Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the first time, this work integrates the problem of liquid-based ship unloading with full truckload vehicle routing and truck driver scheduling. The primary objective is to minimize the total transportation costs during the continuous unloading process, while satisfying extra constraints such as driver rest–break–drive regulations, time windows, a heterogeneous fleet structure, and port-specific constraints such as maintaining a minimum number of backup vehicles at the port during unloading. To address this complex problem, a route-based insertion heuristic is employed as an initial step in a column generation framework designed for exact optimization. The approach incorporates a nested label setting algorithm for column generation, enhanced with acceleration techniques involving multi-search strategies, and refined selection methods. Performance analysis, based on artificial datasets closely resembling real-world scenarios and consisting of 112 instances, demonstrates that optimality gaps below 1% can be achieved within computational times considered reasonable in the context of the existing literature, while the total number of customer nodes and the minimum number of required vehicles at the port are at most 100 and 5, respectively.

In vehicle routing problems, long-distance transportation poses a significant challenge to the optimization of transportation costs while adhering to regulations. This study investigates a special type of logistics problem that focuses on liquid transportation systems involving full truckload delivery and the rest–break–drive periods of truck drivers over long distances according to the regulations of the United States. Based on an exact solution algorithm, this work combines a long-distance full truckload fluid transportation problem with the concept of truck driver schedules for the first time. The goal is to optimize transportation expenses while managing challenges related to the rest–break–drive periods of truck drivers, time windows, trailer varieties, customer segments, food and non-food products, a diverse fleet, starting locations, and the diverse tasks of vehicles. In order to reach optimality, a construction heuristic and the column generation method were employed, supplemented by several acceleration strategies. Performance analysis, carried out with artificial input sets mirroring real-life scenarios, indicates that low optimality gaps can be obtained in an appropriate amount of time for large-scale long-haul liquid transportation.

This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact approach has been applied before. Multiple customer and trailer types, as well as washing operations, are introduced simultaneously during the exact solution process, bringing new aspects to the exact algorithm approach among full-truckload systems in the literature. The objective is to minimize transportation costs while addressing constraints related to multiple time windows, trailer types, customer types, product types, a heterogeneous fleet with limited capacity, multiple departure points, and various actions such as loading, unloading, and washing. Additionally, the elimination or reduction of waiting times is provided along transportation routes. In order to achieve optimal solutions, an exact algorithm based on the column generation method is proposed. A route-based insertion algorithm is also employed for initial routes/columns. Regarding the acquisition of integral solutions in the exact algorithm, both dynamic and static sets of valid inequalities are incorporated. A label-setting algorithm is used to generate columns within the exact algorithm by being accelerated through bi-directional search, ng-route relaxation, subproblem selection, and heuristic column generation. Due to the problem-dependent structure of the column generation method and acceleration techniques, a tailored version of them is included in the solution process. Performance analysis, which was conducted using artificial input sets based on the real-life operations of the logistics firm, demonstrates that optimality gaps of less than 1% can be attained within reasonable times even for large-scale instances relevant to the industry, such as 120 customers, 8 product and 8 trailer types, 4 daily time windows, and 40 departure points.

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.

Üretim ortamlarında, makineler ve otomatik yönlendirmeli araçlar gibi sistem öğelerinin kontrol edilmesi zor olabilmektedir. Etmen tabanlı sistemler, üretim ortamlarında iş kesintileri, makine arızaları gibi dinamik olaylar meydana geldiğinde operasyonları yönetmek için etkili yöntemler sunmaktadır. Bu çalışmada, Çoklu Etmen Sistemi (MAS) mimarisi aracılığıyla esnek bir üretim sistemindeki AGV’lerin ve makinelerin dinamik çizelgeleme problemine uygulanmış ve elde edilen sonuçları literatürde yaygın olarak kullanılan yönlendirme kurallarıyla karşılaştırılmıştır. Çoklu etmen tabanlı yaklaşımlar dağıtılmış, stokastik, sürekli ve yüksek hesaplama karmaşıklığına sahiptir. Etmenler arasında yapılan müzakereler ve teklif verme sonucunda uygun çizelgeler ortaya çıkar. Literatürden alınan bir vaka çalışması etmen mimarisine uygulanmış ve çevrimiçi çizelgeleme kuralları (yönlendirme/sevk kuralları) ile karşılaştırılmıştır. Elde edilen sonuçlara göre, MAS gerçek zamanlı olarak iyi çizelgeler üretebildiği ve makespan performans metriği açısından yaygın olarak kullanılan yönlendirme kuralları ile karşılaştırılabilir olduğu bulunmuştur.

Fossil fuels are the primary energy sources and meet the global energy demands. However, environmental and health problems related with these sources boosted the demand for renewable energy sources. Hydrogen, as an energy carrier has a growing potential for solving these problems. In this article, a mathematical programming model that integrates the production, storage and transportation, safety, location, and staff assignment decisions is presented considering minimization of costs. Although most of the studies focus on location, distribution, storage decisions of hydrogen energy networks, the article also includes production, safety and staff assignment decisions to make this problem more practical. Furthermore, we also investigate the set covering constraint will ensure that each region is covered by minimum number of the hydrogen facilities. The developed model ensures a balance between location, distribution, storage, production, safety and staff decisions by installing two production facilities by assigning total 9 warehouses, 22 tank trucks, 12100 km pipeline, 35 staffs under distance constraint 2000 km in regions 1 and 5. The computational results indicate that the proposed model produces effective solutions for the coverage to all region and minimum total cost for real-case situations.

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®.

Üretim rotalama problemi; üretim, envanter, dağıtım ve rotalama kararlarının bir arada verildiği bir tedarik zinciri problemidir. Bu problemin temel amacı, kararların entegre bir şekilde verilmesiyle birlikte maliyet açısından daha etkin kararlar almaktır. Bu çalışmada, bu problemin kapalı döngü versiyonunu ele alan bir karar destek sistemi önerilmiştir. Önerilen karar destek sisteminde kapalı döngü tedarik zincirlerindeki üretim rotalama problemi için DH algoritması uygulanmıştır. DH algoritması problemi parçalayarak alt problemleri etkileşimli bir şekilde hızla çözmeyi amaçlar. Sistemin gerçek hayat uygulamalarını gösterebilmek adına sıvı yağ üretimi yapan bir firma için bir aylık planlama dönemi için incelenen problem uygulanmış ve maliyetlerin mevcut duruma göre daha düşük olduğu görülmüştür.

Social media users have been growing exponentially in recent years. This growth has evoked researchers and manager to analyze the social media and customer sentiment because it offers significant opportunities to advance business intelligence in supply chain. However, supply chain members are struggling in understanding the general sentiments in today’s business world. Therefore, various methods are used to provide valuable insights in supply chain. In this paper, SentiStrength is used to analyze customer reviews related to one type of product. The output of SentiStrength and demands of the product are then fed into Artificial Neural Network to forecast the customer demands. After, nondominated sorting genetic algorithm II (NSGA-II) based simulation optimization is employed to solve the inventory routing problem using forecasted customer demands. The results of the study demonstrated that the use of hybrid methodology containing sentiment analysis can successfully analyze the inventory routing problem.

A closed-loop supply chain structure organises material and information flows from origin points to consumption points, including production, recycling, disposal, and other reverse logistic activities. Some integration problems arise with this structure including production, inventory, location, routing, distribution, collection, recycling, and routing. The integration problems that are facing scientific researchers include inventory routing, location routing, and location inventory. This study considers the integration problem of a closed-loop supply chain for the production, distribution, collection, and recycling quantities, along with the distribution and collection routes for each time period of a finite planning horizon. We refer to this problem as the “Closed-Loop Supply Chain Integrated Production-Inventory-Distribution-Routing Problem” (CLSC-PRP). A mathematical model is proposed that is the first to determine both quantities and routes for the CLSC-PRP simultaneously. As the problem is known to be NP-hard in terms of computational complexity, a simulated annealing-based decomposition heuristic is developed for solving large-scale CLSC-PRP instances. The results of the proposed mathematical model for the CLSC-PRP are compared with the results of the developed heuristic and two separate models that manage forward and backward production routing problems. An extensive comparative study indicated the following: (i) the proposed model was able to reduce the cost required for operating the total supply chain by an average of 12%, along with providing a positive impact on the environment and (ii) the proposed heuristic is able to generate solutions that are close to optimal in most cases.

Container transportation problems include logistics activities conducted in both terminals and hinterland of the terminal. Transferring containers considering cost, time and environmental issues is a difficult problem. Hinterland container transportation (HCT) is defined as the movement of the containers between terminals and customers by different transportation modes. Processes of the hinterland transportation are the most important cost factors for door-to-door services. These problems aim to decide the distribution of the containers considering the cost and time minimization. In this study, a container distribution, routing problem has been proposed in the hinterland. The problem aims to perform the distribution of the containers at minimum total traveling distance. Allocation of containers and the routes under the constraints of routing, capacity is investigated in this study. An integer programming approach has been proposed and solved.

In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.

This study investigates a multiple-supplier selection problem in which a firm or buyer aims to find an optimal set of suppliers to satisfy its demand for multiple components for a planning horizon. A distinctive feature of our problem formulation is to integrate decisions relevant to supplier selection, such as determining the order quantities from each supplier under price discounts and the order collection routes for multiple vehicles. In other words, the traveling purchaser problem is combined with multiple supplier selection. A new mixed-integer programming model is developed to optimally solve this problem. The model considers costs of inventory holding, ordering, transportation and purchasing along with supplier’s supply capacity, vehicle capacity constraints. A numerical example is provided to illustrate how the model is executed. Scenario analysis is performed to assess the model’s results under varying conditions.

This paper presents a combinatorial problem called a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint, clustered backhauls at the operational level, and train loading at the tactical level for an intermodal transportation network. A two-phase approach, called clustering first, packing-routing second, is proposed for use during the first stage. The clustering of backhauls is carried out using the k-means algorithm. A hybrid approach is provided, which combines the packing of orders by first solving a 3D loading problem for each cluster using machine learning with a best-fit-first strategy, with routing using a genetic algorithm. During the second stage, the train-loading problem is solved using a mixed integer programming approach to minimise the total costs by incorporating various cost types, in which detention and demurrage costs are taken into account. All solution approaches are computationally evaluated on real-world data provided by an international logistics firm and new randomly generated instances. Comparisons are carried out using both exact solution methods and heuristic approaches, and the proposed approach was shown to be more effective for real-world problems. (C) 2019 Elsevier Ltd. All rights reserved.

Inventory routing problems (IRPs) have been one of the most important problems in the last thirty years and include inventory management, vehicle routes and distribution sub problems. Several IRPs have been implemented in various sectors. Maritime inventory routing problem (MIRP) has also been tackled widely. The problem includes the distribution of products and holding the inventory levels between upper and lower limits. In this study, MIRP for the distribution of containers considering available inventory levels aiming minimum total cost has been proposed. Distribution amount and the routes under the constraints of routing and inventory levels have been decided. The model is proposed for deciding both optimal routes of the ships and optimum inventory levels. An integer programming approach for the problem has been proposed and solved using GAMS software.

B lood banking is important for the healthcare system and blood products are vital needed for medical treatment, surgeries, and organ transplants. Delivery time becomes vital when a hospital needs a blood product emergently. Therefore, delivering blood products in the safest and fastest way is the main responsibility of blood banks. In this study, we aim to minimize total delivery times between depots and demand points over a time period. We propose a two-stage mathematical model to determine both location and vehicle delivery routes. This problem is defined as Location and Routing problem for blood bank operations. This study also investigates the effect of distributing blood products among multi-compartment along with routing decisions. The proposed models are tested on a real-world case data in order to demonstrate their effectiveness in producing optimal solutions. By the results the approach can minimize both delivery time and total cost. Furthermore, a computer program with a graphical user interface is developed to implement the proposed approach for different data sets.

Dorduncu Sanayi Devrimi (Endustri 4.0) tum sektorler icin yeni bir cagdir ve akilli tesislere gecistir. Bu calismada, bir lojistik firmasinda Endustri 4.0 yaklasimi; tasimacilik, depolama, yukleme/bosaltma ve bilgi hizmetleri birimlerinde incelenmistir. Bu yaklasimin amaci, Endustri 4.0’ in ilkelerini bu hizmet birimlerinde degerlendirmektir. Calismada, bu konulara odaklanan bir literatur calismasi sunulmustur ve bu alanda lojistik firmalarini tasarlamak icin gerekli temel ilkelere karar verilmistir. Endustri 4.0’a gecis icin onemli olan otonom tasima, otonom stok yonetimi, 3D depolar, kuresel kaynak planlama ve gercek zamanli rotalama gibi kriterlerin onceliklendirilmesi icin bir bulanik yontem kullanilmistir ve yontem, gecis icin gereklilikleri anlamaya yardimci olmaktadir. Ayrica, mevcut uygulamalar, firsatlar ve oneriler, lojistik firmasi icin sunulmustur.

Environmental and social concerns force logistics firms to evaluate various risk factors for intermodal transportation systems. In this context, this paper takes into account economic, social, and ecological risk factors that have attracted considerable attention toward sustainable transportation. In this paper, the allocation of export containers to transportation modes, by incorporating social and ecological risks with a main focus on the minimization of transportation costs, was examined. A mixed-integer-programming-based mathematical model was proposed to decide how the containers can be allocated to different transportation modes. In addition, a fuzzy-based approach was performed to determine the social and ecological risk weights of the transportation modes within the decision process. Each mode was evaluated with social risks (e.g., human accidents and deaths) and ecological risks (e.g., emission values and noise pollution) by decision makers via the assignment of numerical scores. The obtained results show that optimal transportation modes more economical and environmentally friendly were provided by the proposed methods.

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season's data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

Purpose: The main contribution of this paper is to generate an optimum solution for capacity planning and appointment scheduling issues, which are frequently encountered in clinical flows with various route and treatment periods at dental hospitals.Design/methodology/approach: It is essential to define the system well in order to ensure that the working staff and patients use their time very efficiently and that the process flows continuously. By having examined a sample healthcare system through the help of a study addressed in such context, studies on process improvement in line with the dissatisfactions of the working staff and patients have been carried out. Within the scope of the study, the operation of 7 Departments in a dental hospital undergoing a treatment process have been reviewed and examined. The problems encountered as result of the observations made are discussed in detail, and formerly and recently designed system performance analyses are conducted by having performed the respective process improvement studies. The relevant samplings of this study are modeled via the Arena Simulation Program. The data of the previous four months is used in the parameters, which are used through the modellings. The system data are entered by taking into account seasonal characteristics of the data.Findings: The analyses are made as a consequence of such study that has been addressed, it is established that the efficiency of the internal customers of the hospital increases substantially, and that the waiting durations of the dental patients decrease and in turn, the external customer satisfaction increases drastically.Research limitations/implications: Under the scope of the present study, 7 different treatment processes are analysed in a dental hospital in Cukurova Region with a significant patient potential. The treatment clinics present in the hospital are radiology, periodontology, surgery, treatment, orthodontics and prosthesis. These clinics run their own appointment and treatment system independently. Thus, the study has limited with five departments among 7.Originality/value: With this study, given the flow of different existing treatment processes belonging to patients are optimized, and also the continuity of the system is ensured by minimizing the patient waiting times within the existing system.