Person: KÜÇÜKOĞLU, İLKER
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KÜÇÜKOĞLU
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Publication The traveling purchaser problem with fast service option(Elsevier, 2022-01-21) Küçükoğlu, İlker; KÜÇÜKOĞLU, İLKER; Mühendislik Fakültesi; Endüstri Mühendisliği Bölümü; 0000-0002-5075-0876; D-8543-2015The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem, in which a list of products with different quantities has to be purchased from a subset of markets selling various products with different prices. The aim of the problem is to minimize total traveling and purchasing costs while satisfying the products demand in a unique tour. This study introduces a new variant of the TPP, in which the tour has to be completed within a duration time limit by taking into account the traveling and purchasing times of the purchaser. For the purchasing operations, two types of service options are allowed for the purchaser: standard and fast service. The fast service option of a market gives opportunities to the purchaser to complete the purchasing process in a shorter time with an additional cost. This problem is called the traveling purchaser problem with fast service option (TPP-FSO). In addition to presenting a new TPP variant to the literature, this paper proposes an adaptive large neighborhood search (ALNS) algorithm for the TPP-FSO. The proposed ALNS is enriched by a local search procedure, which consists of a set of route-change-based and procurement-changebased heuristics. To evaluate the performance of the ALNS on TPP-FSO, different-sized benchmark problems are generated by using a well-known TPP benchmark problem set. The results of the computations demonstrate the efficiency of the proposed algorithm by introducing better results in shorter computational times.Publication A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking(Springer, 2019-12-01) Küçükoğlu, İlker; Öztürk, Nursel; KÜÇÜKOĞLU, İLKER; ÖZTÜRK, NURSEL; Mühendislik Fakültesi; Endüstri Mühendisliği Bölümü; 0000-0002-5075-0876; 0000-0002-9835-0783; AAG-9336-2021; D-8543-2015Cross-docking is a relatively new logistics strategy that has a great potential to eliminate storage cost and speed up the product flows. This paper considers the vehicle routing and packing problem with cross-docking and presents a mixed integer linear mathematical model. In the model, a set of trucks are used to transport products from suppliers to customers through cross-docking centers. Each supplier and customer node can be visited only once and directly shipping is not allowed from suppliers to customers. Moreover, truck capacities are identified with physical dimensional limits on the contrary of weight or amount of load. The objective of the study is to determine the vehicle routes that minimize the total distance. Due to the complexity of the mathematical model, a hybrid meta-heuristic algorithm (HMA), which integrates tabu search (TS) algorithm within simulated annealing (SA) algorithm, is proposed to solve the problem. Proposed HMA is tested on a well-known benchmark problem data set and compared with the SA and TS solutions. Results show that proposed HMA can produce effective solutions and outperforms the SA and TS especially for the large-sized problems.Publication A tabu search algorithm for the traveling purchaser problem with transportation time limit(Springer-Verlag Berlin, 2023-01-01) Küçükoğlu, İlker; Vansteenwegen, Pieter; Cattrysse, Dirk; Daduna, J. R.; Liedtke, G.; Shi, X.; Voss, S.; KÜÇÜKOĞLU, İLKER; Mühendislik Fakültesi; Endüstri Mühendisliği Bölümü; Daduna, JR; Liedtke, G; Shi, X; Voss, S; 0000-0002-5075-0876; D-8543-2015This study extends the well-known traveling purchaser problem (TPP) by considering a transportation time limit of perishable food in cold-chain logistics. The problem is called the traveling purchaser problem with transportation time limit (TPP-TTL). The objective of the TPP-TTL is to find a route and procurement plan for the purchaser to satisfy the demand of a number of product types with minimum cost. To satisfy the product demand, the purchaser visits a number of capacitated markets, in which the available amount of products is limited. Furthermore, since the travel times cause deterioration on the perishable products, a transportation time limit is taken into account in the TPP-TTL for each product type. The problem is formulated as a mixed-integer programming model and solved by using a tabu search (TS) algorithm. In the computational experiments, TS is carried out for a number of different-sized instances and the results are compared to the results obtained by GUROBI solver to determine the performance of the proposed algorithm. The results of the experiments show that the TS is capable to find many optimal results with less computational time than the GUROBI solver.Publication Adaptive electromagnetic field optimization algorithm for the solar cell parameter identification problem(Hindawi, 2019-04-28) Küçükoğlu, İlker; KÜÇÜKOĞLU, İLKER; Mühendislik Fakültesi; Endüstri Mühendisliği Bölümü; 0000-0002-5075-0876; D-8543-2015Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I-V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.