A two-phase procedure for duplicating bottleneck machines in a linear layout,cellular manufacturing system

We discuss a two-phase procedure for duplicating bottleneck machines in a cellular manufacturing system. Given a preliminary solution by a clustering technique, the first phase solves a cellular layout problem in which it assigns machine-cells to locations to minimize the total inter-cell material handling costs that result from the bottleneck parts. The purpose of this phase is to find an optimal linear layout of cells. The second phase finds the bottleneck machines that need to be duplicated to minimize the costs. A binary (integer) linear programming model is developed in this phase to minimize the total duplication costs and material handling costs (if not duplicated). Finally, a decision is made as to whether a solution with bottleneck machines, or duplication of bottleneck machines to avoid the bottleneck problem is to be accepted. An example is demonstrated to show how such a bottleneck problem in cellular manufacturing is solved.     

An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times

The purpose of this work is to efficiently design disassembly lines taking into account the uncertainty of task processing times. The main contribution of the paper is the development of a decision tool that allows decision-makers to choose the best disassembly alternative (process), for an End of Life product (EOL), and assign the corresponding disassembly tasks to the workstations of the line under precedence and cycle time constraints. Task times are assumed to be random variables with known normal probability distributions. The case of presence of hazardous parts is studied and cycle time constraints are to be jointly satisfied with at least a certain probability level, or service level, fixed by the decision-maker. An AND/OR graph is used to model the precedence relationships among tasks. The objective is to minimise the line cost composed of the workstation operation costs and additional costs of workstations handling hazardous parts of the EOL product. To deal with task time uncertainties, lower and upper-bounding schemes using second-order cone programming and approximations with convex piecewise linear functions are developed. The applicability of the proposed solution approach is shown by solving to optimality a set of disassembly problem instances (EOL industrial products) from the literature.     

Joint optimization of a flow-shop group scheduling with sequence dependent set-up times and skilled workforce assignment

Flow-shop sequence-dependent group scheduling (FSDGS) problem has been extensively investigated in the literature also due to many manufacturers who implemented the concept of group technology to reduce set-up costs, lead times, work-in-process inventory costs, and material handling costs. On the other hand, skilled workforce assignment (SWA) to machines of a given shop floor may represent a key issue for enhancing the performance of a manufacturing system. As the body of literature addressing the group scheduling problems ignored up to now the effect of human factor on the performance of serial manufacturing systems, the present paper moves in that direction. In particular, an M-machine flow-shop group scheduling problem with sequence-dependent set-up times integrated with the worker allocation issue has been studied with reference to the makespan minimization objective. First, a Mixed Integer Linear Programming model of the proposed problem is reported. Then, a well-known benchmark arisen from the literature is adopted to carry out an extensive comparison campaign among three properly developed metaheuristics based on a genetic algorithm framework. Once the best procedure among those tested is selected, it is compared with an effective optimization procedure recently proposed in the field of FSDGS problems, being this latter properly adapted to run the SWA issue. Finally, a further analysis dealing with the trade-off between manpower cost and makespan improvement is proposed.     

Environment conscious manufacturing edited by S.M. Gupta and A.J.D. Lambert

The dynamic facility layout problem (DFLP) aims to minimise the sum of handling and re-layout costs by devising an individual layout for each distinctive production period. In this paper, a hybrid particle swarm optimisation (HPSO) algorithm is proposed to find near-optimal solutions of DFLP. We use a coding and decoding technique that permits a one to one mapping of a solution in discrete space of DFLP to a PSO particle position in continuous space. The proposed PSO will further use this coding technique to explore the continuous solution space. For further enhancement, the proposed PSO is hybridised with a simple and fast simulated annealing. The developed algorithm is capable of being extended to more general cases although equal area machines and standardised handling equipments with identical unit costs are assumed for the time being. Computational results show the efficiency of the proposed algorithm compared to other heuristics.     

Configuring layout in unidirectional loop manufacturing systems

This paper addresses the layout configuration problem of workstations in a unidirectional loop manufacturing system. In this popular unicyclic material handling facility, the material transporters depart from the input/output station, traverse each workstation exactly once, and then return to the input/output station. We show the important properties of this unidirectional material handling loop network. Based on these characterizations, both heuristic and branch-and-bound algorithms are proposed to solve such NP-complete layout problems. Computational experiments with problem sizes up to 100 workstations are reported. Both the problem size and the material flow density have been found to affect the solution quality and the computational efficiency. The proposed methods appear efficient and effective for solving these layout problems.     

Designing a cellular manufacturing system considering decision style,skill and job security by NSGA-II and response surface methodology

Cell formation is a traditional problem in cellular manufacturing systems that concerns the allocation of parts, operators and machines to the cells. This paper presents a new mathematical programming model for cell formation in which operators’ personality and decision-making styles, skill in working with machines, and also job security are incorporated simultaneously. The model involves the following five objectives: (1) minimising costs of adding new machines to and removing machines from the cells at the beginning of each period, (2) minimising total cost of material handling, (3) maximising job security, (4) minimising inconsistency of operators’ decision styles in cells and (5) minimising cost of suitable skill. On account of the NP-hard nature of the proposed model, NSGA-II as a powerful meta-heuristic approach is used for solving large-sized problems. Furthermore, response surface methodology (RSM) is used for tuning the parameters. Lastly, MOPSO and two scalarization methods are employed for validation of the results obtained. To the best of our knowledge, this is the first study that presents a multi-objective mathematical model for cell formation problem considering operators’ personality and skill, addition and removal of machines and job security.     

Sharing clearances to improve machine layout

This paper considers a double-row layout problem with shared clearances in the context of semiconductor manufacturing. By sharing some clearances, reductions in both layout area and material handling cost of approximately 7–10% are achieved. Along with minimal clearances for separating adjacent machines, clearances that can be shared by adjacent machines are considered. The shared clearances may be located on either or both sides of machines. A mixed integer linear programming formulation of this problem is established, with the objective to minimise both material flow cost and layout area. A hybrid approach combining multi-objective tabu search and heuristic rules is proposed to solve it. Computational results show that the hybrid approach is very effective for this problem and finds machine layouts with reduced areas and handling costs by exploiting shared clearances.     

Performance Evaluation of Hybrid Cells

The existence of intercellular moves due to bottleneck machines is a major road block to higher productivity gains in cellular manufacturing systems. One solution to this problem is the formation of a separate cell containing all bottleneck machines from different machine cells (hybrid cell). The formation of hybrid cell is based on the premise that the concentration of bottleneck machines in a single cell makes them more accessible to exceptional parts and simplifies the material flow. This paper presents a procedure for performance evaluation of hybrid cells with the purpose of verifying this premise. The procedure uses the sum of intercellular and intracellular material handling costs as a performance measure.     

Material handling considerations in the FMS loading problem with full routing flexibility

This study exploits machining and routing flexibility to effectively deal with the material handling requirements resulting from a frequently changing demand mix in a manufacturing system where material handling is a bottleneck. For this purpose, the objective function of the operation and tool loading problem is selected as the minimisation of the total distance traveled by parts during their production. Versatile machines and the flexible process plans offer full routing flexibility that enable the same workpiece to be processed using alternative sequences of operations on alternative machines. Three mathematical programming (MP) models and a genetic algorithm (GA) are proposed to solve this problem. The proposed MP formulations include a mixed-integer nonlinear programming (MINLP) model and two mixed-integer programming (MIP) models, which offer different representations for the flexible process plans. The GA is integrated with linear programming for fitness evaluation and incorporates several adaptive strategies for diversification. The performances of these solution methods are tested through extensive numerical experiments. The MP models are evaluated on the basis of the exact solutions they yield as well as how they lend themselves for GA fitness evaluation. The GA–LP integration works successfully for this hard-to-solve problem.     

A genetic algorithm and queuing theory based methodology for facilities layout problem

Facilities layout, being a significant contributor to manufacturing performance, has been studied many times over the past few decades. Existing studies are mainly based on material handling cost and have neglected several critical variations inherent in a manufacturing system. The static nature of available models has reduced the quality of the estimates of performance and led to not achieving an optimal layout. Using a queuing network model, an established tool to quantify the variations of a system and operational performance factors including work-in-process (WIP) and utilisation, can significantly help decision makers in solving a facilities layout problem. The queuing model utilised in this paper is our extension to the existing models through incorporating concurrently several operational features: availability of raw material, alternate routing of parts, effectiveness of a maintenance facility, quality of products, availability of processing tools and material handling equipment. On the other hand, a queuing model is not an optimisation tool in itself. A genetic algorithm, an effective search process for exploring a large search space, has been selected and implemented to solve the layout problem modelled with queuing theory. This combination provides a unique opportunity to consider the stochastic variations while achieving a good layout. A layout problem with unequal area facilities is considered in this paper. A good layout solution is the one which minimises the following four parameters: WIP cost, material handling cost, deviation cost, and relocation cost. Observations from experimental analysis are also reported in this paper. Our proposed methodology demonstrates that it has a potential to integrate several related decision-making problems in a unified framework.     

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.     

A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study

The assembly line worker assignment and balancing problem type-II (ALWABP-2) occurs when workers and tasks (where task times depend on workers’ skills) are to be simultaneously assigned to a fixed number of workstations with the goal of minimising the cycle time. In this study, a two-phase variable neighbourhood search (VNS) algorithm is proposed to solve the ALWABP-2 due to the NP-hard nature of this problem. In the first phase of the algorithm, a VNS approach is applied to assign tasks to workstations with the aim of minimising the cycle time while in the second phase, a variable neighbourhood descent method is applied to assign workers to workstations. The performance of the proposed algorithm is tested on well-known benchmark instances. In addition, the proposed algorithm has been used to solve a real case study from a consumer electronics company that manufactures LCD TVs. The results show that the algorithm is superior to the methods reported in the literature in terms of its higher efficiency and robustness. Furthermore, the algorithm is easy to implement and significantly improves the performance of the final assembly line for the investigated LCD TV real case study.     

Solving coupled task assignment and capacity planning problems for a job shop by using a concurrent genetic algorithm

The problems of task assignment and capacity planning of manufacturing systems have been researched for many years. However, in the existing literature, these two types of problems are researched independently. Namely, when solving the task assignment problem, it is usually assumed that the production capacity of the manufacturing systems has been determined. On the other hand, when solving the capacity planning problem, the production tasks assigned to the workstations in the manufacturing system have also been determined. Actually, the task assignment problem and the capacity planning problem are coupled with each other. When we assign production tasks to workstations, production capacities of these workstations should be regulated so that they are enough for completing the tasks. At the same time, when planning the production capacity, we must know what production tasks are assigned to what workstations. This research focuses on the coupling relations between the two problems for a closed job shop, in which the total work-in-process (WIP) is assumed to be constant. The objective of the task assignment problem is to balance the workloads of the workstations and the objectives of the capacity planning problem are maximising the throughput and minimising total costs of machine purchasing and WIP inventory. We construct the fundamental system architecture for controlling the two coupled optimisation processes, and propose a concurrent genetic algorithm (CGA) to solve the two coupled optimisation problems. The influences of the decision variables of one problem on the objective function of the other problem are taken into consideration when the fitness functions of the CGA are constructed. Numerical experiments are done to verify the effectiveness of the algorithm.     

Determination of the number of containers,production kanbans and withdrawal kanbans; and scheduling in kanban flowshops - Part 2

We consider the problem of determination of the number of containers or container size, production kanbans and withdrawal kanbans, and scheduling of containers in presence of material handling between workstations, and dual blocking mechanisms operative on both workstations and material handling. A heuristic is proposed to address the problem of determining the number of containers, and production and withdrawal kanbans, and also determining the sequence of containers to minimize the sum of weighted flowtime, weighted earliness and weighted tardiness of containers. Recursive equations, developed in part 1 of this paper, are used for time-tabling of the sequence of containers. An extensive performance evaluation of the heuristic and a benchmark procedure, based on random search, has been carried out by generating a number of problems of various sizes.     

A robust cell formation approach for varying product demands

In cellular manufacturing environments, manufacturing cells are generally formed based on deterministic product demands. In this paper, we consider a system configuration problem with product demands expressed in a number of probabilistic scenarios. An optimization model integrating cell formation and part allocation is developed to generate a robust system configuration to minimize machine cost and expected inter-cell material handling cost. A two-stage Tabu search based heuristic algorithm is developed to find the optimal or near optimal solutions to the NP-hard problem. Numerical examples show that this model leads to an appropriate compromise between system configuration costs and expected material handling costs to meet the varying product demands. These example problems also show that the proposed algorithm is effective and computationally efficient for small or medium size problems.     

Modeling for flexible manufacturing systems with an FMS blocking mechanism and a BDSM job routing

We consider a Flexible Manufacturing System (FMS) which is composed of a set of workstations, a common buffer and a Material Handling System (MHS). Each workstation includes a limited input buffer, several machines and a limited output buffer. The MHS consists of several carts moving jobs among the workstations according to the process paths required by the jobs. The carts treat blocked jobs in accordance with a new blocking mechanism, called the 'FMS blocking mechanism'. The function of the common buffer is to temporarily store blocked jobs. Such an FMS is formulated as an open queueing network, in which the MHS is modeled as a central station routing jobs to the workstations. In the model, the machines process jobs with an exponentially distributed processing time, and the carts route jobs to the workstations following a 'Blocking Depended Static Markov (BDSM) job routing' with an exponentially distributed routing time and treat blocked jobs in accordance with the FMS blocking mechanism. It is shown that the equilibrium state distribution of the model has a product-form solution. The blocking probabilities are obtained by computing a fixed point problem whose solution is revealed by an iterative algorithm. Moreover, it is shown that the throughputs of the workstations are independent of the spaces on the local buffers at the workstations. Several numerical examples are presented.     

Loading and scheduling for flexible manufacturing systems with controllable processing times

This study addresses the problem of determining the allocation of operations and their tools to machines, the operation processing times and the allocation/sequence of the parts to be processed on each machine for flexible manufacturing systems with controllable processing times. Tool lives, tool copies and tool sharing are also considered. An integer programming model is developed for the objective of minimizing the sum of operation processing and tardiness costs. Then, iterative algorithms are proposed that solve the two subproblems iteratively, where the loading subproblem is solved by a modified bin packing algorithm under initial processing times and the resulting scheduling subproblem is solved by a priority scheduling method while modifying the loading plans and operation processing times iteratively. Computational experiments were carried out, and the results are reported.     

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.     

Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.     

Generalisation of unidirectional loop layout problem and solution by a genetic algorithm

Unidirectional loop layouts (ULLs) are the preferred layouts in manufacturing systems owing to their relative low investment costs, high material handling elasticity and routing flexibility. Existing formulations of the unidirectional loop layout problem are concentrated on the arrangement of workstations under the assumption that the number and location of loading and unloading stations are known. In this study, the unidirectional loop layout problem is generalised by consideration of potentially attachable loading/unloading equipment to each workstation and releasing of the predetermined number of loading and unloading stations. Thus, more efficient and effective loop layout designs are allowed by eliminating some artificial restrictions. The present ULL model is generalised and a genetic algorithm is developed to solve the problem. Solutions obtained by the genetic algorithm outperformed those obtained by conventional methods. Additionally, comparisons of the generalised model with existing models on randomly generated test problems yielded encouraging results.