Flexible job-shop scheduling problem with resource recovery constraints

Devassia, Jobish Vallikavungal; Salazar-Aguilar, M. Angélica; Boyer, Vincent

doi:10.1080/00207543.2017.1420262

Cited by 23 publications

(4 citation statements)

References 91 publications

(89 reference statements)

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“…The IBM ILOG CPLEX Optimizer (referred to as CPLEX for short) is a powerful optimization solver to solve many different types of complex optimization problems such as nonlinear programming, integer programming, mixed‐integer programming, and so forth. It has been successfully applied to solve one of the most commonly studied manufacturing problems at the factory level, job‐shop scheduling, which can be formulated in mixed‐integer programming problems (for instance, Reference [38–41]). A featured component of the CPLEX software package is the CPLEX Optimizer for the Optimization Programming Language (OPL), which is a built‐in algebraic modeling language for optimization models and can make the coding easier and shorter than with general‐purpose programming languages.…”

Section: The Simulation‐based Integrated Virtual Testbedmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

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In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

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Section: The Simulation‐based Integrated Virtual Testbedmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

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“…Another extension of the problem is resource recovery constraints (Vallikavungal Devassia, Salazar-Aguilar, and Boyer 2018) where resources are available in batches, and recovery times are required between each batch. These types of resources are available in limited quantities but are recovered to process all jobs in different batches.…”

Section: Outlinementioning

confidence: 99%

Minimising total earliness and tardiness with periodically supplied non-renewable resource profiles

Namakshenas

Braaksma

Mazdeh

2021

International Journal of Production Research

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We consider a special class of resource-constrained single machine scheduling problems. In the classical scheduling context, resource types are classified into renewable and non-renewable; however, a large variety of real-world problems may not fit into one of these classes, e.g. labour regulations in project scheduling, budget allocation to different phases of a construction project, and dose management in a medical imaging centre. In this study, we address a class of non-renewable resources supplied, not necessarily immediately, in different periods of the planning horizon. The objective is to assign the jobs to the supply periods and schedule them such that the sum of total tardiness and total earliness is minimised. Several properties and complexity results of the optimal schedules are discussed, then they are used to develop a tractable algorithm. First, we decompose the problem into several single supply problems and then decide the optimal schedule through a polynomialtime optimal algorithm for each single supply problem. The scalability tests indicate the promising performance guarantee of the algorithm compared to provably optimal schedules in the integrated framework.

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“…Each job is composed of a set of partially ordered operations, where each operation has a deterministic processing time and preassigned materials that need to be minimised subject to certain constraints . The constraints can relate to material quantity, job prioritisation and the capacity of the operational centres in addition to deadlines (Vallikavungal Devassia, Salazar-Aguilar, & Boyer, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…A large number of algorithms have been developed in order to solve general scheduling problems, including the JSSP (Hasan, Sarker, Essam, & Cornforth, 2009;Martin et al, 2016). These include the genetic algorithm, particle swarm optimisation, simulated annealing, ant colony optimisation, artificial bee colony, and bee colony optimisation (Hasan et al, 2009;Martin et al, 2016;Vallikavungal Devassia et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Agent-based distributed manufacturing scheduling: an ontological approach

et al. 2019

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The purpose of this paper is the need for self-sequencing operation plans in autonomous agents. These allow resolution of combinatorial optimisation of a global schedule, which consists of the fixed process plan jobs and which requires operations offered by manufacturers. The proposed agent-based approach was adapted from the bio-inspired metaheuristic-particle swarm optimisation (PSO), where agents move towards the schedule with the best global makespan. The research has achieved a novel ontology-based optimisation algorithm to allow agents to schedule operations whilst cutting down on the duration of the computational analysis, as well as improving the performance extensibility amongst others. The novelty of the research is evidenced in the development of a synchronised data sharing system allowing better decision-making resources with intrinsic manufacturing intelligence. The multi-agent platform is built upon the Java Agent Development Environment (JADE) framework. The operation research case studies were used as benchmarks for the evaluation of the proposed model. The presented approach not only showed a practical use case of a decentralised manufacturing system, but also demonstrated near optimal makespans compared to the operational research benchmarks.

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Flexible job-shop scheduling problem with resource recovery constraints

Cited by 23 publications

References 91 publications

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Minimising total earliness and tardiness with periodically supplied non-renewable resource profiles

Agent-based distributed manufacturing scheduling: an ontological approach

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