2

I am trying to convert these two tables into a long table, so I don't have two different tables. Can anyone help me please?

\begin{landscape}
\begin{table}
\begin{center}
\scalebox{0.7}{
    \begin{tabulary}{29cm}{p{3.5cm} p{10cm} p{8cm}  p{8.5cm}}
    \hline
    \textbf{Techniques} & \textbf{Key features} &  \textbf{Advantages}  &  \textbf{Weaknesses}\\ 
    \hline
    \textbf{Weighting}
    \textbf{Sum Method} & Combines various objective functions into a single-objective function by assigning positive weights to each of the objective functions and parametrically varying the weights to generate the Pareto optimal set. 
  &  \tabitem An easy-to-understand approach sufficient for Pareto optimality and simple to implement. 
     \tabitem Compatible with many different algorithms to incorpore preferences in either a prior or a posteriori process
  &  \tabitem A priori selection of weights not necessarily guarantee the final solution will be acceptable.
     \tabitem Different weight sets may generate the same Pareto optimal set.           
     \tabitem May not be able to obtain the Pareto optimal set in case of nonconvexity.
     \tabitem Varying the weights consistently and continuously may not necessarily result in an even distribution of the Pareto optimal set.\\           
\\

\textbf{Genetic}
 \textbf{Algorithms} & A search technique based on the mechanics of natural selection for solving complex combinatorial optimization problems. The search is run parallel to a population of solutions. New generations of solutions are generated through reproduction, crossover, and mutation until a prespecified stopping condition is satisfied. 
 & \tabitem Efficient in many cases in producing good solutions for difficult combinatorial optimization problems.
    \tabitem Potential capability to converge on the Pareto optimal set as a whole.
    \tabitem Applicable for nonconvex functions; Effective regardless of the nature of the objective functions and constraints &  \tabitem As with any heuristic, may not always find the true optimal. 
    \tabitem A relatively high computational cost.   
    \tabitem Requires high programming complexity.\\             
   \\      
  \textbf{Goal}        
  \textbf{Programming} & Minimize the weighted sum of deviations of all objective functions from their respective goals; Can be categorized based on the setting of weights into two types: nonpreemptive and preemptive.     
  & \tabitem Applicable for decision maker to capture the essential elements of a problem and formulate these into goals and constraints.     
    \tabitem Conceptually easy to understand and simple to implement. & \tabitem Global optimality convergence is not guaranteed in some cases.   
    \tabitem Weighted goal programming and preemptive goal programming can result in nonPareto optimization solutions. \\           
  \\           
  \textbf{$\varepsilon$-Constraint}     
  \textbf{Method} & Optimize one arbitrarily selected objective while converting all the remaining objectives into constraints with specified bounds. & \tabitem A properly designed systematic variation of $\varepsilon$ theoretically can yield a set of Pareto optimal solutions.    
  \tabitem Conceptually easy to understand and simple to implement.     
  & \tabitem Improper selection of $\varepsilon$ (or setting the constraints) can result in a formulation with no feasible solution.    
     \tabitem Either different problems (depending on the number of objective functions considered) have to be solved, or a unique solution that is not necessarily easy to verify has to be obtained to ensure Pareto optimality.\\              
    \hline        
\end{tabulary}}
\caption{Summary of Techniques used to Support Multi-Objective Optimization Models \citep{Wu2012}}
\end{center}
\end{table}
\end{landscape}
\clearpage

\begin{landscape}
\begin{table}
\begin{center}
\scalebox{0.7}{
    \begin{tabulary}{29cm}{p{3.5cm} p{10cm} p{8cm}  p{8.5cm}}
    \hline
    \textbf{Techniques} & \hspace{1cm}\textbf{Key features} &     \hspace{1cm}\textbf{Advantages}  &  \hspace{1cm}\textbf{Weaknesses}\\ 
    \hline
    \textbf{Multi-Attribute}     
       \textbf{Utility Theory}        
        & An axiomatized mathematical framework for analyzing and quantifying choices involving multiple competing outcomes.           
        & \tabitem Capable of quantifying a decision maker’s preferences over the available alternatives to a decision.         
            \tabitem Easy to combine with other optimization methods to generate the optimal solution(s). & \tabitem Difficult to construct the individual’s utility function in a practical situation.        
             \tabitem Applications generally must be guided by specialists in the field.         
            \tabitem The validity of this method is debated based on the reasoning that mathematical operations on utility functions are incorrect. \\                  
    \\         
      \textbf{Analytic}        
      \textbf{Hierarchy }       
      \textbf{Process} & Designed for subjective evaluation, providing a vector of weight expressing the relative importance of a set of alternatives based on multiple criteria.      
       & \tabitem Allows for the incorporation into the decision-making process of subjective judgments and user intuition by producing a common formal and numeric basis for solution.    
         \tabitem Easy to understand and simple to implement. & \tabitem In situations in which a vast number of alternatives need to be considered, the comparison process can be lengthy and boring, tending to lead to inaccurate/ biased outcomes.        
         \tabitem Uncertainty about the range of judgments used to express preferences is difficult to handle.\\             
     \\         
       \textbf{Compromise Programming} & Identifies solutions that are closest to the ideal solution by some measures of distance, of which the most common is the minimization of the normalized deviation from the ideal solution measured by the family of metrics.          
       & \tabitem  Results in a reduction of the Pareto optimal set and the procedure is easily understandable.          
         \tabitem Does not have to be restricted to continuous settings; it can be adapted to discrete settings as well. & \tabitem Two types of parameters are in general involved in compromise programming: the exponent that reflects the importance of the maximal deviation from the ideal value and the weights reflecting the relative importance of each objective. Accordingly, how to properly select the exponential parameter and determine the importance of each objective constitutes the main limitations of this method. \\ 
        \hline
 \end{tabulary}}
\caption{Summary of Techniques used to Support Multi-Objective Optimization Models \citep{Wu2012}}
 \end{center}
 \end{table}
 \end{landscape}
5
  • Welcome to TeX.SE. Please extend your code sniped to small but complete document, which we can copy to our computers and compile. In table you use some commands, which are probably determined in your document preamble which you not show. Help us to be able help you.
    – Zarko
    Aug 16 '16 at 17:05
  • What is the \tabitem command?
    – Bernard
    Aug 16 '16 at 17:07
  • 1
    Put a comment sign (%) in front of all lines starting at \hline immediately above the first \end{tabulary}} and ending at the \hline immediately above \textbf{Multi-Attribute}. Is this what you mean by converting the two tables into a lone one?
    – gernot
    Aug 16 '16 at 17:11
  • 2
    You are misusing tabulary all the columns are fixed with (p columns) so tabulary has no possibility of adjusting the column widthd, just use tabular. Also scaling tables is not recomended but if you do do it you need \scalebox{0.7}{% not \scalebox{0.7}{ or you force a spurious space to the left pf the table. Why do you need to make this into a longtable? If you do not want the second half to have a new number just remove the caption Aug 16 '16 at 17:11
  • @Bernard Maybe \newcommand{\tabitem}{~~\llap{\textbullet}~~} as mentioned in some postings.
    – gernot
    Aug 16 '16 at 17:16
2

(edited to incorporate a guess as to how \tabitem should be defined.)

I don't think it's a good idea to try to create a single (long-)table for the material at hand. Instead, I would focus on making the material more easily readable, mainly by not reducing the font size used for the tabular material. I'd use the facilities of the caption package to assure that the two tables are given a single number.

(I've used my own best guess as to \tabitem should defined. If that's not close to what your definition looks like, please let me know.)

enter image description here

enter image description here

\documentclass{article} 
\usepackage{rotating,tabularx,natbib,ragged2e,booktabs,caption}
\newcolumntype{L}{>{\RaggedRight\arraybackslash}X}
\usepackage[a4paper,margin=2.5cm]{geometry} % set the page parameters
\def\tabitem{\par\hangindent=0.9em\hangafter=1\textbullet~}%  what's the real definition?
\begin{document}

\begin{sidewaystable}
\caption{Summary of techniques used to support multi-objective optimization models \citep{Wu2012}}

\begin{tabularx}{\textwidth}{@{}
      >{\bfseries\raggedright}p{1.15in} LLL @{}}
\toprule
Techniques & \textbf{Key features} & \textbf{Advantages}  &  \textbf{Weaknesses}\\ 
\midrule
Weighting Sum Method & Combines various objective functions into a single-objective function by assigning positive weights to each of the objective functions and parametrically varying the weights to generate the Pareto optimal set. 
& \tabitem An easy-to-understand approach sufficient for Pareto optimality and simple to implement.
\tabitem Compatible with many different algorithms to incorpore preferences in either a prior or a posteriori process
& \tabitem A priori selection of weights not necessarily guarantee the final solution will be acceptable.  
  \tabitem Different weight sets may generate the same Pareto optimal set.                
  \tabitem May not be able to obtain the Pareto optimal set in case of nonconvexity.     
  \tabitem Varying the weights consistently and continuously may not necessarily result in an even distribution of the Pareto optimal set.\\           
\addlinespace

Genetic Algorithms 
& A search technique based on the mechanics of natural selection for solving complex combinatorial optimization problems. The search is run parallel to a population of solutions. New generations of solutions are generated through reproduction, crossover, and mutation until a prespecified stopping condition is satisfied. 
& \tabitem Efficient in many cases in producing good solutions for difficult combinatorial optimization problems. 
  \tabitem Potential capability to converge on the Pareto optimal set as a whole.
  \tabitem Applicable for nonconvex functions; Effective regardless of the nature of the objective functions and constraints 
& \tabitem As with any heuristic, may not always find the true optimal.  
  \tabitem A relatively high computational cost.       
  \tabitem Requires high programming complexity.\\             
\addlinespace     

Goal Programming 
& Minimize the weighted sum of deviations of all objective functions from their respective goals; Can be categorized based on the setting of weights into two types: nonpreemptive and preemptive.     
& \tabitem Applicable for decision maker to capture the essential elements of a problem and formulate these into goals and constraints.     
  \tabitem Conceptually easy to understand and simple to implement. 
& \tabitem Global optimality convergence is not guaranteed in some cases.    
  \tabitem Weighted goal programming and preemptive goal programming can result in nonPareto optimization solutions. \\           
\addlinespace 

\boldmath $\varepsilon$-Constraint Method 
& Optimize one arbitrarily selected objective while converting all the remaining objectives into constraints with specified bounds. 
& \tabitem A properly designed systematic variation of $\varepsilon$ theoretically can yield a set of Pareto optimal solutions.     
  \tabitem Conceptually easy to understand and simple to implement.     
& \tabitem Improper selection of $\varepsilon$ (or setting the constraints) can result in a formulation with no feasible solution.     
  \tabitem Either different problems (depending on the number of objective functions considered) have to be solved, or a unique solution that is not necessarily easy to verify has to be obtained to ensure Pareto optimality.\\              
\bottomrule     
\addlinespace
\multicolumn{4}{r@{}}{\emph{Continued on following page}}\\   
\end{tabularx}
\end{sidewaystable}

\begin{sidewaystable}
\ContinuedFloat
\caption{Summary of techniques used to support multi-objective optimization models \citep{Wu2012}, continued}

\begin{tabularx}{\textwidth}{@{}
      >{\bfseries\raggedright}p{1.15in} LLL @{}}
\toprule
Techniques & \textbf{Key features} & \textbf{Advantages}  & \textbf{Weaknesses}\\ 
\midrule
Multi-Attribute Utility Theory       
& An axiomatized mathematical framework for analyzing and quantifying choices involving multiple competing outcomes.           
& \tabitem Capable of quantifying a decision maker’s preferences over the available alternatives to a decision.         
  \tabitem Easy to combine with other optimization methods to generate the optimal solution(s). 
& \tabitem Difficult to construct the individual’s utility function in a practical situation.        
  \tabitem Applications generally must be guided by specialists in the field.         
  \tabitem The validity of this method is debated based on the reasoning that mathematical operations on utility functions are incorrect. \\                  
\addlinespace         
Analytic Hierarchy Process 
& Designed for subjective evaluation, providing a vector of weight expressing the relative importance of a set of alternatives based on multiple criteria.      
& \tabitem Allows for the incorporation into the decision-making process of subjective judgments and user intuition by producing a common formal and numeric basis for solution.    
  \tabitem Easy to understand and simple to implement. 
& \tabitem In situations in which a vast number of alternatives need to be considered, the comparison process can be lengthy and boring, tending to lead to inaccurate\slash biased outcomes.        
  \tabitem Uncertainty about the range of judgments used to express preferences is difficult to handle.\\             
\addlinespace        
Compromise Programming 
& Identifies solutions that are closest to the ideal solution by some measures of distance, of which the most common is the minimization of the normalized deviation from the ideal solution measured by the family of metrics.          
& \tabitem  Results in a reduction of the Pareto optimal set and the procedure is easily understandable.          
  \tabitem Does not have to be restricted to continuous settings; it can be adapted to discrete settings as well. 
& \tabitem Two types of parameters are in general involved in compromise programming: the exponent that reflects the importance of the maximal deviation from the ideal value and the weights reflecting the relative importance of each objective. Accordingly, how to properly select the exponential parameter and determine the importance of each objective constitutes the main limitations of this method. \\ 
\bottomrule
\end{tabularx}
\end{sidewaystable}

\end{document}
5
  • Thank you for your help. I was really struggling with this yesterday. I just started using latex a few month ago but I am loving it. I have learned a lot with your comments on this and other questions. Thank you once again :) Aug 17 '16 at 20:52
  • @ritajustosilva - Glad this answer was useful -- and glad that you're loving LaTeX. :-) Feel free to up-vote and/or accept this answer. ;-)
    – Mico
    Aug 17 '16 at 21:37
  • @ritajustosilva - Just out of idle curiosity: How is \tabitem defined in your document? Was the guess I implemented for this answer anywhere close to your macro?
    – Mico
    Aug 17 '16 at 21:39
  • \usepackage{booktabs} \newcommand{\tabitem}{~~\llap{\textbullet}~~} Aug 19 '16 at 6:52
  • I found this in an example and it works fine. Aug 19 '16 at 6:54

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