Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach (original) (raw)
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Journal of Manufacturing Systems, 2013
We address the two-stage multi-machine assembly scheduling problem. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The objective is to come up with a schedule that minimizes total or mean completion time for all jobs. The problem has been addressed in the scheduling literature and several heuristics have been proposed. In this paper, we propose a new heuristic called artificial immune system (AIS). We conduct experimental analysis for comparing the newly proposed heuristic AIS with the best known heuristic in the literature. Experimental results show that our proposed heuristic AIS performs better than the best known existing heuristic. More specifically, our new heuristic AIS reduces the error of the best known heuristic by 60% while the computational times of both AIS and the best known heuristic are almost the same.
International Journal of Strategic Decision Sciences, 2014
The concern about customer satisfaction and transportation cost in the business environment has spurred an interest in designing a flexible logistics network. This paper proposes a multi-delivery path closed-loop supply chain network to find not only the most cost efficient network design but also the best path to deliver the products to customers. To tackle with such an NP-hard problem an Artificial Immune Algorithm (AIA) is modified. To show the efficiency and accuracy of the proposed method numerical experiments are conducted. Consequently, the computational results validate the quality of the proposed approach by comparing them with those of obtained with exact methods.
Artificial immune system for static and dynamic production scheduling problems
2017
Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival o...
Computers & Industrial Engineering, 2009
This paper investigates an extended problem of job shop scheduling to minimize the total completion time. With aim of actualization of the scheduling problems, many researchers have recently considered realistic assumptions in their problems. Two of the most applied assumptions are to consider sequencedependent setup times and machine availability constraints (MACs). In this paper, we deal with a specific case of MACs caused by preventive maintenance (PM) operations. Contrary to the previous papers considering fixed or/and conservative policies, we consider flexible PM operations, in which PM operations may be postponed or expedited as required. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the given problem, we present a novel meta-heuristic method based on the artificial immune algorithm (AIA) incorporating some advanced features. For further enhancement, the proposed AIA is hybridized with a simple and fast simulated annealing (SA). To evaluate the proposed algorithms, we compare our proposed AIA with three well-known algorithms taken from the literature. Finally, we find that the proposed AIA outperforms other algorithms.
International Journal of Production Research, 2015
Two-stage hybrid flow shop (HFS) scheduling problem followed by single assembly machine is addressed in this paper. To produce the final product, parts need to be processed on the HFS stages and thereafter, several parts are joined under the assembly operations based on the predefined Bill of Materials of the product. The aim of this research is to find the schedule which minimises completion time of the last product, i.e. makespan. For the considered problem, lower bound, heuristic algorithms and two metaheuristic techniques based on artificial immune system are developed. Computational results demonstrate that the proposed lower bound and heuristic algorithms outperform the existent lower bounds and heuristic algorithms.
An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times
Amc, 2006
Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper deals with the hybrid flow shop scheduling problems in which there are sequence dependent setup times, commonly known as the SDST hybrid flow shops. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes an immune algorithm approach to the scheduling of a SDST hybrid flow shop. An overview of the hybrid flow shops and the basic notions of an IA are first presented. Subsequently, the details of an IA approach are described and implemented. The results obtained are compared with those computed by Random Key Genetic Algorithm (RKGA) presented previously. From the results, it was established that IA outperformed RKGA.
Minimize the makespan for job shop scheduling problem using artificial immune system approach
Journal of theoretical and applied information technology, 2015
In the manufacturing industry, scheduling is a process of arranging, controlling and optimizing work and workloads in a production process. This research discussed about job-shop scheduling problem. The main problem in job-shop scheduling is to optimize the usage of machines in order to obtain the shortest time in completing the activities. Several methods have been used to solve job-shop scheduling problems and the method proposed here is artificial intelligence by using the artificial immune system algorithm (AIS). The advantage of this algorithm is fabricated by imitating the natural immune system. The results produced by this method are compared with the best results of the previous research.
Journal of Intelligent Manufacturing, 2012
This paper uses an immune algorithm (IA) metaheuristic optimization method to solve the problem of structure optimization of series-parallel production systems. In the considered problem, redundant machines and buffers in process are included in order to attain a desirable level of availability. A procedure which determines the minimal cost system configuration is proposed. In this procedure, multiple choices of producing machines and buffers are allowed from a list of product available in the market. The elements of the system are characterized by their cost, estimated average up and down times, productivity rates and buffers capacities. The availability is defined as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. The proposed meta-heuristic is used as an optimization technique to seek for the optimal design configuration. The advantage of the proposed IA approach is that it allows machines and buffers with different parameters to be allocated.