Buffer Allocation Problem Research Articles

Most important performance evaluation methods of production lines: A comprehensive review on historical perspective and emerging trends

Production is one of the most significant building blocks that strengthen the sustainable economy of companies and thus contribute to the countries’ welfare. Performance indicators of the production line affect planning operations and the efficiency of the supply chain to which the factory is connected. The key indicators for production line designers and performance analysts to monitor and improve include production rate, resource utilization rate, and average inventory level. The production rate is the most important indicator closely affecting an industrial plant's productivity and efficiency levels. From this perspective, accurate and fast estimation of this indicator is very critical. Production rate can be calculated by simulation, analytical technique, or artificial intelligence methods according to the production line characteristics. In this comprehensive review, the most important performance evaluation methods are discussed historically and systematically about the buffer allocation problem using the snowball sampling method. With this explicit motivation, 145 papers were reviewed and classified according to production line topology, hypothetical/real-case line, machine reliability, previous method on which the method is based, and originality and/or line characteristics. To present a comprehensive comparison, the methods considered were analyzed according to different criteria. This review provides general/in-depth qualitative and quantitative discussions and highlights insights to practitioners and scholars. In addition, the impact of recent key work on production line analysis in the field is assessed along with emerging trends, evolving manufacturing paradigms are discussed, and the challenges associated with performance analysis are addressed.

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.

Design of unreliable flow lines: How to jointly allocate buffer space and spare parts

The design of flow lines is an extensive area of research whereby most publications focus on buffer allocation to cope with random influences like breakdowns or random differences in processing times. In addition, spare parts can directly increase the machines’ availability. However, only a few papers consider the simultaneous optimization of buffer space and the possible number of spare parts in stock. The literature hardly even considers the evaluation of such systems. We are the first to introduce the joint optimization of buffer space and spare parts for flow lines of arbitrary length. First, we aim to allocate buffer capacities and spare parts efficiently. Since the buffer allocation problem is NP-hard, we can expect only to find near-optimal solutions. Second, we demonstrate the algorithmic behavior of different greedy and metaheuristics on this design problem. We illustrate how to exploit the problem structure to solve it almost optimally. Third, we generate managerial insights into allocating spare parts in manufacturing systems with buffers. We show that spare parts tend to be more effective when arranged at or near the center of a flow line, as it is already known for buffers. Moreover, we provide details on the combined buffer and spare part allocations.

Combining balancing, sequencing and buffer allocation decisions to improve the efficiency of mixed-model asynchronous assembly lines

When the production of a variety of models is considered in assembly lines, the more suitable control system is asynchronous: the pace of the line is not imposed but depends on the actual time needed in each work centre to complete a product. In asynchronous lines blocking and starvation phenomena are responsible of performances deterioration. Three popular techniques able to limit these phenomena are: balancing the workload among stations by tasks assignment, allocating buffers between stations, optimizing of the sequence of models entering the line. These three techniques correspond to the Mixed-model Assembly Line Balancing Problem (MALBP), the buffer allocation problem (BAP) and the sequencing problem (SP). The presented approach is able to simultaneously solve these three problems, which are strictly connected, and is based on a Genetic Algorithm procedure that uses discrete event simulation to evaluate the fitness function of individuals. Tests conducted on a set of benchmark instances allow quantifying the effect on line efficiency of sequencing and buffer allocation decisions separately, with the aim to assess which of the two techniques is more effective and to investigate whether if combining sequencing optimization and buffer allocation gives additional advantages in terms of increasing line efficiency.

CRPIM: An efficient compute-reuse scheme for ReRAM-based Processing-in-Memory DNN accelerators

Resistive random access memory (ReRAM) is a promising technology for AI Processing-in-Memory (PIM) hardware because of its compatibility with CMOS, small footprint, and ability to complete matrix–vector multiplication workloads inside the memory device itself. However, redundant computations are brought on by duplicate weights and inputs when an MVM has to be split into smaller-granularity sequential sub-works in the real world. Recent studies have proposed repetition-pruning to address this issue, but the buffer allocation strategy for enhancing buffer device utilization remains understudied. In preliminary experiments observing input patterns of neural layers with different datasets, the similarity of repetition allows us to transfer the buffer allocation strategy obtained from a small dataset to the computation with a large dataset. Hence, this paper proposes a practical compute-reuse mechanism for ReRAM-based PIM, called CRPIM, which replaces repetitive computations with buffering and reading. Moreover, the subsequent buffer allocation problem is resolved at both inter-layer and intra-layer levels. Our experimental results demonstrate that CRPIM significantly reduces ReRAM cells and execution time while maintaining adequate buffer and energy overhead.

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing.Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models.Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document.Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process.Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant.

Data-driven hybrid algorithm with multi-evolutionary sampling strategy for energy-saving buffer allocation in green manufacturing

Buffer allocation, which is an important research topic in manufacturing system design, typically focuses on system performance and cost. However, few previous studies have been performed to investigate energy-saving buffer allocation, which can decrease operational energy consumption in green manufacturing. Furthermore, the computational efficiency of solving the buffer allocation problem requires further investigation. This paper proposes a data-driven hybrid algorithm based on multi-evolutionary sampling strategies for solving energy-saving buffer allocation that can maximize the throughput rate and minimize energy consumption. Two evolutionary sampling strategies, that is, global search and surrogate-assisted local search, are integrated to balance exploitation and exploration. In addition, a database containing historical data pertaining to buffer allocation solutions is used to develop surrogate models that can rapidly predict the throughput and energy consumption and improve the evaluation efficiency of the local search strategy. Experimental results demonstrate the efficacy of the proposed algorithm. This study contributes to an efficient buffer allocation and presents a new perspective on energy-saving measures for green manufacturing designs.

A Model Review on Joint Optimization of Part Quality Inspection Planning, Buffer Allocation, and Preventive Maintenance in SMMS

In serial multi-stage manufacturing systems (SMMS), optimization of part quality inspection planning (PQIP), buffer allocation problem (BAP), and preventive maintenance (PM), individually and jointly, is attracting researchers’ attention. The model formulation for complicated manufacturing systems and the previously mentioned joint decisions is very beneficial given the interdependencies between the various manufacturing functions. As a result, this paper evaluates the literature on joint optimization of the multi-stage serial production system. The literature is classified based on the decision variables basis to represent each manufacturing function [inspection sample size and allocation (PQIP), buffer sizing and allocation (BAP), and preventive maintenance scheduling (PM)], and a general example model is presented in each classification, with a summary of recent studies, solution methods, research gaps, and future research recommendations. In the integrated models, almost all the studies considered only two functions, with that it is worth noting that research into the optimization of over two functions is still in its beginning. Furthermore, most studies neglected many of the real industrial settings that should also be integrated into the model. And finally, there was no specific solution technique recommended in the literature, yet a general simulation optimization method was used to generate and evaluate the combinatorial complex joint models.

A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines

The multi-objective buffer allocation problem of production lines is a non-deterministic-polynomial-hard problem. Many metaheuristic algorithms have been proposed to solve this problem. However, further investigation of new algorithms is still required because metaheuristic algorithms highly depend on the problem types. Furthermore, the balance between the solution quality and computational efficiency requires further improvement. Therefore, a data-driven algorithm consisting of the black widow optimizer and simulated annealing algorithm is proposed to maximize throughput and minimize energy consumption in production lines. Numerical examples demonstrate that the proposed algorithm achieves better solution quality than other state-of-the-art algorithms without losing computational efficiency. This study contributes to multi-objective optimization of resource scheduling in production lines.

An Interactive Two-Stage Framework for Simultaneous Machine Selection and Buffer Allocation

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines

Designing production lines is an important research issue for both academy as well as industry since it should consider both system efficiency and production costs. In this study, the buffer allocation problem (BAP) is solved to maximize the profit of unreliable lines. The profit of a production line is a function of the throughput and various costs, such as WIP, holding and buffer space costs. In this respect, a crucial decision-making problem, i.e., BAP, is how to allocate the finite buffers to deal with the trade-off between maximizing the throughput of the line and production costs. In this study, profit maximization problem is formulated considering two different objective functions and solved under different constraint sets. An adaptive hybrid variable neighborhood search algorithm that incorporates large neighborhood search as a part of the diversification strategy is proposed to solve the problem. Moreover, a new initialization procedure based on the buffer location providing more profit is proposed to reduce the search effort. The efficiency of the proposed algorithm is tested on existing benchmark problems as well as the newly introduced large-sized data sets. In addition to these experiments, a comprehensive experimental design is conducted to determine the influencing factors on the problem at hand. The experimental study reveals that the proposed solution algorithm is capable to solve the profit maximization problem for large production lines, and the number of machines and the reliability parameters are the most influential factors in solving the BAP for profit maximization. Moreover, it has been observed that the proposed initialization procedure significantly reduces the search effort.

A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines

The Buffer Allocation Problem is a well-known optimization problem aiming at determining the optimal buffer sizes in a manufacturing system composed by various machines decoupled by buffers. This problem still has scientific relevance because of problem complexity and trade-off between conflicting goals. Moreover, it assumes industrial relevance in reconfigurable manufacturing lines, where buffer sizes can be easily adapted to the production scenario. This work proposes a novel algorithm integrating performance evaluation and optimization by means of throughput cuts based on a linear approximation. Numerical results show the validity of the proposed approach with respect to the traditional gradient-based method. Moreover, an industrial case study integrating the proposed approach into a decision-support system for the buffer allocation and reallocation is analyzed.

Heuristic Approach for a Combined Transfer Line Balancing and Buffer Allocation Problem Considering Uncertain Demand

In this paper, we refer to a real case study of an industrial partner recently committed to its project on the design of a multi-unit and multi-product manufacturing system. Although the considered problem refers to an actual complex manufacturing system, it can be theoretically classified as a union of two key problems that need to be solved during the transfer line design stage: the transfer line balancing problem (TLBP) and the buffer allocation problem (BAP). As two closely related problems, TLBP and BAP usually have similar optimizing directions and share the same purpose: finding a balance between the performance of the transfer line system as well as minimizing investment costs. These problems are usually solved sequentially, but this leads to solutions close to a local optimum in the solution space and not to the global optimum of the overall problem. This paper presents a multi-objective optimization for concurrently solving transfer line balancing and buffer allocation problems. The new approach is based on a combination of evolutionary and heuristic-based algorithms and takes into account the uncertainty of market demand. To validate the proposed approach, an industrial case study in a multi-unit manufacturing system producing multiple products (four engine blocks) is discussed.

On Local Optima Distribution in Buffer Allocation Problem for Production Line with Unreliable Machines

In this paper, we consider a buffer allocation problem in manufacturing flow lines with series-parallel network structure where nodes correspond to buffers of finite capacity, and arcs correspond to the machines. The machines are supposed to be unreliable, their time to failure and repair time are assumed to be exponentially distributed. Different machines may have different production rates and the production rates of all machines are assumed to be deterministic. The buffer allocation problem is to determine the capacities of all buffers with respect to a given optimality criterion, which is a function of the average production rate of the line, the buffer acquisition and installation cost and the inventory cost. In search for the optimum, the tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with the local search and genetic algorithms. It turns out that the “massif central” or “big valley” structure of the fitness landscape is present but only partially: The fitness of the local optima is negatively correlated with the distance to the best found solution, yet the set of local optima can not be encompassed by a ball of relatively small radius. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The symmetries of the fitness function are discussed and suggested as the possible cause of the local optima clustering. Finally the performance of genetic algorithms is bfiefly discussed with respect to solutions clustering.

A Bottleneck Detection-Based Tabu Search Algorithm for the Buffer Allocation Problem in Manufacturing Systems

Buffer allocation is an important research issue in the design and optimization of manufacturing systems. One objective of buffer allocation is to maximize the throughput subject to the total buffer capacity in manufacturing systems. Previous studies have proposed many approaches to solve the buffer allocation problem, and tabu search has been proven to be effective in obtaining a near-optimal solution. To further improve the computational efficiency, this study proposes a bottleneck detection-based tabu search algorithm to efficiently solve the buffer allocation problem of manufacturing systems with sufficient solution quality. In particular, the tabu search algorithm integrated with diversification strategies is proposed to maintain satisfactory solution quality of the buffer allocation. The bottleneck detection cooperates with the variable neighborhood structure to search for proper locations for allocating buffers, decreasing the computational time of the buffer allocation. In addition, an existing throughput evaluation method is integrated with the proposed approach to evaluate neighborhood solutions by calculating the manufacturing system throughput. Numerical examples show that the proposed approach can obtain the near-optimal solution more efficiently than the simple tabu search and adaptive tabu search algorithms. This study improves the computational efficiency of buffer allocation in manufacturing systems without losing solution quality, contributing to efficient resource reconfiguration and process management.

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

The current context of rising ecological awareness and high competitiveness, reveals a strong necessity to integrate the sustainability paradigm into the design of production systems. The buffer allocation problem is of particular interest since buffers absorb disruptions in the production line. However, despite the rich literature addressing the BAP, there are no studies that use a multi-objective framework to deal with energetic considerations. In this study, the energy-efficient buffer allocation problem (EE-BAP) is studied through a multi-objective resolution approach. The multi-objective problem is solved to optimize two conflicting objectives: maximizing production throughput and minimizing its energy consumption, under a total storage capacity available. The weighted sum and epsilon-constraint methods as well as the elitist non-dominated sorting genetic algorithm (NSGA- II) are adapted and implemented to solve the EE-BAP. The obtained solutions are analyzed and compared using different performance metrics. Numerical experiments show that epsilon-constraint outperforms the NSGA- II when considering comparable computational time. The Pareto solutions obtained are trade-offs between the two objectives, enabling decision making that balances productivity maximization with energy economics in the design of production lines.

Buffer allocation problem in a shoe manufacturing line: A metamodeling approach

Footwear production is subject to the variability inherent in any process, and producers often need to apply tools that allow them to make the right decisions. This work documents the process to optimize the buffer allocation in a shoe manufacturing line minimizing the cycle time in the system, applying a metamodeling approach. It was found that the Front sewing operation, and the interaction between the Lining sewing operation and the assembly operation have the greatest effect on the flow time of the product within the process; the optimum assignment of spaces follows a non-uniform arrangement on the line saturating the slower stations; the cycle time follows a non-linear behavior vs. the total number of spaces (N) in the line. For a certain value of N, the cycle time reaches a minimum value.

Buffer allocation problem in production flow lines: A new Benders-decomposition-based exact solution approach

The Buffer Allocation Problem (BAP) in production flow lines is very relevant from a practical point of view and very challenging from a scientific perspective. For this reason, it has drawn great attention both in industry and in the academic community. However, despite the problem’s relevance, no exact method is available in the literature to solve it when long production lines are being considered, i.e., in practical settings. This work proposes a new Mixed-Integer Linear Programming (MILP) formulation for exact solution of sample-based BAP. Due to the huge number of variables and constraints in the model, an algorithm based on Benders decomposition is proposed to increase the computational efficiency. The algorithm iterates between a simulation module that generates the Benders cuts and an optimization module that involves the solution of an updated MILP model. Multiple Benders cuts after each simulation run are generated by exploiting the structural properties of reversibility and monotonicity of flow line throughput. The new MILP formulation is tighter than the state-of-the-art model from a theoretical point of view, and order of magnitude of computation time saving is also observed in the numerical results.

Energy-efficient buffer allocation problem in unreliable production lines

The buffer allocation problem (BAP) is one of the major optimization problems considered by production systems designers. The BAP is widely studied in the literature, since buffers have a great impact on improving the efficiency of production systems especially for mass production. Nevertheless, with all upcoming changes in world’s economy, industrial requirements and ecological awareness, the necessity of considering energy efficiency in manufacturing systems design becomes crucial. This paper introduces the first variant of energy-efficient buffer allocation problem (EE-BAP). It addresses a novel integrated analytical method to find the best buffer allocation that optimizes both throughput and energy consumption for serial production lines with unreliable machines. A large experimental study, based on different instances well established in the literature, is carried out to analyze the relevance of the proposed method. The obtained results show that considerable energy savings with low or almost no productivity loss are allowed. The energy-efficient BAP is a novel problem that considers the complex trade-off between throughput and energy efficiency performance and introduces new perspectives of research in the field of sustainable manufacturing systems design.