Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.

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Iterated Greedy methods for the distributed permutation flowshop scheduling problem

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times

The trend of globalization has recently seen the study of distributed scheduling problems. This study attempts to solve the distributed hybrid flowshop scheduling problem with multiprocessor tasks, and is the first attempt to address this problem. To solve this strongly NP-hard problem, a mixed integer linear programming formulation and self-tuning iterated greedy (SIG) algorithm that incorporates an adaptive cocktail decoding mechanism are presented to minimize the makespan. Comprehensive computational results demonstrate that the proposed SIG algorithm is extremely efficient and effective. This paper successfully expands the research area of distributed scheduling problems.

Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks

We build a novel model of the developed distributing scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop scheduling (DRPFS).We determine the number of factory needs to use, factory assignment and sequence of job assigned to the factory.A novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed.The various destroy and repair operators are presented. Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Evaluation of turned and milled surfaces roughness using convolutional neural network

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.

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An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.

Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time

Scheduling in flexible manufacturing systems (FMS) is described as an NP-Hard problem. Its complexity has increased significantly in line with the development of FMS over the past years. This paper presents a non-dominated sorting biogeography-based optimization (NSBBO) for scheduling problem of FMS having multi loading-unloading and shortcuts infused in the reentrant characteristics. This model is formulated to identify the near optimal trade-off solutions capable of addressing the bi-objectives of minimization of makespan and total earliness. The goal is to simultaneously determine the best machine assignment and job sequencing to satisfy both objectives. We propose the development of NSBBO by substituting the standard linear function of emigration-immigration rate with three approaches based on sinusoidal, quadratic and trapezoidal models. A selection of test problems was examined to analyze the effectiveness, efficiency and diversity levels of the proposed approaches as compared to standard NSBBO and NSGA-II. The results have shown that the NSBBO-trapezoidal model performed favorably and is comparable to current existing models. We conclude that the developed NSBBO and its variants are suitable alternative methods to achieve the bi-objective satisfaction of reentrant FMS scheduling problem.

Achmad Pratama Rifai

Papers

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Evaluation of turned and milled surfaces roughness using convolutional neural network

An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time

Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling