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A simulation model represents recipes in far greater detail and supports assignment decisions, coordination logic, and plant operation rules, yielding schedules that are realistic, verifiable, adaptable, and generated quickly for offline studies and day-to-day planning.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/using-simulation-for-scheduling-and-rescheduling-of-batch-processes-article/392828/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/using-simulation-for-scheduling-and-rescheduling-of-batch-processes-article/392828.png","ImageObject",442,249,{"name":42,"@type":43},"Fans","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23","2026-09-25",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why do traditional scheduling formulations for batch processes often yield non-actionable schedules?","Question",{"text":63,"@type":64},"They simplify manufacturing recipes (e.g., STN/RTN), assume instantaneous material transfers, ignore shared-utility constraints, and keep scheduling horizons small for computational reasons, which can mismatch real operations.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does simulation improve reliability compared with MILP-based scheduling?",{"text":68,"@type":64},"Simulation can model manufacturing recipes at fine detail and incorporate assignment decisions, coordination logic, and plant operation rules, producing more realistic and verifiable schedules.",{"name":70,"@type":61,"acceptedAnswer":71},"What role do non-zero transfer times play in schedule mismatches?",{"text":72,"@type":64},"When transfer times are not ignored, equipment must be simultaneously active during transfer steps, so simulated or real start times diverge from MILP schedules that assume zero transfer duration.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},392828,1791745141,{"code":4,"msg":82,"data":83},"success",[84,88,93,98,103,108,113,118,123],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":87},11,90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},15,"Posters",60,"posters",{"id":104,"doc_module":22,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},16,"Social Media",50,"social-media",{"id":109,"doc_module":22,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},17,"Forms",40,"forms",{"id":114,"doc_module":22,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},18,"Letters",30,"letters",{"id":119,"doc_module":22,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},21,"Paper Templates",5,"papers-templates",{"id":124,"doc_module":22,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},158,"General","general-158",{"code":4,"msg":82,"data":128},{"doc_id":79,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":22,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":114,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":141},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","processes   \nArticle  \nUsing Simulation for Scheduling and Rescheduling of Batch Processes  \nGirish Joglekar  \nBatch Process Technologies, Inc., 112 Eden Court, West Lafayette, IN 47906, USA; [girish53@gmail.com](girish53@gmail.com)[ ](girish53@gmail.com)Received: 3 September 2017; Accepted: 26 October 2017; Published: 2 November 2017  \nAbstract: The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), transfers of materials are assumed to be instantaneous, constraints due to shared utilities are often ignored, and scheduling horizons are kept small due to the limitson the problem size that can be handled by the solvers. These limitations often result in schedules that are not actionable. A simulation model, on the other hand, can represent a manufacturing recipe to the smallest level of detail. In addition, a simulator can provide a variety of built-in capabilities that model the assignment decisions, coordination logic and plant operation rules. The simulation based schedules are more realistic, verifiable, easy to adapt for changing plant conditions and can be generated in a short period of time. An easy-to-use simulator based framework can be developed to support scheduling decisions made by operations personnel. In this paper, first the complexities of batch recipes and operations are discussed, followed by examples of using the BATCHES simulator for off-line scheduling studies and for day-to-day scheduling.  \nKeywords: batch process; scheduling; simulation; coordination control; rescheduling  \n1. Introduction  \nThe problem of scheduling multiproduct and multipurpose batch processes has been studied extensively over the past 30 years [1] . Scheduling involves making decisions for the assignment of tasks to processing units, and the sequencing of various products through the processing facility. Typically, each product is made according to its unique recipe. In some methodologies, the underlying recipes are modeled using either a State-task network (STN) or resource-task network (RTN), and a mixed integer linear programming (MILP) formulation based on discrete time or continuous time representation is used for solving the optimization problem. In some problems where orders can be treated as individual batches, each order moves through various production stages as a discrete entity, and a sequential MIP formulation with order-indexed decision variables for assignments to equipment units and precedence decisions are used to solve the problem. In both the approaches, the manufacturing recipes are greatly simplified in order to keep the problem size to a manageable level so that it can be solved in a reasonable amount of time.  \nSome of the assumptions made in simplifying the recipes can have significant impact on the solutions generated from these formulations. For example, consider the simple STN shown in Figure 1. It consists of three states, S1, S2 and S3, two tasks T1 and T2, and two units U1 and U2, one for each task. The processing times of the two tasks are t1 and t2, respectively.  \nProcesses 2017, 5, 66; [doi:10.3390/pr5040066 www.mdpi.com/journal/processes](doi:10.3390/pr5040066 www.mdpi.com/journal/processes)  \nProcesses 2017, 5, 66 2 of 18  \nFigure 1. A simple state-task.  \nIn a typical MILP formulation, material transfers S1 to T1, T1 to S2, S2 to T2 and T2 to S3 would be assumed to be instantaneous. The resulting schedule for making two batches of T1 is shown as a Gantt chart in Figure 2. Since the transfers are instantaneous, the end of T1 matches the start of T2 .  \nFigure 2. Schedule with zero transfer times.  \nHowever, in a real process the transfers are rarely instantaneous. The actual recipe of the two operations may be closer to the recipe network shown in Figure 3. As sho","cbCaig1BWnDyWiz3","https://ap.wps.com/l/cbCaig1BWnDyWiz3","pdf",5380075,"English","# Introduction\n## Modeling approaches: STN/RTN and MILP\n## Impact of simplifying assumptions\n### Zero versus non-zero material transfer times\n### Complexity of batch recipe networks","[{\"question\":\"Why do traditional scheduling formulations for batch processes often yield non-actionable schedules?\",\"answer\":\"They simplify manufacturing recipes (e.g., STN/RTN), assume instantaneous material transfers, ignore shared-utility constraints, and keep scheduling horizons small for computational reasons, which can mismatch real operations.\"},{\"question\":\"How does simulation improve reliability compared with MILP-based scheduling?\",\"answer\":\"Simulation can model manufacturing recipes at fine detail and incorporate assignment decisions, coordination logic, and plant operation rules, producing more realistic and verifiable schedules.\"},{\"question\":\"What role do non-zero transfer times play in schedule mismatches?\",\"answer\":\"When transfer times are not ignored, equipment must be simultaneously active during transfer steps, so simulated or real start times diverge from MILP schedules that assume zero transfer duration.\"}]","Using Simulation for Scheduling and Rescheduling of Batch Processes - Article | PDF",1790374755,6]