Scheduling in the FMCG Industry: An Industrial Case Study

Elzakker, M. A. H. van; Zondervan, Edwin; Raikar, N. B.; Grossmann, Ignacio E.; Bongers, Peter

doi:10.1021/ie202106k

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…In general precedencebased models (cf. e.g., Jain and Grossmann 2000, Kopanos et al 2009, Kopanos et al 2011, Méndez and Cerdá 2000, 2002a,b, 2003b, Méndez et al 2001, Sundaramoorthy and Maravelias 2008a,b, and Elzakker et al 2012, all direct and indirect predecessor batches are considered for each batch. The latter approach requires fewer binary variables and allows shared resources such as storage tanks to be handled without the need for additional variables.…”

Section: Exact 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: Exact 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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“…One of the main consumer goods group is the fast-moving consumer goods (FMCG), which are characterized by frequent purchases, rapid consumption and low prices. Elzakker et al [106], presented a problem-specific model for the short-term scheduling problem, considering a fast-moving consumer goods (FMCG) industry. An algorithm based on a unit-specific, continuous time interval MILP model is proposed.…”

Section: Consumer Goods Industriesmentioning

confidence: 99%

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

2019

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Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.

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“…Scheduling is concerned with how and when to execute operations to optimize a chosen objective such as maximizing profits, or minimizing costs, subject to operational constraints such as deadlines, capacity, or available resource limitations. It is common practice for many industries to make use of scheduling as an optimization problem to guide their progress and meet various economic objectives, − and proper scheduling can greatly increase efficiency and therefore is of great practical importance. Scheduling operations over a relatively short period of time, such as a day, a shift, or a week is often referred to as short-term scheduling .…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Approach to Selecting Time Points for Short-Term Scheduling with Application to Multipurpose Facilities

Stevenson

Fukasawa

Ricardez‐Sandoval

2020

Ind. Eng. Chem. Res.

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Choosing an efficient time representation is an important consideration when solving short-term scheduling problems. Improving the efficiency of scheduling operations may lead to increased yield, or reduced makespan, resulting in greater profits or customer satisfaction. When formulating these problems, one must choose a time representation over which to execute to scheduling operations. We propose in this study an iterative framework to refine an initial coarse discretization, by adding key time points that may be beneficial. This framework is compared against existing static discretizations using computational experiments on a scientific services facility. Using case studies from other applications in chemical engineering, we compare the performance of our framework against a previously reported time-discretization approach in the literature. The results of these experiments demonstrate that when problems are sufficiently large, our proposed dynamic method is able to achieve a better tradeoff between objective value and CPU time than the currently used discretizations in the literature.

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Scheduling in the FMCG Industry: An Industrial Case Study

Cited by 21 publications

References 27 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

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

A Dynamic Approach to Selecting Time Points for Short-Term Scheduling with Application to Multipurpose Facilities

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