Abstract
This paper considers the general lot sizing and scheduling problem with rich constraints exemplified by means of rework and lifetime constraints for defective items (GLSP-RP), which finds numerous applications in industrial settings, for example, the food processing industry and the pharmaceutical industry. To address this problem, we propose the Late Acceptance Hill-climbing Matheuristic (LAHCM) as a novel solution framework that exploits and integrates the late acceptance hill climbing algorithm and exact approaches for speeding up the solution process in comparison to solving the problem by means of a general solver. The computational results show the benefits of incorporating exact approaches within the LAHCM template leading to high-quality solutions within short computational times.
Highlights
In many production processes, a fraction of the manufactured products can be defective due to an unreliable production system
We compare the algorithm to benchmark results for data classes that are generated with the same methodology as in [1], which is explained in more detail
We have proposed the Late Acceptance Hill-climbing Matheuristic (LAHCM) as a general framework for solving the optimization problem
Summary
A fraction of the manufactured products can be defective due to an unreliable production system. In the pharmaceutical industry rework often has to take place in a short amount of time due to likely increasing contamination In this context, we formulate a related problem and address a model formulation of the general lot sizing and scheduling problem with rework termed as GLSP-RP, which was first presented by [1]. We formulate a related problem and address a model formulation of the general lot sizing and scheduling problem with rework termed as GLSP-RP, which was first presented by [1] This problem considers lifetime constraints for defective items. Algorithms 2020, 13, 138 information is interchanged along the process Adapting those ideas to well-known single point metaheuristics such as late acceptance hill-climbing (LAHC, [8]) permits, within a hill-climbing framework, to substitute the inherent method for generating candidate solutions by an exact method solving a given sub-problem.
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