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Robots are used in assembly lines because of their higher flexibility and lower costs. The purpose of this paper is to develop mathematical models and simulated annealing algorithms to solve the robotic assembly line balancing (RALB-II) to minimize the cycle time.

Four mixed-integer linear programming models are developed and encoded in CPLEX solver to find optimal solutions for small-sized problem instances. Two simulated annealing algorithms, original simulated annealing algorithm and restarted simulated annealing (RSA) algorithm, are proposed to tackle large-sized problems. The restart mechanism in the RSA methodology replaces the incumbent temperature with a new temperature. In addition, the proposed methods use iterative mechanisms for updating cycle time and a new objective to select the solution with fewer critical workstations.

The comparative study among the tested algorithms and other methods adapted verifies the effectiveness of the proposed methods. The results obtained by these algorithms on the benchmark instances show that 23 new upper bounds out of 32 tested cases are achieved. The RSA algorithm ranks first among the algorithms in the number of updated upper bounds.

Four models are developed for RALBP-II and their performance is evaluated for the first time. An RSA algorithm is developed to solve RALBP-II, where the restart mechanism is developed to replace the incumbent temperature with a new temperature. The proposed methods also use iterative mechanisms and a new objective to select the solution with fewer critical workstations.

Disturbing increase in the use of virgin resources to produce new products has threatened the environment. Many countries have reacted to this situation through regulations which aim to eliminate negative impact of products on the environment shaping the concept of environmentally conscious manufacturing and product recovery (ECMPRO). The first crucial and the most time-consuming step of product recovery is disassembly. The best productivity rate is achieved via a disassembly line in an automated disassembly process. In this chapter, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem. Therefore, an efficient methodology based on the simulated annealing (SA) is proposed to solve the SDDLBP. Case scenarios are considered and comparisons with ant colony optimization (ACO), particle swarm optimization (PSO), river formation dynamics (RFD), and tabu search (TS) approaches are provided to demonstrate the superior functionality of the proposed algorithm.

As far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm.

The efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented.

After presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms.

In this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

The purpose of this study is to optimize the process parameters (wire feed rate (F), voltage (V), welding speed (S) and torch angle (A)) in order to obtain the optimum bead geometry (bead width (W), reinforcement (R) and depth of penetration (P)), considering the ranges of the process parameters using evolutionary algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm.

The modeling of welding parameters in flux cored arc welding process using a set of experimental data and regression analysis, and optimization using GA and SA algorithm.

The adequate mathematical model was developed. The multiple objectives were optimized satisfactorily by the GA and SA algorithms. The feasible solution results are very closer to the optimized results and the percentage error was found to be negligibly small.

The optimal welding parameters were identified in order to increase the productivity. The welding input parameters effect was found.

This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line.

To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time.

The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy.

This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

This paper aims to present a simulated annealing (SA) algorithm to search the optimal solution of reliability‐redundancy allocation problems (RRAP) with nonlinear resource constraints.

The developed SA algorithm is coded in C++ and is applied to reliability design problems which include the series system (P1(a) and P1(b)), the series‐parallel system (P2), and the complex (bridge) system (P3). The numerical experiments are executed on an IBM‐PC compatible with a Pentium IV 2.0 GHz. The results are compared with those of previous studies.

The SA algorithm can find better solutions comparable to the previous studies in all problems except the problem P1(b). The difference on the order of 10⁻⁴ between the best and worst for all problems indicates good solution convergence of the SA algorithm. Note that the CPU times for these problems are within a few seconds by Pentium IV 2.0 GHz (P1(a) =2.78 sec, P1(b) =3.37 sec, P2=1.38 sec, and P3=1.40 sec).

The application of the SA is expanded to the RRAP, which can help reliability engineers design the system reliability.

This research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.

This paper takes curve fitting as an optimization problem of a set of data points. Expressing the data as a function will be very effective to the data analysis and application. This paper will develop the stochastic optimization method to apply to curve fitting. The proposed method is a combination optimization method based on pattern search (PS) and simulated annealing algorithm (SA).

The proposed method is used to solve a nonlinear optimization problem and then to implement it to solve three circular arc‐fitting problems of curve fitting. Based on the analysis performed in the experimental study, the proposed algorithm has been found to be suitable for curve fitting.

Curve fitting is one of the basic form errors encountered in circular features. The proposed algorithm is tested and implemented by using nonlinear problem and circular data to determine the circular parameters.

The developed machine vision‐based approach can be an online tool for measurement of circular components in automated manufacturing systems.

This paper aims to propose comparing the performance of three algorithms based on different population-based heuristics, particle swarm optimization (PSO), artificial bee colony (ABC) and method of musical composition (DMMC), for the districting problem.

In order to compare the performance of the proposed algorithms, they were tested on eight instances drawn from the Mexican electoral institute database, and their respective performance levels were compared. In addition, a simulated annealing-based (simulated annealing – SA) algorithm was used as reference to evaluate the proposed algorithms. This technique was included in this work because it has been used for Federal districting in Mexico since 1994. The performance of the algorithms was evaluated in terms of the quality of the approximated Pareto front and efficiency. Regarding solution quality, convergence and dispersion of the resulting non-dominated solutions were evaluated.

The results show that the quality and diversification of non-dominated solutions generated by population-based algorithms are better than those produced by Federal Electoral Institute’s (IFE’s) SA-based technique. More accurately, among population-based techniques, discrete adaptation of ABC and MMC outperform PSO.

The performance of three population-based techniques was evaluated for the districting problem. In this paper, the authors used the objective function proposed by the Mexican IFE, a weight aggregation function that seeks for a districting plan that represents the best balance between population equality and compactness. However, the weighting factors can be modified by political agreements; thus, the authors decided to produce a set of efficient solutions, using different weighting factors for the computational experiments. This way, the best algorithm will produce high quality solutions no matter the weighting factors used for a real districting process. The computational experiments proved that the proposed artificial bee colony and method of musical composition-based algorithms produce better quality efficient solutions than its counterparts. These results show that population-based algorithms can outperform traditional local search strategies. Besides, as far as we know, this is the first time that the method of musical composition is used for this kind of problems.