A DATA ENVELOPMENT ANALYSIS (DEA)-BASED INTEGRATED LOGISTICS NETWORK SYSTEM DESIGN TO IMPROVE SUPPLY CHAIN EFFICIENCY IN SOUTH CAROLINA

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: EVANS-ALLEN
• Reporting Frequency: Annual
• Accession No.: 1006437
• Project Start Date: May 16, 2015
• Project End Date: May 15, 2018
• Program Code: [(N/A)]- (N/A)

Recipient Organization
SOUTH CAROLINA STATE UNIVERSITY
(N/A)
ORANGEBURG,SC 29117

Performing Department
Engineering Technologies

Non Technical Summary
An integrated logistic network design (ILND) is an important strategic decision that significantly affects the overall performance of supply chain management activities. The primary objective of the traditional logistic network or supply chain design problem is to determine the most cost-effective location of facilities, assignment of lower-level facilities to higher-level facilities, and distribution of products/items throughout the network structure. Traditionally, the transportation or logistics cost has been considered as a main performance measure guiding logistics network design steps. In fact, building a model with the objective of minimizing overall cost using an optimization concept might be the best way to determine network design, but minimizing overall cost does not always lead to the optimal values of other performance measures, such as service level. The performance of such logistics networks can be measured by several performance measures, but some of which may conflict with each other.The objective of the proposed research is to develop and propose a Data Envelopment Analysis (DEA)-based design and benchmarking framework for designing an integrated logistics network (ILN) system under uncertainty to improve supply chain efficiency for various integrated logistics networks in South Carolina. First, after identifying performance measures of the logistics network under uncertainty, we will build a multi-objective optimization model through goal programming (GP), which simultaneously takes all financial and non-financial performance measures into consideration. As the number of performance measures increases, solving the developed mathematical model by GP will generate a great number of alternatives for an ILN design problem, which would cause difficulty in evaluating the efficiency of each alternative yielded by GP. Second, the DEA, which is a relatively new "data-oriented" approach for evaluating the performance of a set of peer entities called Decision Making Units (DMUs) that transform multiple inputs to multiple outputs for their operations. Every alternative generated by GP will be considered as a DMU and all performance measures considered in GP will be classified as inputs or outputs, DEA will be applied to evaluate all alternatives to provide benchmarking information such as efficiency rating for each one, efficiency reference set, and a target for the inefficient alternatives.The outcomes of this project will include mathematical models and design and benchmarking framework that can be applied to a general integrated logistics network model. The DEA-based design and benchmarking framework would help not only major logistics companies but also major industries whose supply chain management has become an important issue and eventually would improve overall supply chain efficiency in South Carolina. Specifically, the results are expected to benefit economic development in South Carolina that the 1890 program at South Carolina State University (SCSU) is presently committed too.

Animal Health Component

(N/A)

Research Effort Categories

Basic

100%

Applied

(N/A)

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
604	6199	2080	100%

Knowledge Area
604 - Marketing and Distribution Practices;

Subject Of Investigation
6199 - Economy, general/other;

Field Of Science
2080 - Mathematics and computer sciences;

Keywords

logistics network design

goal programming

data envelopment analysis

Goals / Objectives
The major goal of this proposed research is to develop an innovative framework for designing an integrated logistics network, as it is an important strategic decision that significantly affects the overall performance of supply chain management activities. The proposed research distinguishes from previous studies in the following two points. First, we will identify other important performance measures beyond cost for the integrated logistics network (ILN), develop a multi-objective model through goal programming (GP), which simultaneously takes relevant financial and nonfinancial performance measures into consideration. Second, contrary to other works on multi-objective models, the proposed project will provide a universal procedure to select the most desirable alternative option(s) out of all alternative options generated by GP with minimized or without subjective judgment from decision makers.The objective of this research is to develop a comprehensive design and benchmarking framework to identify the most effective and efficient ILN overall by simultaneously considering various performance measures which may conflict with each other. More specifically the proposed research will pursue the following objectives:Model Formulation: Investigate the performance measures, develop a procedure on how to formulate a mathematical programming model under single-and multi-objective optimization through GP.Development of Spreadsheet Model: Development of spreadsheet model based upon the mathematical model formulated.Assessment and Improvement: Apply the conventional Data Envelopment Analysis and context-dependent DEA for the assessment and improvement of overall performance in the integrated logistics network.Validation. Demonstrate the applicability of the design and bench marking framework and the improvement in supply chain efficiency in South Carolina (SC) by conducting case studies for several integrated logistics network systems including biofuel logistics network and emergency logistics network in SC.The research plan of this proposal is integrated with an educational plan. Specific educational objectives include:Course Revising and Update: Research findings will be incorporated in to courses in the Master of Science in Transportation (MST) program, Agribusiness (AGBU), and Industrial Engineering Technology (IET). Curricula design and course materials development for the following graduate and undergraduate courses, TRP 651 -Transportation Logistics, AGBU 545 - Supply Chain Management, IET 457 - Facility Location, and IET 357 - Industrial Operations Research I.Promotion of Teaching, Training, and Learning: The investigators will support the training and the development of students in these three academic programs (AGBU, MST, IET) at South Carolina State University.The successful completion of this proposed research would provide a powerful decision support to practitioners as well as researchers working for supply chain management area, helping them design an efficient integrated logistics network to improve overall supply chain efficiency significantly.

Project Methods
Given the research goals/objectives of this research, the following tasks/methodologies have been identified and are explained below:Task 1 - Literature ReviewThis task is to identify prior work suitable as a basis for this research through an expanded review of relevant literature. The review will help the research team identify sources where model input data can be obtained. In addition, the research team will conduct a survey of current practice to analyze several logistics costs. The purpose of the survey is to better understand the many different factors affecting the integrated logistics and to make sure the results of this research are both theoretically and practically sound.Task 2 - Data CollectionAccurate and detailed input data are critical for modeling integrated logistics cost and other performance measures. To ensure our research results can be used by both researchers and practitioners, the research team plans to visit one or two major companies, such as Boeing and BMW in South Carolina, and a logistics company, such as, Premier Logistics Solutions. We will also try to collaborate with South Carolina Emergency Management Division. The purposes of these trips are to identify a comprehensive list of critical components and operational parameters affecting the cost and the efficiency of their logistics system.Task 3 - Development of Optimization Model and Spreadsheet ModelBased on the evaluation of existing models reviewed in Task 1, the research team will develop a procedure how to formulate various logistics network models under single-and multi-objective optimization through goal programming (GP). This model will be developed to better consider the uncertainties involved in the logistics network modeling, which include the uncertainties due to disruptions. This approach is the robust optimization approach which is useful in the absence of sound probabilistic distributions. The robust optimization model developed in Task 3 is a large-scale mixed integer program (MIP) with many binary (for location, capacity selection, and routing decisions) and real (for transportation and inventory decisions) variables along with numerous constraints capturing network structure and system tactical/operational requirements.The integrated model and two robust models can be solved by a variety of optimization software packages, such as LINDO, LINGO, or GAMS. However, coding the developed MIP model using these tools may not be an easy task, since so many decision variables and constraints are involved. Recently, many researchers and practitioners are paying significant attention to Microsoft Excel spreadsheet-based optimization modeling because of its non-algebraic approach. Several powerful software packages based on the Excel spreadsheet model, such as Frontline Solver, What's Best!, CPLEX, etc., make Excel spreadsheet-based modeling attractive. In this research project, Frontline Solver for Microsoft Excel Add-In and LINDO's What's Best! will be used to solve the proposed MIP models. Task 4 - Application of Data Envelopment Analysis (DEA)We will modify the mathematical formulation of the conventional DEA and the context-dependent DEA to apply to the alternative options generated by GP formulated for the integrated logistics network design (ILND) problem in Task 3, to assess the alternative options for identifying the most efficient options from all. DEA-Solver Pro and DEA-Frontier will be used for application of DEA.Task 5 - Development of Design and Benchmarking FrameworkBased upon results produced by Tasks 1 through 4, a general design and benchmarking framework for ILND problem will be developed to improve supply chain efficiency.Task 6 - Case StudyAt least two case studies will be developed to demonstrate the applicability of the design and benchmarking framework for ILND problem. The data collected in Task 2 will be used as the input for the case study, and additional data may need to be collected. The research team first will generate a set of alternative solutions by solve the spreadsheet model for a GP model by using the Solver software packages. These alternative solutions will then be evaluated using DEA and context-dependent DEA to select the most desirable alternative options for each case study. Findings from the case study will be shared with researchers and practitioners via the project website. There might be problems encountered in this case study, which require the research team to go back to Tasks 3-5 and to improve the results.Task 7 - DeliverablesA final project report will be prepared to document the project's entire research efforts and findings. In addition to these documents, the following deliverables will be provided: 1) quarterly project progress reports submitted before the end of each quarter; 2) an annual project report submitted within 90 days after the end of each year.Given the educational goals/objectives of this research, the following plan have identified and explained below:Task 1 - Developing Lectures on the integrated logistics network design (ILND)Task 2 - Including GP and DEA Topics for the Following Courses: TRP 651-Transportation Logistics, IET 357 - Industrial Operations Research I, IET 457- Facility Location Include ILND Topics AGBU 545 - Supply Chain ManagementTask 3 - Development of a New Course, DEA Application in Industrial Engineering

Progress 05/16/15 to 05/15/18

Outputs
Target Audience: Students in Industrial Engineer (IE) program at SCSU. Faculty members of IE, Transportation, Business at SCSU. Faculty members of Engineering Management, University of Houston at Clear Lake. Colleagues attending SEDSI, SWDSI, NEDSI, SEINFORMS, ARD, DSI, PAWC Conferences Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Professional development: Hong, J. and Jeong K. presented the paper, "A Min-Max Normalized Ranking Method for Finding the Most Efficient Dmus in Data Envelopment Analysis," at the 2016 SEDSI Conference, Colonial Williamsburg, VA, 2/17-19/2016. Hong, J. and Taylor, S. presented the paper, "A Cross Efficiency Method-Based Approach to Emergency Relief Supply Chain Design (Best Paper Award in Application of Theory)," at the 2016 NEDSI Conference, Alexandria, VA, 3/31/2016-4/2/2016. Hong, J., Taylor, S., and Narinesingh, R. presented the paper, "Humanitarian Supply Chain Design Problem Combining Data Envelopment Analysis (DEA) and Goal Programming (GP) Approach" at the 1st Annual College of Graduate and Professional Studies Research Symposium, South Carolina State University, Orangeburg, SC, April 14, 2016. Hong, J., Taylor, S., and Rambert, D. presented the paper, "Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design," at the 47th Annual Meeting of SEDSI, Charleston, SC, February 22-24, 2017. Hong, J. and Jeong, K. presented the paper, "Using Stratification Data Envelopment Analysis for the Multi-Objective Facility Location-Allocation Problem," at the 48th Annual Meeting of SWDSI, Little Rock, AR, March 8-11, 2017. Jeong, K. and Hong, J. presented the paper, "Impact of Information Sharing and Ordering Policies on a Supply Chain (Nominated for Best Paper Award in Application of Theory)," at the 2017 Annual Meeting of NEDSI, Springfield, MA, March 22-25, 2017. Taylor, S., Rambert, D. and Hong, J. presented the paper, "Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design,"at the 2017 Association of 1890 Research Directors (ARD) Research Symposium, Atlanta, GA, April 1-4, 2017. Hong, J. presented the paper, "Multi-Objective Facility Location-Allocation Problems Combining Context DEA and Goal Programming, " at the 2017 ARD Research Symposium, Atlanta, GA, April 1-4, 2017. Hong, J. presented the paper, "Flexible Facility Location-Allocation Design Problem under the Risk of Disruptions," at the 2017 Annual Meeting of SEINFORMS, Myrtle Beach, SC, October 5-6, 2017. Taylor, S. and Hong, J. presented the paper, "Multi-Objective Mathematical Models to Design Biomass to Biofuel Supply Chain System in South Carolina (Student Paper Award)," at the 2017 Annual Meeting of SEINFORMS, Myrtle Beach, SC, October 5-6, 2017. Hong, J. presented the paper, "Data Envelopment Analysis Approach and Its Application for Biomass to Biofuel Supply Chain Design," at the 2017 Annual Meeting of DSI, Washington, D.C., November 18-20, 2017. Taylor, S., and Hong, J. presented the paper, "Design of Balanced and Efficient Biomass to Biofuel Supply Chain Network Systems Using Multi-Objective Mathematical Programming Models," at the 75th Professional Agricultural Workers (PAW) Conference, Tuskegee University, AL, December 3-5, 2017. Rambert D., and Hong, J. presented the paper, "Efficiency-Driven Procedure for Biomass-Bioenergy Supply Chain Network Design in South Carolina," at the 75th PAW Conference, Tuskegee University, AL, December 3-5, 2017. Hong, J. presented the paper, "An Efficiency-Driven Approach to Facility Location-Allocation Decision under the Risk of Disruptions," at the 48th Annual Meeting of SEDSI, Wilmington, NC, February 21-23, 2018. Hong, J. presented the paper, "Design of Efficient Facility Location-Allocation System in Case of Disruptions," at the 2018 Annual Meeting of WDSI, Kauai, HI, April 3-6, 2018. How have the results been disseminated to communities of interest?Seminar: Dr. Ki-Young Jeong, an 1890 Research sub-awardee, was invited to present a seminar, "Application of Data Envelopment Analysis to Engineering and Management Problems." He successfully presented the seminar in the Auditorium at the Engineering Building at SC State University on April 7, 2016, to our engineering technology students and faculty members. Conference Hong, J., and K. Jeong, "A Min-Max Normalized Ranking Method for Finding the Most Efficient DMUs in Data Envelopment Analysis," Proceedings of the 2016 SEDSI Conference, 37-47, Colonial Williamsburg, VA, 2/17-19/2016. Hong, J., and S. Taylor, "A Cross Efficiency Method -Based Approach to Emergency Relief Supply Chain Design (Best Paper Award in Application of Theory)," CD of Proceedings of the 2016 Annual Meeting of the NEDSI, 446-461, Alexandria, VA, March 31-April 2, 2016. Hong, J., S. Taylor, and D. Rambert, "Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design," CD of the proceedings of the 47th Annual Meeting of SEDSI, Charleston, SC, February 22-24, 2017. Hong, J., and K. Jeong, "Using Stratification Data Envelopment Analysis for the Multi-Objective Facility Location-Allocation Problem," Proceedings of the 48th Annual Meeting of SWDSI, 32-39, Little Rock, AR, March 8-11, 2017. Jeong, K., and J. Hong, "Impact of Information Sharing and Ordering Policies on a Supply Chain (Nominated for Best Paper Award in Application of Theory)," Proceedings of the 2017 Annual Meeting of NEDSI, 904-914, Springfield, MA, March 22-25, 2017. Hong, J., "Flexible Facility Location-Allocation Design Problem under the Risk of Disruptions," Proceedings of the 2017 Annual Meeting of SEINFORMS, Myrtle Beach, SC, October 5-6, 2017. Taylor, S., and J. Hong, "Multi-Objective Mathematical Models to Design Biomass to Biofuel Supply Chain System in South Carolina (Student Paper Award)," Proceedings of the 2017 Annual Meeting of SEINFORMS, Myrtle Beach, SC, October 5-6, 2017. Hong, J., "Data Envelopment Analysis Approach and Its Application for Biomass to Biofuel Supply Chain Design," Proceedings of the 2017 Annual Meeting of DSI, Washington, D.C., November 18-20, 2017. Taylor, S., and J. Hong, "Design of Balanced and Efficient Biomass to Biofuel Supply Chain Network Systems Using Multi-Objective Mathematical Programming Models," The 75th PAW Conference, Tuskegee University, AL, December 3-5, 2017. Rambert D., and J. Hong, "Efficiency-Driven Procedure for Biomass-Bioenergy Supply Chain Network Design in South Carolina," The 75th PAW Conference, Tuskegee University, AL, December 3-5, 2017. Hong, J., "An Efficiency-Driven Approach to Facility Location-Allocation Decision under the Risk of Disruptions," Proceedings of the 2018 Annual Meeting of the SEDSI, Wilmington, NC, February 21-23, 2018. Hong, J., and K. Jeong, "Application of Data Envelopment Analysis to Relief Logistics Facility Location-Allocation Decisions," Proceedings of the 49th Annual Meeting of SWDSI, Albuquerque, NM, March 7-10, 2018. Hong, J., "Design of Efficient Facility Location-Allocation System in Case of Disruptions," Proceeding of the 2018 Annual Meeting of WDSI, Kauai, HI, April 3-6, 2018. Symposium: Hong, J., S. Taylor, and R. Narinesingh, "Humanitarian Supply Chain Design Problem Combining Data Envelopment Analysis (DEA) and Goal Programming (GP) Approach," The 1st Annual College of Graduate and Professional Studies Research Symposium, South Carolina State University, Orangeburg, SC, April 14, 2016. Taylor, S., D. Rambert, and J. Hong, "Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design,"The 2017 ARD Research Symposium, Atlanta, GA, April 1-4, 2017. Hong, J., "Multi-Objective Facility Location-Allocation Problems Combining Context DEA and Goal Programming, " The 2017 ARD Research Symposium, Atlanta, GA, April 1-4, 2017. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? An important impact for this project is that we develop, present, and demonstrate the use of goal programming (GP) model and Data Envelope Analysis (DEA) framework to the supply chain design problem to help decision-makers who are responsible for supply chain planning and management activities. Contrary to the previous researchers' simple assumption on the fixed inputs and outputs, we generate the inputs and outputs by solving the GP models for the humanitarian supply chain, biomass-biofuel supply chain, and general facility location-allocation problems, propose a procedure how to apply DEA, and determine the relative efficiency for each option. As a result, we can exclude decision makers' subjective judgment and select the best efficient options objectively among the alternatives generated by GP models. We use the following research methodology. Based on geographical data, the necessary data and other performance measures of interest, we formulate and solve the multi-objective optimization problem using GP approach. The GP model generates diverse optimized supply chain layouts with different weights among the conflicting performance measures. Then, we classify the performance measures from the GP into inputs (I) and outputs (O) for DEA. DEA will treat those supply chain layouts as decision-making units (DMUs) and evaluate them. The proposed GP-DEA framework has the following advantages: (1) by adopting the GP, it can generate realistic supply chain layouts, optimized regarding the conflicting measures; (2) DEA can evaluate supply chain layouts and discriminate them without any subjective judgment from the decision-makers. Based on the results from the framework, regression analysis, and robustness analysis are applied to evaluate the impact of the supply chain design factors−locations of facilities and distribution channels−on the efficiency score and the robustness of supply chain layouts, respectively. We propose the following procedure of combining GP model and DEA as a major accomplishment for this project: Step 1: [GP Formulation and Pre-Stratification] Define objectives/goals for performance measures (PMs) to be considered.Then, classify PMs into p inputs and r outputs. Formulate as the GP mode. Set the value of weight for each PM, where each weight changes between 0 and 1 with an increment of Δ, where 0 ≤ Δ ≤ 1. For each set of weights, solve the GP model and call each solution asDMUj, j =1, 2, ..., n. Step 2: [DEA] For each j =1, 2, ..., n, compute efficiency score (ES). Select efficient DMUs whose efficiency score = 1. To rank the efficient supply chain logistics network, go to Step 3 for Cross Efficiency (CE) DEA or Step 4 for Stratification DEA.Otherwise, go to Step 5. Step 3: [Cross Efficiency DEA] Phase I: For each j =1, 2, ..., n, compute ES as in (i) of Step 2. Phase II: Using the multipliers arising I, obtain the CE scores for all DMUs. Rank the DMUs based on the value of CE scores. Go to Step 5. Step 4: [Stratification DEA] Phase I: For each j =1, 2, ..., n, compute efficiency score. Select DMUs with ES=1. Set =1 and construct the stratification level by removing DMUs with ES =1 from the DMU set and moving them to . Setting = +1, repeat this process until there is no DMUs in the DMU set. Phase II:Compute the attractive score of DMUs in . Rank the DMUs in based on the values of attractive scores. Step 5: Identify the efficient supply chain logistics network schemes and do impact and robustness analysis. Our new and innovative proposed procedure enabled us to win the Best Paper Award in Application of Theory for the 2016 NEDSI (Northeast Decision Sciences Institute) Conference, Alexandria, VA, March 31-April 2, 2016, and the Student Paper Award for the 2017 SEINFORMS (Southeastern Chapter of The Institute for Operations Research & The Management Sciences) Conference, Myrtle Beach, SC, October 4-6, 2017. We list the following papers as an accomplishment: Proceedings Paper Hong, J., and S. Taylor, "A Cross Efficiency Method -Based Approach to Emergency Relief Supply Chain Design (Best Paper Award in Application of Theory)," CD of Proceedings of the 2016 Annual Meeting of the Northeast Region of the Decision Sciences Institute (NEDSI), 446-461, Alexandria, VA, March 31-April 2, 2016. Taylor, S., and J. Hong, "Multi-Objective Mathematical Models to Design Biomass to Biofuel Supply Chain System in South Carolina (Student Paper Award)," Proceedings of the 2017 Annual Meeting of Southeastern Chapter of Institute of Operations and Management Sciences (SEINFORMS), Myrtle Beach, SC, October 5-6, 2017. Journal Paper Hong, J., & K. Jeong, "Goal Programming and Data Envelopment Analysis Approach to Disaster Relief Supply Chain Design," International Journal of Logistics Systems and Management (forthcoming), 2018. Hong, J., & K. Jeong, "Combining Data Envelopment Analysis and Multi-Objective Model for the Efficient Facility Location-Allocation Decision," Journal of Industrial Engineering International (forthcoming), 2018. While designing SCNs, the impact of the information sharing (ISR) on the bullwhip effect (BWE) has been identified. We have quantified the impact and reduced BWE based on the quantification. The result shows that overall, the higher ISR values more significantly reduce the BWE than lower ISR values. These results would provide useful implications and insights for better coordination and collaboration in the supply chain. We list the following papers as an accomplishment: Proceedings Paper Jeong, K., and J. Hong, "Impact of Information Sharing and Ordering Policies on a Supply Chain (Nominated for Best Paper Award in Application of Theory)," Proceedings of the 2017 Annual Meeting of NEDSI, 904-914, Springfield, MA, March 22-25, 2017. Journal Paper Jeong, K., and J. Hong, "The Impact of Information Sharing on Bullwhip Effect Reduction in a Supply Chain," Journal of Intelligent Manufacturing (forthcoming), 2018. Since DEA method was developed in 1978, several cross efficiency (CE) methods have been developed as a DEA extension to rank efficient and inefficient DMUs with the main idea of using DEA to do peer evaluation. However, it has been well known that those methods all suffer from lack of discrimination since efficiency scores from those methods may not be unique due to the non-uniqueness of the DEA optimal weights in the Linear Programming (LP) models. We developed two CE bases heuristics (CEHs) for ranking DMUs to overcomes this issue since their CEHs do not use any LP models but show comparable consistency level to other DEA-based full ranking methods. Based on the examples and analysis, we observe that CEH methods show the best performance and the comparable performance in terms of the consistency compared to other ranking methods. The examples also demonstrate that the ranking pattern generated by CEH methods is consistently similar to that generated by the normalized attractive score based ranking method for all stratified levels. We list the following papers as an accomplishment: Proceedings Paper Hong, J.,and K. Jeong, "A Min-Max Normalized Ranking Method for Finding the Most Efficient DMUS in Data Envelopment Analysis," Proceedings of the 2016 Annual Meeting of the SEDSI, 37-47, Colonial Williamsburg, VA, February 17-19, 2016. Journal Paper Hong, J.,and K. Jeong, "Cross-Efficiency Based Heuristics to Rank Decision Making Units in Data Envelopment Analysis,"Computers & Industrial Engineering, 111, 320-330, 2017.

Publications

• Type: Journal Articles Status: Published Year Published: 2017 Citation: 1. Hong, J., and K. Jeong, Cross-Efficiency Based Heuristics to Rank Decision Making Units in Data Envelopment Analysis, Computers & Industrial Engineering, 111, 320-330, 2017.
• Type: Journal Articles Status: Accepted Year Published: 2018 Citation: 2. Jeong, K., and J. Hong, The Impact of Information Sharing on Bullwhip Effect Reduction in a Supply Chain, Journal of Intelligent Manufacturing (forthcoming), 2018.
• Type: Journal Articles Status: Accepted Year Published: 2018 Citation: 3. Hong, J., and K. Jeong, Goal Programming and Data Envelopment Analysis Approach to Disaster Relief Supply Chain Design, International Journal of Logistics Systems and Management (forthcoming), 2018.
• Type: Journal Articles Status: Accepted Year Published: 2018 Citation: 4. Hong, J., & K. Jeong, Combining Data Envelopment Analysis and Multi-Objective Model for the Efficient Facility Location-Allocation Decision, Journal of Industrial Engineering International (forthcoming), 2018.

Progress 10/01/15 to 09/30/16

Outputs
Target Audience: Students in Industrial Engineer (IE) and Industrial Engineering Technology (IET) program at SCSU. Faculty members of IET, Transportation, Business at SCSU. Faculty members of Engineering Management, University of Houston at Clear Lake. Colleagues of SEDSI and NEDSI. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Professional development: Dr. Hong, PI, and Dr. Jeong, Sub-grantee, presented the paper, "A Min-Max Normalized Ranking Method for Finding the Most Efficient DMUs in Data Envelopment Analysis," at the 2016 Southeast Decision Science Institute (SEDSI) Conference, Colonial Williamsburg, VA, 2/17-19/2016. Dr. Hong, PI, and Ms. Taylor, a graduate assistant, presented the paper, "A Cross Efficiency Method-Based Approach to Emergency Relief Supply Chain Design," at the 2016 Northeast Decision Science Institute (NEDSI) Conference, Alexandria, VA, 3/31/2016-4/2/2016. Hong, Jae-Dong, Taylor, Shadae, and Narinesingh, Radcliffe presented the paper, "Humanitarian Supply Chain Design Problem Combining Data Envelopment Analysis (DEA) and Goal Programming (GP) Approach" at the 1st Annual College of Graduate and Professional Studies Research Symposium, South Carolina State University, Orangeburg, SC, April 14, 2016. How have the results been disseminated to communities of interest?Seminar: Dr. Ki-Young Jeong, an 1890 Research sub-awardee, was invited to present a seminar, "Application of Data Envelopment Analysis to Engineering and Management Problems." He successfully presented the seminar in the Auditorium at the Engineering Building at SC State University on April 7, 2016, to our engineering technology students and faculty members. Conference: The following research papers were presented: Hong, J., & Jeong, K. (2016). A min-max normalized ranking method for finding the most efficient DMUs in data envelopment analysis, published in the 2016 Southeast Decision Science Institute (SEDSI) Conference Proceedings, Colonial Williamsburg, VA, 2/17-19/2016. Hong, J.,* & Taylor, S. (2016). A cross efficiency method-based approach to emergency relief supply chain design, published in the 2016 Northeast Decision Science Institute (NEDSI) Conference Proceedings, Alexandria, VA, 3/31/2016-4/2/2016. Hong, Jae-Dong, Taylor, Shadae, and Narinesingh, Radcliffe presented the paper, "Humanitarian Supply Chain Design Problem Combining Data Envelopment Analysis (DEA) and Goal Programming (GP) Approach" at the 1st Annual College of Graduate and Professional Studies Research Symposium, South Carolina State University, Orangeburg, SC, April 14, 2016. The following papers have been accepted for full paper presentation: Hong, J., Taylor, S. & Rambert, D."Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design," has been submitted for presentation and publication in the proceedings of the 47th Annual Meeting of Southeast Decision Sciences Institute, Charleston, SC, February 22-24, 2017. Hong, J. & Jeong, K. "Using Stratification Data Envelopment Analysis for the Multi-Objective Facility Location-Allocation Problem," has been submitted for presentation and publication in the proceedings of 48th Annual Meeting of Southwest Decision Sciences Institute, Little Rock, AR, March 8-11, 2017. Jeong, K. & Hong, J. "Impact of Information Sharing and Ordering Polices on a Supply Chain," has been submitted for presentation publication in the proceedings of the 2017 Annual Meeting of Northeast Decision Sciences Institute, Springfield, MA, March 22-25, 2017. What do you plan to do during the next reporting period to accomplish the goals? During the next annual reporting period, the following will be completed: Main task 5 will continue through 2nd year and the first semester of 3rd year Main task 6 will continue through 2nd year and the first semester of 3rd year By completing these main tasks, we will be ready to start the last task to write the final report.

Impacts
What was accomplished under these goals? Quite a few researchers have utilized data envelope analysis (DEA) efficiency measures to find optimal facility location-allocation schemes. They assume that all inputs and outputs for each facility and its potential sites are given as a fixed data. Their models require the huge data required for the inputs and outputs and consequently the huge number of the constraints for their combined location and simultaneous DEA model (SDEA) as the numbers of facilities and their potential sites increase. In addition to those huge data and constraints required by their models, it would be not only difficult to quantify all inputs and outputs for a facility to be located to cover the allocated sites, but also very subjective for a decision maker to decide the magnitudes of such inputs and outputs. An important impact for this project is that, contrary to the previous researchers' simple assumption on the fixed inputs and outputs, we generate the inputs and outputs by solving the multi-objective programming (MOP) model for the humanitarian supply chain (HTSC), propose a procedure how to apply Data Envelope Analysis (DEA), and determine the relative efficiency for each option. As a result, we can exclude decision makers' subjective judgment and select objectively the best efficient options among the alternatives generated by MOP models. Our new and innovative proposed procedure enabled us to win the Best Paper Award in Application of Theory for the 2016 Northeast Decision Sciences Institute Conference, Alexandria, VA, March 31-April 2, 2016. To achieve goals, the following main tasks have been established: Task 1 - Literature Review Task 2 - Data Collection Task 3 - Development of Optimization Model and Spreadsheet Model Task 4 - Application of Data Envelopment Analysis (DEA) Task 5 - Development of Design and Benchmarking Framework Task 6 - Case Study Task 7 - Deliverables The actual progress of this research uses a spiral project management life cycle model where each task is recursively iterated with other tasks. During the second reporting year, Tasks 1 and 2 have been completed (100%), and a major portion of Task 4 (70%) has also been successfully completed. Tasks 5 and 6 have been partially completed and will be completed by Fall Semester in 2017. We rate that this actual progress is well aligned with this original plan. To achieve above main tasks, the following sub tasks have been completed (sub tasks 1 and 2) or partially completed (sub tasks 3 and 4) by the sub-grantee, Dr. Jeong: sub task 1 - Verify and analyze optimization model (for main task 3) sub task 2 - Develop Data Envelopment Analysis (DEA) models (for main task 4) sub task 3 - Develop the design and benchmarking framework (for main task 5) sub task 4 - Apply developed DEA and framework to data obtained from case studies (for main task 6) The sub tasks 3 and 4 will be continued and completed by Summer Semester in 2017. In conclusion, by the end of this second reporting year, we have progressed successfully and will be able to complete Task 1 through Task 6 by the end of year 2017.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2016 Citation: 1. Hong, J. & Jeong, K. A min-max normalized ranking method for finding the most efficient DMUs in data envelopment analysis, be published in the 2016 Southeast Decision Science Institute (SEDSI) Conference Proceedings, Colonial Williamsburg, VA, 2/17-19/2016. The link is http://programme.exordo.com/sedsi2016/proceedings.sedsi.pdf
• Type: Conference Papers and Presentations Status: Published Year Published: 2016 Citation: 2. Hong, J., & Taylor, S. (2016). A cross efficiency method-based approach to emergency relief supply chain design, published in the 2016 Northeast Decision Science Institute (NEDSI) Conference Proceedings, Alexandria, VA, 3/31/2016-4/2/2016.The link is http://nedsi.org/proc/2016/proceedings_final.pdf
• Type: Conference Papers and Presentations Status: Published Year Published: 2016 Citation: 3. Hong, Jae-Dong, Taylor, Shadae, & Narinesingh, Radcliffe (2016). Humanitarian Supply Chain Design Problem Combining Data Envelopment Analysis (DEA) and Goal Programming (GP) Approach. 1st Annual College of Graduate and Professional Studies Research Symposium, South Carolina State University, Orangeburg, SC, April 14, 2016.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: 4. Hong, J., Taylor, S. & Rambert, D. Productivity-Driven Approach to Integrated Biomass-to-Biofuel Supply Chain Design, has been submitted for presentation and publication in the proceedings of the 47th Annual Meeting of Southeast Decision Sciences Institute, Charleston, SC, February 22-24, 2017.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: 5. Hong, J. & Jeong, K. Using Stratification Data Envelopment Analysis for the Multi-Objective Facility Location-Allocation Problem, has been submitted for presentation and publication in the proceedings of 48th Annual Meeting of Southwest Decision Sciences Institute, Little Rock, AR, March 8-11, 2017.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: 6. Jeong, K. & Hong, J. Impact of Information Sharing and Ordering Polices on a Supply Chain, has been submitted for presentation publication in the proceedings of the 2017 Annual Meeting of Northeast Decision Sciences Institute, Springfield, MA, March 22-25, 2017.

Progress 05/16/15 to 09/30/15

Outputs
Target Audience: Students in Industrial Engineer (IE) and Industrial Engineering Technology (IET) program at SCSU Faculty members of IET, Transportation, Business at SCSU Faculty members of Engineering Management, University of Houston at Clear Lake Colleagues of Southeast Decision Science Institute (SEDSI) and Northeast Decision Science Institute (NEDSI) Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training: Performing Data Envelopment Analysis (DEA) using spreadsheet modeling Technique for a graduate research assistant and the students in IET 357- Industrial Operations Research I, class in Fall, 2015. Developing mathematical models for the emergency relief supply-chain design Preparing the graduate research assistant for presenting the paper, "A cross efficiency method-based approach to emergency relief supply chain design," at the 2016 Northeast Decision Science Institute (NEDSI) Conference, Alexandria, VA, 3/31/2016-4/2/2016. How have the results been disseminated to communities of interest?Based on the current progress, submitted two article and received the following response: "A min-max normalized ranking method for finding the most efficient DMUs in data envelopment analysis," accepted to be presented at the 2016 Southeast Decision Science Institute (SEDSI) Conference, Williamsburg, VA, 2/17-19/2016. "A cross efficiency method-based approach to emergency relief supply chain design," accepted to be presented at the 2016 Northeast Decision Science Institute (NEDSI) Conference, Alexandria, VA, 3/31/2016-4/2/2016. What do you plan to do during the next reporting period to accomplish the goals?During the next annual reporting period, the following will be completed: Main tasks 3 and 4 will be completed by 2nd Semester in 2nd reporting year Main task 5 will start at 1st reporting year and continue through 2nd and 3rd year Main task 6 will start at 2nd semester and continue through 2nd and 3rd year By completing these main tasks, we will complete the remaining portion of the first two objectives (Model Formulation; Development of Spreadsheet Model) and a significant portion of the last two objectives (Assessment and Improvement; Validation).

Impacts
What was accomplished under these goals? To achieve goals, the following main tasks have been proposed Task 1 - Literature Review Task 2 - Data Collection Task 3 - Development of Optimization Model and Spreadsheet Model Task 4 - Application of Data Envelopment Analysis (DEA) Task 5 - Development of Design and Benchmarking Framework Task 6 - Case Study Task 7 - Deliverables The actual progress of this research uses a spiral project management life cycle model where each task is recursively iterated with other tasks. During the first reporting year, Task 1 has been completed (100%), and a major portion of Task 2 (70%) has also been successfully completed. According to the original plan, tasks 3 and 4 start at 2nd semester in 2015 and will be completed by 2nd semester in 2016. This actual progress is well aligned with this original plan. To achieve above main tasks, the following sub-tasks have been proposed by the sub grantee, Dr. Jeong: sub task 1 - Verify and analyze optimization model (for main task 3); sub task 2 - Develop Data Envelopment Analysis (DEA) models (for main task 4); sub task 3 - Develop the design and benchmarking framework (for main task 5); and sub task 4 - Apply developed DEA and framework to data obtained from case studies (for main task 6). During this reporting year, sub tasks 1 and 2 have been progressed as planned to support main tasks 3 and 4. These two sub tasks will be continued and completed by 2nd semester of the next year. In conclusion, by the end of this first reporting year, a major portion of the original objectives (Model Formulation and Development of Spreadsheet Model) have been successfully accomplished. These goals may be updated and further modified needed during the remaining periods.

Publications

• Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: 1. Hong, J. and Jeong, Ki. (2016). A min-max normalized ranking method for finding the most efficient DMUs in data envelopment analysis, accepted to be published in the 2016 Southeast Decision Science Institute (SEDSI) Conference Proceedings, Colonial Williamsburg, VA, 2/17-19/2016.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: 2. Hong, J. and Taylor, S. (2016). A cross efficiency method-based approach to emergency relief supply chain design, accepted to be published in the 2016 Northeast Decision Science Institute (NEDSI) Conference Proceedings, Alexandria, VA, 3/31/2016-4/2/2016.