Collaborative truckload transportation procurement with multiple coalitions

Gönderici işbirliği, son yıllarda ortaya çıkmış yeni bir işbirliği türüdür ve tedarik zinciri yönetiminde kurumlar arası yatay işbirliği sınıfına girmektedir. Sert rekabet koşulları, kaynak yetersizliği, iklim değişimi, güvenlik sorunları ve yeni kanuni düzenlemeler firmalar üzerindeki baskıyı artırmış ve geleneksel düşünce kalıplarını zorlayan yeni çözümler aramaya itmiştir. İşbirliği; daha geniş ve bütün sistemi kapsayan bir bakış açısı getirmesi nedeniyle yeni fırsatlar sunan bir strateji olarak görülmektedir. Gönderici işbirliğinde taşıyıcı firmalardan taşımacılık hizmeti alan bir grup gönderici firma bir araya gelir; ve taşıyıcı firmalarla grup olarak pazarlık yaparlar. Göndericiler işbirliği yapmak istediklerinde; hangi göndericilerin işbirliğine dahil edileceği, hangi göndericilerin rotalarının arka arkaya ekleneceği, ve oluşturulan rota birleştirme çözümünden doğan toplam maliyetin göndericilere ve hatta her bir rotaya dağıtılması konularında en iyi kararları vermek durumundadırlar. Literatürdeki çalışmalar işbirliği yapan göndericilerin tek bir koalisyon kurduğunu varsaymış ve bu tek koalisyonunun kurulması, devamı ve genişletilmesi konularını ele almışlardır. Verilen bir koalisyon ve bu koalisyon için hesaplanan en küçük maliyetli çözüm için adil bir maliyet paylaşımı olup olmadığını konu almışlardır. Buna ek olarak, verilen bir koalisyon, çözüm ve maliyet paylaşma mekanizması için koalisyona katılmak isteyen yeni bir göndericinin koalisyona alınıp alınmaması kararını konu alan çalışmalar da mevcuttur. Büyük ölçekli gönderici işbirliği ağlarında sadece tek bir koalisyona izin verilmesi koordinasyonu zorlaştırmakta ve kabul edilebilir maliyet dağıtımına sahip geniş çaplı bir çözüm bulunmasını zorlaştırmaktadır. Bu çalışmada birden fazla ayrışık koalisyon içerebilen tam kamyon yükü gönderici işbirliği ağları ele alınmıştır. Her biri adil maliyet dağıtımına sahip, ayrık koalisyonlardan oluşan en düşük toplam maliyetli koalisyon yapısının bulunması amaçlanmıştır. Gönderici ve rota sayıları arttıkça ve operasyonel kısıtlar eklendikçe bu kararları en iyi biçimde vermek gittikçe zorlaşmaktadır. Gerçek hayat durumlarında problem boyutlarının çok büyük olması beklendiği için özellikle büyük ölçekli problem örneklerinin çözümüne yönelik sezgisel algoritmalar geliştirilmiştir.We study formation of stable coalitions given a set of shippers and their lanes corresponding to regularly scheduled truckload shipment. In this thesis, selecting participants, deciding who should participate with whom, calculating the lowest cost operational solution and allocating the system-wide cost to the participants stand out as important problems. Collaborating shippers try to identify tours which consist of regularly scheduled shipment with minimal empty truck movements. Then, they must allocate the total cost of the collaborative solution to the participated firms and individual lanes such that the collaborative solution remains attractive to the participants. In the literature, solving the optimization problem minimization the total cost and allocating the calculated minimum cost are treated as successive but distinct phases. The cost minimizing optimization problem is solved with well-known operation research methods, while cooperative game theory concepts are used for cost allocation. The minimum cost solution may render finding an acceptable cost allocation impossible. Besides, similar works in the literature assume that the collaborating firms will forge a single grand coalition. However, as the collaboration grows in size, a single grand coalition may become impractical and also it might leave several lanes out of the coalition, depriving shippers of significant cost savings. In this study, we propose algorithm to design coalition structure which consist of multiple disjoint stable coalitions. Each coalition must have a minimum cost collaborative solution with an acceptable cost allocation. Due to the complexity of the task hand, we devised a heuristic to find good quality solutions to this problem

Shipper collaboration matching: fast enumeration of triangular transports with high cooperation effects

The logistics industry in Japan is facing a severe shortage of labor. Therefore, there is an increasing need for joint transportation allowing large amounts of cargo to be transported using fewer trucks. In recent years, the use of artificial intelligence and other new technologies has gained wide attention for improving matching efficiency. However, it is difficult to develop a system that can instantly respond to requests because browsing through enormous combinations of two transport lanes is time consuming. In this study, we focus on a form of joint transportation called triangular transportation and enumerate the combinations with high cooperation effects. The proposed algorithm makes good use of hidden inequalities, such as the distance axiom, to narrow down the search range without sacrificing accuracy. Numerical experiments show that the proposed algorithm is thousands of times faster than simple brute force. With this technology as the core engine, we developed a joint transportation matching system. The system has already been in use by over 150 companies as of October 2022, and was featured in a collection of logistics digital transformation cases published by Japan's Ministry of Land, Infrastructure, Transport and Tourism.Comment: 16 pages, 7 figure

Combining Facility Location and Routing Decisions in Sustainable Urban Freight Distribution under Horizontal Collaboration: How Can Shippers Be Benefited?

This article investigates the potential economic, environmental, and social effects of combining depot location and vehicle routing decisions in urban road freight transportation under horizontal collaboration. We consider a city in which several suppliers decide to joint deliveries to their customers and goods are delivered via intermediate depots. We study a transportation optimization problem from the perspective of sustainability development. This quantitative approach is based on three-objective mathematical model for strategic, tactical, and operational decision-making as a two-echelon location routing problem (2E-LRP). The objectives are to minimize cost and CO2 emissions of the transportation and maximize the created job opportunities. The model was solved with the ε-constraint method using extended known instances reflecting the real distribution in urban area to evaluate several goods’ delivery strategies. The obtained results by comparing collaborative and noncollaborative scenarios show that collaboration leads to a reduction in CO2 emissions, transportation cost, used vehicles, and travelled distances in addition to the improvement of the vehicles load rate but collaboration affects negatively social impact. To evaluate the effect of the method used to allocate the total gains to the different partners, we suggest to decision makers a comparison between well-known allocation methods

The Benefits of Information Sharing in Carrier-Client Collaboration

This dissertation includes three related papers to investigate different methods that can help transport providers improve their operational efficiency. The first paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The core research method is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second day ALI can improve the profit by an average of 22%. We also found that the impact of ALI depends on radius of service and trip length but statistically independent of load density and fleet size. The second paper investigates the following question of relevance to truckload dispatchers striving for profitable decisions in the context of dynamic pick-up and delivery problems: since not all future pick-up/delivery requests are known with certainty, how effective are alternative methods for guiding those decisions? We propose an intuitive policy and integrate it into a new two-index mixed integer programming formulation, which we implement using the rolling horizon approach. On average, in one of the practical transportation network settings, the proposed policy can, with just second-day ALI, yield an optimality ratio equal to almost 90% of profits in the static optimal solution. We enhance the proposed policy by adopting the idea of a multiple scenario approach. In comparison to other dispatching methods, our proposed policies were found to be very competitive in terms of solution quality and computational efficiency. Finally, inspired by a real-life third party logistic provider, the third paper addresses a dynamic pickup and delivery problem with full truckload (DPDFL) for local operators. The main purpose of this work is to investigate the impact of potential factors on the carriers’ operational efficiency. These factors, which are usually under managerial influence, are vehicle diversion capability, the DPDFL decision interval, and how far in advance the carrier knows of clients’ shipment requirements; i.e., ALI. Through comprehensive numerical experiments and statistical analysis, we found that the ALI and re-optimization interval significantly influence the total cost, but that diversion capability does not. A major contribution of this work is that we develop an efficient benchmark solution for the DPDFL’s static version by discretization of time windows. We observed that three-day ALI and an appropriate decision interval can reduce deviation from the benchmark solution to less than 8%

A spatial decomposition based math-heuristic approach to the asset protection problem

This paper addresses the highly critical task of planning asset protection activities during uncontrollable wildfires known in the literature as the Asset Protection Problem (APP). In the APP each asset requires a protective service to be performed by a set of emergency response vehicles within a specific time period defined by the spread of fire. We propose a new spatial decomposition based math-heuristic approach for the solution of large-scale APPs. The heuristic exploits the property that time windows are geographically correlated as fire spreads across a landscape. Thus an appropriate division of the landscape allows the problem to be decomposed into smaller more tractable sub-problems. The main challenge then is to minimise the difference between the final locations of vehicles from one division to the optimal starting locations of the next division. The performance of the proposed approach is tested on a set of benchmark instances from the literature and compared to the most recent Adaptive Large Neighborhood Search (ALNS) algorithm developed for the APP. The results show that our proposed solution approach outperforms the ALNS algorithm on all instances with comparable computation time. We also see a trend with the margin of out-performance becoming more significant as the problems become larger

Vendor-Buyer Coordination in Supply Chains

Collaboration between firms in order to coordinate supply chain operations can lead to both strategic and operational benefits. Many advanced forms of collaboration arrangements between firms exist with the aim to coordinate supply chain decisions and to reap these benefits. This dissertation contributes to the understanding of the conditions that are necessary for collaboration in such arrangements and the benefits that can be realized of such collaboration arrangements. This dissertation focuses on the vendor-buyer dyad in the supply chain. We identify and categorize collaboration arrangements that exist in practice, based on a review of the literature and combine this with formal analytical models in the literature. An important factor in the benefits of collaboration is the benefit of reduced costs of transport, by realization of economies of scale in the context of capacity-constrained trucks. As a contribution to the understanding of the dependence of transport costs on the volume transported, we demonstrate how transport tariffs for orders of less-than-a-truckload in size on a single link can be deduced from a basic model. The success of a collaboration arrangement depends on agreement about the distribution of decision authority and collaboration-benefits. We study a collaboration arrangement in which the vendor takes responsibility for managing the buyer's inventory and makes it economically attractive to the buyer by offering a financial incentive, dependent on the maximum level the buyer permits to be stocked. This dissertation demonstrates that this incentive alignment leads to considerable cost savings and near-optimal supply chain decisions

Collaborative Logistics in Vehicle Routing

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city routes served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads there may exist opportunities for carriers to collaborate over such routes. That is, Carrier A will also deliver one or more shipments of Carrier B. This will improve vehicle asset utilization and reduce asset-repositioning costs, and may also lead to reduced congestion and pollution in cities. We refer to the above coordination as “collaborative routing”. In our framework for collaboration, we also propose that carriers exchange goods at logistics platforms located at the entry point to a city. This is referred to as “entry-point collaboration”. One difficulty in collaboration is the lack of facilities to allow transfer of goods between carriers. We highlight that the reduction in pollution and congestion under our proposed framework will give the city government an incentive to support these initiatives by providing facilities. Further, our analysis has shown that contrary to the poor benefits reported by previous work on vehicle routing with transshipment, strategic location of transshipment facilities in urban areas may solve this problem and lead to large cost savings from transfer of loads between carriers. We also present a novel integrated three-phase solution method. Our first phase uses either a modified tabu search, or a guided local search, to solve the vehicle routing problems with time windows that result from entry-point collaboration. The preceding methods use a constraint programming engine for feasibility checks. The second phase uses a quad-tree search to locate facilities. Quad-tree search methods are popular in computer graphics, and for grid generation in fluid simulation. These methods are known to be efficient in partitioning a two-dimensional space for storage and computation. We use this efficiency to search a two-dimensional region and locate possible transshipment facilities. In phase three, we employ an integrated greedy local search method to build collaborative routes, using three new transshipment-specific moves for neighborhood definition. We utilize an optimization module within local search to combine multiple moves at each iteration, thereby taking efficient advantage of information from neighborhood exploration. Extensive computational tests are done on random data sets which represent a city such as Toronto. Sensitivity analysis is performed on important parameters to characterize the situations when collaboration will be beneficial. Overall results show that our proposal for collaboration leads to 12% and 15% decrease in route distance and time, respectively. Average asset utilization is seen to increase by about 5% as well

Public-private perspectives on supply chains of essential goods in crisis management

Public authorities are responsible to maintain the population’s supply with essential goods like food or drugs at any time. Such goods are produced, transported and sold by companies in supply chains. Past supply crises all over the world have showcased numerous examples of spontaneous collaboration between public authorities and companies in supply chains. However, insights on formal collaboration which is agreed upon in the preparedness phase is rare in both practice and literature. Therefore, this dissertation’s first research objective is to identify under which circumstances companies are most willing to collaborate with public authorities. In this context, public authorities\u27 and companies\u27 characteristics, resources and roles in a collaboration are identified from literature research as well as real-life cases in Study A. Study B empirically determines companies\u27 preferred preconditions for collaboration: Companies value the continuity of their business processes and expect to be compensated monetarily or by lifted restrictions. The second research objective is to develop collaborative supply chain concepts and evaluate them from public and private perspectives. Study C develops a collaboration concept in a real-time setting in which commercial trucks are jointly re-routed into crisis regions. In Study D, public authorities coordinate tactical use of commercial last-mile delivery vehicles for the home supply with food and drugs. In Study E, strategic collaboration in using dual-use warehouses is investigated with a focus on logistics networks. Study F determines the impact of demand shortfalls and payment term extensions on financial and physical flows in food supply chains. In Studies C-F, the main drivers for effectiveness and efficiency are investigated. By examining collaboration between companies and public authorities in supply crises, this dissertation contributes to the research streams of supply chain risk management and so-called extreme supply chain management. The results provide public decision-makers with insights into companies\u27 motivation to engage in public crisis management. The developed collaborative supply chain concepts serve public authorities as a basis for collaboration design and companies as starting points for integrating public-private collaboration into their endeavors to make supply chains more resilient