Multi-product Production System Research Articles

The Strategy of Strengthening Efficiency and Environmental Performance of Product Changeover in the Multiproduct Production System

Product changeovers are especially critical for multiproduct environments where key production system requirements are flexibility, time, and quality. Massive waste of production time and environmental pollution increase with changeovers significantly. It is noted that the green supply chain is gradually emerging, and the environmental policies in countries are also increasing pressure on manufacturers globally. However, how to improve the changeovers’ environmental performance in manufacturing enterprise are not entirely focused. The present study aims to raise the changeovers’ time efficiency and reduce environmental pollution in the multiproduct production system. This paper first analyzes the characteristics of multiproduct production systems and the causes of inefficient work and pollution and then extracts the problems that need to be optimized. They are the frequent changeover work, complex operation programs, and load imbalance. Multiproduct Production Fast Changeover (MPFC) is developed based on these problems, which integrates the Analytic Hierarchy Process, Entropy Weight method, Divisive Analysis, and Firefly algorithm. In addition, Divisive Analysis’s distance calculation is improved for flexible clustering targets. Firefly algorithm’s exploration, exploitation, and population coordination mechanisms have also been enhanced. The effectiveness of MPFC is proved in a real multiple flow-lines production case: time efficiency was increased, while the multiple industrial pollutions and key resource consumption were also reduced.

Hybrid Make-to-Stock and Make-to-Order (MTS-MTO)Scheduling Model in Multi-Product Production System

In production process, scheduling isan important role in meeting the orders and reducing the cost. This process becomes more complicated when the factory produces various products. Scheduling model in the production process can be divided into two groups: make-to-stock (MTS) and make-to-order (MTO). General constraint in the MTSmodel is limited production capacity and inventory cost. This work aims to propose hybrid MTS-MTO model that can improve lead time and maintain low inventory. In this work, we propose three hybrid MTS-MTO scheduling models for the multi-product production system. In these models, we modify several scheduling algorithms that are used in computer system, such as shortest remaining time(SRT), shortest processing time(SPT), and Round Robin(RR). These models are hybrid (s, S)-first-come-first served (FCFS)model, hybrid modified (s, Q)-SPT model, and modified (s, Q)-SRT model. This model then is implemented into production simulation. In this simulation process, we compare the proposed models with the existing item-by-item based (s, S)model. Based on the simulation result, these proposed models perform better than the basic MTS model. The hybrid (s, Q)-modified SPTmodel performs as the best model in creating high completion ratio, low lead time, and low inventory ratio. In certain condition, our proposed model performs 344 percent in completion ratio, 19.8 percent in lead time, and 3 percent in inventory ratio compared with the existing model.

Research on Improved OEE Measurement Method Based on the Multiproduct Production System

The multiproduct production system has been applied extensively in factories worldwide due to the diverse consumption habits of consumers. However, current Overall Equipment Effectiveness (OEE) measurement methods are not suitable for it properly. With the prevailing of multiproduct production system, it is essential to measure the effectiveness accurately in this kind of production system. In order to fill this gap, based on analyzing former OEE models, we propose the multiproduct production system effectiveness (MPSE), including the calculating steps and application framework, in this paper using the heuristic method. This MPSE is verified by a case study. The principal results show that the proposed MPSE can significantly enhance overall production effectiveness and improve the measurement of indicators in the multiproduct production system, which enriches the theory of OEE at the theoretical level and proposes a novel way to measure and improve the effectiveness of the multiproduct production system effectively at the practical level.

Инновационный подход к разработке технологических процессов изготовления изделий в многономенклатурном производстве

The relevance of the study is in the solution of an important problem – the improvement of the development of engineering procedures for producing goods within the production systems of conventional machine-building enterprises performing the transition to automation and intellectualization of their production cycle. To solve the above-stated task, the authors propose an innovative approach involving the development of efficient manufacturing procedures of producing goods by a multiproduct production system based on making effective project technology solutions. The suggested method of designing effective manufacturing procedures for producing goods by a multiproduct production system is implemented in the System of Computer-Aided Planning of Manufacturing Procedures developed by the authors. The System of Computer-Aided Planning of Manufacturing Procedures is a modern tool for automation of engineering process preparation corresponding to the relevant concept of production digitalization. The set of monitoring and measuring procedures developed by the authors and promoting the improvement of the System of Computer-Aided Planning of Manufacturing Procedures is aimed at the modernization of machining productions with the traditional production cycle and supporting their digital transformation process. The set of monitoring and measuring procedures performs the automated designing of efficient individual manufacturing procedures within a small-series production based on the information about actual dimensional parameters of the part blank surfaces at the initial stage of creation of a manufacturing procedure and based on the integration into the manufacturing procedure structure of an efficient set of monitoring and measuring tools formed on the base of the complex of monitoring and measuring procedures to evaluate the prescribed accuracy of part production. The paper presents methodological and algorithmic provisions of implementing complex of monitoring and measuring procedures, which include the development of the technique of the parts’ coordinate metrology serving as a structural element of an efficient manufacturing procedure and the algorithm of formation of the efficient set of monitoring and measuring tools when designing efficient manufacturing procedure.

Research on optimization of setup time for product in the multi-product production system

Consumers' consumption habits are more and more personalized and diversified, which makes the multi-product production system has been applied extensively in the factory worldwide. This brings a difficult problem to a large number of manufacturing enterprises: how to optimize the setup time of the product to achieve the purpose of improving the time efficiency. Based on this problem, this paper proposes the TCP technology for the optimization of setup time, that is, using the Times Series model, the Clustering Algorithm, and the Parallel Job technology in the Single Minute Exchange of Die (SMED), to form an application framework focusing on optimizing the product setup time. The validity of the technology is verified by a case study. This paper enriches the research field of setup time optimization, production planning, and the application of the Clustering Algorithm in the multi-product production system. It provides a new way for manufacturing enterprises to pursue an excellent efficiency of product setup time.

Jointly optimizing lot sizing and maintenance policy for a production system with two failure modes

In the reliability literature, studies that jointly investigate maintenance and production are typically restricted to one failure mode and fail to address the case where multiple failure modes may exist. This study investigates the problem of joint optimization of lot sizing and maintenance policy for a multi-product production system subject to two failure modes. The failure of the first mode is a soft failure that occurs after defects arrive. The failure of the second mode is a hard failure that occurs without any early warning signals. Products are sequentially produced by the system and a complete run of all products forms a production cycle. The system needs to be re-set up before producing a different product. Both the production cycle and the time of set-up depend on the lot sizes of products. Models are proposed for two maintenance policies: 1) arranging the maintenance during the set-up at the end of each production cycle; 2) arranging the maintenance during each of set-ups. The expected profit per unit time is formulated to obtain the optimal lot sizing and maintenance policy. Some properties of the proposed models are provided and they show that the optimal lot sizing and maintenance policy can be obtained under certain conditions. Case studies and sensitivity analyses are presented to illustrate the proposed models of two maintenance policies. The results show that the manufacturer will gain the most profit if the optimal lot sizing and maintenance policy are adopted. The results of comparing both maintenance policies reveal that the excessive maintenance is not cost-effective. The sensitivity analyses illustrate that reducing the cost due to failures and improving system reliability are effective ways to increase the expected profit per time unit.

Development of Scanning Line Tool Path Generation Algorithm Using Boundary Position Information of Approximate Polyhedron of Complex Molds

In the manufacturing industry, molds are required for mass production operations, and the industry’s recent small lot, multi-product production systems call for such molds to be made by NC machine tools in short periods of time. The tool path point coordinates of NC machine tools are derived by geometric computations, which are used in turn to derive the polyhedron-approximated mold surface and the contact positions of the tool. In the conventional method, however, placing surplus tool path points on the planar section makes it difficult to acquire the boundary position coordinate values in the vicinity of the boundaries of the polyhedrons that constitute the curved surface, resulting in errors in the path point coordinates for the polyhedron-approximated shape of the mold surface. In this study, therefore, we have developed CAM algorithms that can reduce the tool path errors and suppress the number of tool path points by not deriving the path point coordinates in the linearly approximated section. This is done by using the boundary information of the approximate polyhedrons that constitute the concave section of the mold model.

Note on “Multiproduct Single-Machine Production System with Stochastic Scrapped Production Rate, Partial Backordering and Service Level Constraint”

An economic production quantity (EPQ) inventory model for a multiproduct single-machine production system with stochastic scrapped production rate, partial backordering and service level constraint is proposed. This research work revisits this EPQ inventory model and identifies some technical inconsistencies in it. Additionally, some observations and corrections to the shortcomings are provided as well as the solution procedure is improved.

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

The advertising of any smart product is crucial in generating customer demand, along with reducing sale prices. Naturally, a decrease in price always increases the demand for any smart product. This study introduces a multi-product production process, taking into consideration the advertising- and price-dependent demands of products, where the failure rate of the production system is reduced under the optimum energy consumption. For long-run production systems, unusual energy consumption and machine failures occur frequently, which are reduced in this study. All costs related with the production system are included in the optimum energy costs. The unit production cost is dependent on the production rate of the machine and its failure rate. The aim of this study is to obtain the optimum profit with a reduced failure rate, under the optimum advertising costs and the optimum sale price. The total profit of the model becomes a complex, non-linear function, with respect to the decision variables. For this reason, the model is solved numerically by an iterative method. However, the global optimality is proved numerically, by using the Hessian matrix. The numerical results obtained show that for smart production, the maximum profit always occurs at the optimum values of the decision variables.

An integrated preventive maintenance and production planning model with sequence‐dependent setup costs and times

AbstractThis paper considers the integration of preventive maintenance and tactical production planning in a multiproduct production system, where setup costs and times are sequence dependent. A set of products needs to be produced in lots during a finite planning horizon, where preventive maintenance is conducted periodically at the end of some production periods and corrective maintenance is always performed when there is a failure. The system downtime—as caused by maintenance and setup—affects the system's available production capacity. We use a sequence‐oriented method to search for the optimal setup sequence and develop some steps to prune the searching tree. Our objective is to find the optimal preventive maintenance interval, production lot size, and production sequence in every period, by minimizing the sum of maintenance, production, inventory, and setup costs within the planning horizon. Numerical examples are presented to illustrate our model.

A closed-loop model of a multi-station and multi-product manufacturing system using bond graphs and hybrid controllers

Production plans and production schedules are fundamentally dynamic and may be disturbed by several events. In the manufacturing research domain, however, dynamic models for scheduling and production control usually receive less attention as static models. In this paper, a bond graph model for depicting a multi-product production system with job shop configuration is proposed. A case study based on a real production system is presented to illustrate the modeling process. The state model derived from the pictorial representation (i.e., derived from the bond graphs) is simulated, in order to observe the dynamic response of the system. Also, a hybrid proportional controller (HPC) and a hybrid adaptive proportional controller (HAPC) are proposed. In this sense, this research extends the findings of a previous work reported in the literature, in which constant and proportional controllers were tested. The results demonstrated that the HAPC and the HPC outperforms the mentioned controllers, and that the bond graphs are a viable methodology to represent and study the dynamics of manufacturing systems. This approach is innovative since no other closed-loop model based on bond graphs for multiple products has been previously reported in the literature, nor its combination with a hybrid adaptive controller.

An integrated production and delay-time based preventive maintenance planning model for a multi-product production system

An integrated production and delay-time based preventive maintenance planning model for a multi-product production system

An integrated production, inventory and preventive maintenance model for a multi-product production system

This paper considers a production system that can produce multiple products alternately. Products go through the system in a sequence and a complete run of all products forms a production cycle. An integrated production, inventory and preventive maintenance model is constructed, which is characterized by the delay-time concept. Two different situations are studied based on whether the unqualified products and downtime caused by the failures of the system, set-up and preventive maintenance can be ignored or not. Three cases are considered for each situation, depending on the position of the preventive maintenance epochs: the first case, where preventive maintenance is carried out at the end of each production cycle; the second case, where preventive maintenance is carried out at each set-up time of the products; and the third case, where preventive maintenance is carried out at some set-up times only, since it may not always be optimal to carry out preventive maintenance at the end of the production cycle or at each set-up time. The modeling objectives are to find the optimal number of production cycles per year and the optimal position of preventive maintenance that will maximize the expected profit per unit time. Numerical examples, using real data, are presented to illustrate the model.

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failures, repairs and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviors of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production/subcontracting control policies (general, urgent, regular) are compared to one another. The proposed joint production/regular subcontracting control policy involves a cost decrease of up to 20%, as compared to results obtained by Dror et al. [1], who used a simplified control policy in addition to a heuristic solution approach for a real case study. This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.

An imperfect multi-product production system with rework

This study develops an imperfect, multi-product production system with rework. Our objective is to minimize the joint total cost of the system, subject to service level and budget constraints. Since the objective function in the problem is convex, the existence of global optimality is assured. A solution procedure is proposed and numerical examples are provided to demonstrate the applicability of the model in real-world manufacturing problems. Finally, sensitivity analyses are provided to provide managerial insights, as well as to investigate the interdependency of the parameters.

Optimization and Effectiveness of a Production Smoothing Policy in SCM

Kanban system has been known to be a superior tool to control complex production and inventory systems operating in the just-in-time (JIT) environment. The kanban system works more effectively at low inventory levels when the consumption rates of parts necessary to produce finished goods are leveled. The parts consumption leveling in JIT systems is called “Heijunka”. In this research, we investigate a multi-stage multi-product production and inventory system in which the production order quantity is exponentially smoothed. The paper presents an efficient algorithm based on simulation to determine the near optimal exponential smoothing factor and safety stock level at each workstation. We use numerical experiments to evaluate the performance of the exponentially smoothed ordering policy in a supply chain as compared to the original kanban system. And also the performance of the two types of optimization methods, global and local optimizations, in the supply chain are compared. We conclude that the locally optimized production smoothing is quite effective as a production and material replenishment ordering policy as an SCM.

Scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

Multi-product production systems with sequence-dependent setup times are typical in the manufacturing of semiconductor chips and other electronic products. In such systems, the scheduling policies coordinating the production of multiple product types play an important role. In this paper, we study a multi-product manufacturing system with finite buffers, sequence-dependent setup times and various scheduling policies. Using continuous-time Markov chain models, we evaluate the performance of such systems under seven scheduling policies, i.e. cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing time), longest queue, longest processing time, and longest overall time. The impacts of these policies on system throughput are compared, and the conditions characterising the superiority of each policy are investigated. The results of this work can provide production engineers and supervisors practical guidance to operate multi-product manufacturing systems with sequence-dependent setups.

Simulation-based optimisation of inspection stations allocation in multi-product manufacturing systems

In this paper a multi-product production systems (MPPS) with in-line quality control is examined. The problem of determining the optimal inspection strategy that results in lowest total inspection cost, while assuring required outgoing quality level is modelled discrete-event simulation is used to model the production system. The optimal allocation is determined by using a tabu search algorithm.

Capacitated production planning problem considering the detailed scheduling constraints in a flow shop environment

In this paper, a multi-product multi-period production system organized as a flow shop is considered. To overcome the shortcomings of hierarchical production planning, detailed production planning scheduling and sequencing constraints are integrated. A new integrated mixed-integer programming (MIP) model, capable of simultaneously performing production planning and nonpermutation scheduling, is proposed for the considered problem. For precise machine workload calculation, detailed scheduling constraints are used to determine a feasible production plan. The objective function includes production, inventory, backlog, set-up costs, and the costs associated with machine idle time and overtime. A hybrid simulated annealing (HSA) algorithm with dispatching rules and an exact solution method is proposed to solve the integrated model. The proposed algorithm searches the solution space for both problems and finds a combination of production planning and scheduling that is feasible and close to optimum. To evaluate the performance of the proposed model and solution method, problems of different scales are studied. The total costs of the proposed solution method are smaller than the results of hierarchical production planning.

A genetic algorithm with tournament selection for optimising inspection allocation in multiproduct multistage production systems

Multiproduct multistage production systems have been widely implemented in recent years. One of the important considerations in such systems is identifying the location of inspection stations. This paper considers a multiproduct setting where part types compete with each other for common production resources. In this environment, it is important to consider factors such as throughput time variability and to include the corresponding queuing aspects into the model. Each workstation is modelled as a GI/G/c queue. The optimal allocation is determined by using a genetic algorithm with tournament selection, one-point crossover and uniform mutation.