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Optimization of Energy and Water Use in Multipurpose Batch Plants Using an Improved Mathematical Formulation
Published in Thokozani Majozi, Esmael R. Seid, Jui-Yuan Lee, Understanding Batch Chemical Processes, 2017
Thokozani Majozi, Esmael R. Seid, Jui-Yuan Lee
In recent years, batch processes have been getting more attention due to their suitability for the production of small volume, high value–added products. The flexibility of batch plants allows the production of different products within the same facility. Batch manufacturing is typically used in the pharmaceutical, polymer, food and specialty chemical industries as demand for such products are highly seasonal and are influenced by changing markets. A common feature of many batch plants is that they utilize fossil fuels as the energy source and use water for process equipment cleaning, due to inherent sharing of equipment by different tasks. Despite the advantage of batch plants being flexible, they also pose a challenging task to operate in a sustainable way. In the past, batch industries could tolerate high inefficiencies in energy and water consumption due to the high value of final products which outstrips the production costs. However, greater public awareness of the impact of industrial pollution, more stringent environmental regulations and escalating raw materials, energy, and waste treatment costs have now motivated energy and water saving measures for more sustainable operations (Halim and Srinivasan, 2011). Since scheduling, energy and wastewater minimization for multipurpose batch plants go hand in hand, published works in those areas are reviewed.
Production planning and control in construction
Published in Rafael Sacks, Samuel Korb, Ronen Barak, Building Lean, Building BIM, 2017
Rafael Sacks, Samuel Korb, Ronen Barak
In the manufacturing world, batch size refers to the number of products that travel together between workstations. For example, moving products in lots of 50 or 100, or however many fit on a forklift pallet. Lean thinkers are always on the lookout for ways to shrink batch sizes, to attain the Lean ideal of “one-piece flow” (make one, move one). Reducing batch sizes reduces waiting, lowers the amount of WIP inventory, reveals quality problems more quickly and in general helps to reduce the amount of waste in the production system.
Best practices in inventory management in the digital age
Published in Stuart M. Rosenberg, The Digitalization of the 21st Century Supply Chain, 2020
Batch tracking is sometimes referred to as lot tracking, and it’s a process for efficiently tracing goods along the distribution chain using batch numbers. A ‘batch’ refers to a particular set of goods that were produced together and which used the same materials. Use an automatic batch tracking system in order to enter information about all the products within your batch – keeping that information at your fingertips if you need to access it quickly, as in the case of a product recall.
A method combining rules with genetic algorithm for minimizing makespan on a batch processing machine with preventive maintenance
Published in International Journal of Production Research, 2020
Jingying Huang, Liya Wang, Zhibin Jiang
Batch processing is implemented in many manufacturing industries, such as semiconductor wafer fabrication industry (Chakhlevitch, Glass, and Kellerer 2011), casting industry (Mathirajan, Sivakumar, and Chandru 2004), aircraft industry (Van De RZee et al. 2010) and so on. The advantages of batch processing are the avoidance of setups, facilitation of material handling and reduction of processing time (Xu, Chen, and Li 2013). Batch processing machine (BPM) problem combines two sub-problems, which are batching jobs and scheduling batches on the batch processing machine (Jia and Leung 2015). BPM problems can be divided into two parts, compatible job families and incompatible job families (Yao, Jiang, and Li 2012). For compatible job families, jobs from different families can be processed together. Jobs in a batch start and complete processing at the same time. The processing time of a batch is equal to the longest processing time among the jobs in the batch. Uzsoy (1994) firstly proved that both minimising makespan and total completion time on a single batch processing machine with compatible job families are NP-hard problems and also proposed several heuristics to solve these complex problems. Dupont and Jolai Ghazvini (1998) researched the same problem, proposed a BFLPT (Best fit-longest processing time) heuristics which based on Best-fit algorithm. Zhou et al. (2014) addressed the problem of minimising makespan on a single batch-processing machine with dynamic job arrivals as well as arbitrary job sizes and developed a number of efficient construction heuristics.
Use of DES to develop a decision support system for lot size decision-making in manufacturing companies
Published in Production & Manufacturing Research, 2022
Lukas Budde, Shuangqing Liao, Roman Haenggi, Thomas Friedli
Lot sizing decisions are crucial operational key planning activities in manufacturing companies to minimize changeover and inventory costs while keeping a high service level (Glock et al., 2014; Jans & Degraeve, 2007). The lot size (batch size) refers to the quantity of products or parts in one production order that are produced directly one after the other without an interruption in production. The decision for smaller or larger lot sizes has to consider the trade-off between flexibility and resource utilization. Lot sizing decisions thus are crucial to run operations competitively (Bookbinder & H’ng, 1986), but belong to one of the most complex managerial decision problems in production planning (Karimi et al., 2003).
A concise guide to scheduling with learning and deteriorating effects
Published in International Journal of Production Research, 2023
Jun Pei, Ya Zhou, Ping Yan, Panos M. Pardalos
Batch scheduling is characterised by the fact that machines can process multiple jobs at the same time. Most batch scheduling problems with learning effects assume that the actual processing time is a decreasing function of the job's position under the consideration of job and batch learning indicators. Moreover, a number of papers introduced the combined effects of learning and group technology into scheduling problems. In group scheduling problems, job and group learning indicators were also analysed. Compared with scheduling problems within unit-capacity processors, it is more difficult to solve batch and group scheduling problems with learning effects (Geiger and Uzsoy 2008).