Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithms proposed to find approximate solutions. Through computational experiments, the heuristic algorithms\u27 performance is evaluated with problems up to 500 jobs

Cut generation based algorithms for unrelated parallel machine scheduling problems

Research on scheduling in the unrelated parallel machine environment is at best scarce. Moreover, almost all existing work in this area is focused on the minimization of completion time related performance measures and the solution approaches available in the literature suffer from scalability issues. In this dissertation, we leverage on the success of the mathematical programming based decomposition approaches and devise scalable, efficient, and effective cut generation based algorithms for four NP-hard unrelated parallel machine scheduling problems. In the first part,we develop a newpreemptive relaxation for the totalweighted tardiness and total weighted earliness/tardiness problems and devise a Benders decomposition algorithm for solving this preemptive relaxation formulated as a mixed integer linear program. We demonstrate the effectiveness of our approach with instances up to 5 machines and 200 jobs The second part deals with the problem of minimizing the total weighted completion time and proves that the preemptive relaxation developed in part one is an exact formulation for this problem. By exploiting the structural properties of the performance measure, we attain an exact Benders decomposition algorithm which solves instances with up to 1000 jobs and 8 machines to optimality within a few seconds. In the last part, we tackle the unrestricted common due date just-in-time scheduling problemand develop a logic-based Benders decomposition algorithm. Aside from offering the best solution approach for this problem, we demonstrate that it is possible to devise scalable logic-based algorithms for scheduling problems with irregular minsum objectives

Aproximações heurísticas para um problema de escalonamento do tipo flexible job-shop

Mestrado em Engenharia e Gestão IndustrialEste trabalho aborda um novo tipo de problema de escalonamento que pode ser encontrado em várias aplicações do mundo-real, principalmente na indústria transformadora. Em relação à configuração do shop floor, o problema pode ser classificado como flexible job-shop, onde os trabalhos podem ter diferentes rotas ao longo dos recursos e as suas operações têm um conjunto de recursos onde podem ser realizadas. Outras características de processamento abordadas são: datas possíveis de início, restrições de precedência (entre operações de um mesmo trabalho ou entre diferentes trabalhos), capacidade dos recursos (incluindo paragens, alterações na capacidade e capacidade infinita) e tempos de setup (que podem ser dependentes ou independentes da sequência). O objetivo é minimizar o número total de trabalhos atrasados. Para resolver o novo problema de escalonamento proposto um modelo de programação linear inteira mista é apresentado e novas abordagens heurísticas são propostas. Duas heurísticas construtivas, cinco heurísticas de melhoramento e duas metaheurísticas são propostas. As heurísticas construtivas são baseadas em regras de ordenação simples, onde as principais diferenças entre elas dizem respeito às regras de ordenação utilizadas e à forma de atribuir os recursos às operações. Os métodos são designados de job-by-job (JBJ), operation-by-operation (OBO) e resource-by-resource (RBR). Dentro das heurísticas de melhoramento, a reassign e a external exchange visam alterar a atribuição dos recursos, a internal exchange e a swap pretendem alterar a sequência de operações e a reinsert-reassign é focada em mudar, simultaneamente, ambas as partes. Algumas das heurísticas propostas são usadas em metaheurísticas, nomeadamente a greedy randomized adaptive search procedure (GRASP) e a iterated local search (ILS). Para avaliar estas abordagens, é proposto um novo conjunto de instâncias adaptadas de problemas de escalonamento gerais do tipo flexible job-shop. De todos os métodos, o que apresenta os melhores resultados é o ILS-OBO obtendo melhores valores médios de gaps em tempos médios inferiores a 3 minutos.This work addresses a new type of scheduling problem which can be found in several real-world applications, mostly in manufacturing. Regarding shop floor configuration, the problem can be classified as flexible job-shop, where jobs can have different routes passing through resources and their operations have a set of eligible resources in which they can be performed. The processing characteristics addressed are release dates, precedence constraints (either between operations of the same job or between different jobs), resources capacity (including downtimes, changes in capacity, and infinite capacity), and setup times, which can be sequence-dependent or sequence-independent. The objective is to minimise the total number of tardy jobs. To tackle the newly proposed flexible job-shop scheduling problem (FJSP), a mixed integer linear programming model (MILP) is presented and new heuristic approaches are put forward. Three constructive heuristics, five improvement heuristics, and two metaheuristics are proposed. The constructive heuristics are based on simple dispatching rules, where the main differences among them concern the used dispatching rules and the way resources are assigned. The methods are named job-by-job (JBJ), operation-by-operation (OBO) and resource-by-resource (RBR). Within improvement heuristics, reassign and external exchange aim to change the resources assignment, internal exchange and swap intend changing the operations sequence, and reinsert-reassign is focused in simultaneously changing both parts. Some of the proposed heuristics are used within metaheuristic frameworks, namely greedy randomized adaptive search procedure (GRASP) and iterative local search (ILS). In order to evaluate these approaches, a new set of benchmark instances adapted from the general FJSP is proposed. Out of all methods, the one which shows the best average results is ILS-OBO obtaining the best average gap values in average times lower than 3 minutes

Energy aware hybrid flow shop scheduling

Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years

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

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

Due to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness. The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice. The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. A series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied. Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtaiUrlings ., T. (2010). Heuristics and metaheuristics for heavily constrained hybrid flowshop problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8439Palanci

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

An investigation of changeover sensitive heuristics in an industrial job shop environment

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An Adaptive Simulation-based Decision-Making Framework for Small and Medium sized Enterprises

Abstract The rapid development of key mobile technology supporting the ‘Internet of Things’, such as 3G, Radio Frequency Identification (RFID), and Zigbee etc. and the advanced decision making methods have improved the Decision-Making System (DMS) significantly in the last decade. Advanced wireless technology can provide a real-time data collection to support DMS and the effective decision making techniques based on the real-time data can improve Supply Chain (SC) efficiency. However, it is difficult for Small and Medium sized Enterprises (SMEs) to effectively adopt this technology because of the complexity of technology and methods, and the limited resources of SMEs. Consequently, a suitable DMS which can support effective decision making is required in the operation of SMEs in SCs. This thesis conducts research on developing an adaptive simulation-based DMS for SMEs in the manufacturing sector. This research is to help and support SMEs to improve their competitiveness by reducing costs, and reacting responsively, rapidly and effectively to the demands of customers. An adaptive developed framework is able to answer flexible ‘what-if’ questions by finding, optimising and comparing solutions under the different scenarios for supporting SME-managers to make efficient and effective decisions and more customer-driven enterprises. The proposed framework consists of simulation blocks separated by data filter and convert layers. A simulation block may include cell simulators, optimisation blocks, and databases. A cell simulator is able to provide an initial solution under a special scenario. An optimisation block is able to output a group of optimum solutions based on the initial solution for decision makers. A two-phase optimisation algorithm integrated Conflicted Key Points Optimisation (CKPO) and Dispatching Optimisation Algorithm (DOA) is proposed for the condition of Jm|STsi,b with Lot-Streaming (LS). The feature of the integrated optimisation algorithm is demonstrated using a UK-based manufacture case study. Each simulation block is a relatively independent unit separated by the relevant data layers. Thus SMEs are able to design their simulation blocks according to their requirements and constraints, such as small budgets, limited professional staff, etc. A simulation block can communicate to the relative simulation block by the relevant data filter and convert layers and this constructs a communication and information network to support DMSs of Supply Chains (SCs). Two case studies have been conducted to validate the proposed simulation framework. An SME which produces gifts in a SC is adopted to validate the Make To Stock (MTS) production strategy by a developed stock-driven simulation-based DMS. A schedule-driven simulation-based DMS is implemented for a UK-based manufacturing case study using the Make To Order (MTO) production strategy. The two simulation-based DMSs are able to provide various data to support management decision making depending on different scenarios