<abstract xmlns="http://www.w3.org/1999/xhtml">We address the <italic>m</italic>-machine no-wait flowshop scheduling problem; where the objective is to minimize total tardiness. To the best of our knowledge, the considered problem has not been addressed so far. We propose heuristic solutions since the problem is <italic>NP</italic>-hard. Initially, we consider a number of dispatching rules commonly used for the considered objective in other scheduling environments. We identify through computational experiments the best performing dispatching rule; and then propose simulated annealing (<italic>SA</italic>) and genetic algorithms (<italic>GA</italic>) by using the best performing dispatching rule as an initial solution. This achieves at least 50% improvement in the <italic>SA </italic>and <italic>GA </italic>performances. Next, we propose enhanced versions of <italic>SA </italic>and <italic>GA </italic>and show through computational experiments that the enhanced versions provide over 90% further improvement. The performance of enhanced <italic>GA </italic>is slightly better than that of enhanced <italic>SA</italic>; however, the computation time of enhanced <italic>GA </italic>is about 10 times that of enhanced <italic>SA</italic>. Therefore, we conclude that the enhanced <italic>SA </italic>outperforms the enhanced <italic>GA</italic>.</abstract>

Department of Industrial and Management Systems Engineering, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait, Fax: +965 2481 6137

Minimizing total tardiness in no-wait flowshops

<abstract xmlns="http://www.w3.org/1999/xhtml">Methods based on fuzzy outranking relations constitute one of the main approaches to multiple criteria decision problems. The use of ELECTRE methods require the elicitation of a large number of parameters (weights and different thresholds); but direct eliciting is often a demanding task for the decision-maker (<italic>DM</italic>). For handling intensity-ofpreference effects on concordance levels, a generalized concordance model was proposed by Roy and Slowinski which is more complex than previous outranking models. In this paper, an evolutionary multi-objective-based indirect elicitation of the complete ELECTRE III model-parameter set is proposed. The evolutionary multi-objective inference method is successfully extended to inferring reinforced-preference model parameters. Wide experimental evidence is provided to support the proposal, which performs well even working on small size reference sets.</abstract>

Corresponding author, Autonomous University of Sinaloa.

Evolutionary multi-objective optimization for inferring outranking model’s parameters under scarce reference information and effects of reinforced preference

<abstract xmlns="http://www.w3.org/1999/xhtml"> Experimental design is concerned with the problem of allocating resources within an experiment to ensure that objectives of the experiment are achieved at the minimum cost. This paper focuses on the generation of optimal or near-optimal designs for large and complex experiments where it is infeasible to carry out an ex- haustive search of the design space. Optimal designs for gene expression studies, aimed at investigating the behaviour of genes, are considered, where the optimality criterion employed is Pareto optimality. We develop an adaptation of the metaheuris- tic method of Pareto simulated annealing to generate an approximation to the set of Pareto optimal designs for large and complex experiments. We develop algorithms that utilise response surface methodology to search systematically for the optimal values of parameters associated with Pareto simulated annealing and performance is evaluated using quality measures.</abstract>

University of South Australia, School of Mathematics and Statistics, Mawson Lakes Campus, Mawson Lakes, South Australia, 5095, Australia

University of Adelaide, School of Mathematical Sciences, Adelaide, South Australia, 5005, Australia

Pareto simulated annealing for the design of experiments: illustrated by a gene expression study

<abstract xmlns="http://www.w3.org/1999/xhtml"> Graph coloring is one of the best known, popular and extensively researched subject in the field of graph theory, having many applications and conjectures, which are still open and studied by various mathematicians and computer scientists along the world. In this paper we present a survey of graph coloring as an important subfield of graph theory, describing various methods of the coloring, and a list of problems and conjectures associated with them. Lastly, we turn our attention to cubic graphs, a class of graphs, which has been found to be very interesting to study and color. A brief review of graph coloring methods (in Polish) was given by Kubale in [32] and a more detailed one in a book by the same author. We extend this review and explore the field of graph coloring further, describing various results obtained by other authors and show some interesting applications of this field of graph theory.</abstract>

Poznań University of Technology, Institute of Computing Science

Institute of Bioorganic Chemistry, Polish Academy of Sciences

A survey of graph coloring - its types, methods and applications

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Foundations of Computing and Decision Sciences

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international journal published since 1975. One of its main aims is its focus on the links between Computing (understood in the sense defined in the report of the ACM Task Force on the Core of Computer Science chaired by Peter J. Denning: Computing as a Discipline, CACM, Vol. 32, No. 1, 1989) and broadly understood Decision Sciences.  Scope of topics  The journal provides a forum for exchange of ideas for researchers in the fields of computing and decision sciences. The scope of the journal covers various areas of computing and decision sciences, particularly:    approximate reasoning,  artificial intelligence,  combinatorial optimization,  computational complexity theory,  databases and data warehouses,  intelligent decision support,  knowledge engineering,  machine learning and data mining,  metaheuristics,  multiple criteria decision analysis,  networking and distributed systems,  parallel computing and concurrency,  production and project scheduling,  scheduling theory,  soft and granular computing,  software engineering.     Why submit   International editorial board composed of distinguished scholars highly representative of the field of computing and decision sciences,  Quick turnaround,  Comprehensive peer review with at least two highly competent referees assessing each manuscript,  Promotion of published papers in the vast scientific community of researchers working in the field.   Rejection Rate   ca. 60%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process (iThenticate, https://www.ithenticate.com/ ). 