Search results

A fast production scheduling system, the very fast scheduler (VFS), has been developed by the authors. It creates a capacity constrained production schedule within one minute of elapsed time for problems of a size encountered in industry. The quality of the schedules is comparable with the best alternative heuristic scheduling techniques. The speed of the scheduler is such that it can be used on a real‐time basis to plan capacity, adjust priorities and other parameters and derive new schedules.

In practical environments, machines subject to maintenance are prevalent in many production systems. This paper aims to find a schedule that minimizes the completion time (or equivalently, the total setup time) subject to maintenance and due dates.

An efficient heuristic is presented to provide the near‐optimal solution for the problem. The performance of the heuristic is evaluated by comparing its solution with the optimal solution obtained from the integer linear programming model.

In many production systems, the sequence‐dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. The paper studies the sequence‐dependent setup time problem with periodic maintenance, where several maintenances are required. Computational results show that problems with larger time interval and smaller maintaining time can produce a smaller completion time.

Here an efficient heuristic is developed to provide the near‐optimal schedule for the problem. The proposed integer linear programming model is also presented to provide the optimal schedule. However, the proposed heuristic and the integer linear programming model developed in the paper are appropriate for those companies where maintenance is performed periodically and the sequence‐dependent setup times of their jobs are required.

The paper presents the heuristic and the integer linear programming model to deal with sequencing and maintenance problems.

Design, balancing, and sequencing are the key issues associated with assembly lines (ALs). The purpose of this paper is to identify AL design issues and to develop an integrated methodology for mixed‐model assembly line balancing (MMALB) and sequencing. Primarily, mixed‐model lines are utilized for high‐variety, low‐volume job shop or batch production. Variation of a generic product is important for the manufacturers as the demand is mostly customer driven in the present global market.

Different AL design norms, performance indexes, and AL workstation indexes have been identified in the initial stage of this work. As the paper progresses, it has focused towards an integrated approach for MMALB and sequencing addressed for small‐ and medium‐scale assembly plants. A small‐scale practical problem has been justified with this integrated methodology implemented by MATLAB.

ALs execution in the production floor require many important factors to be considered. Different line orientations, production approaches, line characteristics, performance and workstation indexes, problem definitions, balancing and product sequencing in accordance with the objective functions are needed to be taken into account by the line designer.

This paper has highlighted the important AL design characteristics and also provided an integrated approach for balancing mixed‐model assembly lines (MMALs) combined with sequencing heuristic. The findings of this paper can be helpful for the designers while designing an AL. The integrated approach for balancing and sequencing of MMALs can be used as a functional tool for assembly‐based contemporary industries.

The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling.

FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi‐objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make‐span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators.

The application of GA to the multi‐objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized.

The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic.

The paper presents a successful implementation of a meta‐heuristic approach to solve a NP‐hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

Grid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.

In the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.

The simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.

The paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.

The purpose of this paper is to compare the effectiveness of two meta‐heuristics in solving the problem of scheduling maintenance operations and jobs processing on a single machine.

The two meta‐heuristic algorithms, tabu search and simulated annealing are hybridized using the properties of an optimal schedule identified in the existing literature to the problem. A lower bound is also suggested utilizing these properties.

In a numerical experimentation with large size problems, the best‐known heuristic algorithm to the problem is compared with the tabu search and simulated annealing algorithms. The study shows that the meta‐heuristic algorithms outperform the heuristic algorithm. In addition, the developed meta‐heuristics tend to be more robust against the problem‐related parameters than the existing algorithm.

A future work may consider the possibility of machine failure along with the preventive maintenance. This relaxes the assumption that the machine cannot fail but it is rather maintained preventively. The multi‐criteria scheduling can also be considered as an avenue of future work. The problem can also be considered with stochastic parameters such that the processing times of the jobs and the maintenance related parameters are random and follow a known probability distribution function.

The usefulness of meta‐heuristic algorithms is demonstrated for solving a large scale NP‐hard combinatorial optimization problem. The paper also shows that the utilization of the directed search methods such as hybridization could substantially improve the performance of a meta‐heuristic.

This research highlights the impact of utilizing the directed search methods to cause hybridization in meta‐heuristic and the resulting improvement in their performance for large‐scale optimization.

The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

Based on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.

This GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.

Traditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a placement machine with multi‐slot feeders. We formulate the problem as a binary linear programming model, and propose a heuristic procedure to find the solution that consists of a board‐assembly sequence, an associated component loading and unloading strategy and a feeder‐assignment plan within reasonable computational effort. Computational results from solving the simulated problem instances by using the heuristic method and the mathematical model are provided and compared. The proposed heuristic procedure can be incorporated into the PCB scheduling optimization software to decrease cycle times and increase overall assembly throughput in a high‐mix, low‐volume PCB manufacturing environment.

Reports on a comparison by computer simulation between conventional periodic loading (PL) and job‐group loading (JGL). When conventional PL is used, the search of the best part input sequence must be performed in order to optimize performance of a flexible manufacturing system (FMS). JGL works, instead, as a dynamic rule for real time scheduling of FMS, defining a part releasing policy able to guarantee the reaching of a periodic steady state without non‐productive times on the bottleneck workstation. However, JGL does not assure, in some cases, the same performance arising from the optimal part input sequence of conventional PL, in terms of non‐productive times in FMS filling and emptying phases, work in progress and throughput time. The paper demonstrates that using any JGL rule or the best PL part input sequence gives rise to negligible differences in FMS performance. Furthermore, the dynamic capabilities of the JGL also allow for spontaneously restoring the FMS periodic steady state without non‐productive times after any transient, for instance when production mix changes occur.