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    Performance comparison of meta-heuristics for the multiblock warehouse order picking problem
    (University of Cincinnati, 2021) Düzgit, Z.; Toy, A.Ö.; Saner, A.C.
    This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics-the tabu search algorithm and the iterated greedy algorithm-to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the best-known solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages. © 2021 University of Cincinnati. All rights reserved.
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    Production Planning at a Chocolate Company: A Two-Phase Approach by Aggregation
    (Springer Science and Business Media Deutschland GmbH, 2020) Düzgit, Z.; Kuzuoğlu, A.B.; Kalelioğlu, H.; Kolay, H.; Güler, M.B.; Akyol, S.; Toy, A.Ö.
    There are some factors to be considered while developing production planning policy of chocolate and chocolate-based products. One of these factors is that chocolate is a perishable food, therefore it has limited shelf life. Another factor is that; its demand is not always easy to forecast. Holidays, mothers’ day, teachers’ day, valentines’ day and new year are examples of the peak periods. For these special days, demand generally follows a seasonal demand pattern where seasonality may also contain trend for some specific product types. Moreover, there are two religious holidays (Ramadan Feast and Feast of Sacrifice) in Turkey whose dates shift each year. This phenomenon makes forecasts challenging. Underestimating demand causes loss of customer goodwill, lost customers and market share whereas overestimating demand causes excess inventory to keep in stock and risk of fat blooming. Accurate forecasting is critical since it provides a fundamental input for the production plan. The study is conducted in a chocolate company in Turkey. The company does not implement a systematic planning method for chocolate production, instead the planning is based on past experiences with respect to experts’ opinions. The objective of this study is to determine the optimal production and ending inventory levels for the period of 2018 so as to minimize total production and inventory holding cost subject to production, inventory and capacity related restrictions. A two-phase optimization method is adopted as a solution method. Firstly, a mathematical model is developed for the monthly aggregate production planning on product group basis. In order to solve the problem, monthly demand for product groups are forecasted based on the sales data of the previous two years. Secondly, a mathematical model for weekly disaggregate production planning is developed for each end item using the outputs of the aggregate planning as input. The disaggregate production plan gives the weekly planned production and inventory levels for end items for year 2018 with minimum deviation from the aggregate plan. Although the proposed solution model is implemented for year 2018, it can be used for the coming years by updating some parameters. © 2020, Springer Nature Switzerland AG.