Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

Perceived production complexity – understanding more than parts of a system

To successfully manage today’s complex production systems it is essential to study operators’ perception of the system. The paper presents perceived production complexity assessed at seven manufacturing companies with the CompleXity Index (CXI) method. While other methods have measured product variants, layout, work content, tools and information items, CXI combines them into three areas. These three complexity areas (Station design, Work variance and Disturbance handling) help to create an understanding of the complex system by visualising how they contribute to complexity, and show the need to support the operators’ ability to handle the varying work.     

Setting optimal production lot sizes and planned lead times in a job shop

In this research, we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal tactical policies that minimise the relevant manufacturing costs subject to workload variability and capacity limits. We consider two tactical decisions, namely the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs, as functions of these tactical decisions. We formulate a non-linear optimisation model and implement it in the Excel spreadsheet. We test the model with actual factory data from our research sponsor. The results are consistent with our intuition and demonstrate the potential value from jointly optimising over these tactical policies.     

A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study

The assembly line worker assignment and balancing problem type-II (ALWABP-2) occurs when workers and tasks (where task times depend on workers’ skills) are to be simultaneously assigned to a fixed number of workstations with the goal of minimising the cycle time. In this study, a two-phase variable neighbourhood search (VNS) algorithm is proposed to solve the ALWABP-2 due to the NP-hard nature of this problem. In the first phase of the algorithm, a VNS approach is applied to assign tasks to workstations with the aim of minimising the cycle time while in the second phase, a variable neighbourhood descent method is applied to assign workers to workstations. The performance of the proposed algorithm is tested on well-known benchmark instances. In addition, the proposed algorithm has been used to solve a real case study from a consumer electronics company that manufactures LCD TVs. The results show that the algorithm is superior to the methods reported in the literature in terms of its higher efficiency and robustness. Furthermore, the algorithm is easy to implement and significantly improves the performance of the final assembly line for the investigated LCD TV real case study.     

Dependability and performance stochastic modelling of a two-unit repairable production system with preventive maintenance

In this paper, a two-unit multistate repairable production system is considered in which preventive maintenance (PM) is implemented in order to improve its dependability and performance. A general model is provided for the production system using a semi-Markov process, for examining system’s limiting behaviour. Apart from combining redundancy with PM, we introduce scenarios like imperfect and failed maintenance which are usually met in real life production systems. For the proposed model, we calculate the availability, the mean time to failure and the total operational cost and we formulate optimisation problems settled with respect to the system’s inspection times. The main aim of our work is to determine the optimal inspection times and consequently the optimal PM policies to be adopted in order to optimise system’s dependability and performance.     

An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS²O) in terms of solution quality and computational robustness.     

Recent progress in continuous and semi-continuous processing of solid oral dosage forms: a review

Context: Continuous processing is an innovative production concept well known and successfully used in other industries for many years. The modern pharmaceutical industry is facing the challenge of transition from a traditional manufacturing approach based on batch-wise production to a continuous manufacturing model.

Objective: The aim of this article is to present technological progress in manufacturing based on continuous and semi-continuous processing of the solid oral dosage forms.

Methods: Single unit processes possessing an alternative processing pathway to batch-wise technology or, with some modification, an altered approach that may run continuously, and are thus able to seamlessly switch to continuous manufacturing are briefly presented. Furthermore, the concept of semi-continuous processing is discussed. Subsequently, more sophisticated production systems created by coupling single unit processes and comprising all the steps of production, from powder to final dosage form, were reviewed. Finally, attempts of end-to-end production approach, meaning the linking of continuous synthesis of API from intermediates with the production of final dosage form, are described.

Results: There are a growing number of scientific articles showing an increasing interest in changing the approach to the production of pharmaceuticals in recent years. Numerous scientific publications are a source of information on the progress of knowledge and achievements of continuous processing. These works often deal with issues of how to modify or replace the unit processes in order to enable seamlessly switching them into continuous processing. A growing number of research papers concentrate on integrated continuous manufacturing lines in which the production concept of “from powder to tablet” is realized. Four main domains are under investigation: influence of process parameters on intermediates or final dosage forms properties, implementation of process analytical tools, control-managing system responsible for keeping continuous materials flow through the whole manufacturing process and the development of new computational methods to assess or simulate these new manufacturing techniques. The attempt to connect the primary and secondary production steps proves that development of continuously operating lines is possible.

Conclusion: A mind-set change is needed to be able to face, and fully assess, the advantages and disadvantages of switching from batch to continuous mode production.     

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.     

起爆索的概念及其特性

本文采用了新的导爆索芯药,得到了一种具有起爆特性的索,称之为起爆索。它能够被常规的电点火头、非电导爆管或延期体点火,并很快形成爆轰输出,对工业炸药实现引爆,因而具备工程雷管的功能。经过对起爆索爆速、殉爆距离、点火感度、起爆能力、轴向威力等的测定,反映出爆速与芯药线密度有关,其殉爆距离是普通导爆索的4倍多,对常规的电、非电导爆管和延期体点火方式则有较好的感度响应,爆速接近5 000 m/s时能够对粉状炸药、乳化炸药或水胶炸药实施引爆,而起爆索的轴向威力只达到2 mm厚铅板炸孔的合格水平。