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In this paper, we propose an original double flexible job-shop scheduling problem (DFJSP), in which both workers and machines are flexible. Because of the characteristics of double flexibility, DFJSP conforms to practical production better than the flexible job-shop scheduling problem (FJSP). In addition, a multi-objective optimization mathematic model according to the DFJSP is proposed, which is concerned with the processing time indicator that is usually optimized by most existing studies; green production indicators, namely, factors regarding environmental protection; and human factor indicators, which are actual indispensable elements that exist in the production system. Furthermore, ten benchmarks of DFJSP are presented and solved using a newly proposed hybrid genetic algorithm (NHGA). With the proposed NHGA, a new well-designed three-layer chromosome encoding method and some effective crossover and mutation operators are developed. To obtain the best combination of key parameters in NHGA, the Taguchi design of experiment method is used for their evaluation. Finally, comparisons between NHGA and NSGA-II show that the proposed NHGA has advantages in terms of the solving accuracy and efficiency of the DFJSP, particularly at a large scale. It would be beneficial to apply our proposed model to the multi-objective optimization of scheduling problems, especially those considering human factor and green production-related indicators. [ABSTRACT FROM AUTHOR]

This research presents an optimization problem associated with scheduling of unrelated parallel machines considering the simultaneous effects of start-time-related deterioration, position-related learning and sequence-related setup times, with the aim to achieve an optimized value for the mean weighted tardiness and power consumption minimization. A workaround is proposed and served as the converter for solving the Mixed Integer Programming (MIP) problem by the continuous-based metaheuristic algorithms. Knowing the NP-hard nature of the problem, three metaheuristic algorithms, namely, Genetic Algorithm (GA), Cat Swarm Optimization (CSO) and Interactive Artificial Bee Colony (IABC), are employed to obtain quality solutions within acceptable computation time. A unique elitism strategy is introduced in the conventional CSO to reduce the computational time of its seeking mode and to provide improved solution, with the new algorithm form called CSO-Elit. Furthermore, a compact solution expression form is proposed for the use of GA, CSO-Elit and IABC to reduce the number of constraints. With the newly proposed model, many essential constraints are automatically built into the solution representation. Accordingly, the efficiency of algorithms is compared with LINGO regarding the quality of solutions, computational time, and overall power consumption using two sets of problems in small and large scales. In solving the small-scale cases, no statistically meaningful difference was observed between LINGO, GA and CSO-Elit, while in large-scale instances CSO-Elit demonstrated higher performance. The contributions of this paper lie in modeling a complex parallel machines problem considering power consumption minimization and proposing a new form of CSO algorithm with elitism strategy to reduce the computational time. • A novel problem has been identified and explored in this paper. • The model includes mathematical modeling and optimization of an unrelated parallel machine scheduling problem. • Several restrictions which are practical and industrially relevant are considered in this paper. • A new metaheuristic approach by adopting an elitism strategy into a metaheuristic algorithm is proposed. [ABSTRACT FROM AUTHOR]

This paper presents a novel algebraic technique of supply network synthesis and analysis. A case study of biomass processing for supply network synthesis using three techniques (i) mathematical modelling (ii) supermatrix scoring concept and (iii) algebraic analysis with graphical visualisation is demonstrated. The results and performances of three techniques are compared. Comparable modelling results are obtained for all three approaches. The introduction of quantitative cum graphical analysis of supply network allows the performance overview of each possible choice of supply network in one picture. Besides, the algebraic method allows the concurrent set-up of material allocation and ranking of possible supply network choices. This outweighs the mathematical modelling technique that allows for optimal supply network synthesis but giving no insight to the performance of next network choices; and the supermatrix scoring technique that ranks the general supply network choices in a whole picture yet incapable to capture the optimal selection which involves only part of the players, especially in supply network problem. [ABSTRACT FROM AUTHOR]

Abstract: In a recent paper, a generic model, based on a multiobjective optimization procedure, for water supply system for a single company and for an eco-industrial park was proposed and illustrated by a park involving three companies A, B and C. The best configuration was identified by simultaneously minimizing the fresh water flow rate, the regenerated water flow rate and the number of connections in the eco-industrial park. The question is now to know what the maximal increase/decrease in pollutant flow rates is, so that the eco-industrial park remains feasible, economically profitable and environmentally friendly. A preliminary study shows that the park can accept an increase of pollutant flow rates of 29% in company A, 12% in company B and only 1% in company C; beyond these limits the industrial symbiosis becomes not feasible. The proposed configuration is not flexible with a very limited number of connections. Indeed, the solution implemented for conferring some flexibility to this network is to increase the number of connections within the park. However, connections have a cost, so the increase of their number needs to remain moderate. The number of connections is augmented until the symbiosis becomes unfeasible, or until the gain for each company to participate to the park becomes lower than a given threshold. Several cases are studied by increasing the pollutant flow rates under two different scenarios: 1) in only one company, 2) in two or three companies simultaneously. [Copyright &y& Elsevier]

Abstract: This paper presents an industrial application of mixed-integer nonlinear programming (MINLP) models for the optimisation of a water network, which was initiated by an integral need for cleaner production within a brewery. Several mathematical models were developed in order to reduce the use of freshwater, whilst considering the specific requirements of each particular production section. These models are based on the design method developed by Kim and Smith [1]. The original formulation is modified to enable efficient integration of discontinuous and semi-continuous water-using processes in the packaging area. Semi-continuous processes are treated as water sources of limited capacity. The option of installing storage tanks for semi-continuous water streams is included in the model, in order to re-use these streams during the shutdown periods of semi-continuous operations. The original model is additionally extended with the option of installing local (on-site) wastewater treatment units operating either in batch or semi-continuous modes. This enables the analysing of opportunities for regeneration re-use within the production area, i.e. the brewhouse with a cellar, because of high contaminant concentrations at these sites. The scheduling of batch wastewater treatment units is performed simultaneously in order to adjust the treatment schedule to a fixed schedule of batch processes. [Copyright &y& Elsevier]