Hybrid capacity modeling for alternative machine types in linear programming production planning

Processing by alternative machines is very common in today's manufacturing environment, especially in the semiconductor industry. Leachman and Carmon (1992) have presented a novel approach to model alternative machine capacitated problems for Linear Programming (LP) production planning. Their approach makes possible the modeling of complex processes with re-entry into alternative machines in large scale LP production planning. However, there are difficulties in using their approach in industrial applications. In order to use their approach, a uniform assumption must be satisfied. But in many industrial applications, this assumption cannot be satisfied. In addition, their approach creates new alternative machine sets by performing union operations on existing alternative machine sets with common machine types. This will increase the number of capacity constraints, which may increase the solution time of the LP formulation. This study compares their approach and the partition approach outlined herein in terms of CPU times used. Finally, this study proposes a hybrid capacity modeling approach that is more suitable for industrial applications.     

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.     

Corrective maintenance through dynamic work allocation and pre-emption: case study and application

This case study develops an innovative management and scheduling system for corrective maintenance of machines in a manufacturing facility. The study also involves a comparative evaluation of the proposed and the existing systems under a spectrum of operating conditions. A comprehensive simulation is used to evaluate system performances under a variety of settings which include reliability, service level, and cost consequences. The analysis is based on a full factorial experimental design. In summary, the developed self-regulating management system which involves dynamic work allocation and pre-emption is shown to yield higher machine availability and higher mechanic utilisation even with fewer mechanics. The study also finds that the new system is more streamlined, agile, and robust although it is subject to more-constrained machine reliability and mechanic service time environments. Further, a major reduction of current manpower can still achieve at least 95% machine availability, illustrating the cost effectiveness and efficacy of the developed system. This rule-based corrective maintenance system can be operated in uncertain environments on a real-time basis without additional reformatting costs and provides a competitive measure to deal with managerial issues such as low retention rate for skilled mechanics, highly uneven training levels and pay scales. The financial consequences and gains in strategic advantage with respect to the facility's operational structure are promising after implementation. Moreover, the system developed in this case study represents a meaningful starting point for a more vigorous theoretical research on the bucket brigade system to different functions in industrial and operations management.     

A new approach for loop machine layout problem integrating proximity constraints

This paper focuses on the machine arrangement problem on common loop network in a flexible manufacturing system. Existing studies aim to place machines at pre-fixed positions around a loop network. This problem is considered as a permutation problem that aims to find the best combination to reduce generated costs. In this work, we try to add more complexity to this problem by respecting the proximity constraints, defined by the experts, between the machines. To do this, we propose an algorithm based on direct distance measure. Logically, proximity constraints are checked using direct distances but costs are calculated using travelled distances. Throughout this study, we seek the best machine layout in four transport system configuration types to minimise the sum of flow time distances. Comparing our algorithm results with two hybrid genetic algorithms, the empirical results show that the proposed algorithm provides the most suitable solutions.     

Models for Understanding Flexible Manufacturing Systems

The basic features of flexible manufacturing systems are reviewed and models for determining the production capacity of such systems are developed. These models show the desirability of a balanced work load, the benefit of diversity in job routing if there is adequate control of the release of jobs (a job shop can be better than a flow shop), and the superiority of common storage for the system over local storage at machines. The models are extended to allow for material handling delays between machines and for unreliable machines. It is also shown that production capacity models can be used to develop good approximations to the mean number of jobs in the system for given job arrival rates and machine utilizations.     

A PRACTICAL APPROACH TO LOT AND SETUP SCHEDULING AT A TEXTILE FIRM

This is a study of scheduling of setups and production activities of a textile firm, located in North Carolina. The firm faces the problem of scheduling customer orders on a number of knitting machines which can be configured differently by installing different knitting cylinders, to knit various types of greige cloth. Given a set of requirements for different styles of cloth, the problem is to decide on the specific configurations to be used on each machine and on the specific orders to be run on these configurations. The problem is formulated as an integer linear programming model. The objective is the maximization of total contribution of all the scheduled orders subject to capacity constraints of machines and that of tooling, which explicitly consider the effect of scheduled setups and constraints on customer orders. Various solution approaches are discussed. An approximate procedure is devised which incrementally adds new setups based on several heuristics by which the "value" of candidate configurations for the machines are evaluated. These heuristics can either be developed into a self contained scheduling procedure or can interactively be utilized by a human scheduler in a microcomputer environment.     

Availability consideration in the optimal selection of multiple-aspect RMS configurations

Machine availability has a profound influence on the performance of manufacturing systems. This paper extends a model for optimizing reconfigurable manufacturing systems (RMS) configurations with multiple-aspects to incorporate the effect of machine availability using the universal generating function (UGF). Two powerful meta-heuristic optimization techniques, namely genetic algorithms (GAs) and tabu search (TS), are used for optimizing the capital cost and system availability of the RMS configurations. The optimized configurations can handle multiple-parts and their structure is that of flow lines allowing paralleling of identical machines in each production stage. The various aspects considered in the RMS configurations include arrangement of machines, equipment selection and assignment of operations. A case study is presented and implementation of the optimization model is carried out using MATLAB software. The results of using both GAs and TS to solve the problem are then reported and compared for validation. Analysis of different cases of availability consideration including infinite and no buffer capacity is performed and results are compared to those obtained when machine availability is not considered. It has been shown that considering availability affects the optimal configuration selection and increases the required equipment. This increases the costs of the near-optimal configurations obtained especially in the case without buffers. The presented model can support the manufacturing systems configuration selection decisions at both the initial design and reconfiguration stages.     

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.     

Component allocation to balance workload in printed circuit card assembly systems

Component allocation in printed circuit card assembly systems is a special case of the classical mixed-model assembly line balancing problem and involves assigning component types to machines to achieve specific production objectives. In this paper the component allocation problem is considered for the scenario where there are two or more placement machines (possibly nonidentical) and the objective is to balance, for every card type, a combination of the card assembly time and the machine setup time. A mathematical formulation of the problem is developed for a class of placement machines. Two alternative solution approaches are presented: a list-processing-based heuristic for a simple version of the problem, and a linear-programming-based branch-and-bound procedure for the general component allocation problem. Industrial case study results are presented for each approach that indicate expected throughput improvements of up to 8-10% over the company's current procedure, with much less direct effort required by the process engineer.     

Einführung in die industrielle Dichtheitsprüftechnik

The industrial leak test technology is today mainly based on the h gas mass spectrometer test method and, according to the specifications, on pressure and air measure techniques (delta pressure increase, pressure decrease, differential pressure etc.) The machines for the industrial leak test technique are manufactured according to customers and product specifications and are applied for the detection of components and component parts, which are produced in great numbers in the capital goods industry. As the kind of machines must meet very severe industrial standards and complex rules, a highly specialized field for testing and measure technology has been developed during the last years in cooperation with the relevant machine manufactures. As typical examples two automatic industrial leak test machines are presented below — automatic leak test machine for hermetic compressors with a cycle time of 12 sec. — automatic leak test machine for drums and barrels with a cycle time of 5 sec.     

A STATIONARY CYCLICAL PRODUCTION SCHEDULING PROBLEM UNDER A NONADDITIVE OUTPUT FUNCTION

The purpose of this paper is to formulate and solve a nonlinear mixed zero-one integer programming problem aimed to maximize total output by scheduling the operational time of N non-identical machines. Properties of the optimal solution are identified under restrictions imposed on machine availability and various budget constraints. A branch and bound algorithm to solve the problem is suggested.     

Stochastic customer order scheduling with setup times to minimize expected cycle time

Short cycle time of customer orders is crucial for companies to achieve mass customization and quick response. However, the complicated and stochastic environment, especially the exist of setup times, makes it extremely challenging to optimize the efficiency of a system. In this study, stochastic customer orders are scheduled to minimize their expect cycle time with the consideration of setup times. Customer orders arrive dynamically, and each order requires multiple product types with random workloads. These workloads will be assigned to a set of unrelated parallel machines to be processed. Particularly, for each machine, setup times are required whenever there is a product type changeover, and the lengthes are both machine- and product type-dependent. This paper intends to minimize the long-run expected order cycle time by proper policies including workload allocation and type sequencing. The impacts of product type sequence and workload variance are evaluated through theoretical study and several analytical properties are developed. With the help of these properties, three scheduling algorithms are proposed, and a lower bound is derived to evaluate the proposed algorithms. Computational experiment is conducted to demonstrate the effectiveness of the lower bound and the algorithms under various circumstances, and several important managerial insights are also provided.     

An improved imperialist competitive algorithm based photolithography machines scheduling

Abstract: Photolithography machine is one of the most expensive equipment in semiconductor manufacturing system, and as such is often the bottleneck for processing wafers. This paper focuses on photolithography machines scheduling with the objective of total completion time minimisation. In contrast to classic parallel machines scheduling, it is characterised by dynamical arrival wafers, re-entrant process flows, dedicated machine constraints and auxiliary resources constraints. We propose an improved imperialist competitive algorithm (ICA) within the framework of a rolling horizon strategy for the problem. We develop a variable time interval-based rolling horizon strategy to decide the scheduling point. We address the global optimisation in every local scheduling by proposing a mixed cost function. Moreover, an adaptive assimilation operator and a sociopolitical competition operator are used to prevent premature convergence of ICA to local optima. A chaotic sequence-based local search method is presented to accelerate the rate of convergence. Computational experiments are carried out comparing the proposed algorithm with ILOG CPLEX, dispatching rules and meta-heuristic algorithms in the literature. It is observed that the algorithm proposed shows an excellent behaviour on cycle time minimisation while with a good on time delivery rate and machine utilisation rate.     

An attribute design method: a new approach to flexible welding machine design

We examine the design of flexible welding machines, particularly the process of matching a group of components with an optimum machine design. Analysis of current design methods highlighted the need for a systematic and less time consuming method for the conceptual design of these machines. Also identified was the need to establish a link between components to be manufactured, and the design of welding machines, within appropriate economic constraints. We present the Attribute Design Method, developed to achieve an optimum machine design for a given group of components. The method is applied to the design of a flexible welding unit, but its application may be extended to the design of flexible manufacturing systems.     

Approach to measure the mix response flexibility of manufacturing systems

The mix response flexibility of a manufacturing system is the ability to change between product types quickly and economically. This paper proposes a new approach to measure this flexibility in terms of both capability and capacity. While the processing capability is represented as the number of operations that the machines can perform, the manufacturing capacity is modelled as the efficiency of different machines. The proposed model is applied to measure the mix response flexibility for both single and multiple machine systems.     

Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

Minimising the makespan in the two-machine job shop problem under availability constraints

Classical scheduling problem assumes that machines are available during the scheduling horizon. This assumption may be justified in some situations but it does not apply if maintenance requirements, machine breakdowns or other availability constraints have to be considered. In this paper, we treat a two-machine job shop scheduling problem with one availability constraint on each machine to minimise the maximum completion time (makespan). The unavailability periods are known in advance and the processing of an operation cannot be interrupted by an unavailability period (non-preemptive case). We present in our approach properties dealing with permutation dominance and the optimality of Jackson's rule under availability constraints. In order to evaluate the effectiveness of the proposed approach, we develop two mixed integer linear programming models and two schemes for a branch and bound method to solve the tackled problem. Computational results validate the proposed approach and prove the efficiency of the developed methods.     

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.     

A genetic algorithm for the multi-stage and parallel-machine scheduling problem with job splitting – A case study for the solar cell industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.     

Re-entrant lines with unreliable asynchronous machines and finite buffers: performance approximation and bottleneck identification

In this paper we study a re-entrant line with unreliable asynchronous exponential machines and finite buffers. First, an approximation method is presented to estimate the throughput of the re-entrant line. The idea of the approximation is to transform an M-machine re-entrant line into a 2M-machine serial line. Then, a system approach to identify a c-bottleneck based on blockage and starvation information is proposed, where a c-bottleneck machine is the machine whose improvement in machine capacity leads to the largest improvement in system throughput compared with improving all other machines. It is shown that the approximation method results in acceptable accuracy, and the bottleneck identification method can correctly detect the bottlenecks in most cases.