Reza Kia

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.

A fuzzy linear programming approach to layout design of dynamic cellular manufacturing systems with route selection and cell reconfiguration

Abstract This paper presents a novel integer non-linear programming model for the layout design of dynamic cellular manufacturing systems (DCMS) under an uncertain environment. A novel aspect of this model is concurrently making the interrelated cell formation and intracell layout decisions in a dynamic and uncertain environment. Other compromising aspects are: considering single-row layout of equal area facilities to locate machines in each cell, machine relocation based on locations assigned to a machine during two successive periods, calculation of material handling cost based on the distance between the locations assigned to machines, and presenting the existence of uncertainty in the model parameters by fuzzy numbers. Such an integrated model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including intracell layout, operation sequence, operation time, alternative process routing, duplicate machines, machine capacity, route selection, production volume of parts and cell reconfiguration. The uncertainty stems from part demand fluctuation and machine capacity. Linearization procedures are used to transform the proposed non-linear programming model into a linearized formulation. Finally, a new fuzzy linear programming approach which can consider the fuzziness of whole parameters in a mathematical model is developed to solve the linearized model. A comprehensive example is solved by the Lingo software to verify the performance of the proposed model and developed fuzzy approach. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems and fuzzy issues.

A MULTI-OBJECTIVE MODEL FOR DESIGNING A GROUP LAYOUT OF A DYNAMIC CELLULAR MANUFACTURING SYSTEM

This paper presents a multi-objective mixed-integer nonlinear programming model to design a group layout of a cellular manufacturing system in a dynamic environment, in which the number of cells to be formed is variable. Cell formation (CF) and group layout (GL) are concurrently made in a dynamic environment by the integrated model, which incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. Additionally, there are some features that make the presented model different from the previous studies. These features include the following: (1) the variable number of cells, (2) the integrated CF and GL decisions in a dynamic environment by a multi-objective mathematical model, and (3) two conflicting objectives that minimize the total costs (i.e., costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead, machine processing, and forming cells) and minimize the imbalance of workload among cells. Furthermore, the presented model considers some limitations, such as machine capability, machine capacity, part demands satisfaction, cell size, material flow conservation, and location assignment. Four numerical examples are solved by the GAMS software to illustrate the promising results obtained by the incorporated features.

A SIMULATED ANNEALING FOR INTRA-CELL LAYOUT DESIGN OF DYNAMIC CELLULAR MANUFACTURING SYSTEMS WITH ROUTE SELECTION, PURCHASING MACHINES AND CELL RECONFIGURATION

This paper develops a novel mixed integer nonlinear programming model for the intra-cell layout design of dynamic cellular manufacturing systems. In dynamic environment, the product mix and part demand are varying during a multi-period planning horizon. As a result, the cell configuration for one period may not be efficient for successive periods and thus necessitates reconfigurations. The proposed model incorporates several design features including intra-cell layout, operation sequence, operation time, alternative process routings, duplicate machines, purchase machine, machine capacity, route selection, production volume of parts, part movements in batch and cell reconfiguration. By considering intra-cell layout and operation sequence, the material handling volume and related cost is calculated more exactly. The objective is to minimize the total costs of inter-cell material handling, forward and backward intra-cell material handling, setting up route, machine relocation, purchasing new machines, machine overhead and machine processing. The main constraints are route selection among flexible routings, machine availability, cell size, machine time-capacity and machine location. The proposed model cannot be solved for large-sized problems optimally within a reasonable amount of computational time. Therefore, an efficient simulated annealing algorithm is developed to overcome NP-hardness of the proposed model.

Designing group layout of unequal-area facilities in a dynamic cellular manufacturing system with variability in number and shape of cells

This paper presents a new mixed-integer non-linear programming model for designing the group layout (GL) of unequal-area facilities in a cellular manufacturing system (CMS) under a dynamic environment. There are some features that make the presented model different from the previous studies. These include: (1) manufacturing cells with variable numbers and shapes, (2) machine depot keeping idle machines, (3) machines of unequal-areas, (4) manufacturing cells with rectangle regular shapes established on the continuous shop floor and (5) integration of cell formation and GL as interrelated decisions involved in the design of a CMS in a dynamic environment. The objective function is to minimises the total costs of intra- and inter-cell material handling, machine overhead, machine relocation, machine processing, purchasing machines and forming cells. Since the problem is NP-hard, an efficient simulated annealing (SA) algorithm is developed to solve the presented model. The performance of this model is illustrated by two numerical examples. It is then tested using several test problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison to the classical genetic algorithm (GA). The obtained results show that the quality of the solutions obtained by SA is better than GA.

Designing a new integrated model for dynamic cellular manufacturing systems with production planning and intra-cell layout

This paper investigates the problem of designing cellular manufacturing systems incorporating several design features including multi-period production planning, sequence of operations, alternate process routings, intra-cell layout, dynamic system reconfiguration, duplicate machines, machine capacity, lot splitting, and material flow between machines in a dynamic environment. The problem is formulated through a comprehensive integer linear programming model. The objective is to minimise the total costs of inter-cell material handling, forward and backward intra-cell material handling, machine operating, machine maintenance and overhead, cell reconfiguration, outsourcing and inventory holding. The main constraints are demand satisfaction, cell size, machine availability, machine time-capacity, material flow conservation and machine location. Computational results are presented by solving some numerical examples to verify the model performance and illustrate its advantageous aspects.

Just-in-time preemptive one-machine problem with costs of earliness- tardiness, interruption and work in process: A mathematical programming

This paper considers preemption and idle time are allowed in a one-machine scheduling problem with just-in-time (JIT) approach. It incorporates earliness/tardiness penalties, interruption penalties and holding cost of jobs which are waiting to be processed as work-in-process (WIP). Generally in non-preemptive problems, earliness/tardiness penalties are a function of the completion time of the jobs. Then, we introduce a non-linear preemptive scheduling model where the earliness penalty depends on the starting time of a job. The model is linearized by an elaborately-designed procedure to reach the optimum solution. To validate and verify the performance of proposed model, computational results are presented by solving a number of numerical examples.

A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system

In this article, a novel bi-objective integer model is presented to integrate reliability and intra-cell layout in designing a cellular manufacturing system (CMS). Minimising the total costs (e.g. inter and intra-cell material handling, machine overhead and operation, and setting up routes) is the first objective with considering operation time, operation sequence, intra-cell layout, alternative process routing, routes selection, machines capacity, parts demand and parts movements in batches. Maximising the processing routes reliability is the second objective. The presented model is capable of modelling different failure characteristics including a decreasing, increasing, or constant value for machine failure rate. An illustrative example is solved to represent the capability of the presented model using the ε-constraint method in order to demonstrate the conflict between the maximum value of the system reliability and the total costs of the system. Next, a multi-objective imperialist competitive algorithm (MOICA) is employed to find near-optimal solutions for medium- and large-sized test problems. Also, the efficiency of the proposed MOICA is revealed by comparison with the performance of a non-dominated sorting genetic algorithm (NSGA-II). The computational results demonstrate that the performance of the proposed MOICA is superior to the NSGA-II. Furthermore, a real-world case study is conducted to validate the proposed model.

Designing an intra-cell layout model in dynamic cellular manufacturing systems with unequal-area facilities

Abstract This paper presents a novel mixed-integer non-linear programming model for an intra-cell layout (ICL) design for a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands vary during a multi-period planning horizon. As a result, the best cell configuration and part routings for one period may not be efficient for successive periods, and necessitating reconfiguration. A novel aspect of this model is the concurrent making of the cell formations and ICL decisions in a dynamic environment. Another aspect of this model is the utilization of a multi-row machine layout for machines of unequal-area. The DCMS model presented incorporates several design features including alternative process routings, operation sequencing, processing time, parts production volume, duplicate machines, machine capacity, ICL, multi-row layout of unequal-area facilities, continuous-area cells and machine relocation. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, machine overheads and machine processing. The main constraints are part processing requirements, machine availability, maximal and minimal cell size, machine time-capacity and the non-overlapping of facilities. A comprehensive example is solved using Lingo software to verify the performance of the proposed model. Also, the computational results show that to some extent the proposed model overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems of unequal-area facilities.

Designing a mathematical model for intra-cell layout of dynamic cellular manufacturing systems considering production planning and system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with incorporating several design features including multi-period production planning, sequence of operations, alternate process routings, intra-cell layout, system reconfiguration, duplicate machines, machine capacity, lot splitting, and material flow between machines in a dynamic environment in which the product mix and part demands are varying during a multi-period planning horizon. The problem is formulated through a comprehensive integer linear programming model. The objective is to minimize the total costs of inter-cell material handling, forward and backward intra-cell material handling, machine operating, machine maintenance and overhead, cell reconfiguration, outsourcing and inventory holding. Computational results are presented by solving comprehensive example.