Multi-product Manufacturing System Research Articles

Quality-centered production and maintenance scheduling for multi-machine manufacturing systems under variable operating conditions

Production, maintenance, and quality represent the three primary tasks in manufacturing systems. Previous studies have predominantly focused on optimizing these tasks separately or jointly optimizing two of them. However, it is worth noting that these tasks exhibit mutual interaction, and models failing to consider them simultaneously cannot achieve global optimality. Therefore, this paper aims to develop a quality-centered production and maintenance scheduling methodology (QPMSM) for multi-machine and multi-product manufacturing systems. Specifically, a delivery time-based production scheduling strategy is introduced to emphasize the impact of maintenance activities. A quality loss model is developed to characterize the mapping relationship between quality deterioration and component degradation. Furthermore, a well-designed maintenance strategy determines the optimal timing and frequency of maintenance activities to improve system performance and reduce quality deterioration. Building upon this foundation, an integrated model is proposed to concurrently optimize production scheduling, maintenance planning, and quality control tasks to minimize the overall system cost. The effectiveness of the proposed QPMSM in terms of cost-savings has been verified using a designed hybrid meta-heuristic algorithm, providing a viable approach for simultaneous optimization.

Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study

By shifting towards renewable energy sources, manufacturing facilities can significantly reduce their carbon footprint. This environmental issue can be addressed by developing sustainable production through on-site renewable electricity generation and demand-side management policies. In this study, the energy required to power the manufacturing system is obtained from different energy sources: the conventional grid, on-site renewable energy, and an energy storage system. The main objective is to generate a production schedule for a flexible multi-process and multi-product manufacturing system that optimizes the utilization and procurement of electricity without affecting the final demand. A mathematical programming model is proposed to minimize both the total production costs and energy costs, considering a time-of-use pricing policy and an incentive-based program. The uncertainty in renewable energy generation, specifically under the worst-case scenario, is taken into account and the model is transformed into a robust two-stage optimization model. To solve this model, a decomposition approach based on a genetic algorithm is applied. The effectiveness of the proposed model and algorithm is tested on a real industry case involving feed-animal products. A sensitivity analysis is conducted by modifying problem parameters. Finally, a comparison with the nested Column and Constraint Generation algorithm is performed. The obtained results from these analyses validated the proposed model and algorithm.

Comprehensive Assessment of the Manufacturability of Products

Introduction. The assessment of the manufacturability of products – as a stage of production planning and a key aspect of the development of modern industrial machining systems — is an urgent task of modern mechanical engineering. In this regard, theoretical and practical research on the development of methodological approaches to determining the weight significance of quantitative indicators in assessing the manufacturability of parts is highly relevant. The objective of the presented work was to develop an evaluation method aimed at improving the quality of part processing and the effectiveness of the performance of multiproduct manufacturing systems based on the development of additional quantitative indicators for assessing production manufacturability.Materials and Methods. To assess the impact of quantitative production indicators associated with time spent during equipment downtime, a model was created. It was aimed at predicting event flows of delivery of batches of parts for manufacturing for a specific operation and flows of processed parts using the queuing theory apparatus. This approach makes it possible to take into account both the design-engineering characteristics of parts, the features of a particular production system, and the emerging manufacturing situation.Results. The degree of influence of the manufacturability indicators at the level of the process operation was determined by assessing the possible impact on the components when calculating piece-calculation time (Тшт.к.). The interrelations between the manufacturability indicators and expenses for all items of the production cost of part processing (СОП), as well as costs associated with organizational downtime of equipment (Спр.о.i) were established. The degree of influence of the indicators of manufacturability relative to other indicators was determined by using the apparatus of paired comparisons in decision-making in relation to all structural elements of production costs.Discussion and Conclusion. The approach to the implementation of this design procedure was described, which provided taking into account the composition and capabilities of processing equipment of a particular production and the actual production situation. The developed formalized models make it possible to comprehensively predict the impact of the manufacturability indicators of parts on the performance effectiveness of machining systems during their manufacture.

Integrated maintenance, inventory and quality engineering decisions for multi-product systems

Abstract It is essential for manufacturers to consider the interrelation among quality, inventory, and maintenance decisions to detect imperfect quality products, keep the production system in good operating condition, and manage quality and inventory costs. Hence, this paper aims to develop an integrated model of inventory planning, quality engineering, and maintenance scheduling in which the expected total cost per time unit is minimised by determining the sample size, sampling interval, control limit coefficient, along with production cycle time. In this regard, an imperfect multi-product manufacturing system is considered, in which the inventory shortage in satisfying the demand for each product type and the idle time during the production cycle are not allowed. It is assumed that the process starts in an in-control condition where most produced units are conforming. However, due to the occurrence of an assignable cause (AC), the process mean moves to an out-of-control condition in which a significant fraction of non-conforming units is produced. The efficiency of the proposed mathematical model is evaluated by a numerical example, and then the sensitivity of the proposed model to important inputs is analysed. Finally, a comparative study based on the Taguchi design approach is given to confirm the capability of the proposed model to achieve remarkable cost savings.

Determining the optimal product mix in multiple constraints manufacturing environment: an application in the textile industry

Theory of constraints (TOC) is an approach to production planning and control by focusing on the constraints of an organization to increase throughput by effectively managing constraints. TOC approach has been applied in many sectors, and efficient results have been taken. One of the application areas of TOC is product mix decisions. Product mix decisions are important for multi-product manufacturing systems because they affect the performance measures of the companies. This study aims to present how TOC is applied to determine the optimum product mix in a multiple- constraint environment in the textile industry. To achieve this, we first select three basic products of a textile company and examine the production processes of these products from TOC perspective. Next, we perform a bottleneck process and identify three bottlenecks for the problem. Then, based on our bottleneck process results, we generate three scenarios. Upon assessing these scenarios, we determine the most appropriate product mix by implementing the TOC approach. Finally, we employ a goal programming approach to solve the product mix problem and compare its results with those obtained by the TOC.

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.

Mathematical modeling for a multiproduct manufacturing system featuring postponement, external suppliers, overtime, and scrap

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Optimizing a multiproduct manufacturing system with delayed differentiation, outsourcing, expedited rate, and rework strategies

This study examines the effect of delayed differentiation, outsourcing, expedited fabrication rate, and rework strategies on optimal cycle-time decisions for a multi-item manufacturing system. Today's manufacturing firms must simultaneously deal with externally increasing client multi-item requirements with rapid lead-time and high-quality products and internally on a limited capacity. This study is aimed at assisting manufacturers in meeting client needs in conditions of restricted-capacity and minimum total operating expenses, and adopts a delayed differentiation two-stage multiproduct manufacturing scheme to manage the end products' commonality. The first stage produces all required common components, and the second stage fabricates individual finished goods. In both stages, we adopt the reworking of the inevitable nonconforming items produced to assure product quality. Furthermore, we implemented partial outsourcing of common parts' batch and expedited the manufacturing rate of finished products to effectively reduce the uptimes in both stages. We explicitly developed a model to describe the characteristics of the problem. Mathematical analyses with optimization proved the cost function's convexity and determined the cost-minimization rotation cycle policy. Finally, we numerically validated our model's and results' applicability and capability with a simulated example. Apart from creating a useful decision model, this study makes another important contribution to the existing literature in that its revelation of collective/individual effect of the manufacturing-relevant methods on the problem's best-operating cycle policy and crucial performance indices helps manufacturers have better control over their operations and make effective and efficient managerial decisions.

Approximations for dynamic multi-class manufacturing systems with priorities and finite buffers

Capacity planning models for tactical to operational decisions in manufacturing systems require a performance evaluation component that relates demand processes with production resources and system state. Steady-state queueing models are widely used for such performance evaluations. However, these models typically assume stationary demand processes. With shorter new product development and life cycles, increasing customization, and constantly evolving customer preferences the assumption of stationary demand processes is not always reasonable. Nonstationary demand processes can capture the dynamic nature of modern manufacturing systems. The ability to analyze dynamic manufacturing systems with multiple products and finite buffers is essential for estimating throughput rates, throughput times, and work-in-process levels as well as evaluating the impact of proposed capacity plans and resource allocations. In this article, we present computationally efficient numerical approximations for the performance evaluation of dynamic multi-product manufacturing systems with priorities and finite buffers. The approximation breaks time into short periods, estimates throughput and arrival rates based on system status and current arrival processes, and then pieces the periods together through flow balance equations. The dynamic nature of product demands is modeled through non-homogeneous Poisson processes. The performance of these approximations is presented for practically sized flowshops and jobshops.

A sampling-based approach for managing lot release in time constraint tunnels in semiconductor manufacturing

For the sake of product yield and quality considerations, time constraints (TCs) are imposed between process operations in various multi-product manufacturing systems. Often spanning a number of operations, time constraints tend to follow each other in close succession and overlap, forming thus time constraint tunnels (TCTs). The regulation problem of releasing lots in these time constraint tunnels is particularly challenging in semiconductor manufacturing systems, because of re-entrant flows, machine heterogeneity and high mix low volume (HM-LV) production configurations, which are typical in many wafer fabrication facilities. In such an evolving and time-varying context, this paper proposes a sequential sampling-based approach to estimate the probability that, prior to its release, a lot leaves a given time constraint tunnel on time. The proposed approach proves to be competitive in various respects by (i) taking into account industry specific features, (ii) being industrially tractable, and (iii) being sensitive and responsive to the current manufacturing system. Based on real-life instances, numerical experiments highlight the computational effectiveness and the industrial soundness of the proposed problem modelling together with the solution approach.

Human factors under uncertainty: A manufacturing systems design using simulation-optimisation approach

Modern manufacturing systems are characterized by waste elimination, cycle time control, and high work specifications. Workers, although being an integral part of manufacturing, are usually neglected or severely simplified in operational research of these systems. Through the years, the need for control over the job in manual manufacturing has been identified as crucial for both system performances and operators’ health. The aim of this research is to integrate time margins, as the mean of control, and human factors under uncertainty into scheduling problem of a multi-product manufacturing system while maintaining performance and workers’ well-being. The proposed method is polynomial and simulation-based, developed in two stages using agent-based methodology. The first stage provides a global schedule with makespan as the objective function and with time margin allocation strategy under uncertainty. The second stage enables rescheduling depending on the human error probability and fatigue level. Experiments and comparisons with the similar literature problem have indicated decrease of human error probability and fatigue. Extended experiments for flow shop system justify the use of this unique approach. The developed tool enables system designers to enhance performance by observing human effects through its factors and different time margins allocation strategies.

On-demand manufacturing of clinical-quality biopharmaceuticals.

Conventional manufacturing of protein biopharmaceuticals in centralized, large-scale single-product facilities is not well-suited to the agile production of drugs for small patient populations or individuals. Solutions for small-scale manufacturing are potentially more nimble, though previous systems are limited in both process reproducibility and product quality, owing to complicated means of protein expression and purification1–4. We describe an automated bench-top multi-product manufacturing system, called Integrated Scalable Cyto-Technology (InSCyT), for the end-to-end production of hundreds to thousands of doses of clinical-quality protein biologics in about three days. We also demonstrate that InSCyT can accelerate process development from sequence to purified drug in 12 weeks. We produced hGH, IFNα-2b, and G-CSF using highly similar processes on InSCyT and found that the purity and potency of these products is comparable to that of marketedreference products.

An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint

Economic production and on-time ordering are among the most important topics related to production and inventory control issues. An economical production needs a comprehensive and precise planning to be implemented in all production stages. To have a controlled and comprehensive planning system, economic order quantity (EOQ) or economic production quantity (EPQ) models are usually used in various production-inventory environments to minimize costs, avoid delays in orders, and achieve high performance. To meet the demands, sometimes a multiperiod production-inventory planning that involves several products requires outsourcing. In this paper, a production-procurement plan that integrates EOQ with EPQ is proposed for a multiperiod multi-product production-inventory system with a limited warehouse capacity. The design aims to determine the optimal values of decision variables including the production quantity, the order quantity, inventory, shortages, and machine assignments to produce the products in a planning horizon. The problem is formulated as a non-linear mixed-integer programming model. As the problem becomes NP-Hard, a genetic algorithm (GA) is utilized to find a near-optimal solution. In addition, a random search (RS) algorithm is applied to validate the results obtained and to justify the efficiency of the GA. Since there is no benchmark available in the literature, some randomly generated numerical examples are solved and a sensitivity analysis is performed in order to demonstrate the effectiveness of the proposed methodology and the solution algorithm.

Possibility–necessity–credibility measures on generalized intuitionistic fuzzy number and their applications to multi-product manufacturing system

As a special intuitionistic fuzzy set on a real number set, intuitionistic fuzzy numbers (IFNs) have the best capability to model ill-known quantities. The purpose of this paper is to investigate generalized intuitionistic fuzzy numbers (GIFNs). The weighted possibility, necessity and credibility measures of generalized trapezoidal intuitionistic fuzzy numbers (GTIFN) are introduced, and expected value of GTIFN has been formulated. By employing the possibility and necessity measures of GIFN, the single period multi-product manufacturing generalized intuitionistic fuzzy inventory models is transformed into an equivalent deterministic problem. The transformed problem has been solved by soft computing technique. Finally, the proposed method is illustrated with one numerical example.

Priority optimization and make‐to‐stock/make‐to‐order decision in multiproduct manufacturing systems

AbstractWe consider a single‐stage multiproduct manufacturing facility producing a large number of end products. In order to reduce overall inventory costs, an efficient approach is to produce some items according to a make‐to‐stock (MTS) policy and others according to a make‐to‐order (MTO) policy. Items priority levels play a key role in the optimal MTO/MTS decisions for such typical large‐scale systems. To tackle this issue, the manufacturing facility is modeled as a multiproduct multipriority classes queuing system. We propose a general optimization procedure that selects near‐optimal priority classes, gives the associated flow control mode (MTO or MTS) for each product, and provides a lower bound and an upper bound with respect to the optimal cost. First, we illustrate efficiency of our optimization procedure for this class of nonlinear integer programs via several examples and by a numerical analysis, including a comparison with two alternative heuristics given in the literature. In addition, we provide managerial insights by exhibiting, under various parameter settings, the significant impact of an efficient priority level allocation among items on the inventory costs and on optimal splitting between MTO and MTS products.

Scalability in manufacturing systems: a hybridized GA approach

As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach.

Solving product mix problem in multiple constraints environment using goal programming

The theory of constraints is an approach to production planning and control that emphasizes on the constraints to increase throughput by effectively managing constraint resources. One application in theory of constraints is product mix decision. Product mix influences the performance measures in multi-product manufacturing system. This paper presents an alternative approach by using of goal programming to determine the product mix of the manufacturing system. The objective of paper is to provide a methodology in order to make product mix decision. Key point of the proposed methodology is considering decision maker idea to determine the weights of objective functions that are throughput and bottleneck exploitation. Therefore the weights of the objective functions are determined by the information get from decision maker. Through an example, inefficiency of theory of constraints in multiple bottleneck problems has been showed. Comparison of theory of constraints, linear programming and other methods to product mix problem has also discussed to show the advantages of the proposed method.

A Heuristic Approach to Solve an Industrial Scalability Problem

In recent years, the rapid change of market demand is increasing the need for scalability as a key characteristic of manufacturing systems. Scalability allows production capacity to be rapidly and cost-effectively reconfigured in different situation with different requirements and constraints. Our industrial partners are facing quarterly scalability problems involving a multi-unit and multi-product manufacturing system. In this paper, an original approach is presented to solve this kind of problems. Starting from the original manufacturing system configuration and process plan, a set of practical principles are introduced to seek for the feasible configurations; a GA is designed to search in the global solution space. A balancing objective function is defined and used to rank the proposed configurations. A real case study with 4-unit / 4-product situation demonstrates both the validity and efficiency of the proposed approach.

Optimized Assembly Design for Resource Efficient Production in a Multiproduct Manufacturing System

Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact.