Researcher:
Aksen, Deniz

Profile Picture
ORCID

Job Title

Faculty Member

First Name

Deniz

Last Name

Aksen

Name

Name Variants

Aksen, Deniz

Email Address

Birth Date

Filters

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.46e79fdc-9a15-465a-b411-72ffcbcaeb65

Search Results

Now showing 1 - 10 of 26
  • Placeholder
    PublicationMetadata only
    Selective multi-depot vehicle routing problem with pricing
    (Elsevier, 2011) Aras, Necati; Tekin, Mehmet Tugrul; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    Firms in the durable goods industry occasionally launch trade-in or buyback campaigns to induce replacement purchases by customers. As a result of this, used products (cores) quickly accumulate at the dealers during the campaign periods. We study the reverse logistics problem of such a firm that aims to collect cores from its dealers. Having already established a number of collection centers where inspection of the cores can be performed, the firm's objective is to optimize the routes of a homogeneous fleet of capacitated vehicles each of which will depart from a collection center, visit a number of dealers to pick up cores, and return to the same center. We assume that dealers do not give their cores back free of charge, but they have a reservation price. Therefore, the cores accumulating at a dealer can only be taken back if the acquisition price announced by the firm exceeds the dealer's reservation price. However, the firm is not obliged to visit all dealers; vehicles are dispatched to a dealer only if it is profitable to do so. The problem we focus on becomes an extension of the classical multi-depot vehicle routing problem (MDVRP) in which each visit to a dealer is associated with a gross profit and an acquisition price to be paid to take the cores back. We formulate two mixed-integer linear programming (MILP) models for this problem which we refer to as the selective MDVRP with pricing. Since the problem NP-hard, we propose a Tabu Search based heuristic method to solve medium and large-sized instances. The performance of the heuristic is quite promising in comparison with solving the MILP models by a state-of-the-art commercial solver.
  • Placeholder
    PublicationMetadata only
    An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem
    (Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/A
    We study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.
  • Placeholder
    PublicationMetadata only
    Solving the multi-depot location-routing problem with lagrangian relaxation
    (Springer, 2007) N/A; N/A; Department of Business Administration; Özyurt, Zeynep; Aksen, Deniz; Master Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 40308
    Multi-depot Location-Routing Problem (MDLRP) is about finding the optimal number and locations of depots while allocating customers to depots and determining vehicle routes to visit all customers. In this study we propose a nested Lagrangian relaxation-based method for the discrete uncapacitated MDLRP. An outer Lagrangian relaxation embedded in subgradient optimization decomposes the parent problem into two subproblems. The first subproblem is a facility location-like problem. It is solved to optimality with Cplex 9.0. The second one resembles a capacitated and degree constrained minimum spanning forest problem, which is tackled with an augmented Lagrangian relaxation. The solution of the first subproblem reveals a depot location plan. As soon as a new distinct location plan is found in the course of the subgradient iterations, a tabu search algorithm is triggered to solve the multi-depot vehicle routing problem associated with that plan, and a feasible solution to the parent problem is obtained. Its objective value is checked against the current upper bound on the parent problem's true optimal objective value. The performance of the proposed method has been observed on a number of test problems, and the results have been tabulated.
  • Placeholder
    PublicationMetadata only
    Solving the multi-depot locationrouting problem with lagrangian relaxation
    (Springer Nature, 2007) Department of Business Administration; N/A; Aksen, Deniz; Özyurt, Zeynep; Faculty Member; Master Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 40308; N/A
    Multi-depot Location-Routing Problem (MDLRP) is about finding the optimal number and locations of depots while allocating customers to depots and determining vehicle routes to visit all customers. In this study we propose a nested Lagrangian relaxation-based method for the discrete uncapacitated MDLRP. An outer Lagrangian relaxation embedded in subgradient optimization decomposes the parent problem into two subproblems. The first subproblem is a facility location-like problem. It is solved to optimality with Cplex 9.0. The second one resembles a capacitated and degree constrained minimum spanning forest problem, which is tackled with an augmented Lagrangian relaxation. The solution of the first subproblem reveals a depot location plan. As soon as a new distinct location plan is found in the course of the subgradient iterations, a tabu search algorithm is triggered to solve the multi-depot vehicle routing problem associated with that plan, and a feasible solution to the parent problem is obtained. Its objective value is checked against the current upper bound on the parent problem's true optimal objective value. The performance of the proposed method has been observed on a number of test problems, and the results have been tabulated.
  • Placeholder
    PublicationMetadata only
    Multi-period travelling politician problem: a hybrid metaheuristic solution method
    (Taylor & Francis Ltd, 2022) Shahmanzari, Masoud; Salhi, Said; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    This paper studies the multi-period travelling politician problem whose objective is to maximise the net benefit accrued by a party leader during a fixed campaign period. The problem is also characterised by flexible depots since the daily tours realised by the party leader may not start and end at the same city. A hybrid multi-start Iterated Local Search method complemented with a Variable Neighbourhood Descent is developed to solve the problem heuristically. Two constructive procedures are devised to generate initial feasible solutions. The proposed method is tested on 45 problem instances involving 81 cities and 12 towns in Turkey. Computational results show that the hybrid metaheuristic approach outperforms a recently proposed two-phase matheuristic by producing 7 optimal solutions and 17 new best solutions. In addition, interesting practical insights are provided using scenario analysis that could assist campaign planners in their strategic decisions.
  • Placeholder
    PublicationMetadata only
    The budget constrained r-interdiction median problem with capacity expansion
    (Springer, 2010) Piyade, Nuray; Aras, Necati; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    In this article, we elaborate on a budget constrained extension of the r-interdiction median problem with fortification (RIMF). The objective in the RIMF is to find the optimal allocation of protection resources to a given service system consisting of p facilities so that the disruptive effects of r possible attacks to the system are minimized. The defender of the system needs to fortify q facilities of the present system to offset the worst-case loss of r non-fortified facilities due to an interdiction in which the attacker's objective is to cause the maximum possible disruption in the service level of the system. The defender-attacker relationship fits a bilevel integer programming (BIP) formulation where the defender and attacker take on the respective roles of the leader and the follower. We adopt this BIP formulation and augment it with a budget constraint instead of a predetermined number of facilities to be fortified. In addition, we also assume that each facility has a flexible service capacity, which can be expanded at a unit cost to accommodate the demand of customers who were serviced by some other interdicted facility before the attack. First, we provide a discrete optimization model for this new facility protection planning scenario with a novel set of closest assignment constraints. Then, to tackle this BIP problem we use an implicit enumeration algorithm performed on a binary tree. For each node representing a different fortification scheme, the attacker's problem is solved to optimality using Cplex 11. We report computational results obtained on a test bed of 96 randomly generated instances. The article concludes with suggestions for future research.
  • Placeholder
    PublicationMetadata only
    A location-routing problem for the conversion to the "click-and-mortar" retailing: the static case
    (Elsevier, 2008) Altınkemer, Kemal; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    The static conversion from brick-and-mortar retailing to the hybrid click-and-mortar business model is studied from the perspective of distribution logistics. Retailers run warehouses and brick-and-mortar stores to meet the demand of their walk-in customers. When they decide to operate on the Web as an e-tailer, also click-and-mortar stores are needed which can serve both walk-in and online customers. While the distance between home and the nearest open store is used as a proxy measure for walk-in customers, a quality of service (QoS) guarantee for online customers is timely delivery of their orders. We describe and solve a static location-routing based problem for companies that embrace the clicks-and-bricks strategy in their retail operations. An augmented Lagrangian relaxation method embedded in a subgradient optimization procedure generates lower bounds, whereas a heuristic method finds feasible solutions. The performance of the Lagrangian-based solution method is tested on a number of randomly generated test problems.
  • Placeholder
    PublicationMetadata only
    A bilevel fixed charge location model for facilities under imminent attack
    (Pergamon-Elsevier Science Ltd, 2012) Aras, Necati; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    We investigate a bilevel fixed charge facility location problem for a system planner (the defender) who has to provide public service to customers. The defender cannot dictate customer-facility assignments since the customers pick their facility of choice according to its proximity. Thus, each facility must have sufficient capacity installed to accommodate all customers for whom it is the closest one. Facilities can be opened either in the protected or unprotected mode. Protection immunizes against an attacker who is capable of destroying at most r unprotected facilities in the worst-case scenario. Partial protection or interdiction is not possible. The defender selects facility sites from m candidate locations which have different costs. The attacker is assumed to know the unprotected facilities with certainty. He makes his interdiction plan so as to maximize the total post-attack cost incurred by the defender. If a facility has been interdicted, its customers are reallocated to the closest available facilities making capacity expansion necessary. The problem is formulated as a static Stackelberg game between the defender (leader) and the attacker (follower). Two solution methods are proposed. The first is a tabu search heuristic where a hash function calculates and records the hash values of all visited solutions for the purpose of avoiding cycling. The second is a sequential method in which the location and protection decisions are separated. Both methods are tested on 60 randomly generated instances in which m ranges from 10 to 30, and r varies between 1 and 3. The solutions are further validated by means of an exhaustive search algorithm. Test results show that the defender's facility opening plan is sensitive to the protection and distance costs.
  • Placeholder
    PublicationMetadata only
    An efficient variable neighborhood search with tabu shaking for a class of multi-depot vehicle routing problems
    (Pergamon-Elsevier Science Ltd, 2021) Sadati, Mir Ehsan Hesam; Çatay, Bülent; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308
    We present a Variable Tabu Neighborhood Search (VTNS) algorithm for solving a class of Multi-Depot Vehicle Routing Problems (MDVRP). The proposed algorithm applies a granular local search mechanism in the intensification phase and a tabu shaking mechanism in the diversification phase of Variable Neighborhood Search. Furthermore, it allows the violation of problem-specific constraints throughout the search in an attempt to escape from local optima and to converge to a high-quality feasible solution. VTNS is a flexible algorithm; with simple adaptations it can be implemented to solve MDVRP, MDVRP with Time Windows (MDVRPTW) and Multi-Depot Open Vehicle Routing Problem (MDOVRP). Our com-putational tests on these three problems show that VTNS provides promising results competitive with state-of-the-art algorithms from the literature in terms of both solution quality and run time. Overall, we achieve six new best-known solutions in the MDVRP, one in the MDVRPTW, and four in the MDOVRP benchmark data sets.
  • Placeholder
    PublicationMetadata only
    A periodic traveling politician problem with time-dependent rewards
    (Springer-Verlag Berlin, 2018) Department of Business Administration; N/A; Aksen, Deniz; Shahmanzari, Masoud; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Business; 40308; N/A
    The Periodic Traveling Politician Problem (PTPP) deals with determining daily routes for a party leader who holds meetings in various cities during a campaign period of.. days. On a graph with static edge costs and time-dependent vertex profits, PTPP seeks a closed or open tour for each day. The objective is the maximization of the net benefit defined as the sum of rewards collected from meetings in the visited cities minus the traveling costs normalized into a compatible unit. The reward of a meeting in a city are linearly depreciated according to the meeting date and recency of the preceding meeting in the same city. We propose a MILP formulation in which we capture many real-world aspects of the PTPP.