Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics

Low costs, high reactivity and high quality products are necessary criteria for industries to achieve competitiveness in nowadays market. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfil these requirements. RMS is one of the latest manufacturing paradigms, where machines components, software or material handling units can be added, removed, modified or interchanged as needed and when imposed by the necessity to react and respond rapidly and cost-effectively to changing. This research work addresses the multi-objective single-product multi-unit process plan generation problem in a reconfigurable manufacturing environment where three hybrid heuristics are proposed and compared namely: repetitive single-unit process plan heuristic (RSUPP), iterated local search on single-unit process plans heuristic (LSSUPP) and archive-based iterated local search heuristic (ABILS). Single-unit process plans are generated using the adapted non-dominated sorting genetic algorithm (NSGA-II). Moreover, in addition to the minimisation of the classical total production cost and the total completion time, the minimisation of the maximum machines exploitation time is considered as a novel optimisation criterion, in order to have high quality products. To illustrate the applicability of the three approaches, examples are presented and the obtained numerical results are analysed.     

A fuzzy approach to process plan selection

Process planning is the systematic determination of the detailed methods by which parts can be manufactured from raw material to finished product. In a real manufacturing environment, usually several different parts need to be manufactured in a single facility sharing constrained resources. The existence of alternative process plans for each part makes the selection of process plan a very important issue in manufacturing. The objectives in process plan selection might be imprecise and conflicting. In this paper, a fuzzy approach is used to deal quantitatively with the imprecision of the process plan selection problem. Each process plan is evaluated and its contribution to shopfloor performance is calculated using fuzzy set theory. A progressive refinement approach is used to first identify the set of process plans that maximize the contributions, and then consolidate the set to reduce the manufacturing resources needed.     

An integrated approach to solving the process plan selection problem in an automated manufacturing system

Selection of a process plan is a crucial decision making problem encountered in manufacturing systems due to the presence of several alternative process plans arising out of availability of several machines, tools, fixtures etc. capable of performing the same operations of the part. Because of its vital impact on the performance of the manufacturing system, several researchers have addressed the plan selection problem in recent years. Although functional integration plays a significant role in the development of current manufacturing systems, many of the functions in manufacturing systems have been developed without a sense of integration. Therefore, it becomes important to emphasize the integration of functions rather than the individual development of the function itself. This paper attempts to address the plan selection problem taking into account the similarity measures among the process plans of the parts. Four algorithms have been developed to integrate the several segments of the process plan selection problem. Application of these algorithms ensures considerable computational simplicity in yielding the feasible process plans of the parts.     

Optimization of process plans using a constraint-based tabu search approach

A computer-aided process planning system should ideally generate and optimize process plans to ensure the application of good manufacturing practices and maintain the consistency of the desired functional specifications of a part during its production processes. Crucial processes, such as selecting machining resources, determining set-up plans and sequencing operations of a part should be considered simultaneously to achieve global optimal solutions. In this paper, these processes are integrated and modelled as a constraint-based optimization problem, and a tabu search-based approach is proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure from good manufacturing practices (penalty function) are the optimization evaluation criteria. Precedence constraints from the geometric and manufacturing interactions between features and their related operations in a part are defined and classified according to their effects on the plan feasibility and processing quality. A hybrid constraint-handling method is developed and embedded in the optimization algorithm to conduct the search efficiently in a large-size constraint-based space. Case studies, which are used for comparing this approach with the genetic algorithm and simulated annealing approaches, and the proposed constraint-handling method and other constraint methods, are discussed to highlight the performance of this approach in terms of the solution quality and computational efficiency of the algorithm.     

Integrated design of cellular manufacturing systems in the presence of alternative process plans

In this paper we consider a generalized group technology problem of manufacturing a group of parts in which each part can have alternative process plans and each operation in these plans can be performed on alternative machines. The objective is to model and analyse how alternative process plans influence the resource utilization when the part families and machine groups are formed simultaneously. Accordingly, we develop three integer programming models to successively study the effect of alternative process plans and simultaneous formation of part families and machine groups. An illustrative example is included.     

Process plan selection

Process planning is the task of generating a plan for transforming raw material to its finished form according to design specifications. The existence of alternative feasible process plans for a part and the manufacture of several parts in a single facility sharing constrained resources, makes careful selection of process plans essential. This paper formalizes the selection of process plans with the objective of minimizing the total processing time and the total number of processing steps. The set of plans are then consolidated to minimize the resources used. This is accompanied by intangible benefits such as simplified planning and scheduling, fewer constraints on designing the layout, and an overall reduction in the manufacturing cost. As a word of caution, it may be added that process plans have wide ranging impact on factory operations and it is unrealistic to expect a single model to be comprehensive in addressing the problem. The model outlined in the paper highlights the key issues and provides an analytical basis for selecting process plans.     

Linguistic-based meta-heuristic optimization model for flexible job shop scheduling

In this paper, a linguistic based meta-heuristic modelling and solution approach for solving the Flexible Job Shop Scheduling Problem (FJSSP) is presented. FJSSP is an extension of the classical job-shop scheduling problem. The present problem definition is to assign each operation to a machine out of a set of capable machines ( the routing problem ) and to order the operations on the machines ( the sequencing problem ), such that a predefined performance measure is optimized. The scope of the problem is widened by taking into account the alternative process plans for each part ( process plan selection problem ) in the present study. Moreover, instead of using operations to represent product processing requirements and machine processing capabilities, machine independent capability units, which are known as Resource Elements (RE), are used. Representation of unique and shared capability boundaries of machine tools and part processing requirements is possible via RE. Using REs in scheduling can also reduce the problem size. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls. Using these controls and the Giffler and Thompson (1960) priority rule-based heuristic, a simulated annealing algorithm is developed to solve FJSSP. This novel approach simplifies the modelling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its solution. The results obtained from the computational study have shown that the proposed algorithm can solve this complex problem effectively within reasonable time. The results have also given some insights on the effect of the selection of dispatching rules and the flexibility level on the job shop performance. It is observed that the effect of dispatching rule selection on the job shop performance diminishes by increasing the job shop flexibility.     

Scheduling with alternatives: a link between process planning and scheduling

The objective of this research is to develop and evaluate effective, computationally efficient procedures for scheduling jobs in a large-scale manufacturing system involving, for example, over 1000 jobs and over 100 machines. The main performance measure is maximum lateness; and a useful lower bound on maximum lateness is derived from a relaxed scheduling problem in which preemption of jobs is based on the latest finish time of each job at each machine. To construct a production schedule that minimizes maximum lateness, an iterative simulation-based scheduling algorithm operates as follows: (a) job queuing times observed at each machine in the previous simulation iteration are used to compute a refined estimate of the effective due date (slack) for each job at each machine; and (b) in the current simulation iteration, jobs are dispatched at each machine in order of increasing slack. Iterations of the scheduling algorithm terminate when the lower bound on maximum lateness is achieved or the iteration limit is reached. This scheduling algorithm is implemented in Virtual Factory, a Windows-based software package. The performance of Virtual Factory is demonstrated in a suite of randomly generated test problems as well as in a large furniture manufacturing facility. To further reduce maximum lateness, a second scheduling algorithm also incorporates a tabu search procedure that identifies process plans with alternative operations and routings for jobs. This enhancement yields improved schedules that minimize manufacturing costs while satisfying job due dates. An extensive experimental performance evaluation indicates that in a broad range of industrial settings, the second scheduling algorithm can rapidly identify optimal or nearly optimal schedules.     

Materials selection using complex proportional assessment and evaluation of mixed data methods

Material selection is a very fast growing multi-criteria decision-making (MCDM) problem involving a large number of factors influencing the selection process. Proper choice of material is a critical issue for the success and competitiveness of the manufacturing organizations in the global market. Selection of the most appropriate material for a particular engineering application is a time consuming and expensive process where several candidate materials available in the market are taken into consideration as the tentative alternatives. Although a large number of mathematical approaches is now available to evaluate, select and rank the alternative materials for a given engineering application, this paper explores the applicability and capability of two almost new MCDM methods, i.e. complex proportional assessment (COPRAS) and evaluation of mixed data (EVAMIX) methods for materials selection. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two illustrative examples are cited which prove that these two MCDM methods can be effectively applied to solve the real time material selection problems. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained which almost match with the rankings as derived by the past researchers.     

Inspection resource assignment in a multistage manufacturing system with an inspection error model

Producing high-quality products at low cost is always one concern for a multi-stage manufacturing system. That is, production costs and inspection efficiency should receive equal importance. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources during process planning. Product quality and the possible costs can then be concurrently considered when evaluating a manufacturing plan. Except for finite inspection station classes, the limited number of inspection stations of each inspection station class is considered to solve the inspection allocation problem in this research. Rather than utilizing a constant inspection error or a specified inspection error probability distribution determined by previous observations, the inspection allocation problem is solved using relative cost models in which the inspection error model is embedded. The inspection allocation problem can then be solved by practically reflecting the inspection error when tolerances are rapidly changed to satisfy customer requirements. Since determining the optimal inspection allocation plan seems impractical as the problem size becomes quite large, two heuristic methods have been developed by considering the defective rate, manufacturing cost and earliest stage priority in this research. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. A feasible manufacturing plan can then be determined and confirmed during process planning by concurrently solving the inspection allocation problem.     

Concurrent optimal allocation of distributed manufacturing resources using extended Teaching-Learning-Based Optimization

The optimal allocation of distributed manufacturing resources is a challenging task for supply chain deployment in the current competitive and dynamic manufacturing environments, and is characterised by multiple objectives including time, cost, quality and risk that require simultaneous considerations. This paper presents an improved variant of the Teaching-Learning-Based Optimisation (TLBO) algorithm to concurrently evaluate, select and sequence the candidate distributed manufacturing resources allocated to subtasks comprising the supply chain, while dealing with the trade-offs among multiple objectives. Several algorithm-specific improvements are suggested to extend the standard form of TLBO algorithm, which is only well suited for the one-dimensional continuous numerical optimisation problem well, to solve the two-dimensional (i.e. both resource selection and resource sequencing) discrete combinatorial optimisation problem for concurrent allocation of distributed manufacturing resources through a focused trade-off within the constrained set of Pareto optimal solutions. The experimental simulation results showed that the proposed approach can obtain a better manufacturing resource allocation plan than the current standard meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation and Harmony Search. Moreover, a near optimal resource allocation plan can be obtained with linear algorithmic complexity as the problem scale increases greatly.     

A priority scheduling approach for flexible job shops with multiple process plans

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported.     

Models and optimisation approaches for scheduling steelmaking–refining–continuous casting production under variable electricity price

This paper studies the steelmaking–refining–continuous casting (SRCC) scheduling problem with considering variable electricity price (SRCCSPVEP). SRCC is one of the critical production processes for steel manufacturing and energy intensive. Combining the technical rules used in iron-steel production practice, time-dependent electricity price is considered to reduce the electricity cost and the associate production cost. A decomposition approach is proposed for the SRCCSPVEP. Without considering the electrical factor, the first phase applies the mathematical programming method to determine the relative schedule plan for each cast. In the second phase, we formulate a scheduling problem of all casts subject to resource constraint and time-dependent electricity price. A heuristic algorithm combined with the constraint propagation is developed to solve this scheduling problem. To investigate and measure the performance of the proposed approach, numerous instances are randomly generated according to the collective data from a well-known iron-steel plant in China. Computational results demonstrate that our algorithm is very efficient and effective in providing high-quality scheduling plans, and the electricity cost can be reduced for the iron-steel plant.     

Integration of process planning and scheduling by exploring the flexibility of process planning

A unique method for the integration of process planning and scheduling in a batch-manufacturing environment is reported. This integration is essential for the optimum use of production resources and for the generation of realistic process plans that can be readily executed with little modification. The integration problem is modelled at two levels: process planning and scheduling, which are linked by an intelligent facilitator. The process-planning module employs an optimization approach in which the whole plan solution space in terms of available machines, tools, tool accessibility and precedence constraints is first generated and a search algorithm is then used to find the optimal plan. For a given set of jobs, the scheduling module takes the optimal plans for each job and generates a schedule based on a given criterion, as well as the performance parameters (machine utilization and number of tardy jobs). An unsatisfied performance parameter is fed back to the facilitator, which then identifies a particular job and issues a change to its process plan solution space. The iteration of process Planning -scheduling-solution space modification continues until a schedule is satisfactory or until no further improvement can be made. The uniqueness of this approach is characterized by the flexibility of the process-planning strategy and the intelligent facilitator, which makes full use of the plan solution space intuitively to reach a satisfactory schedule. (It may not be the optimal, though.) The integrated system was implemented in the manufacturing of prismatic parts. The testing results show that the developed integration method can achieve satisfactory process plans and a schedule in an effective and efficient manner.     

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.     

考虑绿色制造和外协方式的多周期订单安排模型研究

针对多品种小批量企业的多周期订单安排问题,提出一种以绿色制造和外协方式为研究视角的优化方法模型。综合考虑企业的设备操作成本、人工成本、库存成本、外协成本等竞争性指标以及设备能耗、噪声等绿色性指标,构建考虑绿色制造和外协方式的多周期订单安排模型,并基于遗传算法设计了模型的求解方法;最后以一个机加工企业的四周期订单需求为例,应用该方法给出了各阶段订单的安排结果以及企业制造资源的分配方案,既验证了模型和算法的有效性,又在绿色制造和外协方式的研究视角上为多周期订单的安排方式提供理论方法支持。     

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.     

基于Plant Simulation的航空综合机加厂房布局仿真研究

科学的生产设置布局规划对航空制造业降低生产成本、提高产品质量尤为重要.以某民用航空发动机传动系统的齿轮和机匣综合加工厂房的规划设计为例,根据厂房设施布置的一般原则,以齿轮和机匣的年产量目标、产品加工工艺、单工艺加工面积需求为设计输入,并考虑各加工区的加工特点而带来的位置约束性,采用遗传算法与模拟退火算法相结合的混合遗传算法为优化工具,将特定的功能区固化在基因串特定的位置上来满足位置约束．计算得出优化方案后,以Plant Simulation为仿真平台,建立该综合机加厂房的仿真模型,从产量满足率、设备利用率、在制品库存量和生产线稳健性等多个指标进行了设施布局的仿真评价．结果表明,优化后的系统能够很好满足生产纲领,各关键设备负载比较均衡,同时维持低水平的在制品库存量,且生产线稳健性较好．因此,综合运用混合遗传算法与Plant Simulation仿真可以为生产设施布局问题给出一种有效、直观的解决方案,且由离散事件仿真获取的评价指标能深刻体现方案的优劣．     

An agent-based negotiation approach to integrate process planning and scheduling

This paper presents the development of an agent-based negotiation approach to integrate process planning and scheduling (IPPS) in a job shop kind of flexible manufacturing environment. The agent-based system comprises two types of agents, part agents and machine agents, to represent parts and machines respectively. For each part, all feasible manufacturing processes and routings are recorded as alternative process plans. Similarly, alternative machines for an operation are also considered. With regard to the scheduling requirements and the alternative process plans of a part, the proposed agent-based IPPS system aims to specify the process routing and to assign the manufacturing resources effectively. To establish task allocations, the part and machine agents have to engage in bidding. Bids are evaluated in accordance with a currency function which considers an agent's multi-objectives and IPPS parameters. A negotiation protocol is developed for negotiations between the part agents and the machine agents. The protocol is modified from the contract net protocol to cater for the multiple-task and many-to-many negotiations in this paper. An agent-based framework is established to simulate the proposed IPPS approach. Experiments are conducted to evaluate the performance of the proposed system. The performance measures, including makespan and flowtime, are compared with those of a search technique based on a co-evolutionary algorithm.