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

Abstract

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.

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

“…The CPU time needed was gradually depleted to less than 10 s for small problem instances and even to 10 min to larger ones, by applying the proposed approaches and the suggested preordering rules. Baumann and Trautmann [110] proposed a hybrid method for large-scale, short-term scheduling problems of packed consumer goods products. A subset of the operations were scheduled iteratively by solving a general precedence MILP model [24].…”

Operating room scheduling with Generalized Disjunctive Programming

“…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].…”

On the Optimization of Production Scheduling in Industrial Food Processing Facilities

“…In (Kopanos et al, 2010), the authors studied a real-life yoghurt production facility using a novel mixed discrete and continuous MILP model. Furthermore, Baumann and Trautmann, (2014) proposed a hybrid MILP method for make-and-pack processes using a decomposition strategy and a critical-path improvement algorithm. Moreover, Aguirre et al, (2017) combined a novel MILP model that incorporates TSP (Travelling Salesman Problem) constraints, with a rolling horizon algorithm.…”