Solving a large-scale industrial scheduling problem using MILP combined with a heuristic procedure

Roslöf, Janne; Harjunkoski, Iiro; Westerlund, Tapio; Isaksson, Johnny

doi:10.1016/s0377-2217(01)00140-0

Cited by 32 publications

(13 citation statements)

References 18 publications

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“…Méndez and Cerdá (2003a) propose a similar approach for the rescheduling of a single-stage production process with parallel units. Roslöf et al (2002) adapt the approach presented in Roslöf et al (2001) to the problem of generating an initial schedule. In each iteration, new batches are added to the current partial schedule using an MILP formulation, and the relative order of the batches that have already been scheduled is thereby maintained, in a similar fashion to the method of Roslöf et al (2001).…”

Section: Hybrid Iterative Scheduling Methodsmentioning

confidence: 99%

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Baumann

Trautmann

2014

European Journal of Operational Research

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Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The shortterm scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500-1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient.We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

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Section: Hybrid Iterative Scheduling Methodsmentioning

confidence: 99%

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Baumann

Trautmann

2014

European Journal of Operational Research

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“…Caumond et al (2009) provide mathematical formulation and optimal solutions for the flexible manufacturing systems scheduling problem (FMSSP) with one vehicle where the MILP formulation takes into account the maximum number of jobs allowed in the system, limited input/output buffer capacities, empty vehicle trips and no-move-ahead trips simultaneously. Roslöf et al (2002) consider a large-scale industrial production scheduling problem that includes the allocation of a number of production runs with release and due dates in addition to sequence-dependent setup times. They solve the problem combining MILP with a heuristic procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimisation of process audit plan for minimising vehicle launch risk using MILP

Bharatendra

Nepal

Gunasekaran

et al. 2013

IJPM

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Suppliers play a critical role in the success of any organisation. A complex product such as an automobile often involves a large number of suppliers. The need for high supplier quality especially gets highlighted at the time when automobile manufacturers launch new vehicles. A new vehicle launch involves enormous coordination efforts among the original equipment manufacturer (OEM) and suppliers distributed across the globe. In such situations, supplier audits are used for minimising associated launch risk. However, allocating limited qualified engineers to supplier plants for an audit becomes a challenging task. This research proposes a mixed integer linear programming (MILP) model to allocate engineers to supplier plant locations and create an optimal audit schedule by incorporating the level of risk involved.

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“…They are often used for more complex production environments and may include modules for: (i) dividing the full set of jobs into groups of similar characteristics; (ii) constructing a feasible schedule by considering one or a few jobs at a time; (iii) improving the schedule locally by removing and reinserting jobs in different positions of the processing sequence 4 or reassigning them to different machines. Successful approaches have been reported for the steel [14], paper [15], pharmaceutical [16][17] and consumer goods industries [18].…”

Section: Introductionmentioning

confidence: 99%

Operating room scheduling with Generalized Disjunctive Programming

Castro

Marqués

2015

Computers & Operations Research

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Solving a large-scale industrial scheduling problem using MILP combined with a heuristic procedure

Cited by 32 publications

References 18 publications

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Optimisation of process audit plan for minimising vehicle launch risk using MILP

Operating room scheduling with Generalized Disjunctive Programming

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