Scheduling manufacturing systems for delayed product differentiation in agile manufacturing

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.     

An ant colony optimisation algorithm for scheduling in agile manufacturing

Producing customised products in a short time at low cost is one of the goals of agile manufacturing. To achieve this goal an assembly-driven differentiation strategy has been proposed in the agile manufacturing literature. In this paper, we address a manufacturing system that applies the assembly-driven differentiation strategy. The system consists of machining and assembly stages, where there is a single machine at the machining stage and multiple identical assembly stations at the assembly stage. An ant colony optimisation (ACO) algorithm is developed for solving the scheduling problem of determining the sequence of parts to be produced in the system so as to minimise the maximum completion time (or makespan). The ACO algorithm uses a new dispatching rule as the heuristic desirability and variable neighbourhood search as the local search to make it more efficient and effective. To evaluate the performance of heuristic algorithms, a branch-and-bound procedure is proposed for deriving the optimal solution to the problem. Computational results show that the proposed ACO algorithm is superior to the existing algorithm, not only improving the performance but also decreasing the computation time.     

Dynamic scheduling of FMS using a real-time genetic algorithm

The paper presents a genetic algorithm capable of generating optimised production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes and unforeseen situations is particularly stressed (dynamic scheduling). Two contrasting objectives represented by the reduction of machine idle-times, thanks to dynamic scheduling computation and the reduction of the makespan, are taken into account by the proposed system. The key-point is the real-time response obtained by an optimised evolutionary strategy capable of minimising the number of genetic operations needed to reach the optimal schedule in complex manufacturing systems.     

ISTS: A new procedure for the integrated scheduling of multi-stage systems

This paper addresses the problem of scheduling a two-stage manufacturing system. MRP systems have been developed to synchronize production in multistage systems, assigning due dates to production at each stage. However, MRP systems allow just a broad scheduling and it is therefore up to the short term scheduling system to meet such due dates. Previous research work, and industrial practice, have developed independent scheduling systems for each stage, relying on MRP to synchronize them. Resulting performances are not satisfactory because, quite often, many components are late, and assembly schedules are disrupted. Recent research works on assembly shops have shown that improvements in performances can be achieved if assembly constraints and dependencies between jobs are also considered at the short term scheduling level. Based on the idea that these findings can be valid for multi-stage manufacturing systems, this research work presents a new short term scheduling procedure which, on the one hand adopts two different scheduling systems for the machining stage and the assembly stage, and on the other, highly integrates short term scheduling of different stages. The former exploits the different characteristics of the two subsystems. In particular, unlike all previous research work, assembly constraints and dependencies between jobs are taken into consideration at the machining stage, both at the dispatching level, and at the Order Review and Release level.     

Proposal of a concept of future oriented machine tools for advanced manufacturing systems

In order to satisfy the recent various consumers’?needs, an advanced manufacturing system will be required. So, the innovative concept of Future Oriented Machine Tools (FOMT), which consists of four function blocks to realize the advanced manufacturing system, is proposed. The four function blocks are Management, Prediction, Observation and Strategy, and intelligent manufacturing processes are realized by utilizing these function blocks. FOMT can make a significant contribution in four stages: design stage, production scheduling stage, machining stage and post-machining stage. In the design stage, the product information such as accuracy can be evaluated in CAD, considering the manufacturing ability concurrently. In the production scheduling stage, the product schedules can be generated automatically and flexibly for the various products. In the machining stage, cutting conditions adjusted autonomously according to the machining status and machining problems. In the post-machining stage, the machining know-how data are accumulated and stored for future production and human engineers. The feasibility of it, especially the machining stage, is discussed using the developed cutting process simulator called VMSim (virtual machining simulator) from the view points of cutting force, machining error and environmental burden (CO₂ emission).     

Flow shop scheduling problems with assembly operations: a review and new trends

The past few years have witnessed a resurgence of interest in assembly flow shop scheduling as evidenced by increasing number of published articles in this field. A basic assembly flow shop consists of two types of stages: fabrication or machining stage and assembly stage. Machining and assembly stages are composed of either one or a set of machines that are working in parallel. Final products have hierarchical assembly structure with several components and assembly operation(s). The components need to be processed in the machining stage(s) and then assembled based on hierarchical assembly structure. The goal is to find the sequence of jobs that optimises certain objectives. Assembly flow shop scheduling problem has several interesting derivatives and applications in various manufacturing and service industries. This paper provides a consolidated survey of assembly flow shop models with their solution methodology. Finally, the paper concludes by presenting some problems receiving less attention and proposes several salient research opportunities.     

Minimizing production flow time in a process and assembly job shop

Scheduling is one of the most important issues in the planning and operation of production systems, but in medium to large shops, the generation of consistently good schedules has proven to be extremely difficult. The problem is that optimal scheduling solutions involve costly and impractical enumeration procedures. In the literature, most scheduling problems only address jobs with serial or sequential operations. Rarely do they consider jobs in which machining and assembly operations are simultaneously involved. This lack of attention to scheduling problems that involve both machining and assembly goes against what one would normally find in most job shops. In this paper, the problem of scheduling a set of N final products on M machines in a job shop environment that involve both machining and assembly operations is addressed. The objective pursued is the minimization of production flow time (makespan). A mathematical model is developed in an effort to obtain optimal solutions. Because this type of model grows exponentially as the size of the problems increases, an heuristic solution approach is developed to solve the problems more efficiently. The models are tested and compared on several test problems.     

Dynamic scheduling for complex engineer-to-order products

The current paper considers dynamic production scheduling for manufacturing systems producing products with deep and complex product structures and complicated process routings. It is assumed that manufacturing and assembly processing times are deterministic. Dynamic scheduling problems may be either incremental (where the schedule for incoming orders does not affect the schedule for existing orders) or regenerative (where a new schedule is produced for both new and existing orders). In both situations, a common objective is to minimize total costs (the sum of work-in-progress holding costs, product earliness and tardiness costs). In this research, heuristic and evolutionary-strategy-based methods have been developed to solve incremental and regenerative scheduling problems. Case studies using industrial data from a company that produces complex products in low volume demonstrate the effectiveness of the methods. Evolution strategy (ES) provides better results than the heuristic method, but this is at the expense of significantly longer computation times. It was found that performing regenerative planning is better than incremental planning when there is high interaction between the new orders and the existing orders.     

A scheduling approach for a flexible manufacturing system

This paper discusses the scheduling problem of a particular flexible manufacturing system (FMS). The two main components of the FMS are a CNC turret lathe and a CNC machining centre. In the system a wide range of different jobs has to be processed. Each job consists of one or more processing operations on one or both machines. Important characteristics of the scheduling problem are sequence-dependent change-over times (on the turret lathe) and transfer times (on both machines and between the machines). The change-over times are caused by the need to exchange tools in the turret when a new part is going to be processed. The transfer times reflect the time needed to perform manual transportation and clamping activities between two subsequent processing (machining) operations of a part. In this paper a branch and bound algorithm is described based on an active schedule strategy. Solutions are compared to results obtained by a simple dispatching rule     

Integrated scheduling and self-reconfiguration for assembly job shop in knowledgeable manufacturing

The problems of integrated assembly job shop (AJS) scheduling and self-reconfiguration in knowledgeable manufacturing are studied with the objective of minimising the weighted sum of completion cost of products, the earliness penalty of operations and the training cost of workers. In AJS, each workstation consists of a certain number of teams of workers. A product is assumed to have a tree structure consisting of components and subassemblies. The assembly of components, subassemblies and final products are optimised with the capacity of workstations simultaneously. A heuristic algorithm is developed to solve the problem. Dominance relations of operations are derived and applied in the development of the heuristic. A backward insertion search strategy is designed to locally optimise the operation sequence. Once the optimal schedule is acquired, the teams are reconfigured by transferring them from workstations of lower utilisation to those of higher utilisation. Effectiveness of the proposed algorithm is tested by a number of numerical experiments. The results show that the proposed algorithm promises lower total cost and desirable simultaneous self-reconfiguration in accordance with scheduling.     

Maintenance scheduling for a manufacturing system of machines with adjustable throughput

This paper considers the problem of analyzing and optimizing joint schedules of maintenance and throughput adjustment operations in manufacturing systems. The purpose of joint scheduling of maintenance and throughput changing operations is to maximize the cost benefits of maintenance operations in manufacturing systems in which some or all of the machines can execute their function under different process settings, resulting in different machine and system throughputs. Such a capability enables one to strategically slow down more degraded machines or accelerate freshly maintained machines so that production targets can be met and maintenance operations can be offset to times when they are less intrusive on the manufacturing process. A Monte-Carlo-simulation-based method is proposed for the evaluation of cost effectiveness of any schedule of maintenance and throughput changing operations, and a genetic-algorithm-based method is proposed to enable searching for schedules that would maximize the cost benefits of these operations. A matrix chromosome representation of the joint schedules of maintenance and throughput adjustment operations is introduced and several mechanisms of chromosome evolution and selection are proposed and analyzed in numerical simulations of such manufacturing systems. Results indicate a good ability for the newly proposed methods to achieve a tradeoff between cost benefits of production and losses due to maintenance operations through strategic allocation of maintenance and throughput changing actions.     

Divergent production scheduling with multi-process routes and common inventory

This research considers a scheduling problem in a divergent production system (DPS) where a single input item is converted into multiple output items. Therefore, the number of finished products is much larger than the number of input items. This paper addresses two important challenges in a real-life DPS problem faced by an aluminium manufacturing company. One challenge is that one product can be produced following different process routes that may have slightly different capabilities and capacities. The other is that the total inventory capacity is very limited in the company in the sense that a fixed number of inventory spaces are commonly shared by raw materials, WIP (work-in-process) items and finished products. This paper proposes a two-step approach to solving this problem. In the first step, an integer programming (IP) model is developed to plan the type and quantity of operations. In the second step, a particle swarm optimisation (PSO) is proposed to schedule the operations determined in the first step. The computational results based on actual production data have shown that the proposed two-step solution is appropriate and advantageous for the DPS scheduling problem in the company.     

Scheduling of the optimal tool replacement times in a flexible manufacturing system

In Flexible Manufacturing Systems (FMSs). a cutting tool is frequently used for different operations and on different part types to minimize tool change-overs and the number of tools required, and to increase part-routing flexibility. In such situations, the tools become shared resources and work in job-dependent, changeable and nonhomogeneous conditions. It is well known that the tool failure rate depends on both age and machining conditions and that tool reliability is a function of the duration, machining conditions, and the sequence of the operations in FMS. The objective of this paper is to obtain a schedule of the optimal preventive replacement times for the cutting tools over a finite time horizon in a flexible manufacturing system. We assume that the tool will be replaced either upon failure during an operation or preventively after the completion of each operation, incurring different replacement costs. A standard stochastic dynamic programming approach is taken to obtain the optimal tool replacement times. The optimal schedule is obtained by minimizing the total expected cost over a finite time horizon for a given sequence of operations. A computational algorithm is developed and a numerical example is given to demonstrate the procedure.     

A methodology for cutting-tool management through the integration of CAPP and scheduling

Tool management is an important element in the efficiency of flexible manufacturing systems. This paper improves manufacturing flexibility through integrated computer-aided process planning and scheduling with tool management. A new methodology for cutting-tool management is introduced, based on the use of alternative tools. In the methodology proposed, computer-aided process planning singles out all possible tool alternatives for each operation. The tool manager is designed to solve interferences between tool alternatives to make them compatible with scheduling. Interferences occur when the same tools are simultaneously needed in various machining operations. We propose a method to reduce tool alternatives. Thus, interferences disappear, and a collection of tool alternatives compatible with the scheduling of the system is obtained for machining operations. Tool management can use these sets of tools to plan tool changes that are necessary to face tool wear and to react to perturbations in the production system. To prove the response of our proposal, computational experiments are performed on randomly generated test problems.     

Simultaneous versus sequential loading and scheduling of flexible assembly systems

A monolithic and a hierarchical approach is presented for loading and scheduling in a general flexible assembly system and a flexible assembly line. The system is made up of a set of assembly stations of various types each with limited working space and is capable of simultaneously producing a mix of product types. The objective is to determine an assignment of assembly tasks to stations and an assembly schedule for all products so as to complete the products in a minimum time. In the monolithic approach loading and scheduling decisions are made simultaneously. In the hierarchical approach, however, first the station workloads are balanced by solving the loading problem, and then detailed assembly schedule is determined for prefixed task assignments and assembly routes by solving a standard job-shop problem. Mixed integer programming formulations are presented for simultaneous and for sequential loading and scheduling. Loading and scheduling with alternative or with single task assignments are considered. Numerical examples are included to illustrate and compare the two approaches proposed.     

A CAPP framework with optimized process parameters for rotational components

Process planning, as a critical stage integrating the design and manufacturing phase in a manufacturing environment, has been automated to meet the needs for higher productivity and lower production cost. Being an input to various systems such as scheduling and routing, process planning results are of great importance in the manufacturing stage. Though feature extraction and sequence optimization have been given much attention, the process parameters are rarely dealt with. This paper focuses on the development of a new generative computer aided process planning (CAPP) framework for rotational components. The developed framework includes modules for feature extraction based on CAD application programming interfaces, determination of the optimum sequence and generation of optimum process parameters. The optimization of the machining operations is achieved using the evolutionary technique. The approach resulted in the reduction and prediction of machining time and cost. The framework is demonstrated with a case study.     

Monolithic versus hierarchical approach to integrated scheduling in a supply chain

The two approaches, monolithic and hierarchical, with a set of mixed integer programming formulations are proposed and compared for multi-objective integrated scheduling in a customer driven supply chain. The supply chain consists of multiple manufacturers (suppliers) of parts, a single producer of finished products and a set of customers who generate final demand for the products. Each supplier has a number of identical production lines in parallel for the manufacture of parts, and the producer has a flexible assembly line for assembly of products. Given a set of orders, the problem objective is to determine which orders are to be provided with parts by each supplier, find a schedule for the manufacture of parts by each supplier and for the delivery parts from each supplier to the producer, and find a schedule for the assembly of products for each order by the producer, such that a certain performance measure of the supply chain is optimised. The selection of the parts supplier for each order is combined with due date setting for some orders, subject to the suppliers and the producer available capacity. Different objective functions are considered that take into account both customer service level and total manufacturing, delivery and production cost. Numerical examples are presented that are modelled by real-world integrated scheduling in a customer driven supply chain of high-tech products, and some computational results are reported to compare the two approaches.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

Book review of Global Strategic Management,by P. Lasserre,Palgrave Macmillan,Basingstoke, 2003, xx + 454 pp., £24.99, softcover (ISBN 0 333 79375 7)

This paper considers the simultaneous scheduling of material handling transporters (such as automatic guided vehicles or AGVs) and manufacturing equipment (such as machines and workcentres) in the production of complex asembled product. Given the shipping schedule for the end-items, the objective of the integrated problem is to minimize the cumulative lead time of the overall production schedule (i.e. total makespan) for on-time shipment, and to reduce material handling and inventory holding costs on the shop-floor. The problem of makespan minimization is formulated as a transportation integrated scheduling problem, which is NP-hard. For industrial size problems, an effective heuritsic is developed to simultaneouly schedule manufacturing and material handling operations by exploting the critical path of an integrated operation network. The performance of the proposed heuristic is evaluated via extensive numerical studies and compared with the traditional sequential scheduling approach. The superiority of the integrated heuristic is well documented.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.