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Kredi kartı işlemleri için üye işyeri hizmet ücreti optimizasyonu
This thesis aims to enhance the current pricing policies set to optimize customer retention by proposing enhanced methodologies, focusing on finding efficient algorithms that apply to large-scale businesses. Customer retention studies have a vital effect on companies since they affect the generated revenue and market share. This study mainly investigates pricing factors and their relationship with customer churn. The aim is to find commission rates that will be used to charge merchants when making transactions with Point of Sale (POS) devices. An important aspect to remember is the price sensitivity of those merchants as there happens to be a negative relationship between their retention, and pricing policies. We designed a nonlinear mathematical model to solve this problem. However, solving nonlinear models for large-scale problems is not effective when computational power and current solving techniques are considered. As an approach to solving this phenomenon, we discretized the nonlinear form of the model and proposed an alternative integer programming model. By doing so, solvers used to optimize the integer programming models were able to solve higher scales of the problem when compared with global nonlinear solvers. However, it is observed that IP solvers fail to solve the proposed mathematical model after a certain size. To solve this dimensionality problem, stratification techniques are used to divide the problem and optimize subparts of the whole problem separately while limitations and the objective of the problem are considered.
Türk dağıtıcıların karşılaştığı zengin araç rotalama problemi için metasezgisel algoritmaların ve çözüm süreçlerinin araştırılması
Logistical challenges pose a substantial financial burden for distributors worldwide, particularly in light of escalating oil prices and evolving customer demands. This thesis addresses a Rich Vehicle Routing Problem (RVRP) encountered by a distributor in Turkey. The problem involves a multi-objective function that integrates road tariffs, specific customer constraints, and features observed in various VRP variants. Our study explores diverse solving approaches, including an exact method developed through a novel Mixed-Integer Linear Program, as well as non-exact solutions employing heuristic and metaheuristic algorithms. Notably, our findings highlight the efficacy of metaheuristic algorithms, particularly through a two-phase solving strategy: initially dividing and solving the problem into individual sub-problems, then integrating them comprehensively. The study concluded with a 5.13% reduction in operational costs compared to the distributor's existing plan, indicating significant potential for enhanced profitability while optimizing the daily scheduling of the vehicle fleet, and further expanding applications in operations research.
Sehir lojistiğinde yer seçimi–rotalama ve senkronizasyon problemleri
City logistics aims to improve urban freight transportation by considering the costs and benefits of public and private sectors, consolidating segmented freight shipments, and integrating the individual actors in a collaborative environment. This thesis studies network design problems in city logistics systems to address managerial challenges in urban freight transportation. We consider two network design schemes, namely the one and the two-echelon distribution networks, to formulate strategic and tactical level problems in urban freight transportation. In the single-echelon systems, freight is distributed from consolidation centers located on city boundaries to the customers inside the city. In the two-echelon systems, goods are unloaded at the intermediate facilities, called satellites, and consolidated into smaller vehicles suitable for the last-mile delivery in city centers. From an operational level perspective, we highlight the importance of synchronizing first and second echelon vehicles at the satellite locations and discuss the relation of the satellite synchronization problem to the network design problems. We propose mathematical programming formulations for the introduced strategic, tactical, and operational level problems in city logistics, and develop exact and heuristic solution approaches. The exact approaches use column generation to find optimal delivery routes efficiently. The heuristics are based on the hierarchical decomposition of the original problem into its basic decisions. Extensive computational studies in this thesis provide new insights into designing and implementing a practical city logistics system in real-world.
Kuyruk tipi servis sistemlerinde kalite bilincindeki stratejik müşteriler
A service system is a configuration of technology and organizational networks designed to deliver services that satisfy needs, wants and aspirations of customers. Call centers, universities, hospitals, restaurants, shopping centers, hotels, beauty centers are a few examples of the service systems which provide service to customers in different areas. A strategic customer in a service system acts in order to maximize his individual welfare or utility. To maximize his individual utility, a strategic customer compares all of his possible alternatives and chooses the one which brings the highest pay-off to him. The alternative with the highest pay-off is referred as the optimal behavior (action) of this customer. Indeed, each customer's optimal behavior is affected by acts taken by the service provider (system manager) and by the other customers. The result is an aggregate equilibrium pattern of behavior which may not be optimal from the perspective of the society as a whole. So, the optimal decisions of the individual customer, social planner who is responsible from maximizing the social welfare, and the service provider all need to be analyzed in these systems. Many service systems are queueing type systems. So service systems generally include queues, and the individual customer, social planner and the service provider must (directly or indirectly) consider the displeasure of waiting in the queue. Moreover, the service systems that we consider in this thesis have imperfect quality. The service failures which are caused by the service quality problem will require resolutions. Designing the service systems by using different system structures we analyze different service failure resolution alternatives in Strategic Customer Setting. In summary, our goal in this thesis is to integrate service quality issues with strategic behavior and compare different service system structures
Afet sonrasında yolları açmak için çok-araçlı ayrıt rotalama problemleri
Doğal felaketlerden sonra köprüler ve viyadükler çökebilirken, yollar hasar görebilir veya enkazla kaplanabilir. Bu sık görülen tehlike bazı yol bölümlerinin kapanmasına ve hatta yol ağının bağlantısının kesilmesine neden olur. Afet müdahale aşamasında, çalışma ekipleri, yol ağını yeniden bağlamak ve ulaşıma açmak için yolların bir alt kümesini açmak üzere işe gönderilir. Kapalı bir yoldan, bir takım tarafından bu yol açılmadan geçilemez. Bu çalışmanın amacı, yol temizleme ekipleri için senkronize edilmiş bir çalışma programı üretecek etkin bir çözüm yöntemi sunmaktır. Bloke olmuş yolları temizlemek ve tekrar bağlantı sağlamak için gönderilen birden çok iş ekibinin rotalarını bulan iki ayrıt rotalama problemi ele alınmıştır. Birinci problemde amaç ağın tamamen yeniden bağlanması için gereken zamanı enküçüklemektir. İkinci problemde ise amaç, bağlantısı kesilen ağ bileşenlerini belirli bir süre içinde tekrar bağlayarak kazanılan toplam ödülü enbüyüklemektir. Her bir problem için tam sonuç veren bir karışık tam sayılı programlama (KTP) modeli geliştirilmiştir. İlk problem için KTP modelinin bir gevşetmesinden olurlu çözüm üreten bir mat-sezgisel önerilmiştir. Gevşetme çözümünün optimalite boşluğunun, K'nın takım sayısı olduğu durumda, gevşetilmiş modelden elde edilen alt sınırın en fazla K katı olduğu kanıtlanmıştır. İkinci problem için ise, bir mat-sezgisel yöntem geliştirilmiştir. Bu yöntem, tek araç problemlerini sırasıyla güncellenmiş ödüller ile çözer. Bir üst sınır elde etmek için, önce kesin formülasyondaki zamanlama unsurları gevşetilip, daha sonra Lagrange gevşetme yöntemi ile tek araç problemlerine dönüşen gevşetilmiş KTP çözülür. Önerilen yöntemlerin etkinliği, sistematik şekilde rasgele oluşturulan çeşitli büyüklüklerdeki Öklidyen veriler ve tahmini deprem senaryolarına göre üretilen üç farklı İstanbul yol ağı verisi üzerindeki hesaplamalı deneyler ile gösterilmiştir.
Fiyat dalgalanmalarının bulunduğu ortamlarda envanter yönetimi, fiyatlama ve risk azaltımı
Price uncertainties are among the most critical challenges that retailers and manufacturers have to face. For instance, companies whose operations require procuring from commodity markets are exposed to commodity price fluctuations which experience sharp movements frequently. Besides random nature of customer demand, due to this input and/or sales price volatility, there might be considerable variability in firms' profits. It is vital for these firms to consider price fluctuations in adjusting inventory control and pricing policies, and take a variety of risk management measures. In this dissertation, we consider such a firm where continuous price changes during the planning horizon affect both unit payoff from sales as well as customer arrivals. In a multi-period setting, we first investigate optimal price-dependent inventory control policies and numerically illustrate how continuous price fluctuations affect optimal controls and resulting payoffs. Then, we analyze optimal pricing policies assuming that sales prices are determined both by market-driven random prices and firm's markup decision. We show that level of price variability has a negative effect on firms' final profits. Finally, in a minimum-variance framework, we explore financial hedging strategies of the risk-sensitive firm. We assume that inherent price dynamics of the inventory item are correlated with prices of various products which are freely traded in financial markets. This presents an opportunity for the firm to invest in a financial portfolio of these products to manage its exposure to price and demand uncertainties by observing the current inventory, wealth and price levels. In this environment, we explicitly characterize dynamic variance-minimizing investment decisions of the firm using dynamic programming. We then explore the risk reduction effects of minimum-variance financial hedges through numerical examples and show that significant risk reductions may be possible by using the right hedge.
Navigasyon ve arama konusunda çeşitli çevirimiçi eniyileme problemlerinin analizi
Bu tezde, ağ yapıları üstünde navigasyon ve arama ile ilgili çeşitli çevrimiçi eniyileme problemleri üzerinde çalışılmıştır. Çevrimiçi problemlerde bilgiler adım adım açıklanır ve tüm bilgiler mevcut olmadan önce kararlar alınmalıdır. Tez kapsamında, afete müdahale, arama kurtarma, güvenlik ve savunma alanlarında uygulamaları olan birkaç çevrimiçi eniyileme problemi için eniyi stratejiler tasarlanıp bunların performansları teorik olarak analiz edilmiştir. Önerilen stratejilerin performanslarını analiz etmek için en kötü durumda, bilginin baştan elde olduğu (çevrimdışı) durumdaki en iyi çözüme göre, rekabetçi oranlar belirlenmiştir. İlk olarak, çevrimiçi k-Kanadalı Gezgin Problemi (k-KGP), ayrıtları kesişmeyen yollara sahip çizgeler üzerinde incelenmiştir. Daha önce literatürde bu problem için bir eniyi rassal strateji verilmiştir. Bu çalışmada, bazı durumlarda bu stratejinin uygulanamaz olduğu gösterilerek, strateji her durumda uygulanabilir ve eniyi olacak şekilde değiştirilmiştir. Daha sonra çevrimiçi çok katılımcılı k-KGP ele alınmıştır. Bu problemin sınırlı ve sınırsız iletişimin olduğu iki durumuna bakılarak, literatürde verilen, deterministik stratejilerin rekabetçi oranına alt sınırı iyileştirilmiştir. Aynı iki durum, ayrıtları kesişmeyen yollara sahip çizgeler üzerinde incelenerek, iki deterministik strateji geliştirilmiştir. Bunlardan bir tanesinin eniyi olduğu ispatlanmıştır. Problemin iletişimin olmadığı, sınırlı ve sınırsız iletişimin olduğu üç durumuna bakılarak, rassal stratejilerin rekabetçi oranına alt sınırlar geliştirilmiştir. Ayrıca iletişimli durumlar için rassal bir strateji geliştirilerek, bunun ayrıtları kesişmeyen yollara sahip çizgeler üzerinde eniyi olduğu ispatlanmıştır. Ayrıt belirsizliği olan çevrimiçi Minimum Gecikme Problemi de ele alınan bir başka problemdir. Bu problem için bir eniyi deterministik strateji geliştirilmiştir. Ayrıca, rassal stratejilerin beklenen rekabetçi oranına bir alt sınır bulunmuştur. Son olarak, çevrimiçi Ayrık Arama Problemi, yönlendirilmemiş çizgelerdeki seyahat ve arama maliyetleriyle incelenmiştir. Eniyi deterministik ve rassal stratejiler bulunmuştur.
Çeşitli konveks olmayan problemlerin kopozitif formulasyonlarının dıştan yaklaşımları üzerine
Copositive optimization is linear optimization over the convex cone of copositive or completely positive matrices. "The term copositive programming" was first introduced in 2000. In 2009, Burer showed that mixed binary quadratic optimization problems (MBQP), which comprises a rather large class of nonconvex and combinatorial problems, can be equivalently reformulated as a copositive optimization problem. This seminal work has greatly increased the interest in copositive optimization. It is not surprising, however, that since many combinatorial and nonconvex optimization problems can be reformulated as a copositive optimization problem, copositive programs are also NP-hard in general. The difficulty in the reformulation is entirely due to the conic constraint. For this reason, many researchers have proposed outer approximation hierarchies to the intractable completely positive cone. These approximation hierarchies are composed of a sequence of tractable cones that yield increasingly better approximations of the completely positive cone and are exact in the limit. By replacing the intractable cone by outer approximations in the copositive formulation of nonconvex and NP-hard minimization (resp. maximization) problems, a sequence of increasingly tighter lower (upper) bounds can be obtained for the original problem. This provides opportunities to obtain near-optimal solutions and improve the effectiveness of the algorithms for solving the original problem. In this thesis, we study outer approximations of the copositive reformulations of three classes of nonconvex and NP-hard optimization problems. We first study the class of mixed binary programs (MBPs). We compare the lower bounds arising from outer polyhedral approximations to the lower bound provided by the linear programming (LP) relaxation and establish that the lower bounds due to outer approximations are at least as good as that of LP relaxation. We establish various necessary or sufficient conditions under which the lower bound arising from the outer approximations matches that from the LP relaxation. Our results illustrate the weaknesses of polyhedral approximations. On the other hand, we show that the non-polyhedral doubly nonnegative (DNN) approximations, in general, yield tighter lower bounds. Secondly, we focus on the specific 0-1 knapsack problem (KP) in the class of MBPs. We study two different copositive formulations of the knapsack and compare the upper bounds arising from outer polyhedral approximations to the upper bound provided by the LP relaxation of (KP). We prove that upper bounds obtained from outer polyhedral approximations actually coincide with the upper bound provided by the LP relaxation until at least a certain and fairly large level of the hierarchy. On the other hand, we establish that if the LP relaxation has a non-integer unique solution, then the DNN relaxation gives a strictly better upper bound than the LP relaxation. Finally, we consider the standard quadratic programs (StQP) and investigate the instances of (StQP) for which the DNN relaxation is exact. We establish a complete algebraic characterization of the (StQP) instances that admit an exact DNN relaxation. We explicitly identify three different subsets of such (StQP) instances. Furthermore, we propose a recipe for constructing instances of (StQP) with an exact DNN relaxation. In summary, our results reveal that outer polyhedral approximations, in general, yield weak bounds for (MBP) and for the specific 0-1 knapsack problem, whereas doubly nonnegative relaxations usually give rise to tighter lower bounds.
Üretim sistemlerinin işarete bağlı eşik kuralı ile veriye dayalı kontrolü
With shop-floor data becoming more available, production systems can be controlled more effectively by using data-driven control methods. This thesis focuses on the following research question: how can the decision to produce or not to produce at any time be given depending on the real-time information about a production system?; how can the collected data be used directly in optimizing the policy parameters?; what is the effect of using different information sources on the performance of the system? and how can this choice be made? In order to answer these questions, a production/inventory system that consists of a production stage that produces to stock to meet random demand is considered. The system is not fully observable but partial production and demand information, referred to as markings is available. We propose using the marking-dependent threshold policy to decide whether to produce or not based on the observed markings in addition to the inventory and production status at any given time. An analytical method that uses a matrix geometric approach is developed to analyze a production system controlled with the marking-dependent threshold policy when the production, demand, and information arrivals are modeled as Marked Markovian Arrival Processes. A mixed integer programming formulation is presented to determine the optimal thresholds. Then a mathematical programming formulation that uses the real-time shop-floor data for joint simulation and optimization (JSO) of the system is presented. Using numerical experiments, we compare the performance of the JSO approach to the analytical solutions. The results indicate that the marking-dependent policy introduced here is able to make use of the available partial information effectively and the data-driven joint simulation and optimization is an efficient way for setting the parameters of the policy. Next, we investigate how machine learning methods can be employed to facilitate the optimization of systems controlled with the marking-dependent policies and compare the performance of a number of well-known machine learning methods on this task. Through numerical experiments we show that using machine learning and active learning can improve the performance of complex production systems by speeding up time-consuming optimization problems. Finally, we implement the concepts of this thesis using a Lego production system and discuss the usage of Lego production systems in educational and research related to data-driven control.
Yoğun bakım ünitelerinde tekrar denemeli hasta kabul ve taburcu etme kontrollerin incelenmesi
Intensive Care Units (ICUs) are scarce resources and operate most of the time under high occupancy rates. When faced with limited bed availability, arriving patients are sometimes refused or admitted by discharging an existing patient early, which may result in both increased readmission rates and patient/hospital related costs. Yet, there is not a well-defined admission and discharge control policy to decrease such adverse consequences. In this thesis, we consider a conceptual ICU setting that serves multiple types of patients, which reflects the trade-offs between first-time and recurring patients, between different health stages as well as between the early-discharge and rejection decisions. To do this, we define a so-called readmission orbit whose population consists of patients who are to be readmitted after a previous ICU discharge. There are two major approaches available to represent and analyze such a model: Markov Decision Processes (MDPs) and fluid approximations. Since the conceptual model suffers from the curse of dimensionality, we first consider a simple version of the conceptual model with only one patient type and one health condition, by which we isolate the effects various admission and discharge decisions on readmissions. We present a discrete-time MDP formulation for the simple model and investigate the structure of the optimal admission and discharge control policy under such a model. Next, we present discrete-time MDP formulation for the conceptual model, which aims to develop a well-performing and implementable policy, rather than finding an optimal policy. Then, we develop a deterministic fluid model to control admissions and discharges in such an environment and show that the optimal control of the fluid model in the steady state can be obtained by solving a nonlinear problem. We derive a heuristic policy based on the solution of this nonlinear problem. Finally, we develop a discrete-event simulation platform that represents the ICU system. The platform is quite general, so that it can represent ICUs with different characteristics as well as a variety of control policies. We use this platform to benchmark the performance of the proposed heuristic policies, with those of the two state-of-the-art policies. Numerical results show that our proposed policies significantly outperform the the state-of-the-art policies.
Kanser hastalığında önemli gen kümelerini belirlemek için geliştirilen en iyileme modelleri
Using genomic characterizations of tumours biopsied from cancer patients has a great importance in understanding the formation and progression mechanisms in cancer. Survival analysis is one of the research methods that is used to predict overall survival time of cancer patients and to understand the aforementioned progression mechanisms. High dimensional structure of the genomic characterizations with the limited number of training samples makes survival analysis a challenging task. To be able to identify the survival associated biological mechanisms, cancer-specific pathway/gene set collections can be integrated into machine learning models. Existing approaches usually follow a two-stage approach that either identify predictive genes using a feature selection method and map these selected genes to known pathways/gene sets, or train separate models for each pathway/gene set and try to pick informative ones considering each model's predictive performance. Following such a two-stage approach might result in inefficacy of mapping selected genes to a known biological pathway/gene set due to highly correlated structure between feature groups or including related or very similar pathways/gene sets into the final model due to analyzing each pathway/gene set separately. In this thesis, rather than following such two-stage approaches, we propose machine learning models that can conjointly identify disease related biological mechanisms and perform survival prediction using only these identified biological mechanisms. Our algorithms obtain a sparse set of pathways/gene sets for the survival associated biological mechanisms by eliminating the uninformative ones from the model. We test our algorithms using 20 cancer datasets obtained from The Cancer Genome Atlas and two cancer-specific pathway/gene set collections as input data. We first propose a survival analysis model that integrates pathway/gene set collection into the model using multiple kernel learning. Our algorithm with conjoint modelling approach obtains statistically significantly better or comparable predictive performances against survival random forest (RF) and survival support vector machine (SVM) using significantly fewer gene expression features. Predictive performances of machine learning algorithms can be increased using multitask learning. For this purpose, we extend our multiple kernel learning-based algorithm towards multitask learning. Our multitask learning algorithm both models multiple cancer datasets simultaneously and integrates cancer related biological mechanisms into the machine learning model. The algorithm is able to identify common underlying biological mechanisms for cancer by obtaining better or comparable predictive results against survival RF, survival SVM, and our multiple kernel learning survival analysis algorithm. We also extend our multitask learning algorithm towards task clustering to identify the groups of cancer types that share similar underlying biological mechanisms. To this aim, we propose a unified formulation for task clustering, survival analysis, and knowledge extraction. Our clustering algorithm identifies relevant cancer groups by obtaining statistically significantly better or comparable predictive performances against survival RF, survival SVM, our multiple kernel learning and multitask multiple kernel learning survival analysis algorithms. Numbers of gene expression features and gene sets used by our clustering algorithm are significantly fewer than those of benchmark algorithms. These results show that our methods that identify survival associated biological mechanisms, obtain better or comparable predictive performances against survival analysis methods developed on genomic data without using the pathway/gene set information in the literature. In addition, we prove that survival prediction can be performed using fewer number of gene expression features compared to these benchmark algorithms. We also identify the cancer groups that share similar biological mechanisms without decreasing the predictive power.
Kanser biyolojisi için gen kümesi tabanlı sınıflandırma modelleri
As one of most prevalent and fatal diseases worldwide, cancer has been the focus of biomedical research for many decades. A wide range of different cancers with various causes have been identified and studied, and many treatment methods and drugs have been developed for these cancers. However, many open questions remain in understanding the mechanisms of cancer. With the advent of the high throughput sequencing technologies and the ever-increasing availability of genomic information gathered from cancer patients, researchers have successfully employed genomic information in diagnosis, prognosis and treatment of cancers. Still, given the large number of genes, their considerable correlation and the heterogeneity of cancers, genomic information alone often falls short of generating interpretable models for cancers. To alleviate these effects, pathways can be incorporated into models of cancer. In this thesis, using pathways, we have developed classification methods that produce interpretable models for progression of cancers and survival outcome. Patients usually experience the growth of a number of tumors in their life-time. However, not all these tumors pose a danger to the patient's life. To communicate the severity of the cancer, practitioners and researchers use the staging convention. The stage of a cancer encodes the level of its spread in the neighboring tissue and the body in general, where early-stages are assigned to local tumors and late-stages to the cancers that have spread in the body. Hence, understanding what drives cancers from early- to late-stages is an important question in understanding the mechanisms of cancer. The survival outlook of a patient is a similar measure for the severity of a cancer and understanding the mechanisms that affect the survival chances of a patient is immensely important in devising treatment strategies. In this work, we use multiple kernel learning for integrating pathways into the classification models for cancers. This allows for developing models that are more accurate and far more interpretable compared to models generated by conventional methods that do not use the pathway information. We then extend this method in several directions to improve accuracy and interpretability of the models. In the second part of this thesis, observing that the level of sparsity of the solutions can differ largely from caner to cancer and is often only indirectly affected by the parameters of the methods, we develop a model with an adjustable measure of sparsity and propose efficient solution methods for solving instances of this problem. In the third part of this thesis, given the known similarities between cancers, we develop a framework for building multitask classification models to improve the classification accuracy of cohorts with limited data using the similar cohorts with abundant data. Finally, we employ optimization techniques such as the cutting-plane method and Benders decomposition to improve the algorithmic performance of these methods to the point that they can be applied successfully to large-scale problems stemming from considering several cancers together in a multitask framework. The practical application of these methods is examined by applying them to the two aforementioned classification tasks across the Cancer Genome Atlas datasets for 27 cancer types. The results of these experiments indicate that the incorporation of pathways into these classification problems facilitates generating more interpretable and more accurate models, the similarity of cancers can be leveraged using a multitask framework towards the same goals, and the optimization methods developed here allow for solving these large-scale problems in efficient time using parallelization technologies.
Üçüncü-parti bir lojistik taşıyıcısının karayolu nakliye operasyonlarının optimizasyonu
Planning of daily freight operations is one of the main challenges in transport logistics. Our study originates from a complex real-life routing problem faced by a third-party logistics (3PL) firm. Most of the 3PL firms employ a decentralized approach to ease the planning process and manage the complexity in their operations. This leads to suboptimal decisions due to the lack of integrated use of their resources as well as missed consolidation opportunities. The aim of this study is to develop a solution methodology to optimize the planning of day-to-day road transport operations of the firm in a centralized manner within an acceptable time frame. We address three novel problems related to road freight operations in this thesis. We first examine the less-than-truckload (LTL) planning problem, which involves pickup and delivery orders with deadlines and cross-docking opportunities. We assume that an unlimited number of trucks of different capacities can be hired from the spot market on a short-term basis to fulfill the LTL transportation requests. The objective is to find a minimum cost assignment of orders to feasible route-vehicle pairs while satisfying practical constraints. We propose a decomposition-based matheuristic algorithm to tackle this problem. Our solution approach demonstrates a significant reduction in the freight costs over the long term. The 3PL firm also operates a limited number of dedicated trucks which are either directly owned or hired through a long-term contract. The second problem we address deals with the assignment of dedicated vehicles to less-than-truckload (LTL) and full truckload (FTL) routes. The LTL routes obtained in the first part can be re-scheduled in this phase while the spot-hired vehicles are replaced with dedicated ones to maximize savings via an Mixed Integer Linear Programming (MILP)-based solution approach. We finally examine the last-mile delivery operations of the 3PL firm where different types of products are delivered to customers within a certain geographic region from a central depot. We model this part as a Rich Vehicle Routing Problem (RVRP) with heterogeneous vehicle fleet, time windows, selectiveness and several other practical requirements while allowing multiple trips per vehicle. We propose a parallelized hybrid evolutionary algorithm for solving the problem. The contribution of our study is twofold. On one hand, it creates actual value for the business through reducing freight costs with the proposed solution techniques. On the other hand, it introduces novel problems and solution algorithms to the optimization literature which can be extended to a wide range of industry problems in the future.