Mukund Nilakantan Janardhanan

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

ABSTRACT 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.

Scheduling System for Multiple Unmanned Aerial Vehicles in Indoor Environments Using the CSP Approach

In recent years there has been an increased demand in use of multiple unmanned aerial vehicles (UAVs) for surveillance and material handling tasks in indoor environments. However, only a limited number of studies have been reported on UAV scheduling in an indoor 3D environment. This paper presents the indoor UAV scheduling problem and models it as a constraint satisfaction problem (CSP) to find a feasible solution in less computation time. A numerical example of the problem is presented to illustrate the proposed methodology.

Application of Particle Swarm Optimization to Maximize Efficiency of Straight and U-Shaped Robotic Assembly Lines

This paper focuses on implementing particle swarm optimization (PSO) to optimize the robotic assembly line balancing (RALB) problems with an objective of maximizing line efficiency. By maximizing the line efficiency, industries tend to utilize their resources in an efficient manner. In this paper, two layout configurations of robotic assembly lines are proposed. In this robotic assembly line balancing problem, the tasks are assigned to the workstations and the efficient robots to perform the assigned tasks are chosen based on the objective of maximizing line efficiency. Performance of the proposed PSO algorithm is evaluated on benchmark problems and compared with the best known results reported in the literature. Computational time of the proposed algorithm is better than the one reported in the literature. Comparative studies on the performance of the two layouts are also done and the results are reported.

Traffic flow routing and scheduling in a food supply network

Purpose The purpose of this paper is to focus on the reference model of a grid-like supply network that enables formulation of delivery routing and scheduling problems in the context of the periodic vehicle routing problem. Design/methodology/approach The conditions for seamless (collision-free) synchronization of periodically executed local transport processes presented in this paper guarantee cyclic execution of supply processes, thereby preventing traffic flow congestion. Findings Systems that satisfy this characteristic, cyclic deliveries executed along supply chains are given and what is sought is the number of vehicles needed to operate the local transport processes in order to ensure delivery from and to specific loading/unloading points on given dates. Determination of sufficient conditions guaranteeing the existence of feasible solutions that satisfy these constraints makes it possible to solve the considered class of problems online. Practical implications The computer experiments reported in this paper show the possibilities of practical application of the proposed approach in the construction of decision support systems for food supply chain management. Originality/value The aim of the present work is to develop a methodology for the synthesis of regularly structured supply networks that would ensure fixed cyclic execution of local transport processes. The proposed methodology, which implements sufficient conditions for the synchronization of local cyclic processes, allows one to develop a method for rapid prototyping of supply processes that satisfies the time windows constraints given.

Input Analysis of the Distributor’s Pallet Loading Problem

Numerous studies have been conducted on the distributor’s pallet loading problem (DPLP) in order to find solution methods that are time efficient and produces results that are applicable in the real world. It is well known, that the complexity of the problem increases by the number of boxes to be packed on the pallet, but not much research focuses on other factors of input affecting the complexity. This paper proposes a model for solving the three-dimensional single pallet DPLP. Datasets are created specifically to conduct selected experiments to identify causes to increased computational time. The results yield a strong link between computation time and certain ratios of total volume of boxes to maximum capacity of the pallet as well as the amount of small vs. large boxes to be packed.

3D pallet stacking with rigorous vertical stability

A number of studies have been conducted on the distributor’s pallet loading problem. However, the problem has rarely been solved for and applied on a real robot. Therefore, a model for solving such problems is created with focus on the solutions being applicable in real life. The main findings of the presented research is that for large problems it is necessary to utilize other approaches than optimization such as meta-heuristics. Furthermore, some model aspects are necessary to address such as stability in order to ensure working solutions.

Artificial bee colony algorithms for two-sided assembly line worker assignment and balancing problem

Worker assignment is a new type of problem in assembly line balancing problems, which typically occurs in sheltered work centers for the disabled. However, only a few contributions consider worker assignment in a two-sided assembly line. This research presents three variants of artificial bee colony algorithm to solve worker assignment and line balancing in two-sided assembly lines. The utilization of meta-heuristics is motivated by the NP-hard nature of the problem and the chosen methods utilize different operators for onlooker phase and scout phase. The proposed algorithms are tested on 156 cases generated from benchmark problems. A comparative study is conducted on the results obtained from the three proposed variants and other well-known metaheuristic algorithms, such as simulated annealing, particle swarm optimization and genetic algorithm. The computational study demonstrates that the proposed variants produce more promising results and are able to solve this new problem effectively in an acceptable computational time.

Minimizing energy consumption in a straight robotic assembly line using differential evolution algorithm

This paper focuses on implementing differential evolution (DE) to optimize the robotic assembly line balancing (RALB) problems with an objective of minimizing energy consumption in a straight robotic assembly line and thereby help to reduce energy costs. Few contributions are reported in literature addressing this problem. Assembly line balancing problems are classified as NP-hard, implying the need of using metaheuristics to solve realistic sized problems. In this paper, a well-known metaheuristic algorithm differential evolution is utilized to solve the problem. The proposed algorithm is tested on benchmark problems and the obtained results are compared with current state. It can be seen that the proposed DE algorithm is able to find a better solution for the considered objective function. Comparison of the computational time along with the cycle time is presented in detail.

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

Abstract 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.