Abstract
The integration problem of production and transportation (IPPT) is one of the most important decision issues in real-life manufacturing and flow industries. To illustrate its potential for improving difficult production and transportation processes, we focus on a challenging case of IPPT, namely the distributed no-wait flow-shop and transportation integrated scheduling problem (DNWFSTISP) considering third party logistics (3PL), which occurs in many practical industries. DNWFSTISP consists of integrating the well-known distributed no-wait flow-shop scheduling and dedicated vehicle routing in the sense of 3PL while minimizing the total cost. First, we investigate the integration approach of the production and transportation of DNWFSTISP and present three problem-specific loading heuristics, which account for both a make-to-order strategy and 3PL providers to improve customer satisfaction. Second, we propose an active learning based hyper-heuristic algorithm (ALHHA) to solve the problem. ALHHA is unique in that it relies on an active learning based high-level exploration to discover promising search regions and a neighborhood search based low-level exploitation to intensively examine given search regions. Third, we assess the composing ingredients of the proposed ALHHA on 40 instances with up to 200 jobs and 7 factories to shed light on their impacts on the performance of the algorithm. Moreover, we make the 40 instances and solved upper bounds publicly available, which would be valuable for future research on DNWFSTISP.
Published Version
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