This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)-one of the most efficient metaheuristic optimization search techniques. Assembly lines of a specific car factory considered as a case study are balanced by the ACS to optimize their energy resource management. The workload variance of the line is performed as the objective function to be minimized in order to increase the productivity. In this work, the ACS is used to address the number of tasks assigned for each workstation, while the sequence of tasks is assigned by factory. Three real-world assembly line balancing (ALB) problems from a specific car factory are tested. Results obtained by the ACS are compared with those obtained by the genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms. By using the ACS, the productivity can be increased and the energy consumption of the lines can be decreased significantly.

Abstract-This paper aims to apply a modified current search method, adaptive current search (ACS), for assembly line balancing problems. The ACS algorithm possesses the memo ry list (M L) to escape fro m local entrapment and the adaptive radius (AR) mechanis m to speed up the search process. The ACS is tested against five benchmark unconstrained and three constrained optimization problems co mpared with genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms and provides superior results. The ACS is used to address the number of tasks assigned for each workstation, while the heuristic sequencing (HS) technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance and the idle t ime are performed as the mult iple-object ive functions. The proposed approach is tested against four benchmark A LB problems co mpared with the GA, TS and CS. As results, the ACS associated with the HS technique is capable of producing solutions superior to other techniques. In addition, the ACS is an alternative potential algorithm to solve other optimization problems.

Abstract-The adaptive current search (ACS) is one of the novel metaheuristic optimization search techniques proposed for solving the combinatorial optimization problems. This paper aimed to present the application of the ACS to optimize the real-world traveling transportation problems (TTP) of a specific car factory. The total distance of the selected TTP is performed as the objective function to be minimized in order to decrease the vehicle's energy. To perform its effectiveness, four real-world TTP problems are conducted. Results obtained by the ACS are compared with those obtained by genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS can provide very satisfactory solutions superior to other algorithms. The minimum total distance and the minimum vehicle's energy of all TTP problems can be achieved by the ACS with the distant error of no longer than 3.05%.

The textile industry is a complicated manufacturing industry because it is a fragmented and heterogeneous sector dominated by Small and Medium Enterprises (SMEs). There are various energy-efficiency opportunities that exist in every textile plant. However, even cost-effective options often are not implemented in textile plants mostly because of limited information on how to implement energy-efficiency measures. This paper presents the expansion of problem formulation of consummation management based on load shifting in textile industry. The mathematical model is a Non Polynomial (NP) hard optimization problem to determine the start time of the process in order to minimize the total electricity cost under varying tariffs such as flat rate and Time of Use (TOU). For solve this problem, Ant Colony Optimization (ACO) is applied and compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To show its efficiency, the case studies in case of Single Process Multiple Jobs (SPMJ) in term of small, medium and large scales are demonstrated. The results show that the proposed method is able to achieve the best solution efficiently and easy to implement.