As the U.S. military faces an increasing need to deploy across a range of military operations and environments, the ability to establish and sustain logistics support remains a major challenge. The Engineer Research and Development Center is currently developing the Planning Logistics Analysis Network System (PLANS), a decision support tool, to facilitate strategic and operational logistics planning. This paper describes a site selection protocol for logistics operations occurring without a suitable port, commonly referred to as Logistics over-the Shore (LOTS) operations. The model uses multi- objective decision analysis techniques to weight different operational criteria to determine the best overall site for logistics over the shore operations. This tool will enhance the time and accuracy in determining an optimal site that meets the decision maker’s specific operational needs

Evans, Mike

Green, Chris

Lami, Mike

Madarasz, Brendan

Smith, Benjamin

English

Industrial and Systems Engineering Review (ISER)

Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
 
ISER © 2016 88 
http://iser.sisengr.org 
Site Selection for Joint Logistics over the Shore (JLOTS) Operations Using Multi- 
Objective Decision Analysis 
 
Mike Evans, Mike Lami, Brendan Madarasz, Benjamin Smith, and Chris Green 
 
Department of Systems Engineering 
United States Military Academy 
West Point, NY 
 
Corresponding author's Email:  Michael.Evans@usma.edu  
 
Author Note: Cadets Lami, Evans, Smith, and Madarasz are working on this project as a capstone with the Department 
of Systems Engineering (DSE). We would like to thank the U.S. Army Engineer Research and Development Center 
(ERDC) for the mentorship, guidance and financial support through this project. 
 
Abstract: As the U.S. military faces an increasing need to deploy across a range of military operations and environments, 
the ability to establish and sustain logistics support remains a major challenge. The Engineer Research and Development 
Center is currently developing the Planning Logistics Analysis Network System (PLANS), a decision support tool, to 
facilitate strategic and operational logistics planning. This paper describes a site selection protocol for logistics operations 
occurring without a suitable port, commonly referred to as Logistics over-the Shore (LOTS) operations. The model uses 
multi- objective decision analysis techniques to weight different operational criteria to determine the best overall site for 
logistics over the shore operations. This tool will enhance the time and accuracy in determining an optimal site that meets 
the decision maker’s specific operational needs. 
 
Keywords: Decision Analysis, JLOTS, Logistics, Decision-Focused Transformation 
 
1. Introduction 
 
The Army currently lacks a ubiquitous system to assist logistics planners in developing a complete 
sustainment concept from a point of origin to an operational location. Engineer Research and Development Center 
(ERDC) is creating Planning Logistics Analysis Network System (PLANS) to facilitate strategic and operational planning 
for the Army Logistics community (Bednar, 2015). PLANS will analyze a set of early-entry alternatives to optimize 
effectiveness in adapting to environmental conditions in support of JLOTS operations in austere entry environments. 
Figure 1 outlines JLOTS operations showing the potential complexity and variations of these operations. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. JLOTS Operation Visual Image 
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 89 
http://iser.sisengr.org 
As a subset of PLANS, the JLOTS model analyzes the over-the-shore logistics operation from the 
Intermediate Staging Base (ISB) to the Central Receiving Shipping Point (CRSP). The ISB, located off the coast, is where 
vessels stage as they prepare for beach infiltration. The CRSP, located on the shore, is where supplies are organized and 
distributed in support of follow-on operations. The development of a decision support model will allow sustainment 
planners to conceptualize a JLOTS operation and optimize limited resources during the development of a deployment plan. 
 
 
2. Background 
 
From the invasion of Normandy to humanitarian operations in Haiti, U.S. forces have used JLOTS as a 
transportation technique to move personnel and equipment to locations without suitable ports. Specifically, the role of 
JLOTS is to conduct operations that facilitate the loading and unloading of ships through inadequate or damaged ports, or 
over a bare beach. JLOTS operations can last between 60 – 90 days and involve the integrations of many organizations. 
The main responsibility of JLOTS operations is to be an asset for commanders to support and sustain their forces in any 
environment (CJCS, 2013). 
JLOTS requires extensive planning and precision execution in a timely and efficient manner. A successful 
JLOTS operation entails a comprehensive analysis of landing sites, operational considerations, and asset availability. 
Several operational considerations are communication plans, ship discharge plans, ship to shore movement plans, 
lighterage repair, weather, retrograde operations, and force protection.  The success of JLOTS operations relies on the 
throughput of transporting cargo from ship to shore. Throughput is dependent on beach size, type of lighterage, transit 
system and marshalling yard capacity. Lighterage are vessels that transport cargo from deep-sea ships to the shore. JLOTS 
operations involve many types of transit systems to include the causeway ferry, floating causeways, and roll-on, roll off 
vessels (CJCS, 2013). 
Logisticians must understand the specific mission requirements and operational considerations in order to 
determine an optimal site for a JLOTS operation. Site selection criteria must include both land and sea considerations. 
The current site selection process uses a qualitative checklist available on the Joint Publication on JLOTS planning 
(CJCS, 2013). This process involves a thorough map and imagery analysis, site reconnaissance, and risk mitigation by the 
planning team. 
 
 
3. Methodology 
 
Multi-Objective Decision Analysis (MODA) is the foundation of the decision model. This type of model 
translates qualitative input of experts into a numeric scoring system used to show utility for each specific site landing 
(Parnell, 2013). After conducting research and extracting expert opinion from stakeholders, a qualitative value model 
translates the information gathered from stakeholders into a usable diagram that identifies the functions, objectives and 
value measures of site selection (Parnell, 2008). Objectives represent a statement of preference, usually in the form of 
maximize, minimize or optimize. Value measures identify the scale to assess how well the objective is achieved (Parnell, 
2008). There are two types of value measures: discrete and continuous. 
The objectives and value measures assist in developing a swing weight matrix and value functions. The 
swing weight matrix assesses the level of importance to the operation against the variation in the measure ranges of 
the value functions. The relative importance represents the impact the stakeholder believes it will have on the decision. The 
variation of the measure ranges represents the impact that the measure would have on the decision if it varied across 
its entire range (Parnell, 2008). Value measures in the swing weight matrix with the highest importance and variation 
receive the largest swing weights, while low importance and variation receive the lowest swing weights. Swing weights 
vary between one and one-hundred. After the assignment of the swing weights, the individual measure weight is 
obtained by dividing the value measure swing weight by the sum of all the swing weights of the value measures (Parnell, 
2008). Value measures can be a direct or proxy measure, or a natural or constructed scale. Direct and natural value 
measures are preferred over proxy and constructed when available. 
Value functions return a unit-less value score based on the desirability of the raw data. The value functions 
identify threshold values that serve as feasibility screening criteria for alternatives. The objective of the value measures, as 
identified by the qualitative value model, determines the shape of the graph (Belton, 2002). 
The overall model is additive and uses the measure weights and raw data from each site to arrive at a value score. In 
the model, VVVVV represents the value of the jth alternative, VV represents n scores for the jth alternative, VV represents 
the measure weight for the ith value measure, and VVVVVV represents the value of the jth alternative on the ith value 
measure (Parnell, 2008). 
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 90 
http://iser.sisengr.org 
Decision-focused transformation (DFT) provides further analysis to the decision-maker. Decision focused 
transformation rescales the value attained from each site in the model by eliminating all unattainable and common value 
between the sites.  Additionally, it rescales the swing weights based on the earned value for each site and redistributes the 
weights amongst the value measures. The output of the transformation shows where each site derives most of its value, which 
allows the decision maker to choose the site that offers the most value in a particular area (Dees, 2010). DFT will 
automatically account for a lack of data at a particular site. If data is not available for a specific measure, then the value is 
unattainable and redistributed to other measures. 
 
 
4. Analysis 
 
The critical first step in developing the model was engaging stakeholders and experts about site selection. We 
engaged stakeholders through surveys and interviews to gain knowledge on the factors and complexities of JLOTS 
operations. The stakeholders include the Center for Army Analysis (CAA), 7th Transportation Brigade 
(Expeditionary), Beach Naval Group-  Pacific, TRANSCOM, and U.S Expeditionary Warfare Training Group Pacific. We 
gathered information on the relative importance of value measures, which is crucial to assigning swing weights and 
creating the value functions. Our iterative engagement with these stakeholders led to the development of nineteen value 
measures and corresponding functions and three feasibility screening criteria that are crucial to the viability and 
accuracy of our model (Figure 2). Differentiating between land and water factors prevents diluting the importance of each 
factor. The additive model generates a score for land and water factors that sums both for an overall site score. The 
breakdown of water and land considerations allows the weighting of the considerations separately and more clearly 
demonstrates the tradeoffs for each. This allows the decision maker to conduct his own analysis on which of the two 
considerations are most important in his decision (Kirkwood, 1997). The graphs to the right of Figure 2 are two of our 
value functions for the model. Each value measure has a function that returns a score based on raw data input. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2. Complete List of Nineteen Value Measures and Three Screening Criteria 
  
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 91 
http://iser.sisengr.org 
The outputs of the model are a land score, a water score and a total score. For further analysis, the model produces 
a Land vs. Water Value Chart (Figure 3), a Comparison of Value Breakdown and a DFT Analysis (Figure 4). The 
tradeoffs between the water and land scores of each site (Figure 3) show what the user will see by selecting a control on 
the Graphic User Interface. This is useful because it allows the decision maker to compare all the sites simultaneously. 
The visual representation of the sites makes it easier to comprehend the value and analyze land versus water scores. 
This chart also displays deterministic dominance when one site has both a higher land and water score than the other 
alternatives. Sites below the efficient frontier are deterministically dominated and can be eliminated from consideration 
(Keeney, 1993). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3. Example of a Land vs. Water Value Chart 
 
 
The second graph is a comparison of the value breakdown for each site. This allows the user to observe which 
value measures contribute the most value to the site (Figure 4). The third graph displays the individual breakdown of each 
total value score according to the value from each measure after DFT (Figure 4). This allows the decision maker to 
perform his own tradeoff analysis by comparing the specific value breakdown of the sites to gain insights into which sites 
have superior scores for each of the measures (Kirkwood, 1997). The DFT provides valuable insight by showing the 
additional value gained for each value measure in choosing one site over another. 
 
 
 
Figure 4. Example of a Regular Stacked Value Graph vs. DFT Analysis 
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 92 
http://iser.sisengr.org 
5. Results, Discussion, and Future Plans 
 
In order to validate the model, the team conducted a case study with two different scenarios. The first is an 
operational scenario, landing on the Mid-Atlantic coast of the United States. This scenario intends to model landing 
on foreign soil for sustainment support of combat operations. The second is a humanitarian effort that would land in 
Florida in case of flooding due to a natural disaster such as a hurricane. Each scenario models five sites in both the winter 
and summer. All sites are in the Unites States due to availability of data. Modeling at different times of year takes into 
account the changes in weather that occur in different seasons. The reason for two different scenarios is the type of cargo 
transported in different missions. In a humanitarian effort, combat vehicles and ammunition are not required. We acquired 
data for the case studies from multiple sources including geospatial imagery, the National Oceanic and Atmospheric 
Administration (NOAA), and several other historical weather data sources. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5. Scenario 2 Site Locations 
 
 
According to Figure 6, Miami receives the best water score of 82.17 and the best overall score of 139.55. 
However, the best site in terms of land objectives is Naples. On the comparative value chart, Pompano Beach, Jupiter and 
Cape Coral are below the efficient frontier and can be removed from consideration because they are dominated (Figure 6) . 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 93 
http://iser.sisengr.org 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6. Scenario 2 Land vs. Water Chart 
 
 
The tradeoff analysis for water considerations displays that the distance to the safe haven and distance to beach 
from ISB value measures yield significant value over the other alternatives. Figure 7 shows the reason why Miami is 
better than Naples for water considerations. Miami possesses more value from distance to safe haven and distance to 
beach from ISB. This type of analysis gives the decision-maker the tools necessary to make an educated decision while 
weighing the positives and negatives of the best available sites. Additionally, by eliminating all common and 
unattainable values, Naples is the worst site for water considerations. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7. Water Score Comparison and Breakdown and DFT Analysis 
 
 
To verify the model, our team conducted sensitivity analysis on all of the value measures for each scenario of the 
case study. Area for Marshalling Yards is the only value measure that is sensitive. The value measure is sensitive between 
the values of 45 and 80. Between these two values, site 1 has a greater value. Otherwise, site 5 is the best site for this 
value measure. The sensitivity analysis of the value measures supports the model. Due to only one of the swing 
weights being sensitive, the model does not over-rate or under-rate a specific measure. 
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 94 
http://iser.sisengr.org 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8. Sensitivity Analysis of Area for Marshalling Yard Value Measure 
 
 
The model proves the MODA methodology is applicable to JLOTS site selection. Future work would 
include applying to worldwide sites and expanding to use equipment not in APS. We can also expand our model to 
differentiate between combat and non-combat operations by accounting for different factors that are organic to each. Some 
of these considerations are additional space and transportation for combat vehicles, ammunition, and greater fuel 
requirements. Accounting for port operations would be valuable to decision makers if that opportunity is available to them. 
Choosing a p ort site, damaged or otherwise, would be useful to compare the capability, it has on a LOTS operation. 
 
 
6. Conclusion 
 
The MODA methodology with decision-focused transformation is an appropriate technique to apply to site 
selection for JLOTS operations. The model provides a normative framework with decision support tools to assist the 
operational planner in selecting the optimal JLOTS site. Although there is still work to do on this project, it will be 
possible to integrate our site selection model into PLANS in development by the Engineer Research and Development 
Center. The initial groundwork, stakeholder analysis, and analytical decision platform is completed. Synthesizing our 
model with a data collection platform will increase the usability of the tool. Simplifying the process of collecting data 
would increase the ease of use and ultimately the utility of the model by making it applicable worldwide. 
 
 
7. Acknowledgement 
 
This paper was previously published and presented in the Donald R. Keith Memorial Capstone Conference at 
USMA in May of 2015. 
 
 
8. References 
 
Belton, V., & Stewart, T. J. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Boston: Kluwer 
Academic. 
Bednar, A. E. W., Boler, C. A. (2015). PLANS (Planning Logistics Analysis Network System) [PDF file].  
Industrial and Systems Engineering Review, 4(2), 2016 ISSN (Online): 2329-0188 
Evans, et. al. 
ISER © 2016 95 
http://iser.sisengr.org 
Chairman of the Joint Chiefs of Staff (CJCS). (2013, October). Joint Logistics. Washington, DC: Government Printing Office. 
Dees, R. A., Dabkowski, M. F., & Parnell, G. S. (2010). Decision-focused transformation of additive value models to 
improve communication. Decision Analysis, 7(2), 172-184.  
Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: preferences and value trade-offs. New York, NY: 
Cambridge University Press.  
Kirkwood, C. W. (1997). Strategic Decision Making: Multiobjective Decision Analysis with Spreadsheets. United States of 
American: Wadsworth Publishing Company. 
Parnell, G. S., Driscoll, P. J., & Henderson, D. L. (2008). Decision Making in Systems Engineering and Management 
(2nd ed.). Hoboken, NJ: Wiley-Interscience. 
Parnell, G. S., Terry Bresnick, M. B. A., Tani, S. N., & Johnson, E. R. (2013). Handbook of Decision Analysis. Hoboken, 
NJ: John Wiley & Sons, Inc. 


Site Selection for Joint Logistics over the Shore (JLOTS) Operations Using Multi- Objective Decision Analysis

http://watsonojs.binghamton.edu/index.php/iser/article/download/59/pdf_20

Site Selection for Joint Logistics over the Shore (JLOTS) Operations Using Multi- Objective Decision Analysis

Abstract

Similar works

Full text

Available Versions

Industrial and Systems Engineering Review (ISER)