R. S. Lashkari

Machine grouping problem in cellular manufacturing systems— an integer programming approach

In this paper a methodology is proposed to group the machines in cellular manufacturing systems based on the tooling requirements of the parts, toolings available on the machines and the processing times. Two 0-1 integer programming formulations are proposed. These formulations assume that the part families are known. The first formulation groups the machines based on the compatibility of parts with machines. The second formulation groups the machines in order to minimise the cost of allocating the machines and the cost of intercell movement. These formulations take into account the limitations on the number of machines in a group and the number of machines available of a particular type. The application of these formulations is illustrated using an example.

A new formulation of operation allocation problem in flexible manufacturing systems: mathematical modelling and computational experience

This paper extends the formulation of the operation allocation problem to include the important planning aspects of refixturing and limited tool availability. A 0–1 integer programming formulation is proposed with two objective functions and a set of realistic constraints. The computational behavior of the solution is discussed and a number of observations prompted by the solution methodology have been made.

A fuzzy goal-programming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach

Fuzzy set theory has been widely accepted in modelling of some of the vague phenomena and relationships that are non-stochastic in nature. The problem of machine-tool selection and operation allocations in a flexible manufacturing system usually involves parameters that are non-deterministic and imprecise in nature. This paper adopts a fuzzy goal-programming model having multiple conflicting objectives and constraints pertaining to the machine-tool selection and operation allocation problem, and a new random search optimization methodology termed Quick Converging Simulated Annealing (QCSA) is being used to resolve the underlying issues. The main feature of the proposed QCSA algorithm is that it outperforms genetic algorithm and simulated annealing approaches as far as convergence to the near optimal solution is concerned. Moreover, it is also capable of eluding local optima. Extensive experiments are performed on a problem involving real-life complexities, and some of the computational results are reported to validate the efficacy of the proposed algorithm.

A mathematical programming approach to joint cell formation and operation allocation in cellular manufacturing

Abstract We introduce a zero-one integer programming model for the simultaneous solution of the cell formation and operation allocation problem in cellular manufacturing. We assume that there is a set of existing machines in a manufacturing shop and that there is a set of part types to be processed on these machines. Each part type is assumed to have more than one process plan, and each operation of a part type may be performed on more than one machine. The objective of the model is to simultaneously form machine groups (cells) and allocate operations of the part types to the regrouped machines in such a way as to minimize the sum of operation costs, refixturing costs and transportation costs. Computational results are provided to demonstrate the viability of the model.

Simultaneous grouping of parts and machines in cellular manufacturing systems—an integer programming approach

Abstract In this paper, a methodology is proposed to simultaneously group machines and parts in cellular manufacturing systems based on the tooling requirements of the parts, tools available on the machines and the processing times. Two non-linear 0–1 integer programming formulations are proposed. The first formulation forms machine-part groups on the basis of the compatibility of the parts with the machines. The second formulation groups the machines and the parts in order to seek a trade-of between the cost of duplicating the machines and the cost of intercell movement. The formulations take into account the limitations on the number of parts and machines in a group as well as the number of machine types available. The application of these formulations is illustrated using an example.

A production planning model for cellular manufacturing systems with refixturing considerations

In this paper we consider the problem of assigning operations of part types to one or more machines in a cellular manufacturing system. We develop a mixed integer linear model considering trade-off between refixturing and material handling movement. Examples are included to illustrate the applications of the models developed.

Model for integrating the supply chain of an appliance company: a value of information approach

A supply chain management model is presented in the context of a major household appliance manufacturer in Mexico. Specifically, it provides a capacitated, multistage, multiperiod, multicommodity, multifacility inventory planning model. The mixed-integer programming model deals with the efficient allocation of resources in the supply chain network under the premise that information is a valuable resource that also requires optimal allocation in order to enhance the flow of products and to minimize system-wide costs. The model employs the strategy of risk pooling or time postponement as a cost-reduction driver to account for the provision of safety stocks in the system. Numerical results are presented to demonstrate the feasibility of the application of the real-world, large-scale supply chain models.

Selection of scheduling rules in FMSs—A simulation approach

This paper deals with the development of a microcomputer-based simulation model of a random flexible manufacturing system aimed at solving scheduling problems. The model is written in SLAM II and can be used interactively. It offers five alternative scheduling rules, but other rules could be incorporated if required. The selection process is demonstrated through an example, and an experimental design is conducted to evaluate the effect of changes in the levels of various resources on system performance.

A Lagrangian relaxation approach to machine allocation in cellular manufacturing systems

Abstract The allocation of machines to part families in cellular manufacturing systems is formulated as a 0–1 integer programming model, and a solution procedure based on Lagrangean relaxation is presented. The application of the formulation is illustrated using large, randomly generated problems.

Risk readiness and resiliency planning for a supply chain

This paper proposes supply chain (SC) risk readiness and resiliency measures and formulates a model for planning and controlling select internal business factors to create desired risk resiliency in order to avert potential risks and mitigate their after-effect. SCs may be exposed to events that affect their business operations, and primarily impact the production processes (i.e. production-related risks), or events (such as natural calamities or terrorism) that affect the way the business interacts with the market, and primarily impact the transportation and distribution processes (i.e. market-related risks). Although a business cannot control such disasters as natural calamities or terrorism, it is possible to identify and control the factors that are responsible for production-related risks and that influence several market-related risks or disasters. The proposed model and the measures will guide SCs through the process of identification, planning and controlling the internal factors that make the chain resilient to these various risks. The resiliency measures and the mixed integer programming model will also enable SCs to conduct what-if analyses of cost and performance trade-off options. A numerical example illustrates the planning in typical scenarios.