A Performance-Based Dispatching Rule for Decentralised Manufacturing Planning and Production Control System

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.

Development of Mechanical Engineering: State and Prospects

The paper seeks to intensify innovation-driven development of Russian mechanical engineering enterprises and industries. The results of combined administrative and technological investigations unambiguously indicate the necessity and feasibility of restructuring the existing industrial system into large mechanical engineering companies and complexes. Integrating the efforts of every company department should ensure maintaining a competitive quality level and restoring the volume of engineering production, diversifying and modernising the manufacturing processes. Practical implementation of the investigation results in terms of substantiating the directions and steps involved in innovation-driven development will ensure not only high efficiency of introducing them into mechanical engineering and machine utilisation, but also the desired technological breakthrough in production, science and education

Storage of Wood Chips

To make forest biomass more competitive, increased efficiency in the handling and supply system is needed, thus producing high-quality fuel at a lower cost. Operating costs can be reduced if the target chip size is increased, as this increases productivity and reduces chipper fuel consumption. However, the chips need to be storedin order to meet fluctuating seasonal demand and maintain high machine utilisation. Due to biomass degradation, storage of comminuted biomass can lead to high energy losses, but can also increase fuel quality, e.g. by reducing moisture content and increasing net calorific value. This study evaluated the effects of storage on dry matter losses and differences in fuel quality of the stored biomass for three target chip sizes and three materials during six months of storage. The results showed that coarse chips had significantly lower moisture content and lower energy losses after storage than fine chips. Overall, changes during storage resulted in an economic loss of 3–4% per oven-dry ton for fine chips, but an economic gain of 2–6% for coarse chips. Thus increased target chip size can increase the competitiveness of forest biomass through decreased production costs and reduced storage costs. It can also ensure higher, more consistent fuel quality.

Development of a Method to Compute the Overall Key Performance Index for a Spinning Mill to Aid Supply Chain Management

This paper aims to provide a convenient measure of evaluating the performance of spinning mills using the overall Key Performance Index. Although many authors have advocated the concept of the key performance index, their studies were confined to departments other than spinning. The methodology followed in this paper is based on providing weightages to machine utilisation, spindle production, end breaks, HOK (number of operative hours required to produce 100 kg of yarn), roving production, yarn realisation, CSP (count strength product), units/kg of Yarn and to compute the overall Key Performance Index in a logical manner. It has come to light that some mills have achieved an overall Key Performance Index (OKPI) of more than 100 and less than 100 in certain other mills. Also the important factors which are significant for supply chain management have been highlighted. A very useful source of information and advice for various spinning mills to develop their own supply chain strategies is provided. Especially with the introduction of technical textiles, supply chain management has become a critical area.

Factors Affecting Leadtime in Spinning Mills

An investigation of the factors which affect lead time in spinning mills producing carded and combed counts is reported. Data were collected from 27 mills producing 40 Ne Carded Yarn and 9 mills producing 40 Ne Combed Yarn. A number of 13 parameters which affect lead time were identified and they are FQI (Fibre Quality Index), Roving Production, CSP (Count Strength Product), Yarn Realisation, Spindle Production, Top Arm Roller Pressure (Front), Top Arm Roller Pressure (Middle), Top Arm Roller Pressure (Back), End Breaks, UKG, HOK (the number of Operative Hours required to produce 100 kg of yarn), YQI (Yarn Quality Index), Machine Utilisation. Multiple linear regression analysis have been carried out to find out the dominant parameters which affect lead time. While 71.4% of the variables account for lead time in respect of carded counts, about 78.4% of the variables have dominating influence in combed yarns.

A tabu search algorithm for the cell formation problem with part machine sequencing

This paper presents extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously and part machine utilisation is considered. More specifically, an integration of inter-cell movements of parts and machine set-up costs within the objective function, and also a combination of machine set-up costs associated with parts revisiting a cell when the part machine operation sequence is taken into account are examined and an enhanced model is formulated. Based upon this model’s requirements, an initial three stage approach is proposed and a tabu search iterative procedure is designed to produce a solution. The initial approach consists of the allocation of machines to cells, the allocation of parts to machines in cells and the evaluation of the objective function’s value. Special care has been taken when allocating parts to machine cells as part machine operation sequence is preserved making the system more complex but more realistic. The proposed tabu search algorithm integrates short term memory and an overall iterative searching strategy where two move types, single and exchange, are considered. Computational experiments verified both the algorithm’s robustness where promising solutions in reasonably short computational effort are produced and also the algorithm’s effectiveness for large scale data sets.

Hybrid Algorithms for Manufacturing Rescheduling

This chapter investigates manufacturing rescheduling of customised production and compares the results with those found for commodity production in earlier research by the authors. The hybrid rescheduling algorithms presented in this chapter were obtained by combining two key rescheduling-related elements found in the literature (a) rescheduling criteria (i.e., job priority, machine utilisation and right-shift delay) with (b) level of disruption transmitted to the shop-floor due to rescheduling (i.e., High disruption and Low disruption). The main advantage of hybrid rescheduling algorithms over individual rescheduling algorithms consists of their ability to combine the main features of two different algorithms, in order to achieve enhanced performance, depending on the objective of the organisation. The five hybrid rescheduling algorithms taken into account in this chapter are: Priority High, Priority Low, Utilisation High, Utilisation Low and Right-Shift. The authors’ case study research in three manufacturing companies has identified the use of a set of these hybrid algorithms in practice. Each of the case studies is evaluated in terms of time-based performance in three main areas: suppliers’ interface, internal production and customers’ interface. This evaluation is carried out for both customised and commodity production, using the same hybrid rescheduling algorithms and performance measure the authors used in their previous research work, for comparability purposes (i.e. the entropic-related complexity). The findings show that customised production exhibits a lower entropic-related complexity than commodity production. Although this behaviour may seem unexpected, the entropic-related complexity analysis allows for an interpretation / understanding of its underlying reasons. For example, companies making customised products first agreed the specifications of the products with the customer, and then they mutually agreed on a contract which would financially protect manufacturers (should last minute customer changes occur), by specifying analytically determined penalties or premium charges. Furthermore, a set of recommendations were made to the companies involved in this research study based on the analysis presented in this chapter, such as the need for manufacturing organisations of customised products to ensure they have dependable suppliers, and that, internally, they plan for and embed sufficient spare capacity to cope with internal or external disturbances.