tumutegyereize Milton (2013). Public procurement reforms: Issues and challenges: The case of Uganda. CIPS Pan African Conference. Ghana. National Theatre.##Uganda Investment Authority (2018). Small and Medium Enterprises. http://www.ugandainvest.go.ug/sme/##