Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ fexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality metrics, providing more versatile and better solutions for production and energy efciency

A Linear Programming Model for Renewable Energy Aware Discrete Production Planning and Control

Industrial production in the EU, like other sectors of the economy, is obliged to stop producing greenhouse gas emissions by 2050. With its Green Deal, the European Union has already set the corresponding framework in 2019. To achieve Net Zero in the remaining time, while not endangering one's own competitiveness on a globalized market, a transformation of industrial value creation has to be started already today. In terms of energy supply, this means a comprehensive electrification of processes and a switch to fully renewable power generation. However, due to a growing share of renewable energy sources, increasing volatility can be observed in the European electricity market already. For companies, there are mainly two ways to deal with the accompanying increase in average electricity prices. The first is to reduce consumption by increasing efficiency, which naturally has its physical limits. Secondly, an increasing volatile electricity price makes it possible to take advantage of periods of relatively low prices. To do this, companies must identify their energy-intensive processes and design them in such a way as to enable these activities to be shifted in time. This article explains the necessary differentiation between labor-intensive and energy intensive processes. A general mathematical model for the holistic optimization of discrete industrial production is presented. With the help of this MILP model, it is simulated that a flexibilization of energy intensive processes with volatile energy prices can help to reduce costs and thus secure competitiveness while getting it in line with European climate goals. On the basis of real electricity market data, different production scenarios are compared, and it is investigated under which conditions the flexibilization of specific processes is worthwhile

Modeling and Solving Flow Shop Scheduling Problem Considering Worker Resource

In this paper, an uninterrupted hybrid flow scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, fuzzy programming method has been used to control the parameters of processing time and preparation time. In the proposed model, there are several jobs that must be processed by machines and workers, respectively. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine and each worker at each stage, so that the total completion time (Cmax) is minimized. Also this paper, fuzzy programming method is used for control unspecified parameter has been used from GAMS software to solve sample problems. The results of problem solving in small and medium dimensions show that with increasing uncertainty, the amount of processing time and consequently the completion time increases. Increases from the whole work. On the other hand, with the increase in the number of machines and workers in each stage due to the high efficiency of the machines, the completion time of all works has decreased. Innovations in this paper include uninterrupted hybrid flow storage scheduling with respect to fuzzy processing time and preparation time in addition to payment time. The allocation of workers and machines to jobs is another innovation of this article

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Energy-aware coordination of machine scheduling and support device recharging in production systems

Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

Modeling, design and scheduling of computer integrated manufacturing and demanufacturing systems

This doctoral dissertation work aims to provide a discrete-event system-based methodology for design, implementation, and operation of flexible and agile manufacturing and demanufacturing systems. After a review of the current academic and industrial activities in these fields, a Virtual Production Lines (VPLs) design methodology is proposed to facilitate a Manufacturing Execution System integrated with a shop floor system. A case study on a back-end semiconductor line is performed to demonstrate that the proposed methodology is effective to increase system throughput and decrease tardiness. An adaptive algorithm is proposed to deal with the machine failure and maintenance. To minimize the environmental impacts caused by end-of-life or faulty products, this research addresses the fundamental design and implementation issues of an integrated flexible demanufacturing system (IFDS). In virtue of the success of the VPL design and differences between disassembly and assembly, a systematic approach is developed for disassembly line design. This thesis presents a novel disassembly planning and demanufacturing scheduling method for such a system. Case studies on the disassembly of personal computers are performed illustrating how the proposed approaches work

A novel Tiki-Taka algorithm to optimize hybrid flow shop scheduling with energy consumption

Hybrid flow shop scheduling (HFS) has been thoroughly studied due to its significant impact on productivity. Besides the impact on productivity, the abovementioned problem has attracted researchers from different background because of its difficulty in obtaining the most optimum solution. HFS complexity provides good opportunity for researcher to propose an efficient optimization method for the said problem. Recently, research in HFS has moved towards sustainability by considering energy utilization in the study. Consequently, the problem becomes more difficult to be solved via existing approach. This paper modeled and optimized HFS with energy consumption using Tiki-Taka Algorithm (TTA). TTA is a novel algorithm inspired by football playing style that focuses on short passing and player positioning. In different with existing metaheuristics, the TTA collected information from nearby solution and utilized multiple leaders’ concept in the algorithm. The research began with problem modeling, followed by TTA algorithm formulation. A computational experiment is then conducted using benchmark problems. Then, a case study problem is presented to assess the applicability of model and algorithm in real-life problems. The results indicated that the TTA consistently was in the first and second ranks in all benchmark problems. In addition, the case study results confirmed that TTA is able to search the best fitness solution by compromising the makespan and total energy utilization in the production schedule. In future, the potential of TTA will be further investigated for flexible hybrid flow shop scheduling problems

A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles

Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs

Integer linear programs and heuristic solution approaches for different planning levels in underground mining

Natürlich vorkommende Mineralien werden seit Tausenden von Jahren aus der Erde gefördert. Im Bergbau wird Operations Research (OR) hauptsächlich angewendet, um die Materialgewinnung zu vereinfachen und die Ressourcen für die Gewinnung effizienter zuzuordnen. Optimierungsprobleme im Bergbau werden üblicherweise nach ihrem Planungshorizont eingeordnet. Dabei werden Layout- und Designprobleme auf strategischer, Produktions- und Planungsprobleme auf taktischer und Ressourcenzuordnungsprobleme auf operativer Planungsebene behandelt. In dieser kumulativen Dissertation betrachten wir eine der größten deutschen Kalibergwerke und befassen uns mit drei Optimierungsproblemen auf drei verschiedenen Planungsebenen. Zunächst betrachten wir eine sogenannte „Gewinnungsprogrammplanung“ für einen Planungshorizont von einem Monat auf taktischer Planungsebene. Die betrachtete qualitätsorientierte Zielfunktion zielt auf eine gleichmäßige Kalisalzgewinnung hinsichtlich des beinhalteten Kaliums ab. Da die Menge der Gesamtförderung a priori unbekannt ist, kann die in der Gesamtförderung enthaltene Kaliummenge mithilfe nicht-linearer Nebenbedingungen in der mathematischen Formulierung bestimmt werden. Die Herausforderung besteht in der Linearisierung der entsprechenden Nebenbedingungen, damit ein gemischt ganzzahliges lineares Programm eingeführt werden kann. Darüber hinaus schlagen wir eine Heuristik vor, welche mindestens eine zulässige Lösung für realitätsnahe Probleminstanzen innerhalb eines angemessenen Zeitraums findet. Die Performanceanalyse an 100 zufällig generierten Probleminstanzen zeigt, dass eine subtile Kombination des vorgeschlagenen mathematischen Programms mit der eingeführten Heuristik nahezu optimale Lösungen für praxisrelevante Probleme findet. Als Nächstes betrachten wir eine „Grobplanung des Maschineneinsatzes“ innerhalb eines Planungshorizonts von einer Woche, welche zwischen der taktischen und der operativen Planungsebene eingeordnet werden kann und untersucht, ob die Ergebnisse der Gewinnungsprogrammplanung für die erste Woche des folgenden Monats umgesetzt werden können. Hierzu wird ein Maschinenplanungsproblem zur Minimierung des maximalen Fertigstellungszeitpunkts berücksichtigt. Wir stellen ein gemischt ganzzahliges lineares Programm vor, welches bestimmte Umstände in einem untertägigen Bergwerk wie die Wiederholung der Erstfreigabe berücksichtigt. Die größte Herausforderung besteht darin, einen Lösungsansatz zu entwickeln, der nahezu optimale Lösungen für große Probleminstanzen findet. Also wird eine Heuristik vorgeschlagen, der absichtliche Verzögerungen von Jobs vor Bearbeitungsstufen einbezieht, d. h. sogenannte aktive Pläne erzeugt. Die Performanceanalyse zeigt, dass kleine Probleminstanzen mit CPLEX optimal gelöst werden können. Bei größeren Instanzen liefert die vorgeschlagene Heuristik die besten Ergebnisse. Schließlich wird auf der operativen Planungsebene eine „Feinplanung des Maschinen- und Personaleinsatzes“ berücksichtigt. Das betrachtete Problem verfolgt einen gleichmäßigen Fortschritt im untertägigen Bergwerk innerhalb einer Arbeitsschicht. Um realistische Lösungen zu erstellen, müssen verschiedene Arten von Rüstzeiten in Betracht gezogen werden, die abhängig von der Bearbeitungsreihenfolge der Operationen an Maschinen und Arbeitern entstehen. Die größte Herausforderung besteht darin, die spezifischen Umstände einer Arbeitsschicht mathematisch darzustellen, z. B. die Berücksichtigung der Pausen der Mitarbeiter für eine eventuelle Verzögerung der Bearbeitungszeit, das Bestimmen des bearbeiteten Prozentsatzes eines Jobs während einer Arbeitsschicht, die Berechnung der Entfernungs- und Umrüstzeiten usw. Wir stellen eine Heuristik vor, die aus zwei Schritten besteht. Im ersten Schritt wird eine Relaxation des Problems unter Einhaltung einen Teil der genannten Nebenbedingungen gelöst. Die gefundene, typischerweise unzulässige Lösung wird im zweiten Schritt durch Einfügen der vernachlässigten Zeiten repariert. Die Ergebnisse zeigen, dass die vorgeschlagene Heuristik für 70 Prozent der realitätsnahen Probleminstanzen eine bessere Lösung als eine bestehende Heuristik finden kann. Anschließend formulieren wir ein neues, kompaktes, gemischt ganzzahliges lineares Programm, das mithilfe von TSP-Variablen alle Problemspezifikationen berücksichtigt. Wir zeigen, dass das vorgeschlagene gemischt ganzzahlige lineare Programm die vorgeschlagene zweistufige Heuristik erheblich übertrifft.Humans have been extracting naturally occurring minerals from the earth for thousands of years. In mining, operations research (OR) has been mainly used to help the mine planners decide how the material can be extracted and what to do with the material removed, what kind of resources to use for the extraction, and how to allocate the resources. It is very widespread to classify decision problems according to their time horizons, where 1. layout and design problems, 2. production and scheduling problems, and 3. operational equipment allocation problems are considered on strategic, tactical, and operational planning levels, respectively. In this cumulative dissertation thesis, we consider one of the biggest German potash mines and address three optimization problems on three different planning levels. First, we consider a so-called “extraction program planning” for a time horizon of one month on the tactical planning level. The related quality-oriented objective function aims at an even extraction of potash regarding the potassium content. For mathematically formulating the objective function, the amount of potassium contained in the output must be determined. Since the amount of total output is a priori unknown, the potassium amount can be determined primarily using non-linear constraints. The principal challenge is the linearization of the corresponding constraints to introduce a mixedinteger linear program with a quality-related objective function. We also propose a heuristic solution procedure that finds for realistically-sized problem instances at least one feasible solution within a reasonable amount of time. The performance analysis conducted on 100 randomly generated problem instances shows that a sophisticated combination of the proposed mixed-integer linear program and the introduced heuristic approach finds high-quality, near-optimal solutions for practice-relevant problems. Next, we deal with a “preliminary (conceptual) planning of machines” within a time horizon of one week. That problem can be classified between the tactical and operational planning levels and investigates whether the results of the extraction program planning can be implemented for the first week of the following month. For this purpose, a machine scheduling problem to minimize the makespan is taking into account. We propose a mixed-integer linear program considering particular circumstances in an underground mine, e.g., reentry. The main challenge is to provide a solution approach that can find near-optimal solutions for large-sized problem instances. For this purpose, we suggest a heuristic approach considering conscious delays of jobs in front of production stages, i.e., active scheduling is applied. The performance analysis shows that small problem instances can be optimally solved with CPLEX-solver. For larger problem instances, the best performance is achieved by the suggested advanced multi-start heuristic. Finally, a “detailed shift planning” considering a simultaneous assignment of machines and workers is taken into account on the operational planning level. That problem pursues an even progress in the underground mine within a work shift. During a work shift, in addition to a machine scheduling problem, a personnel allocation problem must be considered. Moreover, to provide realistic solutions, different kinds of setup times must be observed, depending on the processing sequence of the operations on machines and workers. The major challenge is to express the specific circumstances of a work shift mathematically, e.g., considering workers' breaks for a possible delay in the processing time of a job, determining the processed percentage of a job during a work shift, observing removal and changeover times, etc. A part of real constraints is formulated in a relaxed program as part of a heuristic solution approach. The proposed heuristic procedure consists of two steps. In the first step, a relaxed program neglecting some setup times is solved, and the typically unfeasible solution achieved is repaired in the second step by inserting the neglected times. The results show that the proposed heuristic can find for 70 percent of the realistic problem instances a better solution than an existing heuristic approach. Subsequently, we introduce a new, compact mixed-integer linear program using TSPvariables considering all the problem specifications. We show that the proposed mixed-integer linear program outperforms the proposed two-stage heuristic considerably