Christos Dimopoulos

Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.

A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems

The problem of identifying machine cells and corresponding part families in cellular manufacturing has been extensively researched over the last thirty years. However, the complexity of the problem and the considerable number of issues involved in its solution create the need for increasingly efficient algorithms. In this paper the use of genetic programming for the solution of a simple version of the problem is investigated. The methodology is tested on a number of problems taken from the literature and comparative results are presented.

Investigating the use of genetic programming for a classic one-machine scheduling problem

Abstract Genetic programming has rarely been applied to manufacturing optimisation problems. In this paper the potential use of genetic programming for the solution of the one-machine total tardiness problem is investigated. Genetic programming is utilised for the evolution of scheduling policies in the form of dispatching rules. These rules are trained to cope with different levels of tardiness and tightness of due dates.

Multi-objective optimization of manufacturing cell design

Whereas the single-objective cell-formation problem has been studied extensively during the past decades, research on the multi-objective version of the problem has been relatively limited, despite the fact that it represents a more realistic modelling of the manufacturing environment. This article introduces multi-objective GP-SLCA, an evolutionary computation methodology for the solution of the multi-objective cell-formation problem. GP-SLCA is a hybrid algorithm, comprising of GP-SLCA, a genetic programming algorithm for the solution of single-objective cell-formation problems, and NSGA-II, a standard evolutionary multi-objective optimization technique. The proposed methodology is capable of providing the decision maker with a range of non-dominated solutions instead of a single compromise solution, which is usually produced as an outcome of alternative multi-objective optimization techniques. The application of multi-objective GP-SLCA is illustrated on a large-sized test problem taken from the literature.

A review of evolutionary multiobjective optimization applications in the area of production research

Evolutionary computation methods have been used extensively in the past for the solution of manufacturing optimization problems. This work examines the impact of the fast-growing evolutionary multiobjective optimization field in this area of research. A considerable number of significant applications are reported for a wide range of relevant optimization problems. The review of these applications leads to a number of conclusions and establishes directions for future research.

Explicit consideration of multiple objectives in cellular manufacturing

Although many methodologies have been proposed for solving the cell-formation problem, few of them explicitly consider the existence of multiple objectives in the design process. In this article, the development of multi-objective genetic programming single-linkage cluster analysis (GP-SLCA), an evolutionary methodology for the solution of the multi-objective cell-formation problem, is described. The proposed methodology combines an existing algorithm for the solution of single-objective cell-formation problems with NSGA-II, an elitist evolutionary multi-objective optimization technique. Multi-objective GP-SLCA is able to generate automatically a set of non-dominated solutions for a given multi-objective cell-formation problem. The benefits of the proposed approach are illustrated using an example test problem taken from the literature and an industrial case study.

A genetic programming heuristic for the one-machine total tardiness problem

Genetic programming has rarely been applied to manufacturing optimisation problems. In this report we investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Combinations of dispatching rules are employed as an indirect way of representing permutations within a modified genetic programming framework. Hybridisation of genetic programming with local search techniques is also introduced, in an attempt to improve the quality of solutions. All the algorithms are tested on a large number of benchmark problems with different levels of tardiness and tightness of due dates.

Evolving knowledge for the solution of clustering problems in cellular manufacturing

Hierarchical clustering has been widely used for the solution of problems in the area of cellular manufacturing. Hierarchical clustering procedures utilize coefficients that quantify the level of similarity between pairs of machines or parts in the plant. An evolutionary methodology is proposed for the construction of new similarity coefficients that can be used by standard hierarchical clustering methodologies for the solution of cell-formation problems. A typical application is presented for the simplest case of the cell-formation problem. However, alternative similarity coefficients can be evolved for advanced formulations of the problem by suitably modifying the set of fitness cases that constitute the environment of the evolutionary process.

Interdisciplinary design of scheduling decision support systems in small-sized SME environments: The i-DESME framework

This paper introduces i-nterdisciplinary DEsign for SMEs (i-DESME), a structured interdisciplinary framework for the design of IT scheduling decision support systems, with a focus on small-sized Small and Medium Enterprises (SME) industrial environments. The proposed framework initially models the interdisciplinary characteristics of the current (‘as-is’) implementation of scheduling processes within a particular SME industrial environment. This information provides the basis for the implementation of function allocation and algorithm selection studies on the scheduling processes considered. As a result, an interdisciplinary specification of the information technology (IT) decision support system which will support the future (‘to-be’) implementation of the scheduling processes within the industrial environment is produced, and subsequent software lifecycle phases are implemented. A summary of case study results from the application of the i-DESME framework within the environment of a typical micro-sized SME company is provided.

The Expression and Prognostic Impact of Immune Cytolytic Activity-Related Markers in Human Malignancies: A Comprehensive Meta-analysis

Background Recently, immune-checkpoint blockade has shown striking clinical results in different cancer patients. However, a significant inter-individual and inter-tumor variability exists among different cancers. The expression of the toxins granzyme A (GZMA) and perforin 1 (PRF1), secreted by effector cytotoxic T cells and natural killer (NK) cells, were recently used as a denominator of the intratumoral immune cytolytic activity (CYT). These levels are significantly elevated upon CD8+ T-cell activation as well as during a productive clinical response against immune-checkpoint blockade therapies. Still, it is not completely understood how different tumors induce and adapt to immune responses. Methods Here, we calculated the CYT across different cancer types and focused on differences between primary and metastatic tumors. Using data from 10,355, primary tumor resection samples and 2,787 normal samples that we extracted from The Cancer Genome Atlas and Genotype-Tissue Expression project databases, we screened the variation of CYT across 32 different cancer types and 28 different normal tissue types. We correlated the cytolytic levels in each cancer type with the corresponding patient group’s overall survival, the expression of several immune-checkpoint molecules, as well as with the load of tumor-infiltrating lymphocytes (TILs), and tumor-associated neutrophils (TANs) in these tumors. Results We found diverse levels of CYT across different cancer types, with highest levels in kidney, lung, and cervical cancers, and lowest levels in glioma, adrenocortical carcinoma (ACC), and uveal melanoma. GZMA protein was either lowly expressed or absent in at least half of these tumors; whereas PRF1 protein was not detected in almost any of the different tumor types, analyzing tissue microarrays from 20 different tumor types. CYT was significantly higher in metastatic skin melanoma and correlated significantly to the TIL load. In TCGA-ACC, skin melanoma, and bladder cancer, CYT was associated with an improved patient outcome and high levels of both GZMA and PRF1 synergistically affected patient survival in these cancers. In bladder, breast, colon, esophageal, kidney, ovarian, pancreatic, testicular, and thyroid cancers, high CYT was accompanied by upregulation of at least one immune-checkpoint molecule, indicating that similar to melanoma and prostate cancer, immune responses in cytolytic-high tumors elicit immune suppression in the tumor microenvironment. Conclusion Overall, our data highlight the existence of diverse levels of CYT across different cancer types and suggest that along with the existence of complicated associations among various tumor-infiltrated immune cells, it is capable to promote or inhibit the establishment of a permissive tumor microenvironment, depending on the cancer type. High levels of immunosuppression seem to exist in several tumor types.