جستجوی مقالات مرتبط با کلیدواژه « uniform parallel machines » در نشریات گروه « صنایع »

This paper provides a mathematical model and a bi-phase heuristic algorithm for the uniform parallel machines scheduling problem to maximize benefits and the number of jobs processed before their due dates as the weighted objective function. In the first phase of this heuristic, named “the neighborhood combined dispatching rules algorithm” (NCDRA), an initial sequence by the segmentation of the dispatching rules (DRs) is generated. Then, the output sequence is segmented, and required efforts are made to derive a sequence combined with these rules to improve the objective. The second phase involves a local search in which operators such as swapping, insertion, and reversion are concurrently implemented there on. The proposed algorithm is examined on four classes of problems with 50, 100, and 1000 jobs on 5, 10, and 50 machines, respectively. Results obtained by NCDRA and a Simulated Annealing (SA) algorithm developed on problem instances indicate that the NCDRA provides high-quality results on objective function for solving problems in different scales.

The two-stage assembly flowshop scheduling problem has been studied in this research. Suppose that a number of products of different kinds are needed to be produced. Each product is consists of several parts. There are m uniform machines in the first stage to manufacture the components (parts) of products and there is one assembly station in the second stage to assembled parts into products. Setup operation should be done when a machine starts processing a new part and setup times are treated as separate from processing times. Two objective functions are considered: (1) minimizing the completion time of all products (makespan) as a classic objective, and (2) minimizing the cost of energy consumption as a new objective. Processing speed of each machine is adjustable and the rate of energy consumption of each machine is dependent of its processing speed. At first, this problem is described with an example, and then needed parameters and decision variables are defined. After that, this problem is modeled as a mixed integer linear programming (MILP) and GAMS software is applied to solve small problems. To solve this bi-objective model, Epsilon Constraint algorithm is used on some test problems obtained standard references. Data of test problems were obtained from previous references and new parameters have been adjusted for considered problem. Conflicting of two considered objective functions has been valid through the result. In additional, result of solving test problems and sensitivity analysis show that how we can reduce energy consumption by adjusting completion times.