“… Fig. 1 State-task-network of the chemical processes based on Papageorgiou and Pantelides [2] , p. 30, and Burkardt and Hatzl [7] , p. 1178, and developed further by Hahn and Brandenburg [1] . …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The production system and processes of the benchmark problem are based on a case example of a chemicals manufacturer. This case example has been used to numerically analyze procedures for short-term campaign scheduling over a planning horizon of about ten weeks (see, e.g., [2] , [6] , [7] ). However, the manufacturing system and processes of the case example are adequate to numerically illustrate and analyze mid-term APP approaches.…”
Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). The need for powerful and efficient approaches to solve complex APP problems persists. Problem-specific solution approaches are advantageous compared to standardized approaches that are designed to provide basic decision support for a broad range of planning problems but inadequate to optimize under consideration of specific settings. This in turn calls for methods to compare different approaches regarding their computational performance and solution quality. In this paper, we present a benchmarking problem for APP in the chemical process industry. The presented problem focuses on (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates and (ii) integrated campaign planning with production mix/volume on the operational level. The mutual trade-offs between economic, environmental and social factors can be considered as externalized factors (production-related carbon emission and overtime working hours) as well as internalized ones (resulting costs). We provide data for all problem parameters in addition to a detailed verbal problem statement. We refer to Hahn and Brandenburg [1] for a first numerical analysis based on and for future research perspectives arising from this benchmarking problem.
“… Fig. 1 State-task-network of the chemical processes based on Papageorgiou and Pantelides [2] , p. 30, and Burkardt and Hatzl [7] , p. 1178, and developed further by Hahn and Brandenburg [1] . …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The production system and processes of the benchmark problem are based on a case example of a chemicals manufacturer. This case example has been used to numerically analyze procedures for short-term campaign scheduling over a planning horizon of about ten weeks (see, e.g., [2] , [6] , [7] ). However, the manufacturing system and processes of the case example are adequate to numerically illustrate and analyze mid-term APP approaches.…”
Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). The need for powerful and efficient approaches to solve complex APP problems persists. Problem-specific solution approaches are advantageous compared to standardized approaches that are designed to provide basic decision support for a broad range of planning problems but inadequate to optimize under consideration of specific settings. This in turn calls for methods to compare different approaches regarding their computational performance and solution quality. In this paper, we present a benchmarking problem for APP in the chemical process industry. The presented problem focuses on (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates and (ii) integrated campaign planning with production mix/volume on the operational level. The mutual trade-offs between economic, environmental and social factors can be considered as externalized factors (production-related carbon emission and overtime working hours) as well as internalized ones (resulting costs). We provide data for all problem parameters in addition to a detailed verbal problem statement. We refer to Hahn and Brandenburg [1] for a first numerical analysis based on and for future research perspectives arising from this benchmarking problem.
“…Teunter and Flapper (2006) presented work to identify the best bottling alternative for produced batches in the pharmaceutical industry when the batches have to undergo several lengthy quality tests. Burkard and Hatzl (2006) investigated a heuristic aimed to minimise the makespan in batch processing problems occurring in the chemical and pharmaceutical industry. A hybrid technique called 'partial parameter uniformisation', which ignores values of some parameters to facilitate the solution of complex batch sizing and scheduling problems, was proposed by Wang and Guignard (2006).…”
This paper addresses a complex scheduling problem encountered in a major pharmaceutical industry setting. Specifically, the problem deals with assigning tasks to technicians as part of the quality control phase in order to minimise the total flowtime, and the number of jobs not meeting a required time window. The problem considers test batching, overlapping tests, and resource assignments constrained by test specific capability requirements. Furthermore, batching tasks of similar types is possible, but batch sizes are particular to each product-test type combination. This is a significant difference from previous literature in batching parallel machines. The particular problem described in the paper is highly relevant to the pharmaceutical industry and has not been previously addressed in the literature. Various approaches to solve this particular problem are described and compared via statistical analyses. Finally, the authors present a software prototype with implemented solution algorithms.
“…The multi-product, multistage batch plant is widely applied in the chemical [1][2][3][4][5], petroleum [6,7], steel [8,9], food [10,11] and pharmaceutical industries [12,13]. The multi-product, multistage batch plant is a batch production process with several processing stages and some parallel processing units in every stage [14].…”
A scheduling model for a multi-product, multistage batch plant with parallel units is presented. The objective is to maximize the weighted completion times of orders in every processing stage while imposing a penalty on the slower orders. The proposed model uses the continuous-time representation mode and describes the allocations of tasks, units and stages by a set of binary variables. In order to reduce the model size and provide a more effective solution to the model, a pre-ordering approach that sorts the processing sequence of orders is developed. The pre-ordering approach identifies the infeasible assignments through which the number of binary variables is significantly reduced. Illustrative examples are provided to show that the size of the proposed model is small, and therefore, needs much less computational effort in comparison with the existing models in the literature.
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