Part type selection,machine loading and part type volume determination problems in FMS planning

The solution to subproblems of FMS planning problems require an integrated approach. The mathematical programming approaches, which are often followed, are not suitable for large problems. In this paper, we have proposed a three stage approach to solving part type selection, machine loading and part type volume determination problems. In contrast to the usual approach of maximizing the part types in each batch (or minimizing the number of batches), we have attempted to maximize the routeing flexibility of the batches. A heuristic has been proposed for the part type selection problem and simple mathematical programs for the other two problems. The illustrative examples show that by improving the routeing flexibility of the batches the overall system performance has improved.     

A heuristic procedure for loading problems in flexible manufacturing systems

The loading problem in a flexible manufacturing system (FMS) is viewed as selecting a subset of jobs from a job pool and allocating the jobs among machines. In this paper a heuristic solution to the loading problem has been suggested by developing the concept of essentiality ratio for the objective of minimizing the system unbalance and thereby maximizing the throughput. The proposed heuristic is tested on ten problems and the results show that the algorithm developed is very reliable and efficient.     

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.     

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.     

Incorporating dynamism in traditional machine loading problem: an AI-based optimisation approach

Machine breakdowns have been recognised in flexible manufacturing systems (FMS) as the most undesirable characteristic adversely affecting the overall efficiency. In order to ameliorate product quality and productivity of FMS, it is necessary to analyse, as well as to minimise the effect of breakdowns on the objective measures of various decision problems. This paper addresses the machine loading problem of FMS with a view to maximise the throughput and minimise the system unbalance and makespan. Moreover, insufficient work has been done in the domain of machine loading problem that considers effect of breakdowns. This motivation resulted in a potential model in this paper that minimises the effect of breakdowns so that profitability can be augmented. The present work employs an on-line machine monitoring scheme and an off-line machine monitoring scheme in conjunction with reloading of part types to cope with the breakdowns. The proposed model bears similarity with the dynamic environment of FMS, hence, termed as the dynamic machine loading problem. Furthermore, to examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on different dataset from previous literature has been investigated with application of intelligence techniques such as genetic algorithms (GA), simulated annealing (SA) and artificial immune systems (AIS). The results incurred under breakdowns validate the robustness of the developed model for dynamic ambient of FMS.     

Production planning for flexible flow systems with limited machine flexibility

A flexible manufacturing system (FMS) is highly capital-intensive and FMS users are concerned with achieving high system utilization. The production planning function for setting up an FMS prior to production should be developed in order to make the most of the potential benefits of FMSs. We consider two production planning problems of grouping and loading a flexible flow system, which is an important subset of FMSs where the routing of parts is unidirectional. We show that considering this routing restriction as well as limited machine flexibility strongly affects both the solution techniques and the quality of the solutions. Because of the complexity of the problem, we present a heuristic approach that decomposes the original problem into three interrelated subproblems. We show that the proposed approach usually finds a near-optimum solution and is superior to an approach that exists in the literature of FMS production planning. We also introduce effective heuristic methods for two new subproblems that arise because of the unidirectional flow precedence and flexibility constraints. Computational results are reported and future research issues are discussed.     

Control policy simulation based on machine age in a failure prone one-machine,one-product manufacturing system

This paper deals with the production planning problem of a flexible manufacturing system (FMS). This problem is within the class of problems which are usually faced during the life cycle of FMS. There are basically two approaches used to solve this problem: analytical and simulation. The objective of this paper is to develop a methodology for determine the control policy for a given FMS by using the combination of both analytical and simulation approaches. The production rate control of a one-machine one-product system is presented to illustrate our approach. The contribution of the analytical part of the proposed model consists of generating a near-optimal controller based on the concept of an age-dependent hedging point policy. This sub-optimal policy is used as input of the simulation part and the improvement of operating conditions (control policies) achieved with a constant demand rate. The proposed model includes also a stochastic demand and lot sizes in production which consists of similar products batches with bulk arrivals. The robustness of the controller against both demand rates and production types has been validated.     

A loading and dispatching problem in a random flexible manufacturing system

A scheduling problem in a flexible manufacturing system (FMS) is considered to be a composite of two interdependent tasks: loading and sequencing. Formulations are presented for the loading problem with two objectives:

(i) minimization of the system workload unbalance, and

(ii) minimization of system unbalance and the number of late jobs;

including constraints such as the number of tools slots with duplications, unique job routing, nonsplitting of jobs and machine capacity. For both the objectives, heuristic methods are developed and performance is compared with the exact mixed integer programming solutions. A simulation model is developed for investigating the system performance for the problem of minimizing the system unbalance using heuristic and sequential loading methods in conjunction with four—FIFO, SPT, LPT and MOPR—dispatching rules.     

Part ordering through simulation-optimization in an FMS

One of the most important scheduling and control problems that must be solved concerning the efficient operation of FMS is the part ordering problem, i.e. finding the optimal sequence of parts to be released into the manufacturing system. Methods currently available for handling this problem are based on heuristic dispatching rules. In this paper an approach for solving the problem using the simulation-optimization technique is proposed. Currently available heuristic approaches can only try to get the optimum at a local level because of the complexities of the system and the dependencies of its components, whereas the proposed approach tries to get the global optimum.     

A note on the problem of scheduling a flexible manufacturing system for Just-in-time customers

In supplying just-in-time customers, one common industry practice is for the vendor to set up a warehouse near the point of demand. We assume that the plant is a flexible manufacturing system (FMS) where the changeover times between parts of the same family axe. negligible, but not between parts of different families. Thus the FMS must be configured to produce one family of parts at a time. For each part type, we assume that the vendor makes deliveries to the warehouse at fixed intervals. We examined two related issues: the run length for each part family and the number of deliveries to the warehouse in each cycle. We first showed that it is not necessary for the vendor to produce for each delivery, nor is it desirable to complete the entire production lot before delivery. We then investigated the FMS loading problem for two or more part-families. We developed the expressions for the optimal run length and delivery frequency, (1) when loading the part types sequentially and (2) when loading the part types simultaneously. The resulting models can be shown to be consistent with the classical EOQ model.     

Experimental investigation of flexible manufacturing system scheduling decision rules

This paper reports the results of an experimental investigation of scheduling decision rules for a dedicated flexible manufacturing system. A simulation model of an existing flexible manufacturing system (FMS) comprised of 16 computer numerical controlled machines (CNC) was constructed using actual operation routings and machining times to evaluate the performance of various part loading and routing procedures. The results indicate that FMS performance is significantly affected by the choice of heuristic parts scheduling rules.     

Implementing a loading heuristic in a discrete release job shop

The scheduling literature has developed two almost mutually exclusive problems; a static and a dynamic scheduling problem. The result has been that different tools have been developed to solve each problem. In this research, a model is developed such that it contains elements of both the static and the dynamic problems. This model, called here the discrete release model, considers the routing, loading, and release decisions as well as the customary sequencing decision evident in most job shop research. The objective of the research is to implement a method for examining tentative loading assignments prior to their release to the shop floor.

The research develops a single pass, sequential routing and loading system. A heuristic is then developed that will enable jobs to be reloaded and rerouted prior to their start of processing. The system is tested in various shop environments to ascertain the effectiveness of this loading heuristic.     

A heuristic for setting up a flexible assembly system

We consider setup problems in flexible assembly systems (FASs) with an objective involving job priorities and station workloads. Because of the difficulty in solving the FAS setup problem, it is partitioned into two subproblems: the part type selection problem and the loading problem. We suggest a solution procedure in which branch-and-bound methods and heuristic methods are used for solving the subproblems. Part types are tentatively selected first without explicitly considering the system's restrictions, such as precedence relationships among tasks. As a result, the alternative for part type selection may not always be feasible for the loading problem. For a feasibility check and for a use in assigning tasks of selected part types, we develop an algorithm for finding a feasible sequence of the tasks. In the loading problem, tasks are assigned to stations using LPT-type rules with improvements tried through interchanging tasks. Results of computational tests on randomly generated problems are reported.     

Scheduling of machines and automated guided vehicles in FMS using differential evolution

This paper addresses the problem of simultaneous scheduling of machines and two identical automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). For solving this problem, a new meta-heuristic differential evolution (DE) algorithm is proposed. The problem consists of two interrelated problems, scheduling of machines and scheduling of AGVs. A simultaneous scheduling of these, in order to minimise the makespan will result in a FMS being able to complete all the jobs assigned to it at the earliest time possible, thus saving resources. An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs as an integral part of the overall scheduling activity. The algorithm is tested by using problems generated by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers and analysed.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

Scheduling flexible flow lines with no in-process buffers

An heuristic algorithm is proposed for scheduling a flexible flow line with no intermediate buffers. The line is made up of several processing stages in series, where each stage has one or more identical parallel machines. In the line different part types can be manufactured simultaneously, each of which is processed by at most one machine in every stage. Intermediate queues of parts waiting between the stages for their next operations are not allowed. The problem objective is to minimize the makespan of the schedule for a set of part types selected for processing. The algorithm proposed is a part-by-part heuristic, in which during every iteration a complete processing schedule is determined for one part type selected for loading into the line. The selection of the part type and its complete schedule are based on the cumulative partial schedule obtained for all parts selected so far. The decisions in every iteration are made using a local optimization procedure aimed at minimizing total blocking and waiting time of the machines along the route of the selected part type. The efficiency of the algorithm is tested on several groups of random test problems     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

The part mix and routing mix problem in FMS: a coupling between an LP model and a closed queueing network

In the near future many companies will face the problem of the optimal use of newly installed manufacturing technology (e.g. a flexible manufacturing system or FMS). Very often this will involve decisions on what parts to produce using the new system (the part mix problem) and how to produce these parts (the routing mix problem). We show that traditional operational research tools such as linear programming and queueing network theory are well suited to tackle these problems. In particular, LP models are combined with queueing network models in an iterative procedure. As such the strengths of both techniques can be exploited in making optimal use of the part mix and routing mix flexibility of the FMS.     

Multiple routeings and capacity considerations in group technology applications

This paper addresses the problem of manufacturing cell formation, given multiple part routeings, and multiple functionally similar workcentres. Cellular manufacturing is intended to facilitate production, and thus should be based on projected production requirements. The originality of the approach lies in considering both the manufacturing system as well as projected production, and distributing the demand among alternate routeings in order to obtain a better manufacturing cell design. The suggested choice of part routeings favours the decomposition of the manufacturing system into manufacturing cells in a way that minimizes part traffic, along with satisfying the part demand and workcentre capacity constraints. We show that the problem can be formulated as a linear programming type problem which simultaneously addresses two problems: (i) routeing selection, and (ii) cell formation. The common objective is to minimize the inter-cell traffic in the system. The proposed algorithm iteratively solves two problems. The first problem is formulated as a linear-programming problem, while the latter is approached by an existing heuristic bottom-up aggregation procedure, known as Inter-Cell Traffic Minimization Method (ICTMM), enhanced appropriately.