This paper presents a general review of intellectual capital literature, which includes definitions, components and indicators. We aim to gather a useful collection of indicators and definitions. After studying several related works from the literature, many definitions and indicators are identified and explain that choosing appropriate indicators depends on organizational targets. Every kind of organization can use this study to identify its more applicable and appropriate indicators to know about its intellectual capital. Identifying all of indicators in previous studies can help organization select right indicators for the organization.

In this paper, we consider job shop scheduling and machine location problem, simultaneously. Processing, transportation, and setup times are defined as deterministic parameters. The purpose of this paper is to determine machine location and job scheduling such that the make span and transportation cost is minimized. Therefore, the proposed model is a multi-objective problem one, where the first objective function minimizes make span and another minimizes the transportation cost. To solve the multi-objective problem, two methods are evaluated. Considering combination of job shop scheduling problem and machine location problem makes the proposed model more complex than job shop scheduling problem, which is an NP-hard problem. Therefore, to solve the proposed model, genetic algorithm as a meta-heuristic algorithm is implemented. To show the efficiency of the proposed genetic algorithm, 6×6 job shop scheduling problems are considered.