A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines

This paper presents a hybrid memetic algorithm for the problem of scheduling n jobs on m unrelated parallel machines with the objective of maximizing the weighted number of jobs that are completed exactly at their due dates. For each job, due date, weight, and the processing times on different machines are given. It has been shown that when the numbers of machines are a part of input, this problem is NP-hard in the strong sense. At first, the problem is formulated as an integer linear programming model. This model is practical to solve small-size problems. Afterward, a hybrid memetic algorithm is implemented which uses two heuristic algorithms as constructive algorithms, making initial population set. A data oriented mutation operator is implemented so as to facilitate memetic algorithm search process. Performance of all algorithms including heuristics (H1 and H2), hybrid genetic algorithm and hybrid memetic algorithm are evaluated through computational experiments which showed the capabilities of the proposed hybrid algorithm.     

An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors.     

Developing a novel approach to assess the efficiency of resource utilisation in organisations: a case study for an automotive supplier

There are different business processes in each organisation. Each business process utilises some resources to perform its related activities, produces specified outputs (products/services) and reaches organisational goals. In this paper, an approach is presented to assess the ability of business processes to utilise resources. To apply the presented framework, a manufacturing firm in the automotive industry was selected. Initially, four main business process groups were chosen for the assessment. Then, 15 processes of the four determined process groups and resources utilised by each process were identified. All of the recognised 19 resources were classified into six major categories, including physical, relational, organisational, informational, human and legal resources. Afterward, a hierarchical top-down analysis was performed to determine the ability of process groups and processes to utilise resource categories and resources. The results of the analysis show which resources have been strongly/weakly utilised by which business processes. In other words, by applying the suggested framework, it is possible to accurately identify the strengths and weaknesses of the resource utilisation. Therefore, the company can focus on weaknesses, prioritise them and develop improvement actions to increase the ability to utilise resources in the specified areas.     

Proposing a graph ranking method for manufacturing system selection in high-tech industries

All manufacturing centers are looking for the solutions to reduce costs and increase their competitive advantages. One of the practical solutions for cost reduction is to select a suitable manufacturing system in order to optimize usage of limited resources. In high-tech industries, the manufacturing system selection is extremely difficult because of the complex features and structures of their products. Generally, selecting the best manufacturing system of high-tech products is a multiple-criteria decision-making (MCDM) problem. Graph ranking method is one of the most used techniques among MCDM methods, which is originated from combinatorial mathematics. Simple computational procedure, ability to consider relationships between criteria, etc., are some perfect characteristics of this method for modeling and solving decision-making problems with complexity. Therefore, this study attempted to determine the most suitable manufacturing system in high-tech industries using graph ranking method. Moreover, because of vagueness and imprecision in human judgments, fuzzy set theory is utilized in the evaluation procedure. The suggested approach was used to select the most appropriate system for LCD manufacturing at Sanam Electronic Company. Finally, Obtained results indicated the efficiency of the proposed approach and selection of a cloud-based manufacturing system as the most suitable manufacturing system in high-tech industries.