Multi-product Line Research Articles

Optimizing modular equipment in the design of multi-product reconfigurable production lines

This work addresses the problem of the design and balancing of a modular multi-product line in a reconfigurable environment. The study emphasizes the importance of modularity in the reconfigurable manufacturing systems (RMS), which allows fast, efficient, and cost-effective adaptation to changes by rearranging, moving, or removing modules. The line consists of a fixed number of modular machines arranged linearly and connected by a material handling system. Each machine has a limited number of slots for modules. The line can handle several product types per batch, and each batch requires a specific line configuration to be produced. The objective consists in identifying the minimum number of modules needed and their arrangement in different configurations to produce each product type. To solve this problem, a compact integer linear programming (ILP) formulation is initially investigated. Then an ILP reformulation, based on a decomposition approach, as well as a heuristic that uses this ILP reformulation are developed. The results demonstrate the effectiveness of the proposed methods.

Dynamic hoist scheduling for multi-recipe and multi-stage production lines: A logical framework

This article studies the dynamic multi-hoist scheduling problem in a robotic cell with multi-function and parallel processing units. We consider a general case of a multi-recipe and multi-stage material handling process, which can be encountered in many manufacturing industries such as electronics, chemical, food, automotive, and steelmaking. Scheduling such a process, even for a line with a single hoist, is very complex and has been merely dealt with using exact mathematical methods. We propose a logic-based optimization model to solve the task scheduling and resource allocation problem for a line with multiple hoists. Then necessary and sufficient conditions are presented to avoid collision between the hoists. A compact formulation for these constraints is then integrated into the model. We consider the units with multi-capacity as a special case of parallel units; nevertheless, we drive a more efficient model to deal with this problem in particular. Eventually, the logical constraints are translated into a MILP model that can be solved to optimality to minimize both productivity and hoist movement. In the end, we verify the effectiveness of the proposed method by applying it to various practical problems.

An optimization model for the downstream supply chain network, considering consolidated warehouses and the selection of transportation mode

PurposeThe supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.Design/methodology/approachThe model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.FindingsTotal cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.Research limitations/implicationsThe study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.Originality/valueThe issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Energy-efficient control in multi-stage production lines with parallel machine workstations and production constraints

Nowadays, the growing interest in industry for enhancing manufacturing processes sustainability is a major trend. One of the most supported strategies to increase the energy-efficiency of manufacturing activities is the control of machine state towards the optimum trade-off between production rate and energy demand. This method is referred to as energy-efficient control and it triggers machines in a standby state with low power request. In this article, multi-stage production lines composed of identical parallel machine workstations are the systems of interest, and the energy-efficient control policies make use of buffer level information. Each machine can be switched off instantaneously and switched on with a stochastic startup time. Problem objective is to minimize the energy demand while ensuring production constraints. This article proposes a novel approach to solve the problem at hand. An exact model for two-stage system is formulated using a Markov Decision Process to be solved with a linear programming methodology. A novel technique, namely the Backward-Recursive approach, is used to address systems with more than two stages. Numerical experiments confirm the effectiveness of the proposed approach.

Dynamic Hoist Scheduling for Multi-Recipe and Multi-Stage Production Lines: A Logical Framework

Dynamic Hoist Scheduling for Multi-Recipe and Multi-Stage Production Lines: A Logical Framework

Development of Machine Learning prediction models for their integration in a Digital Twin for a tapered roller bearing production line

The aim of this work is to develop the prediction models that are integrated in a digital twin for a tapered roller bearing multi-stage production line. The manufacturing process consists of rings machining and component assembly processes, including intense quality control. This work proposes the use of machine learning techniques for a 4-step strategy which consists of: a data analysis, the development of one prediction model for the manufacture of the double outer ring, one model for the two inners rings, and finally their integration in the digital twin. The strategy is validated with real data. Several regression techniques are tested and the selected model is the exponential regression due to its better performance when compared with other algorithms. Once incorporated in the digital twin, the developed models can predict the process behaviour under potential changes so determining the optimum operating conditions can be fairly facilitated; as well as predict the final bearing setting under different machining conditions.

Quantifying the Impact of Inspection Processes on Production Lines through Stochastic Discrete-Event Simulation Modeling

Inspection processes are becoming more and more popular beyond the manufacturing industry to ensure product quality. Implementing inspection systems in multistage production lines brings many benefits in productivity, quality, and customer satisfaction. However, quantifying the changes necessary to adapt the production to these systems is analytically complicated, and the tools available lack the flexibility to visualize all the inspection strategies available. This paper proposed a discrete-event simulation model that relies on probabilistic defect propagation to quantify the impact on productivity, quality, and material supply at the introduction of inspection processes in a multistage production line. The quantification follows lean manufacturing principles, providing from quite basic quantity and time elements to more comprehensive key performance indicators. The flexibility of discrete-event simulation allows for customized manufacturing and inspection topologies and variability in the tasks and inspection systems used. The model is validated in two common manufacturing scenarios, and the method to analyze the cost-effectiveness of implementing inspection processes is discussed.

Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm

Buffer capacity allocation plays a key role in the design of production lines. By means of polymorphism analysis on production capacity and capability, we investigate the buffer allocation problem in multistage production lines with unreliable machines. The allocated buffers simultaneously maximize the system theoretical production rate and minimize the system state entropy. A mathematical model for buffer capacity optimization was established and optimized using an extended vector universal generating function (UGF) and an improved adaptive nondominated sorting genetic algorithm II (NSGA-II). The feasibility and effectiveness of the proposed method were verified by applying the model to a production line of engine heads.

Transients in flexible manufacturing systems with setups and batch operations: Modeling, analysis, and design

Significant research and practice efforts have been devoted to flexible manufacturing systems. Many of them focus on performance analysis, production and inventory control, planning, and scheduling. Steady state analysis is prevalent in these studies. The transient behavior of flexible lines is less investigated. However, the dynamic changes in customer demands and the uncertain nature in production make the transient performance critical for system control, scheduling, and improvement. Due to non-negligible setups during product change, batch operation is typically carried out in many flexible lines. How to design the system to allocate multiple products, determine batch size, and schedule part sequence is of significant importance to system performance during transients. In this article, an analytical method is developed to evaluate the system throughput, work-in-process, and other performance measures in transient periods for multi-product lines with Bernoulli reliability machines, finite buffers, non-negligible setups, and batch productions. System properties, such as monotonicity, are discussed. Moreover, optimal order assignment and part scheduling in systems with multiple flexible lines are studied. Both centralized and decentralized optimization policies are investigated.

Optimal Buffer Allocation in Multi-Product Repairable Production Lines Based on Multi-State Reliability and Structural Complexity

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system’s multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.

Multiple software product lines to configure applications of internet of things

Software product lines (SPL) emulate the industrial production lines that are capable of generating large volumes of products through reuse schemes and mass production. A multi product line (MPL) aims to reuse of several SPL. Feature models are often used to manage the existing resources of SPLs and define valid products through notations and relationships such as mandatory, optional, and alternative features. The main contribution of this study is a method to manage the variability of multiple SPL and generate a new portfolio of products for Internet of Things (IoT). For this, the problem of developing a universal feature model (FM) for an MPL from merging the FMs of the individual SPLs with a Search-Based Software Engineering (SBSE) technique is addressed. In addition, the authors propose a multi-objective optimisation model to maximise the reusability and compatibility between features and minimise the development cost. The model facilitates the design of an MPL-feature model. Authors' empirical results show that the proposed model solved by genetic algorithms allows to configure a variety of software products and to determine the scope of the MPL.

Cost Minimization for Unstable Concurrent Products in Multi-stage Production Line Using Queueing Analysis

This research and resulting contribution are results of Assumption University of Thailand. The university partially supports financially the publication.

Branding Vertically Differentiated Product Lines: Branded House vs. House of Brands

The decision whether a multi-product firm offers its goods under a joint or separate brands is essential for its success. When selling vertically differentiated products, it needs to consider pricing, cannibalization, and---when branding jointly---product confusion, i.e., consumers associating characteristics with the wrong product. The extent of product confusion crucially depends on the accuracy of available information. In this paper, we study the branding and pricing problem of a firm selling vertically differentiated products to a mix of naive and sophisticated consumers. The analysis accounts for the spillover arising from product confusion. Our findings suggest that joint branding is optimal when the spillover is either high or low but not in between. When low, firms jointly brand to save the cost of erecting a second brand. In contrast, when spillover effects are high, the firm brands jointly since it is inherently more profitable even if building additional brands is free. In between, firms opt for separate branding despite the additional cost of building more brands. We also find that a higher fraction of sophisticated consumers does not necessarily push the firm towards joint branding. Moreover, firms are inclined to jointly brand more similar products, and, if doing so, decrease price dispersion. Similarly vertically differentiated firms choose different branding regimes under competition, while low-end (high-end) strategic competition pushes a multi-product line firm towards joint branding. Finally, we investigate when firms want to provide consumers voluntarily with accurate information about the quality of its products.

Sequential optimisation of reconfigurable machine cell feeders and production sequence during lean assembly

ABSTRACTThis paper presents a manufacturing model for integrating reconfigurable machine cells as feeders of components to a multi-product assembly line exhibiting a prominent lean characteristic; synchronisation with Takt time. Two mathematical formulations in the form of combinatorial optimisation problems are developed to identify the best reconfiguration for the machines by optimum selection of the modules in the cells to minimise the total error with Takt time and then the optimum sequence of assembling the products to minimise the total reconfiguration time and effort. An elitist genetic algorithm (GA) meta-heuristic is employed twice in succession to search the optimal solutions for both the minimisation problems. The best machine configuration found from the first search is utilised further to identify the optimal assembly sequence. A hypothetical data set representing the features and parameters of the model in conformance with any typical mixed-model assembly is presented for illustration of computational procedure. Synchronisation among machine cells and tardiness are considered as performance evaluation measures to validate the effectiveness of the proposed model.

Constraint programming heuristics for configuring optimal products in multi product lines

Nowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with inter-model constraints representing the existing relations among domain concerns, and with non-functional properties modeled as attributes attached to product features. Multiple techniques use constraint programming to automate the cumbersome task of manually configuring a suitable product. Currently, there are some proposals to improve the performance of constraint solvers when configuring single-model product lines, however configuration scenarios with multiple interrelated and attributed models are not yet targeted. This paper proposes and evaluates three search heuristics used to configure optimal products regarding multi-objective criteria. Results are compared against the default search strategy of the Choco constraint solver. We evaluated the performance for configuring optimal products in four state-of-the-art product lines and 130 generated variability models representing multi-product lines that scale up to 6400 features and 960 constraints. As a result, we observe that the proposed heuristics perform better than the default solver strategy when the variability models scale in terms of features. In contrast, the default strategy and one of the proposed heuristics perform better as the number of interdependencies between variability models increases.

Scheduling of Products for Reconfiguration Effort in Reconfigurable Manufacturing System

Reconfigurable manufacturing system is a responsive manufacturing system which can easily adjust its capacity and functionality with least effort and time when demand of the product changes. Reconfigurable manufacturing system (RMS) has the exactly capacity and functionality whenever is required.In the present work a methodology has been presented for scheduling of the products on the basis of reconfiguration effort in a multi-product line. For scheduling of products, three criteria have been considered; reconfiguration effort, profit over cost and due date. Integrated approach of Shannon entropy and TOPSIS has been used for scheduling of the products.

스마트 공장을 위한 유연 제조 시스템 : 다 품종 제품의 재생 도착 과정과 전용 대기공간을 고려한 베르누이 라인의 성능 척도 분석

스마트 공장을 위한 유연 제조 시스템 : 다 품종 제품의 재생 도착 과정과 전용 대기공간을 고려한 베르누이 라인의 성능 척도 분석

A separation principle for resource allocation in industrial wireless sensor networks

Industrial production lines have been used to assemble a wide range of commercial goods such as metallurgy, automobile, and electronic devices. Since these production lines create tens of trillions of dollars annually, their production efficiency, cost, and safety are critical for global economy. This paper uses industrial wireless sensor networks (IWSNs) to monitor multi-stage production lines. Unlike traditional surveillance WSNs, IWSNs feature a unique cascaded network topology, which can be leveraged to optimize network performance (e.g., end-to-end delay). To our best knowledge, research along this direction is lacking. Specifically, considering the physical characteristics and functional requirements of production lines, we introduce the cascaded FieldNets where each FieldNet is a field sub-net corresponding to one process stage. In particular, the end-to-end minimization oriented resource allocation problem is concerned. It is a nonlinear mixed integer programming problem formulated by both (1) channel allocation among FieldNets and (2) multichannel transmission scheduling within each FieldNet. To solve it, a separation principle is proposed, by which we prove that the resource allocation within each FieldNet can be determined independently from the channels allocation among FieldNets. Performance evaluation demonstrates that the proposed resource allocation approach provides a $$10{\times }$$10× larger region of schedulability and achieves as low as 10 % of end-to-end delay compared with the scheduling approach in WirelessHART, and only consumes half of the energy based on some existing MACs such as Y-MAC and EM-MAC under high-traffic condition.

The Analysis of Performances of an Assembly Line in Strike-Zone Supply Method Managed with Kanban and Conwip

The present paper is part of a larger study of the authors, which consists in the analysis of performances of several production systems. On a multiproduct assembly line supplied in Strike-Zone, are made researches on the influence of the method of production flow management, the method of launching in production and by varying the clients demand size on several performance indicators. The assembly line has mainly manual activities, divided in 5 workstations. The line produces two types of similar products. Using dynamic simulation (Arena software) that mimics the lines functioning mode, different experimental conditions are created, determined by the variation of the above mentioned parameters, following the evolution of the performance indicators: average work in process, cost per unit, systems reactivity to client demand and throughput. The obtained results were interpreted and structured, in a manner to help the process managers to take adequate decisions considering the wanted objectives.

Manufacturing lines under surplus-based control: multiple products and bounded buffers

Challenged by the scheduling complexity for production flow processes in industrial facilities, we study the performance of multi-product producing lines. We analyse the performance of multi-product lines that consist a number of machines and bounded buffers with preselected base stock levels. It is assumed that each manufacturing machine in the line is capable of working with several product types, but only operate on one product at a time. The network is operated under a surplus-based production control policy in the presence of perturbations and production demand fluctuations. We derive bounds on the demand tracking accuracy for each product type, regardless its flow direction in the multi-product line. In addition, for a multi-product line with unidirectional product flow, we obtain a quantitative relation between demand tracking accuracy, its inventory levels, numbers of product types, buffer capacity limits and perturbations. The accuracy of the obtained demand tracking bounds is illustrated by numerical simulations. By means of simulation experiments, we show that the obtained results have a valuable meaning and can be used as a reference tool in practice.