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This paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the communication-aware DDP (C-DDP), which incorporates handover and outage constraints into the conventional multi-depot multi-trip green vehicle routing problem with time windows. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. A mixed integer programming (MIP) model is developed to minimize the total flight distance while satisfying communication constraints. We present a genetic algorithm (GA) that can solve large instances and compare its performance with an off-the-shelf MIP solver. Computational study shows that the GA and MIP solver performances are equivalent to solving smaller instances. We also compare the GA performance against another evolutionary algorithm, particle swarm optimization (PSO), for larger instances and find that the GA outperforms the PSO with slightly longer CPU times. The results indicate that ignoring the communication constraints would cause significant operational disruption risk and this risk can be easily mitigated with a slight sacrifice from flight distances by incorporating the proposed communication constraints. In particular, the communication performance can be improved by up to 28.9% when the flight distance is increased by 19.1% at most on average.

Son yıllarda fosil kaynaklardan elde edilen enerji fiyatlarının artması, tedarik problemleri ve çevresel sorunlar gibi faktörler nedeniyle yenilenebilir enerji kaynakları giderek daha büyük bir öneme sahip olmaktadır. Bu enerji kaynaklarına yönelik yapılan yatırımların etkili olması için doğru bir şekilde ekonomik olarak analiz edilmesi gerekmektedir. Son dönemde artan karbon fiyatlandırması uygulamaları da bu analizlerde göz önünde bulundurulması gereken bir faktördür. Özellikle rüzgâr gibi düşük emisyonlu enerji kaynakları için, karbon fiyatlandırması ek bir gelir kaynağı sunmaktadır. Bu çalışmada, belirli bir bölgedeki geçmiş rüzgâr hızı verileri kullanılarak gelecek dönemler için rüzgâr hızı tahminleri yapılmış ve bu tahminlere dayanarak elektrik üretim miktarları hesaplanmıştır. Daha sonra, farklı rüzgâr türbinlerinin ekonomik olarak değerlendirilmesi için birim maliyetler karşılaştırılmıştır. Tahmin yöntemi olarak, mevsimsel ARIMA modeli tercih edilmiştir. Ayrıca, ekonomik analizde Net Bugünkü Değer yaklaşımı kullanılmıştır. Böylelikle, yatırımların maliyetleri, enerji üretimi ve karbon fiyatlaması gibi faktörler dikkate alınarak projenin ekonomik durumu değerlendirilmektedir. Özellikle karbon fiyatlamasının dahil edilmesiyle birlikte, rüzgâr türbinlerinin büyüklüğüne bağlı olarak birim maliyetlerde %9,4 ila %16 arasında azalmalar gözlemlenmiştir. Bu, yatırımcılar ve enerji şirketleri için yenilenebilir enerji projelerinin daha cazip hale gelmesini sağlamaktadır. Ayrıca, çevresel etkilerin azaltılması ve sürdürülebilir enerji üretimi hedeflerine ulaşma açısından da önemli bir adımdır

This paper studies the assignment problem of multi product assembly jobs to days. The problem aims to minimize the amount of overtime while avoiding assembly delays for jobs that can be fragmented into smaller sub-tasks. When sequence-dependent setup times are negligible, the problem considered transforms into the bin packing problem with restricted item fragmentation where jobs represent items and days stand for bins. We present a mixed integer programming model of the problem by extending earlier formulations in the literature. Computational experiments show that the mathematical model obtained optimal solutions for majority of instances tested within reasonable computation times.

This paper studies the multi-period home healthcare routing and scheduling problem with homogeneous electric vehicles and time windows. The problem aims to construct the weekly routes of healthcare nurses, which provide service to the patients located at a scattered geographic area. Some patients may require to be visited more than once in the same workday and/or in the same workweek. We consider three charging technologies; normal, fast, and super-fast. The vehicles might be charged during the working day at a charging station or at the end of the working day at the depot. Charging a vehicle at a depot at the end of a working day requires the transfer of the corresponding nurse from the depot to her/his home. The objective is to minimize the total cost that comprises the fixed cost of utilizing healthcare nurses, the energy charging costs, the costs associated with depot-to-nurse home transfer services, and the costs of a patient left unserved. We formulate a mathematical model and develop an adaptive large neighborhood search metaheuristic that has been efficiently crafted to handle specific problem features. We conduct extensive computational experiments on benchmark instances to assess the competitiveness of the heuristic and to deeply analyze the problem. Our analysis shows the importance of competency level matching as mismatching competency levels could increase the costs of home healthcare providers.

Production scheduling affects the cost of production. Hence, it is one of the important problems for operational problems. Especially for productions that require assembly precedencies, an inappropriate production schedule may be the reason for overtime decisions for a finite planning period. An inappropriate production schedule may lead to tardiness in assembly jobs as subassembly parts may not be ready on time. To meet the deadline of products, tardiness must be compensated using overtime. In this study, we considered an assembly job shop scheduling problem for multigroup jobs with sub-assembly precedence's from a real-world application. Our aim is to determine the production schedule that minimizes overtime for a finite planning time interval. We developed a mixed integer programming model that minimizes the overtime complying with the delivery dates for the problem. We conducted a case-study on a construction machinery industry in Thailand. Construction machines have several welded body parts with subassembly processes. Welded body parts with sub-assembly processes have cutting, bending, machining and prewelding processes. The readiness of the welded body parts before the machine assembly is critically important for the continuity of the machine assembly line.

Bu çalışmada Çoklu Şarj Teknolojisine Dayalı Kısmi Şarj Politikalı Karma Filolu Elektrikli Araç Rotalama Problemi (KF-E-ARP) ele alınmıştır. Karma filo, elektrikli araçları (EA) ve konvansiyonel araçları (KA) içermektedir. Konvansiyonel araçların emisyon fonksiyonu ve elektrikli araçların şarj tüketim fonksiyonu içerisinde katedilen mesafe ile birlikte araçta taşınan yük miktarı da dikkate alınmıştır. Ayrıca şarj istasyonunda çoklu şarj konfigürasyonlarından birinin seçilmesi, karma filolu problemlerde ilk defa ele alınmıştır. Problem, müşteri dağıtım taleplerini karşılarken toplam maiyeti enazlayan araç rotalarının elde edilmesi olarak tanımlanabilir. KF-E-ARP için öncelikle karma tamsayılı matematiksel model geliştirilmiştir. Problem NP-zor olduğundan dolayı, orta ve büyük boyutlu problemlere makul sürelerde çözüm elde edebilmek amacıyla ekleme tabanlı bir çözüm kurucu sezgisel algoritma önerilmiştir. Deneysel analizlerde, matematiksel model ile 2 saat içerisinde çözüm bulunamayan tüm orta ve büyük boyutlu problemlere önerilen çözüm kurucu sezgisel algoritmalar ile yaklaşık 1 saniye gibi çok kısada sürede uygun çözümler bulunabildiğini gözlemlenmiştir.