Mixed Integer Programming Approaches to Planning and Scheduling in Electronics Supply Chains

This paper discusses mixed integer programming (MIP) approaches to planning and scheduling in electronics supply chains. First, the short-term detailed scheduling of wafer fabrication in semiconductor manufacturing and detailed scheduling of printed wiring boards assembly in surface mount technology lines are discussed. Then, the medium-term aggregate production planning in a production/assembly facility of consumer electronics supply chain is described, and finally coordinated aggregate planning and scheduling of manufacturing and supply of parts and production of finished products is presented. The decision variables are defined and MIP modelling frameworks provided. The two decision-making approaches are discussed and compared: integrated (simultaneous) approach, in which all required decisions are made simultaneously using a complex, large monolithic MIP model; and hierarchical (sequential) approach, in which the required decisions are made successively, using hierarchies of simpler and smaller-size MIP models. The paper highlights also the research on stochastic MIP applications to planning and scheduling in electronics supply chains with disrupted material and information flows due to natural or man-made disasters

Meta-heurísticas para o sequenciamento de famílias de tarefas em máquinas paralelas idênticas de processamento em lote: Meta-heuristics for scheduling job families on identical parallel batch processing machines

Este artigo aborda um problema de sequenciamento de tarefas em máquinas paralelas idênticas de processamento em lote. Neste problema uma máquina pode executar um lote de tarefas simultaneamente. Além disso, as tarefas são classificadas em famílias, onde uma família agrupa tarefas que possuam alguma característica em comum. Assim, os lotes devem conter somente tarefas de uma mesma família. O problema também considera tarefas com diferentes tempos de chegada (release times). As tarefas possuem ainda uma data de entrega e um peso. O objetivo do problema é determinar os lotes de tarefas para serem sequenciados nas máquinas de tal maneira que o atraso total ponderado das tarefas seja minimizado. O problema envolvendo sequenciamento de lotes é uma extensão do sequenciamento de tarefas clássico (onde uma máquina processa apenas uma tarefa por vez) e possui muitas aplicações reais.  Para resolver o problema abordado, três algoritmos baseados em meta-heurísticas foram desenvolvidos: Adaptive Large Neighborhood Search (ALNS), Iterated Greedy (IG) e Simulated Annealing (SA). Todos estes algoritmos utilizam técnicas de busca em vizinhança para melhorar a qualidade de uma solução. Experimentos computacionais, utilizando dados da literatura, foram realizados a fim de avaliar o desempenho dos algoritmos. Para instâncias de grande porte, os algoritmos propostos são comparados com dois algoritmos da literatura (Memetic Algorithm e Variable Neighborhood Search). Os experimentos e testes realizados demonstram que os algoritmos desenvolvidos neste trabalho geram soluções válidas de excelente qualidade superando as melhores soluções encontradas na literatura

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

A DECOMPOSITION-BASED HEURISTIC ALGORITHM FOR PARALLEL BATCH PROCESSING PROBLEM WITH TIME WINDOW CONSTRAINT

This study considers a parallel batch processing problem to minimize the makespan under constraints of arbitrary lot sizes, start time window and incompatible families. We first formulate the problem with a mixed-integer programming model. Due to the NP-hardness of the problem, we develop a decomposition-based heuristic to obtain a near-optimal solution for large-scale problems when computational time is a concern. A two-dimensional saving function is introduced to quantify the value of time and capacity space wasted. Computational experiments show that the proposed heuristic performs well and can deal with large-scale problems efficiently within a reasonable computational time. For the small-size problems, the percentage of achieving optimal solutions by the DH is 94.17%, which indicates that the proposed heuristic is very good in solving small-size problems. For large-scale problems, our proposed heuristic outperforms an existing heuristic from the literature in terms of solution quality

Traveling Salesman Problem with a Drone Station

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 문일경.The importance of drone delivery services is increasing. However, the operational aspects of drone delivery services have not been studied extensively. Specifically, with respect to truck-drone systems, researchers have not given sufficient attention to drone facilities because of the limited drone flight range around a distribution center. In this paper, we propose a truck-drone system to overcome the flight-range limitation. We define a drone station as the facility where drones and charging devices are stored, usually far away from the package distribution center. The traveling salesman problem with a drone station (TSP-DS) is developed based on mixed integer programming. Fundamental features of the TSP-DS are analyzed and route distortion is defined. We show that the model can be divided into independent traveling salesman and parallel identical machine scheduling problems for which we derive two solution approaches. Computational experiments with randomly generated instances show the characteristics of the TSP-DS and suggest that our decomposition approaches effectively deal with TSP-DS complexity problems.Chapter 1. Introduction 1 Chapter 2. Literature Review 5 Chapter 3. Truck-drone routing Problem 9 3.1 Notation 10 3.2 Mathematical formulation 12 Chapter 4. Fundamental Features of the TSP-DS 14 4.1 Route distortion 14 4.2 Condition for the elimination of route distortion 18 4.3 Decomposition of the TSP-DS 20 Chapter 5. Computational Experiments 24 5.1 Computation times 25 5.2 Comparison between the TSP-DS and TSP 28 5.3 Number of drones in a drone station 30 5.4 Discussion 32 Chapter 6. Conclusions 33 References 35 초록 40Maste

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

Non-identical parallel machines batch processing problem with release dates, due dates and variable maintenance activity to minimize total tardiness

[EN] Combination of job scheduling and maintenance activity has been widely investigated in the literature. We consider a non-identical parallel machines batch processing (BP) problem with release dates, due dates and variable maintenance activity to minimize total tardiness. An original mixed integer linear programming (MILP) model is formulated to provide an optimal solution. As the problem under investigation is known to be strongly NP-hard, two meta-heuristic approaches based on Simulated Annealing (SA) and Variable Neighborhood Search (VNS) are developed. A constructive heuristic method (H) is proposed to generate initial feasible solutions for the SA and VNS. In order to evaluate the results of the proposed solution approaches, a set of instances were randomly generated. Moreover, we compare the performance of our proposed approaches against four meta heuristic algorithms adopted from the literature. The obtained results indicate that the proposed solution methods have a competitive behaviour and they outperform the other meta-heuristics in most instances. Although in all cases, H + SA is the most performing algorithm compared to the others.Beldar, P.; Moghtader, M.; Giret Boggino, AS.; Ansaripoord, AH. (2022). Non-identical parallel machines batch processing problem with release dates, due dates and variable maintenance activity to minimize total tardiness. Computers & Industrial Engineering. 168:1-28. https://doi.org/10.1016/j.cie.2022.10813512816