A new line remedial policy for the paced lines with stochastic task times

This study is based on investigating the effect of incompleted tasks on the line efficiency after balancing the stochastic ALB problem. For this purpose, a model allowing buffer storage between the stations in order to reduce the effect of incompleted tasks, is proposed.     

Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.     

Job rotation in assembly lines employing disabled workers

In this paper we consider the programming of job rotation in the assembly line worker assignment and balancing problem. The motivation for this study comes from the designing of assembly lines in sheltered work centers for the disabled, where workers have different task execution times. In this context, the well-known training aspects associated with job rotation are particularly desired. We propose a metric along with a mixed integer linear model and a heuristic decomposition method to solve this new job rotation problem. Computational results show the efficacy of the proposed heuristics.     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

Optimally locating in-house logistics areas to facilitate JIT-supply of mixed-model assembly lines

In modern-day production systems, ever-rising product variety poses a great challenge for the internal logistics systems used to feed mixed-model assembly lines with the required parts. As an answer to this challenge many manufacturers especially from automobile industries have identified the supermarket-concept as a promising part feeding strategy to enable flexible small-lot deliveries at low cost. In this context, supermarkets are decentralized in-house logistics areas in the direct vicinity of the final assembly line, which serve as intermediary stores for parts. Small tow trains are loaded with material in a supermarket and deliver parts Just-in-Time to the stations lying on their fixed route. This paper discusses the general pros and cons of the supermarket-concept and treats the decision problem of determining the optimal number and placement of supermarkets on the shop floor. A mathematical model is proposed, an exact dynamic programming algorithm presented, and the validity of the proposed approach for practical purposes as well as the trade-off resulting from fixed installation and maintenance cost is investigated in a comprehensive computational study.     

A comparison of search techniques for minimizing assembly time in printed wiring assembly

In the robotics assembly of DPP model, the coordinates of assembly point and magazine are dynamically changed during robotics assembly so that evaluation of the assembly efficiency is extremely complicated. To route the robotics travel, most related investigations have utilized the fixed coordinate of insertion points and magazine using the Traveling Salesman Problems (TSP) method to sequence the insertion points after arbitrarily assigning the magazine. However, robotics travel routing should be based on a relative coordinate to obtain a better solution because the robotics, board and magazine are simultaneously moved at different speeds during assembly. To resolve such a dynamically combinatorial problem, this study presents the Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS) based algorithms. These approaches can simultaneously arrange the insertion sequence and assign the magazine slots by the computer and yield a better performance compared to the conventional approach. Results presented herein also demonstrate that the larger the number of insertion points and/or part numbers the better the performance. These approaches are also compared.     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

The joint replenishment and freight consolidation of a warehouse in a supply chain

We have developed joint replenishment and consolidated freight delivery policies for a third party warehouse that handles multiple items, which have deterministic demand rates in a supply chain. Two policies are proposed and mathematical models are developed to obtain the optimal parameters for the proposed policies. Four efficient algorithms are presented to solve the mathematical models for the two policies. The performances of the two policies with the parameters obtained from the proposed algorithms are then compared with the common cycle approach for 1600 randomly generated problems. The results show the robust performance of the proposed algorithm for both policies.     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

Backup strategy for robots’ failures in an automotive assembly system

Automotive assembly lines are often characterized by robots’ failures that may result in stoppages of the lines and manual backup of tasks. The phenomena tend to impair throughput rate and products’ quality. This paper presents a backup strategy in which working robots perform tasks of failed robots. The proposed Mixed-Integer Linear-Programming based approach minimizes the throughput loss by utilizing the robots’ redundancy in the system. Two algorithms are developed to comply with stochastic conditions of a real-world environment. The performance of these algorithms is compared with several heuristics, and the downstream-backup based algorithm is found superior to all other methods.