Statistical design of sustainable composites from poly(lactic acid) and grape pomace

Biocomposites from poly(lactic acid) (PLA) and grape pomace (GP) were created via injection molding to examine the effects of GP in a PLA matrix. To optimize the mechanical performance the biocomposites were compatibilized with maleic anhydride grafted PLA (MA-g-PLA). The objective of this work was to create a model that could accurately predict the mechanical properties of GP/PLA biocomposites. A region of feasibility for the biocomposites was determined using a statistical design of experiments. Linear regression was used to model the mechanical performance and predicted results with an error of 10% for both tensile and flexural strength and 16% for impact strength. The model was verified with a biocomposite of PLA/GP/MA-g-PLA with a ratio of 62/36/2. This biocomposite had a tensile strength, flexural modulus, and impact strength of 25.8 MPa, 40.0 MPa, and 18.4 J/m, respectively. It was found that a linear model can accurately predict the mechanical properties of PLA/GP/MA-g-PLA biocomposites.     

A hybrid genetic algorithm for flowshop lot streaming with setups and variable sublots

Lot streaming is the process of splitting a given lot or job to allow the overlapping of successive operations in flowshops or multi-stage manufacturing systems to reduce manufacturing lead time. Recent literature shows that significant lead time improvement is possible if variable sublots, instead of equal or consistent sublots, are used when production setup time is considered. However, lot streaming problems with variable sublots are difficult to solve to optimality using off-shelf optimisation packages even for problems of small and experimental sizes. Thus, efficient solution procedures are needed for solving such problems for practical applications. In this paper, we develop a mathematical programming model and a hybrid genetic algorithm for solving n-job m-machine lot streaming problems with variable sublots considering setup times. The preliminary computational results are encouraging.     

Machine cell formation using a mathematical model and a genetic-algorithm-based heuristic

This paper presents a comprehensive mathematical model and a genetic-algorithm-based heuristic for the formation of part families and machine cells in the design of cellular manufacturing systems. The model incorporates dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, subcontracting cost, tool consumption cost, set-up cost and other practical constraints. To solve this model efficiently, a two-phase genetic-algorithm-based heuristic was developed. In the first phase, independent cells are formed which are relatively simple to generate. In the second phase, the solution found during the first phase is gradually improved to generate cells optimizing inter-cell movement and other cost terms of the model. A number of numerical examples of different sizes are presented to demonstrate the computational efficiency of the heuristic developed.     

A parallel multiple Markov chain simulated annealing for multi-period manufacturing cell formation problems

Simulated annealing (SA) is a general purpose optimization technique capable of finding optimal or near optimal solutions in various applications. The major disadvantage of this technique is its slow convergence making it not suitable for solving many complex optimization problems. This limitation may be alleviated by parallel computing using a multiprocessor computer or a cluster of workstations. In this paper, we present an integer programming model for solving a multi-period cell formation problem in cellular manufacturing system. In order to solve the mathematical model efficiently, we developed a multiple Markov chain simulated annealing algorithm which allows multiple search directions to be traced simultaneously. Our computational results on a single processor machine showed that multiple Markov chain SA is much more efficient than a conventional single Markov chain SA. The parallel implementation of the multiple Markov chain SA further improves its computational efficiency in terms of solution quality and execution time.     

A statistical approach to engineer a biocomposite formulation from biofuel coproduct with balanced properties

A 3²‐full factorial design of experiment (DOE) and regression modeling were implemented together as a practical approach to attain a distillers' grains‐filled biocomposite with balanced mechanical and physical properties. The effects of compatibilizer and lubricant on tensile strength, flexural modulus, impact strength and melt flow index of the biocomposites were studied. Analysis of variance (ANOVA) was implemented to develop least square regression models containing statistically significant main effects (linear and quadratic) and interaction effect. The developed models showed good predictability for the new measurements. The statistical approach adopted in this work including overlaying contour plots of the response surfaces in the studied level domain was effective in highlighting an optimized region that leads to balanced mechanical and physical properties. © 2014 The Authors Journal of Applied Polymer Science Published by Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40443.     

Environmental and economic prospects of biomaterials in the automotive industry

Clean Technologies and Environmental Policy - This study provides an overview of the application of biomaterials in automotive industries and their economic and environmental implications. It also...     

Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem

Lot streaming is the technique of splitting a given job into sublots to allow the overlapping of successive operations in multi-stage manufacturing systems thereby reducing production makespan. Several research articles appeared in literature to solve this problem and most of these studies are limited to pure flowshop environments where there is only a single machine in each stage. On the other hand, because of the applicability of hybrid flowshops in different manufacturing settings, the scheduling of these types of shops is also extensively studied by several authors. However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this paper, we aim to contribute in bridging the gap between the research efforts in flowshop lot streaming and hybrid flowshop scheduling. We propose a mathematical model and a genetic algorithm for the lot streaming problem of several jobs in multi-stage flowshops where at each stage there are unrelated parallel machines. The jobs may skip some of the stages, and therefore, the considered system is a complex generalized flowshop. The proposed genetic algorithm is executed on both sequential and parallel computing platforms. Numerical examples showed that the parallel implementation greatly improved the computational performance of the developed heuristic.     

A simulated annealing with multiple‐search paths and parallel computation for a comprehensive flowshop scheduling problem

Recent studies have demonstrated that the performance of a simulated annealing algorithm can be improved by following multiple‐search paths and parallel computation. In this paper, we use these strategies to solve a comprehensive mathematical model for a flexible flowshop lot streaming problem. In the flexible flowshop environment, a number of jobs will be processed in several consecutive production stages, and each stage may involve a certain number of parallel machines that may not be identical. Each job has to be split into several unequal sublots by following the concept of lot streaming. The sublots are to be processed in the order of the stages, and sublots of certain products may skip some stages. This complex problem also incorporates sequence‐dependent setup times, the anticipatory or nonanticipatory nature of setups, release dates for machines, and machine eligibility. Numerical examples are presented to demonstrate the effectiveness of lot streaming in hybrid flowshops, the performance of the proposed simulated annealing algorithm, and the improvements achieved using parallel computation.     

Jobshop lot streaming with routing flexibility,sequence-dependent setups,machine release dates and lag time

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging.     

A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems

Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.