Three-stage Assembly Research Articles

Mathematical models for multi-stage hybrid assembly flow-shop scheduling with preventive maintenance and release times

Effective job scheduling is crucial in production to reduce costs, improve customer satisfaction, enhance efficiency, and boost competitiveness, leading to transformative impacts in production environments. This article presents a pioneering approach that addresses assembly and processing at multiple stages in the scheduling process. Specifically, we introduce the first formulation of mixed integer linear programs (MILP) for a Multi-stage Hybrid Assembly Flow-shop with identical parallel machines. The models incorporate considerations for job release times and service tasks to implement preventive maintenance and effectively captures the intricacies of a Multistage Assembly flow-shop. Existing literature in Assembly flow-shop scheduling primarily focuses on the two-stage assembly flow-shop scheduling problem (TSAFSP) and its extensions, such as the three-stage assembly flow-shop scheduling problem, the Distributed two-stage assembly flow-shop scheduling problem (DTSAFSP), the Distributed assembly permutation flow-shop Scheduling Problem (DAPFSP), and the multi-stage assembly flow shop (MSAF). Our work deals with a challenging NP-hard problem. Inspired by a real-life production site dedicated to manufacturing heavy machinery, we validate our proposed mathematical models by solving various instances of the problem using exact methods. The experimental results confirm the effectiveness and robustness of our approach in tackling multi-stage hybrid assembly flow shop scheduling challenges.

Three-Stage Assembly Flow Shop Scheduling Problem with a Fixed Maintenance Period

This paper investigates the three-stage assembly flow shop scheduling problem, provided that there is a fixed maintenance period (MP) imposed on one of the machines in the first stage, the objective is to minimize the makespan. The starting time and duration of MP are known in advance, during MP no job can be processed on the corresponding machine. Only the non-resumable scenario is considered, i.e., if a job fails to finish before MP, it must restart from the beginning after the machine becomes available again, rather than continuing its processing. The problem generalizes the three-stage assembly flow shop scheduling problem without MPs and is therefore strongly NP-hard. To the best of our knowledge, the problem has not been explored so far. We propose three approximation algorithms for the problem.

N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

AbstractSystem-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing.

Q-learning driven multi-population memetic algorithm for distributed three-stage assembly hybrid flow shop scheduling with flexible preventive maintenance

The distributed assembly flow shop scheduling (DAFS) problem has received much attention in the last decade, and a variety of metaheuristic algorithms have been developed to achieve the high-quality solution. However, there are still some limitations. On the one hand, these studies usually ignore the machine deterioration, maintenance, transportation as well as the flexibility of flow shops. On the other hand, metaheuristic algorithms are prone to fall into local optimality and are unstable in solving complex combinatorial optimization problems. Therefore, a multi-population memetic algorithm (MPMA) with Q-learning (MPMA-QL) is developed to address a distributed assembly hybrid flow shop scheduling problem with flexible preventive maintenance (DAHFSP-FPM). Specifically, a mixed integer linear programming (MILP) model targeted at the minimal makespan is first established, followed by an effective flexible maintenance strategy to simplify the model. To efficiently solve the model, MPMA is developed and Q-learning is used to achieve an adaptive individual assignment for each subpopulation to improve the performance of MPMA. Finally, two state-of-the-art metaheuristics and their Q-learning-based improvements are selected as rivals of the developed MPMA and MPMA-QL. A series of numerical studies are carried out along with a real-life case of a furniture manufacturing company, to demonstrate that MPMA-QL can provide better solutions on the studied DAHFSP-FPM..

A Q-Learning-Based Artificial Bee Colony Algorithm for Distributed Three-Stage Assembly Scheduling with Factory Eligibility and Setup Times

The assembly scheduling problem (ASP) and distributed assembly scheduling problem (DASP) have attracted much attention in recent years; however, the transportation stage is often neglected in previous works. Factory eligibility means that some products cannot be manufactured in all factories. Although it extensively exists in many real-life manufacturing processes, it is hardly considered. In this study, a distributed three-stage ASP with a DPm→1 layout, factory eligibility and setup times is studied, and a Q-learning-based artificial bee colony algorithm (QABC) is proposed to minimize total tardiness. To obtain high quality solutions, a Q-learning algorithm is implemented by using eight states based on population quality evaluation, eight actions defined by global search and neighborhood search, a new reward and an adaptive ε−greedy selection and applied to dynamically select the search operator; two employed bee swarms are obtained by population division, and an employed bee phase with an adaptive migration between them is added; a new scout phase based on a modified restart strategy is also presented. Extensive experiments are conducted. The computational results demonstrate that the new strategies of QABC are effective, and QABC is a competitive algorithm for the considered problem.

Optimization study of three-stage assembly flowshop problem in pharmacy automation dispensing systems

Along with the rising prevalence of chronic diseases and the increasing complexity of pharmaceutical care, pharmacy automation dispensing systems (PADS)s are utilized to improve the quality of patient care, reduce dispensing-related medication errors, and cut down medication-related expenses. After PADSs fulfill prescription orders, patients still have to dispense medications for each meal by themselves. It is well known that dispensing medications is time-consuming and exhausting, especially for chronic patients. During the manual dispensing process, some unexpected medical errors might appear, which would result in discomfort, harm, and even death. To provide efficient and patient-centered pharmacy care, we proposed automatically dispensing prescriptions by meal orders in PADS. Specifically, we formulated a novel three-stage assembly flowshop problem to fulfill prescription orders at the level of meal orders to minimize the total completion times of packing prescription orders. In our studied PADS, there are fully flexible parallel machines dispensing medication jobs and collating sub-orders (meal prescription orders) in the first and second stages, and one assembly machine packing prescription orders in the third stage. Based on the properties of the optimal schedule, we proposed a branch-and-bound (B&B) algorithm embedded with a constructive heuristic (CH) to obtain a tight upper bound. Since the three-stage assembly flowshop problem in PADS is strongly NP-hard, we also presented an improved tabu search (iTS) heuristic for the practical data size. Finally, we conducted a series of numerical experiments based on artificial data sets guided by characteristics obtained from practical pharmacy data sets. The computational results showed that the proposed CH algorithm is efficient, and when the computational time was allowed, the iTS algorithm could further enhance the performance. Moreover, the efficiency of collating sub-orders has a significant impact on the overall performance of PADS.

An adaptive artificial bee colony with reinforcement learning for distributed three-stage assembly scheduling with maintenance

Distributed three-stage assembly scheduling problem extensively exists in the real-life assembly production process and is seldom considered. The integration of reinforcement learning with meta-heuristic can effectively improve the performance of meta-heuristic and effectively solve the problem; however, the integration is seldom used to cope with the problem. In this study, distributed three-stage assembly scheduling problem with DPm→1 layout and maintenance at three stages is considered and a mathematical model is provided. A new artificial bee colony with Q-learning (QABC) is proposed to minimize maximum tardiness. An effective Q-learning algorithm is implemented to dynamically select search operator, which consists of 12 states based on population quality evaluation, 8 actions defined by global search and neighborhood search, a new reward and an effective action selection. Two employed bee swarms are formed, an adaptive communication and an adaptive competition process between them are adopted to intensify exploration ability and improve search efficiency. QABC and its four comparative algorithms are tested on 80 instances. The computational results demonstrate that the new strategies of QABC really improve its search performance and QABC is a competitive algorithm for the considered problem.

Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation

In this paper, we address the assembly flowshop scheduling problem, which is a generalisation of two well-known scheduling problems in the literature: the three-stage Assembly Scheduling Problem (ASP) and its variant with two stages denoted as the two-stage ASP. For this problem, we prove several theoretical results which are used to propose a speed-up procedure. This acceleration mechanism can be applied in any insertion-based method for the problem under study and, consequently, also for their special cases. In addition, we propose four efficient constructive heuristics for the problem, based on both Johnson’s algorithm and the NEH heuristic. These proposals are compared against 47 algorithms existing in the literature for related problems. The results show the excellent performance of the proposals.

Improved discrete cuckoo optimization algorithm for the three-stage assembly flowshop scheduling problem

The three-stage assembly flow shop scheduling problem, where the first stage has parallel machines and the second and the third stages have a single machine, is addressed in this study. Each product has made of several components that after processing at the first stage are collected and transferred to the third stage to assemble them as the product. The goal is to find products’ sequence to minimize completion time of the last product, makespan. Since the problem is NP-hard, an improved version of Cuckoo Optimization Algorithm (COA), a bio-inspired meta-heuristic, is proposed which incorporates new adjustments such as clustering, egg laying and immigration of the cuckoos based on a discrete representation scheme. These novel features result in an Improved Discrete version of COA, called IDCOA, which works efficiently. Also, for the addressed problem, a lower bound and some dispatching rules are proposed. The performance of the employed algorithms through randomly generated instances is evaluated which endorses the capability of the proposed IDCOA algorithm.

Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints

In this paper, a three-stage assembly flow shop scheduling problem with machine availability constraints is taken into account. Two objectives of minimising total weighted completion times (flow time) and minimising sum of weighted tardiness and earliness are simultaneously considered. To describe this problem, a mathematical model is presented. The problem is generalisation of three-machine flow shop scheduling problem and two-stage assembly flow shop scheduling problem. Since these problems are known to be NP-hard, the considered problem is also strongly NP-hard. Therefore, two multi-objective meta-heuristics are presented to efficiently solve this problem in a reasonable amount of time. Comprehensive computational experiments are performed to illustrate the performance of the presented algorithms.

A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times

Abstract This paper considers a three-stage assembly flowshop scheduling problem with sequence-dependent setup times at the first stage and blocking times between each stage in such a way that the weighted mean completion time and makespan are minimized. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. Thus, this paper proposes a meta-heuristic method based on simulated annealing (SA) in order to solve the given problem. Finally, the computational results are shown and compared in order to show the efficiency of our proposed SA.

Three-stage Assembly of the Cysteine Synthase Complex from Escherichia coli

Control of sulfur metabolism in plants and bacteria is linked, in significant measure, to the behavior of the cysteine synthase complex (CSC). The complex is comprised of the two enzymes that catalyze the final steps in cysteine biosynthesis: serine O-acetyltransferase (SAT, EC 2.3.1.30), which produces O-acetyl-L-serine, and O-acetyl-L-serine sulfhydrylase (OASS, EC 2.5.1.47), which converts it to cysteine. SAT (a dimer of homotrimers) binds a maximum of two molecules of OASS (a dimer) in an interaction believed to involve docking of the C terminus from a protomer in an SAT trimer into an OASS active site. This interaction inactivates OASS catalysis and prevents further binding to the trimer; thus, the system exhibits a contact-induced inactivation of half of each biomolecule. To better understand the dynamics and energetics that underlie formation of the CSC, the interactions of OASS and SAT from Escherichia coli were studied at equilibrium and in the pre-steady state. Using an experimental strategy that initiates dissociation of the CSC at different points in the CSC-forming reaction, three stable forms of the complex were identified. Comparison of the binding behaviors of SAT and its C-terminal peptide supports a mechanism in which SAT interacts with OASS in a non-allosteric interaction involving its C terminus. This early docking event appears to fasten the proteins in close proximity and thus prepares the system to engage in a series of subsequent, energetically favorable isomerizations that inactivate OASS and produce the fully isomerized CSC.

A Centrifugal Contactor Design to Facilitate Remote Replacement

Advanced designs of nuclear fuel recycling and radioactive waste treatment plants are expected to include more ambitious goals for solvent extraction-based separations, including higher separation efficiency, high-level-waste minimization, and a greater focus on continuous processes to minimize cost and footprint. Therefore, annular centrifugal contactors (ACCs) are destined to play a more important role for such future processing schemes. Previous efforts defined and characterized the performance of commercial 5-cm, model V-02; and 12.5-cm, model V-05, single-stage ACCs in a nonradioactive environment. The next logical step, the design and initial evaluation of remote-capable, pilot-scale ACCs for use in a “hot” or radioactive environment has been completed. This work continues the development of remote designs for ACCs that can process the large throughputs needed for future nuclear fuel recycling and radioactive waste treatment plants. Novel designs were developed for the remote interconnection of contactor units, clean-in-place (CIP) and drain connections, and a new solids removal collection chamber. A three-stage, 12.5-cm-diam rotor module has been constructed and is being evaluated for use in highly radioactive environments. This prototype assembly employs three standard CINC V-05 CIP units modified for remote service and replacement via new methods of connection for solution inlets, outlets, drain, and CIP. Hydraulic testing and functional checks were successfully conducted, and then the prototype was evaluated for remote handling and maintenance. Removal and replacement of the center position V-05R contactor in the three-stage assembly was demonstrated using an overhead rail mounted PaR manipulator. Initial evaluation indicates a viable new design for interconnecting and cleaning individual stages while retaining the benefits of commercially reliable ACC equipment. Replacement of a single stage via remote manipulators and tools is estimated to take [approximately]30 min, perhaps fast enough to support a contactor change without loss of process steady-state equilibrium. The design presented in this work is scalable to commercial ACC models from V-05 to V-20 with total throughput rates ranging from 20 to 650 l/min.

Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times

In this paper, we consider a three-stage assembly flowshop scheduling problem with bi-objectives, namely the mean flow time and maximum tardiness. This problem can be considered as a production system model consisting of three stages: (1) different production operations are done in parallel, concurrently and independently, (2) the manufactured parts are collected and transferred to the next stage, and (3) these parts are assembled into final products. In this paper, sequence-dependent setup times and transfer times are also considered as two important presumptions in order to make the problem more realistic. We present a novel mathematical model for a production system with a new lower bound for the given problem. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. Thus, we propose two meta-heuristics, namely simulated annealing and tabu search, to solve a number of test problems generated at random. Finally, the computational results are illustrated and compared in order to show the efficiency of the foregoing meta-heuristics.

Engineering-Driven Factor Analysis for Variation Source Identification in Multistage Manufacturing Processes

Variation source identification is an important task of quality assurance in multistage manufacturing processes (MMPs). However, existing approaches, including the quantitative engineering-model-based methods and the data-driven methods, provide limited capabilities in variation source identification. This paper proposes a new methodology that does not depend on accurate quantitative engineering models. Instead, engineering domain knowledge about the interactions between potential variation sources and product quality variables is represented as qualitative indicator vectors. These indicator vectors guide the rotation of the factor loading vectors that are derived from factor analysis of the multivariate measurement data. Based on this engineering-driven factor analysis, a procedure is presented to identify multiple variation sources that are present in a MMP. The effectiveness of the proposed methodology is demonstrated in a case study of a three-stage assembly process.

The three-stage assembly flowshop scheduling problem

We consider the three-stage assembly flowshop scheduling problem with the objective of minimizing the makespan. The three-stage assembly problem generalizes both the serial three machine flowshop problem and the two-stage assembly flowshop scheduling problem and is therefore strongly NP-hard. We analyze the worst-case ratio bound for several heuristics for this problem. We also analyze the worst-case absolute bound for a heuristic based on compact vector summation techniques and we point out that, for a large number of jobs, this heuristic becomes asymptotically optimal. Scope and purpose The three-stage assembly flowshop scheduling problem models situations which arise frequently in manufacturing when various fabrication operations are performed concurrently and then collected and transported into an assembly area for a final assembly operation. The main criterion for this problem is the minimization of the maximum job completion time (makespan). The objective of this paper is to derive algorithms for minimizing the makespan. In doing so, we also demonstrate the reduction of assembly flowshop problems to their embedded serial flowshop problems.

Efficient asymmetric synthesis of radicicol dimethyl ether: a novel application of ring-forming olefin metathesis.

A concise, stereospecific synthesis of radicicol dimethyl ether is presented. The strategy relies on a convergent three-stage assembly of the 14-membered lactone which has, as a key transformation, a novel ring-forming metathesis reaction utilizing a vinyl epoxide.

Application of a behaviour-based scheduling approach for distributed scheduling of an assembly shop

Past approaches to solving the assembly-scheduling problem have mainly been from the consideration of a hierarchically controlled shop. Thus, the problem is considered in its entirety and solved in a monolithic fashion. However, in recent times, attempts have been made to impose heterarchical or distributed control. This paper extends our previous work in the area of distributed behaviour-based control and examines the performance of the behaviour-based control model for scheduling assembly problems. Using this approach, the performance of a set of scheduling problems for a three-stage four-station assembly problem is investigated. Model performance is compared with a mixed integer programming (MIP) model of the same situation.

The incorporation of cytochrome oxidase into newly-forming yeast mitochondrial membranes

Cytochrome c oxidase extracted from a yeast auxotroph grown on different fatty acid supplements, and assayed at various temperatures (0→37°), gives transition points (Tt) when the data are expressed in an Arrhenius plot: Tt for linoleic, oleic and elaidic acids were 7.7°, 10.2° and 21.8° respectively. Lipid anlaysis of isolated mitochondria established that there is a change in lipid profile when yeast is first grown aerobically on linoleic acid and then transferred to elaidic acid under anaerobic consitions. These two observations bave been used as a basis for the investigation of the assembly of cytochrome c oxidase when cells are oxygen induced for mitochondriogenesisvia a linoleic, N2 elaidic, O2 transfer experiment. A three-stage assembly process is proposed consisting of (i) 0.0→0.25 h, auto-assembly of oxidase from preformed, chloramphenicol-sensitive precursors and newly synthesized cycloheximide-sensitive precursors, (ii) 0.25 h→0.5h, a continuation of (i) plusde novo synthesis of new precursors of both kinds and (iii) 0.5 h onwards; normal, synchronisedde novo synthesis. This model predicts one population of oxidase molecules at 0.0 h (linoleic Tt) and two populations of oxidase at 0.5 h (end of stage (ii)); one from the anaerobic precursors (linoleic Tt) and one for thede nove, aerobic oxidase (elaidic Tt): these predictions are confirmed by Arrhenius profiles constructed from samples taken at 0.0, 0.5, 1.0 and 3.0 h after oxygen challenge.

Transistor Regulation System for Fine Current Control of an Arc Jet

A current regulator was built to reduce fluctuations in arc-heated flows and to maintain set values of arc current for long periods of time. Regulation is obtained with 145 transistors in a three-stage assembly that is operated in series with the arc and is designed for use at current levels up to 1000 A. Results are presented that show its operating characteristics, its effect on arc current oscillations, and the corresponding reduction of fluctuations in test section flow properties.