Operational Planning and Scheduling Benchmark Problem in an Automatic Picking System

Graph-based heuristics for operational planning and scheduling problem in automatic picking system

“…We show the computational results in Table 2. In Table 2, the column of random is the best objective values among 100,000 random assignments of products and scheduling requests reported by Iima and Kawano (2014). The column of CP shows the best objective values using a constraint programming model by Miyamoto et al (2015).…”

“…Readers could refer to papers by Iima et al (2015) and Kondo et al (2012) for more details. We then prove the NP-hardness of the problem in Section 2.3.…”

“…One possible strategy is random assignment; we assign N K product types into N L lanes randomly. Iima and Kawano (2014) applied random assignments repeatedly, and reported statistical computational results on benchmark instances. Another possible and promising strategy is local search (and metaheuristics).…”

“…Iima et al (2015) proposed a polynomial time algorithm that decides time points (or converted locations) of supplying products optimally. We note that they assumed that a lane group is designated for each product even if more than one lane groups can supply this product type.…”

“…For the operational planning and scheduling problem in the automatic picking system, Iima et al (2015) proposed an efficient algorithm to solve a subproblem of the original problem; when an assignment of products into lanes and a schedule of requests to be processed are given, the algorithm determines how to operate the automatic picking system optimally. By using this algorithm, the search space can be decreased effectively and it becomes easier to search for a good assignment of products and a good schedule of requests by some strategies (such as local search and metaheuristics).…”