The bullwhip effect with correlated lead times and autocorrelated demand

We quantify the bullwhip effect in a two-echelon supply chain when demand follows a first-order autoregressive random process and the lead times form a correlated stationary sequence of random variables. We assume future demands are predicted with the minimum mean squared error method; the random lead times can be estimated using any method. We analyse the impact of the autocorrelated demands and autocorrelated lead times on the bullwhip effect. We consider several cases of mutual lead time dependence, such as a first-order autoregressive random process and a first-order integer autoregressive random process. We explore the use of naïve, moving average, minimum mean squared error, and exponential smoothing forecasting methods for predicting lead times. We show how the bullwhip is influenced by demand correlation, lead time autocorrelation, and the parameters of the lead time forecasts. We reveal that minima and maxima exist in the bullwhip effect. With moving average forecasts of negatively correlated lead times, we observe an even-odd phenomenon in the bullwhip measure. Theoretical results are confirmed by a Monte Carlo simulation and a practical approach. We also derive a formula for the variance of the lead time demand forecast error. </p

Heuristics for solving a multi-model robotic assembly line balancing problem

Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail

Heuristics for solving a multi-model robotic assembly line balancing problem

Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail

Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems

Methods for reducing the carbon footprint is receiving increasing attention from industry as they work to create sustainable products. Assembly line systems are widely utilized to assemble different types of products and in recent years, robots have become extensively utilized, replacing manual labor. This paper focuses on minimizing the carbon footprint for robotic assembly line systems, a topic that has received limited attention in academia. This paper is primarily focused on developing a mathematical model to simultaneously minimize the total carbon footprint and maximize the efficiency of robotic assembly line systems. Due to the NP-hard nature of the considered problem, a multi-objective co-operative co-evolutionary (MOCC) algorithm is developed to solve it. Several improvements are applied to enhance the performance of the MOCC for obtaining a strong local search capacity and faster search speed. The performance of the proposed MOCC algorithm is compared with three other high-performing multi-objective methods. Computational and statistical results from the set of benchmark problems show that the proposed model can reduce the carbon footprint effectively. The proposed MOCC outperforms the other three methods by a significant margin as shown by utilizing one graphical and two quantitative Pareto compliant indicators

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial - hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study

Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems

Methods for reducing the carbon footprint is receiving increasing attention from industry as they work to create sustainable products. Assembly line systems are widely utilized to assemble different types of products and in recent years, robots have become extensively utilized, replacing manual labor. This paper focuses on minimizing the carbon footprint for robotic assembly line systems, a topic that has received limited attention in academia. This paper is primarily focused on developing a mathematical model to simultaneously minimize the total carbon footprint and maximize the efficiency of robotic assembly line systems. Due to the NP-hard nature of the considered problem, a multi-objective co-operative co-evolutionary (MOCC) algorithm is developed to solve it. Several improvements are applied to enhance the performance of the MOCC for obtaining a strong local search capacity and faster search speed. The performance of the proposed MOCC algorithm is compared with three other high-performing multi-objective methods. Computational and statistical results from the set of benchmark problems show that the proposed model can reduce the carbon footprint effectively. The proposed MOCC outperforms the other three methods by a significant margin as shown by utilizing one graphical and two quantitative Pareto compliant indicators

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial - hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently

Data for: Accelerated carbonation of steel slag monoliths at low CO2 pressure – microstructure and strength development.

carbonation method + in-situ thermal XRD method + dat