PUBLICATION IN INTERNATIONAL JOURNALS
Faculty members of the Laboratory of Logistics & Supply Chain Management are actively involved in research. Area of research include various aspects of logistics and supply chain management such as supply chain strategy, lot-sizing / inventory management, schedule instability, supply chain risk management, traceability, information sharing, sustainable supply chain, pricing and revenue management, container stowage, distribution planing and optimization, and many others. Research done by faculty members have been published in many international journals. Here are the publication lists from faculty members of the Logistics & Supply Chain Management Laboratory of ITS.
Prof. I Nyoman Pujawan
1. Jauhari, W., Pujawan, I N. (2012). Joint economic lot size (JELS) model for single-vendor single-buyer model with variable production rate and partial backorder. International Journal of Operational Research (Accepted).
2. Widodo, E., Takahashi, K., Morikawa, K., Pujawan, I. N. and Santosa, B. (2011). Managing sales return in dual sales channel: an analysis of primary versus secondary market resale strategies, International Journal of Industrial and Systems Engineering (Accepted).
3. Erwin Widodo, I Nyoman Pujawan, and Budi Santosa, Katsuhiko Takahashi, Katsumi Morikawa (2012). “Compromised Stackelberg scheme towards smoothened profit-distribution in dual channel supply chain,” International Journal of Logistics and SCM Systems, Vol. 6, No. 1, pp. 111-121.
4. Pujawan, I N. And Smart, A. U. (2012). Factors affecting schedule instability in manufacturing companies. International Journal of Production Research 50 (8), 2252-2266.
5. Widodo, E., Takahashi, K., Morikawa, K., Pujawan, I N. and Santosa, B. (2011). Managing sales return in dual sales channel: Its product substitution and return channel analysis, International Journal of Industrial and Systems Engineering 9 (1), 67 – 97.
6. Jauhari, W., Pujawan, I N., Wiratno, S. E., Priyandari, Y. (2011). Integrated inventory model for single vendor single buyer with probabilistic demand. International Journal of Operational Research 11 (2), 160 – 178.
7. Pujawan, I N. and Mahendrawathi Er (2009). Managing supply chain complexity in a tea manufacturing company. Operations and Supply Chain Management: An International Journal 2 (3), pp. 167 – 171.
8. Pujawan, I N. and Geraldin, L. H. (2009). House of Risk: A Model for Proactive Supply Chain Risk Management. Business Process Management Journal 15 (6), pp. 953 – 967
9. Vanany, I., Zailani, S., and Pujawan, I N. (2009). Supply chain risk management: literature review and future research. International Journal of Information Systems and Supply Chain Management 2(1), pp. 16 – 33.
10. Pujawan, I N., Kurniati, N., and Wessiani, N. (2009). Supply Chain Management for Disaster Relief Operations: Principles and Case Studies. International Journal of Logistics Systems and Management 5(6), pp. 679 – 692.
11. Law, K., and Pujawan, I N. (2009). Collective Efficacy and Manufacturing Schedule Instability- A study in Hong Kong and the Pearl River Delta Region. International Journal of Industrial and Systems Engineering 4(1), pp. 1 – 17.
12. Pujawan, I N. (2008). Operations and supply chain management: an editorial. Operations and Supply Chain Management: An International Journal 1(1), pp. 1 – 3.
13. Piplani, R., Pujawan, I N., and Ray, S. (2008). Sustainable supply chain management. International Journal of Production Economics 111 (2), pp. 193 – 194.
14. Pujawan, I N. and Silver, E. A. (2008). Augmenting the lot sizing order quantity when demand is probabilistic. European Journal of Operational Research 188 (3), pp. 705-722.
15. Pujawan, I N. (2008). Schedule instability in a supply chain: An experimental study. International Journal of Inventory Research 1 (1), pp. 53 – 66.
16. Mahendrawathi Er., and Pujawan, I N. (2007). Integrated inventory-production decisions under uncertain demand: A simulation study. International Journal of Logistics and Transport, 1 (1), pp. 41 – 51.
17. Pujawan, I N. (2005). Schedule nervousness in a manufacturing system: A case study. Production Planning and Control 15 (5), pp. 515 – 524.
18. Pujawan, I N. and Goyal, S. K. (2005). Electronic procurement and manufacturing strategic objectives. International Journal of Logistics Systems and Management 1 (2/3), pp. 227 – 243.
20. Pujawan, I N. (2004). The effect of lot sizing rules on order variability. European Journal of Operational Research 159 (3), pp. 617-635.
21. Pujawan, I N. (2004). Assessing supply chain flexibility: A conceptual framework and case study. International Journal of Integrated Supply Management 1(1), pp. 79-97.
22. Pujawan, I N. and Kingsman, B. G. (2003). Properties of lot sizing rules under lumpy demand. International Journal of Production Economics 81-82, 295-307.
23. Pujawan, I. N. and Kingsman, B. G. (2002). Joint Optimisation and Timing Synchronisation in a Buyer Supplier Inventory System. International Journal of Operations and Quantitative Management 8 (2), pp. 93 – 109.
Dr. Ahmad Rusdiansyah
1. Rusdiansyah, A. and Tsao, D. (2005). An integrated model of the periodic delivery problems for vending-machine supply chains, Journal of food engineering 70 (3).
2. Rusdiansyah, A. and Tsao, D. (2005). Coordinating deliveries and inventories for a supply chain under vendor managed inventory system, JSME International Journal Series A 48 (2).
3. Garside, A.K. and Rusdiansyah, A. (2010). An integrated model for production, inventory and distribution problem with direct shipment, International Journal of Business Performance and Supply Chain Modelling 2 (1).
4. DP Sari, A Rusdiansyah, L Huang (2012). Models of Joint Economic Lot-sizing Problem with Time-based Temporary Price Discounts. International Journal of Production Economics 139 (1), 145-154.
5. Nurwidiana, Rusdiansyah, A. (2010). Modeling And Solving Common Replenishment EPOCHS Considering Shipment Consolidation. International Journal of Logistics and Transport.
6. A Rusdiansyah, H Pradhana, D Mariana, N A Wessiani (2011). Joint Dynamic Pricing Model for Two Parallel Flights Considering Overbooking, Cancellations, and No-Show Customers. Journal of the Eastern Asia Society for Transportation Studies 9, pp. 2113-2128.
Dr. Imam Baihaqi
1. I Baihaqi, N Beaumont, A Sohal (2008). Information sharing in supply chains: a survey of Australian manufacturing, International Review of Business Research Papers 4 (2), 1-12
2. I Baihaqi, AS Sohal (2012). The impact of information sharing in supply chains on organisational performance: an empirical study, Production Planning & Control.
Dr. Iwan Vanany
1. Vanany, I., Zailani, S., and Pujawan, I N. (2009). Supply chain risk management: literature review and future research. International Journal of Information Systems and Supply Chain Management 2(1), pp. 16 – 33.
2. Vanany, I. and Shaharoun, A. M. (2011). The Comprehensive Framework for RFID Justification in Healthcare. International Business Management 5 (2), pp. 76-84.
3. Vanany, I. and Shaharoun, A. M. (2010). A multi-stage approach for RFID selection decision. Meiji Business review 58.
4. Vanany, I. (2011). An AHP Based Method to Prioritize the Barriers and Critical Success Factors of RFID Adoption in Healthcare, International Business Management 5 (6), pp. 427-435
Beside all lecturers, assistants of the Laboratory of Logistics & Supply Chain Management are also publishing their researches. Here are some publications on those researches.
Promotion Planning and Inventory Policy Modelling to Optimize Supply Chain Profit
To achieve an efficient and effective supply chain, all members of supply chain needs coordination. This research analyze and develop coordination model that focus on promotion plan (consist of discount rate and promotion frequency) that being done by retailer and regulation adjustment using newsvendor inventory model. This research aims to achive a coordination that might optimize profit sharing supply chain. There are 2 coordination scenarios, which are Off-Invoice trade deal (OI) and Scan back trade deal (SB). From both scenario promotion plan are calculated and inventory regulation by using newsvendor inventory model. The new developed model are tested with numerical testing by using several parameter that influence promotion and inventory regulation. The biggest total supply chain profit achieved when SB trade deal implemented. Numerical test result shows that retailer have tendency to choose OI trade deal coordination, on the other hand manufacture more likely choose SB trade deal.
Keywords: supply chain coordination, customer promotion plan, Markov switching time-series, newsvendor inventory model, buyback contract.
Wahyu Bagus Anshori
Developing Model and Tabu Search Algorithm for Solving Tanker Schedulling Problem (TSP) Considering Product Loading Compatibility Constraint
In this research we develop a model for solving Multi Product Tanker Schedulling Problem (m-TSP) considering product loading compatibility constraint. Those products will be shipped from single depot to several consumption ports using a fleet of undedicated multi compartment tankers. In this model, we attempt to determine schedules for the tankers and the quantity of products should be loaded into them referring the demand received. During the loading process, we restrict that the products to be loaded on the neighboring compartments should be compatible. The objective of the problem is to minimize the total distribution costs consist of port charge, management fee, bunker consumption cost, and compartment utility cost as well as ensuring the availability of products at every port. Due to the complexity of the problem, we propose to use Tabu Search algorithms to implement the proposed model. The developed algorithm has been tested to solve the real problem and the result can represent the system behavior well.
Keyword : Multi Product Tanker Schedulling Problem, Product Compatibility, Single Depot, Undedicated Compartment, Tabu Search Algorithm
Dynamic Pricing with Competition Model Based on Time and Seat Inventory for Two Paralel Flight in Low Cost Carrier Considering Competitor’S Price and Seat Inventory
The Low Cost Carrier (LCC) is an airline which provide lower cost than traditional airline. Airlines competition becomes more strict with attendance of LCC. The two parallel flights can be defined as a two flight with equal departure time. The departure time of one flight and the other flight on two parallel flights is closed. The competition of two parallel flights between LCC’s airlines will be a harder competition. Since the pricing decision for one flight will affect demand and revenue for the other flight. In this research, dynamic pricing model is developed for competition od two parallel flight in LCC. The development is based on time, seat inventory, anad competitor pricing. In dynamic pricing, the model developed is a dynamic programming model, which can optimizes ticket price dynamically. Optimizes ticket prices is ticket price which is chosen considering with inventory, tine, and competitor’s ticket price. The result is the best response for competitor is being a follwer of competitor. The computation shows that dynamic pricing model based on time and seat inventory has higher expectation revenue than dynamic pricing model based on inventory only. Numerical experiment also shows that seat allocation of every sub classes is important to be considered. It needs to be considered to get the optimize expectation revenue.
Keywords : revenue management, dynamic pricing with competition, parallel flight, dynamic programming.
Inventory Model for Build-To-Order Supply Chain under Product-Mix Uncertainty
We developed periodic review inventory model for build to order supply chain. BTO strategy allows the company to hold inventory consisting of certain number of module with various types until the customer demand occur. Costumers have flexibility in choosing their own product configuration that will increase product-mix uncertainty. Assuming that one module with various types can be purchased from one supplier, this model will combine both stochastic periodic review system (T, s, S) and joint replenishment problem where T is included as decision variable with dynamic S and s. The model allows backorder so the unmet demand will be fulfilled once the stock is replenished. Under BTO environment, we prove that the proposed model performs better than other periodic review model in both cost minimization and backorder occurrence. Evidently we provide numerical example of proposed model and other model (static periodic review and non-joint replenishment periodic review model) to demonstrate the efficiency of proposed model.
Keywords: build-to-order supply chain (BOSC), periodic review, backorder, product-mix uncertainty, joint replenishment.
Sobiroh Ulin Nuha
Integration Model Between Production Scheduling and Distribution Planning on Perishable Food Product
Perishable food products have specific characteristics such as rate of decline in quality, temperature and shelf life that different for each product.Management of these products can be started at the beginning of the production process until the products are accepted by consumers.This research attempt to integrate production scheduling with distribution planning on perishable food products considering energy cost, quality and shelf life products. This integration aims to maximize the total profitcompany and ensure product quality. The integration is begun with the delivery of planning beforehand.The results of this distribution planning is the information about the consumer to be served on the k-th vehicle so that making it easier for company to do the scheduling of production, especially for company that have only one production line.Distribution planning in this research using two methods, namely vehicle routing problem with time windows (VRPTW) and vehicle routing problem with quality windows(VRPQW).The results obtained in this study that the distribution planning using VRPQW requires more number of vehicles but with average of less quality lossthan the delivery of planning using VRPTW. On the production scheduling, quality consideration the rate of decline in quality in the production sequence to minimize the total cost of distribution.
Keywords: perishable food products, production scheduling, distribution planning, vehicle routing problem with time windows, vehicle routing problem with quality windows.