The objective of this thesis is to present the basics of production scheduling problems for workstations and to create an application of dynamic model. This application should help the UNIKOV company to produce similar schedules for their own operations. The theoretical part provides a brief description of terms and characteristics of the scheduling problems. Detailed characteristics are provided on the open shop problem - the task applied on the real world example. The paper then follows with a practical part, covering an explanation of the heuristics model written in VBA for MS Excel, allowing dynamic expansion and management of the production.

The objective of this thesis is to introduce MES systems with their properties and relations to other automation systems. Furthermore production scheduling theory is introduced with applicable mathematical methods. For given scheduling problem is created optimization model and basic serie of simulations is accomplished. The core of an existing MES system is transformed to web non-comercial platform. All necessary changes are listed in order to integrate production scheduling subsystem to the existing MES system.

Master´s thesis analyses the current state of the production scheduling in a manufacturing company in the automotive industry. It describes the setup system management of the module Advanced Production Planning by Helios Orange program, which company uses. This thesis introduces a new process of order and describes procedures for control of this module. Two essential steps of this new process were tested – creation of production orders and scheduling of production batches. In the end of the master´s thesis there are estimated financial savings after the implementation of the new process production scheduling.

To provide high service level, an organisation must maintain flexibility in production planning. This allows them to react to changes in demand and supply information. Changes in production plan decreases planning stability. Low stability has knock on effect on supply of material. This causes disruptions in production and delivery as well as increased cost due to higher inventory cost, stockout costs, expediting cost and sub optimal setups cost. This study aims to determine the methods to improve planning stability. The factors that influence stability like planning horizon, safety stock, inventory policies are examined in detail in a real world setting. Literature review of existing work on planning stability was carried out. Single case study was used to study this phenomenon in depth at a pharmaceutical manufacturing company. Qualitative data regarding planning method, flow of information, interaction between departments, time horizons were studied. Quantitative data includes the production plans across different planning cycles. Existing stability levels were computed to determine the future levels of stability required. The literature was compared with existing working methods to find improvements. Plans away from the frozen period will have low stability due to forecast volatility. But as we move closer to frozen period, higher stability should be achieved. Low planning horizon in the immediate periods are due to material supply uncertainty which can be prevented by having responsible suppliers and increasing safety stocks. Using a stabilisation parameter can prevent rescheduling when small changes in demand occurs. This ensures unnecessary reaction to small changes are prevented.  Increasing the planning horizon will provide suppliers with accurate information regarding quantity and timing which will in turn help them plan better. Lastly, safety capacity in production can help towards handling unexpected production loses and thereby prevent re-planning.

Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.

This thesis presents a case study of mathematical production scheduling optimization applied on Arla Foods AB’s production of dairy products. The scheduling was performed as a possible remedy for problems caused by overcrowded finished goods warehouse. Based on the scheduling, conclusions were made on whether the existing two-shift production is sufficient or if an additional night shift should be introduced. In parallel, an empirical and theoretical analysis on the perceived effects of night shift work on employees was conducted. For the optimization, mixed integer programming was used to model the production context through a discrete time scheduling lot-sizing model developed in this thesis. The model developed and implemented on Arla Foods AB contributes to the research field through its feature of relatively low complexity enabling scheduling of extensive production systems when applied in industrial contexts where products may be categorized. The thesis concludes that mathematical production scheduling can solve Arla Foods AB’s production problematics and suggests reallocation of the existing shifts for the purpose of reduced costs and acceptable warehouse levels. This reallocation would incur production during inconvenient hours whereas management remedies reducing negative effects of night shift work are identified. / Denna avhandling innefattar en studie av matematisk optimering av produktionsscheman applicerad på Arla Foods ABs produktion av mejeriprodukter. Schemaläggningen utfördes som en möjlig lösning på produktionsproblematik orsakad av överfyllda färdigvarulager. Utifrån de optimerade produktionsschemana drogs slutsatser kring om dagens produktionsstruktur på två skift är tillräcklig eller om introduktion av ett andra nattskift skulle vara fördelaktig. Parallellt med detta presenteras en empirisk och teoretisk studie kring de produktionsanställdas uppfattning kring effekter av att arbeta nattskift. För optimeringen har heltalsoptimering (eng: mixed integer programming) använts för modellering av produktionen genom en produktionsplaneringsmodell med diskret tidsrepresentation (eng: discrete time scheduling lot-sizing model ) som utvecklas i denna avhandling. Denna model, som även appliceras på Arla Foods ABs produktion, presenteras i detalj och karaktäriseras av låg komplexitet vilket möjliggör schemaoptimering av omfattande produktionssystem givet att produktportföljen kan kategoriseras i produktgrupper med liknande egenskaper ur ett produktionsperspektiv. Avhandlingen fastslår att matematisk optimering av produktionsscheman har potential att lösa produktionsproblematiken på Arla Foods AB och föreslår en reallokering av den nuvarande produktionen för minskade kostnader och utjämnade nivåer i färdigvarulager. Produktionsomläggningen skulle innebära produktion under obekväm arbetstid vilket föranleder en analys av initiativ som har potential att minska de negativa effekterna av nattskiftarbete för de produktionsanställda.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaf 76. / by Daniel Olivier Bursaux. / M.S.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Peter Gregory Furth. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Mordechai Porath. / M.S.

No description available.