Material Handling Cost Research Articles

Integrated cell formation and layout problem considering multi-row machine arrangement and continuous cell layout with aisle distance

Cellular manufacturing is an important application of group technology and the cell formation process is one of the important steps in designing cellular manufacturing system. In recent years, researchers have noticed potential benefits when the layout problem is considered within the cell formation process. Nevertheless, there are not sufficient studies about consideration of real-life features in the cell formation and layout design process. In this paper, a new approach is presented to integrate the cell formation and its layout design. The proposed approach has three important design features not found in other papers. These design features are multi-row intra-cell layout (layout of machines within the cells), continuous inter-cell layout (layout of rectangular shape cells on the planar area), and aisle distance. The objective of the proposed approach is to form machine cells, find the layout of machine cells, and obtain the arrangement of machines within the cells such that the total material handling cost is minimized. In order to have a more accurate layout design, the material handling cost is calculated in terms of the actual position of machines within the cell. Due to the computational complexity of the proposed problem, a heuristic method is proposed to solve medium- and large-scale problems in a reasonable computational time. Three lower bounds are developed for the proposed integrated problem in which the tightest of them is chosen for evaluating the solution of the heuristic method. Finally, numerical examples adopted from the literature are solved to verify the performance of the proposed heuristic method and illustrate the advantages of the proposed integrated approach. The results indicated that the heuristic method is both effective and efficient in solving real-sized problems. The results also demonstrated that the proposed layout approach gives better layout design in comparison with the existing approaches.

Solving an Extended Double Row Layout Problem Using Multiobjective Tabu Search and Linear Programming

Facility layout problems have drawn much attention over the years, as evidenced by many different versions and formulations in the manufacturing context. This paper is motivated by semiconductor manufacturing, where the floor space is highly expensive (such as in a cleanroom environment) but there is also considerable material handling amongst machines. This is an integrated optimization task that considers both material movement and manufacturing area. Specifically, a new approach combining multiobjective tabu search with linear programming is proposed for an extended double row layout problem, in which the objective is to determine exact locations of machines in both rows to minimize material handling cost and layout area where material flows are asymmetric. First, a formulation of this layout problem is established. Second, an optimization framework is proposed that utilizes multiobjective tabu search and linear programming to determine a set of non-dominated solutions, which includes both sequences and positions of machines. This framework is applied to various manufacturing situations, and compared with an exact approach and a popular multiobjective genetic algorithm optimization algorithm. Experimental results show that the proposed approach is able to obtain sets of Pareto solutions that are far better than those obtained by the alternative approaches.

MATERIAL FLOW ANALYSIS AND PLANT LAY-OUT OPTIMIZATION OF A MANUFACTURING SYSTEM

The paper proposes the results of a research project developed by the authors in collaboration with a production system working in the field of manufacturing wood products. The first step of the project was an accurate analysis of the system and the design of new production documents in order to collect data about the system itself. The collected data have been used to propose an initial solution of plant lay-out. The second step of the project was the construction of a simulation model. The model has been used to find an optimal plant- layout configuration by means of genetic algorithms with the goal of material handling cost and shop order flow time minimization.

APPLYING SIMULATED ANNEALING TO A GENERALIZED CELL FORMATION PROBLEM CONSIDERING ALTERNATIVE ROUTINGS AND MACHINE RELIABILITY

One of the most important steps in designing a cellular manufacturing system is cell formation which includes grouping the machines in cells and the parts as part families, so that the costs are minimized. Several aspects of the problem should be taken into account in cell formation; more specifically, machines and their reliability are among the most important issues that should be modelled correctly. Another important facet of a cellular manufacturing system is material handling cost consisting of inter-cellular and intra-cellular movement costs. In addition, setup cost may play a significant role in decision-making in many real world problems of cell formation. Obviously, cell formation cannot be completed without considering the demands for parts. Considering all these aspects of the problem in this research, a generalized model for solving cell formation problem is proposed. Exact methods, e.g., B&B, are cumbersome in solving such complex models for large-size problems. Therefore, in this paper, a modified version of simulated annealing algorithm is designed and numerical examples are provided to show that the proposed method is efficient and effective.

A note on “Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem”

Abedzadeh et al. (Int J Adv Manuf Technol 65:197–211, 2013) proposed a mixed integer programming formulation for dynamic flexible bay layout problem with three objectives: minimizing material handling and relayout cost, maximizing adjacency ratio, and minimizing shape ratio difference. In this paper, we correct the formulation proposed by Abedzadeh et al. (Int J Adv Manuf Technol 65:197–211, 2013). This correction is mainly focused on the three objectives of their formulation. It is shown that the relayout cost was miscalculated, the adjacency ratio was improperly linearized, the third objective was incorrectly linearized and fuzzy programming technique for multi-objective linear programming problems was not applied correctly for this problem.

A new 0-1 linear programming approach and genetic algorithm for solving assignment problem in flexible manufacturing system

Flexible manufacturing system (FMS) consists of some multi-functional machines that are linked together through a material-handling system and the whole of the system is controlled by a central computer. One of the most important problems in production planning of FMS is machine–tool selection and operation allocation problem that directly influences the production costs and time. This paper presents a 0-1 integer linear programming model for the multi-objective machine–tool selection and operation allocation problem in FMS. The proposed model attempts to simultaneously minimize machining cost, material-handling cost, setup cost, and maximum machine work load time and tool life. We also consider the constraints of tool magazine capacity, tool life, and machine capacity in the problem. Due to the nondeterministic polynomial (NP)-hard nature of the derived problem, a genetic algorithm is developed to solve the model. Computational results indicate the effectiveness of the proposed algorithm for solving the problem.

Métodos discretos basados en quimiotaxis de bacterias y algoritmos genéticos para solucionar el problema de la distribución de planta en celdas de manufactura.

<p><span style="font-family: Arial;">Este trabajo presenta la solución mono-objetivo y multi-objetivo del problema de la distribución de planta en celdas de manufactura a través de dos nuevos algoritmos híbridos discretos basados en quimiotaxis de bacterias y en algoritmos genéticos. Los modelos propuestos resuelven simultáneamente los dos inconvenientes que constituyen el problema de la distribución de planta en celdas de manufactura: la formación de las celdas y la distribución de planta intra e inter celdas, considerando el agrupamiento de las celdas y el costo de transporte y manipulación de materiales. El desempeño de las propuestas se evaluó con problemas de prueba de distribución de planta de celdas de manufactura, agente viajero (TSP) y el caso multi-objetivo del problema de las mochilas. Los resultados mono-objetivo se compararon con AG, BFOA y <em>Bacterial</em>-GA, mientras que los resultados multi-objetivo se compararon con los reconocidos algoritmos NSGA2 y SPEA2 en los que se obtuvo un mejor desempeño en los dos casos.</span></p>

Optimization of P.E. area division and arrangement based on product mix

The shipbuilding industry in recent times has witnessed a decline in the demand for commercial ships and a sharp increase in the demand for offshore plants. In response to this change in the industry, shipbuilding companies have sought an optimal product mix for producing commercial ships and offshore plants with minimal additional investment in facilities. This study was aimed at optimizing the pre-erection (P.E.) area partition for ship and offshore blocks based on the forecasted product mix of the planning horizon. The offshore P.E. area requires additional investment in facilities because the blocks are usually much heavier than those of ships. For given production schedules, and taking the flexibility of the forecasted change into consideration, we determined the optimal P.E. area partition ratio using the dynamic layout problem methodology. In this study, material handling cost, operating cost, rearrangement cost, and additional P.E. area construction cost were used to determine the optimal division. The optimal division of the P.E. area for offshore plants was then determined for a 10-year planning horizon. The results were applied to a shipyard to evaluate their business feasibility.

A new model for robust facility layout problem

The Facility Layout Problem (FLP) is the problem of locating each department in a long plant floor without any overlap between departments in order to minimize the material handling cost. The main purpose of this study is to show the effectiveness of a robust approach to solve FLP.In this study, it is assumed that the departments’ length and width are not predetermined. For modeling this kind of uncertainty, the size of each department is considered as a bounded variable and two new parameters are also introduced to implement a robust approach. Moreover, a new adaptive algorithm is designed to determine the robust layout with respect to the decision makers’ requirements. Furthermore, the lower and upper bounds of objective function are obtained in this model. Due to the complexity of the problem, a set of problems is generated and tested by the proposed algorithm. The results show the efficiency of our model.

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.

A novel approach for considering layout problem in cellular manufacturing systems with alternative processing routings and subcontracting approach

In this paper a new integrated approach is presented for designing cellular manufacturing system and its inter- and intra-cell layouts. Various production factors such as part demands, alternative processing routings, operation sequences, processing times, capacity of machines, etc. are incorporated in the problem in order to extend its applicability. To increase the accuracy of the inter- and intra-cell layout design, the material handling cost is calculated in terms of the actual position of machines within the cells and regarding the dimensions of the machines and aisle distances. Also, a subcontracting approach is proposed to determine the production volume of parts within the CF and layout design process regarding the production, material handling and outsourcing costs and under demand and machine capacity constraints. To the best of our knowledge, this is the first study that addresses all these design features simultaneously. As the proposed problem is NP-hard, an efficient GA is employed to solve it. Finally, numerical examples adopted from the literature are used to verify the proposed approach.

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.

Production Layout Improvement for Steel Fabrication Works

This paper focuses on the study of a steel fabrication works production line in a particular company in Malaysia. The company’s main business is steel cutting and bending service. Due to high number of customer orders, the current production layout has to be improved. The objectives of the study was to improve the current layout in order to minimise material handling cost, improve flexibility for operation, utilise the available area and minimise overall production time. The improvement process layout was studied using WITNESS software. Production analysis has been conducted in the process flow of the company in order to identify improvement at the problematic areas. Finally, improvement alternative solutions were proposed based on Witness simulation.

An efficient local search heuristic for the double row layout problem with asymmetric material flow

The double row layout problem (DRLP) consists of arranging a number of rectangular machines of varying widths on either side of a corridor to minimize the total cost of material handling for products that move between these machines. This problem arises in the context of many production environments, most notably semiconductor manufacturing. Because the DRLP contains both combinatorial and continuous aspects, traditional solution approaches are not well suited to obtain solutions within a reasonable time. Moreover, previous approaches to this problem did not consider asymmetric flows. In this paper, an effective local search procedure featuring linear programming is proposed for solving the DRLP with asymmetric flows (symmetric flows being a special case). This approach is compared against several constructive heuristics and solutions obtained by a commercial mixed integer linear programming solver to evaluate its performance. Computational results show that the proposed heuristic is an effective approach, both in terms of solution quality and computational effort.

An Adaptive Genetic Algorithm for Digital Facility Layout Optimization Under the Multi-Species and Variable-Batch Production Mode

For the NP-hard characteristic of facility layout problem (FLP) and its importance to industry, many optimal and heuristic algorithms have been designed to solve the problem. But when optimizing the production, with fixed prob- abilities, traditional GA has its flaws with slow convergence speed and the less-than ideal accuracy of the optimal solu- tion. According to the characteristics of multi-species and variable-batch production mode, this paper analyzed those weak points and proposed an improved adaptive genetic algorithm with the objective of minimizing the material handling cost. This genetic algorithm has adaptive probabilities based on the fitness. It can keep the diversity and excellence of the genes. The proposed model of GA also has been tested and verified by simulation with MTALAB. According to the results, it shows that the proposed approach improved the convergence speed and the accuracy of the optimal solution and can help the factory make right decision.

Two-stage method for solving cell formation and layout problems with alternative routings and machine capacity constraints

This paper presents a two-stage method which can be used for the precise evaluation of the material handling cost within the CF process. In the first stage, a mathematical model is presented for minimizing the weighted sum of the total inter- and intra-cell movement cost by considering alternative processing routings and under constraints on cell size and machine capacity. In this stage, depending on the weight proposed in the model, different machine cells can be formed. In the second stage, another mathematical model is presented for minimizing the total material handling cost by taking into account the dimensions of the machines, the widths of the cells and the aisle distances; the linear arrangement of machines within the cells as well as the cell layout are obtained in this stage. Each machine cell formed in the first stage is evaluated using the proposed model in the second stage. Finally, the solution with the lowest material handling cost is selected as the best solution. Several numerical examples adopted from the literature are solved by the CPLEX solver to verify the performance of the proposed method. Finally, the computational results are presented.

Dynamic Cell Formation based on Multi-objective Optimization Model

In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM) formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.

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.

A multi-objective mixed-integer programming model for a multi-floor facility layout

We consider a multi-floor facility layout problem in which the overall length and width of the facility, the size and location of each department, the number and the location of elevators and the number of floors in the facility are all modelled as decision variables. We adapt a linear approximation scheme to represent the area of each department. We consider two objective functions in our model, namely minimising material handling and facility building costs, and propose a lexicographic ordering technique to handle multiple objectives. The numerical experiments show that the slack used in the lexicographic ordering approach has a significant impact on the optimal solution. The experiments also show that the material handling cost can be significantly reduced in a multi-floor facility compared with a single-floor facility.

A two-step heuristic algorithm for layout design of unequal-sized facilities with input/output points

The facility layout problem (FLP) is to arrange facilities within a given area so as to minimise the total material-handling cost. An efficient arrangement of facilities plays an important part in the achievement of high productivity in a manufacturing system. This paper focuses on the layout design of unequal-sized facilities with fixed shapes and input/output (I/O) points. A mixed-integer programming (MIP) model is developed to obtain optimal solutions. Since the MIP model is not applicable to large-sized problems, a two-step heuristic algorithm is developed to solve the FLPs. In the first step, a layout solution with moderate quality is generated by using an interconnected zone algorithm and a simulated annealing (SA) algorithm. The zone algorithm is a newly developed layout construction technique which places facilities successively within designated zones according to a placing sequence. The zone concept is introduced to reduce the solution space such that the computational effort is decreased, while SA is used to search for the placing sequences. The second step improves that solution further using the reduced MIP by employing the relative position information obtained from the first step. Computational experiments indicate that the algorithm in the first step can yield on average a 1.048% improvement on the best solutions obtained by the existing algorithms. The second step can further improve the solutions obtained in the first step by 0.484% on average. Compared to the commercial software VIP-PLANOPT 2006 (Engineering Optimization Software 2005), the proposed two-step algorithm improves the solutions by 9.113% on average.