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The purpose of this paper is to focus on the reference model of a grid-like supply network that enables formulation of delivery routing and scheduling problems in the context of the periodic vehicle routing problem.

The conditions for seamless (collision-free) synchronization of periodically executed local transport processes presented in this paper guarantee cyclic execution of supply processes, thereby preventing traffic flow congestion.

Systems that satisfy this characteristic, cyclic deliveries executed along supply chains are given and what is sought is the number of vehicles needed to operate the local transport processes in order to ensure delivery from and to specific loading/unloading points on given dates. Determination of sufficient conditions guaranteeing the existence of feasible solutions that satisfy these constraints makes it possible to solve the considered class of problems online.

The computer experiments reported in this paper show the possibilities of practical application of the proposed approach in the construction of decision support systems for food supply chain management.

The aim of the present work is to develop a methodology for the synthesis of regularly structured supply networks that would ensure fixed cyclic execution of local transport processes. The proposed methodology, which implements sufficient conditions for the synchronization of local cyclic processes, allows one to develop a method for rapid prototyping of supply processes that satisfies the time windows constraints given.

The purpose of this paper is to present the design and evaluation of an integrated system that supports planners and dispatchers to deliver enhanced courier operations. In addition to regular deliveries and pickups, these operations include: first, mass deliveries to be served over a horizon of multiple days; and second, real-time dynamic requests (DRs) to be served within the same service period.

To address the aforementioned challenges, the authors developed an architecture that enhances a typical fleet management system by integrating purpose designed methods. Specifically, the authors plan mass deliveries taking into account typical routes of everyday operations. For planning DRs in real time, the authors propose an efficient insertion heuristic.

The results from testing the proposed optimization algorithms for planning mass deliveries and real-time DRs are encouraging, since the proposed algorithms outperform current practices. Testing in a practical courier environment, indicated that the enhanced planning system may improve significantly operational performance.

The proposed optimization algorithm for the dynamic aspect of this problem comprises a heuristic approach that reaches suboptimal solutions of high quality. The development of fast optimal algorithms for solving these very interesting and practical problems is a promising area for further research.

The proposed integrated system addresses significant problems of hybrid courier operations in an integrated, balanced manner. The tests showed that the allocation of flexible orders within a three-day time horizon improved the cost per flexible order by 7.4 percent, while computerized routing improved the cost of initial (static) routing by 14 percent. Furthermore, the proposed method for managing DRs reduced the excess cost per served request by over 40 percent. Overall, the proposed integrated system improved the total routing costs by 16.5 percent on average compared to current practices.

Both the planning problems and the related solution heuristics address original aspects of practical courier operations. Furthermore, the system integration and the proposed systematic planning contribute to the originality of the work.

The purpose of the paper is to present an integrated model for the location of warehouse, the allocation of retailers to warehouses, and finding the number of vehicles to deliver the demand and the required vehicle routing in order to minimize total transportation costs, fixed and operating costs, and routing costs.

The model assumes that the number of plants has already been determined and answers the following questions: what is the number of warehouses to open? How warehouse are allocated to plants? How retailers are allocated to warehouses? Who are the retailers that will be visited and in what order? How many vehicles are required for each route? What are the total minimum costs?

The model was formulated as a mixed integer linear programming model and solved using Lagrange relaxation and sub‐gradient search for the location/allocation module and a traveling salesman heuristic for the routing module. The results for the randomly selected problems show that the deviation in objective function value ranges between 0.29 and 2.05 percent from the optimum value. Also, from the CPU time point of view, the performance was very good.

An attempt is made to integrate location, allocation, and routing decisions in the design of a supply chain network.

The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm.

A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution.

Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level.

The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.

In the Norwegian oil and gas industry the upstream logistics includes providing the offshore installations with needed supplies and return flow of used materials and equipment. This paper considers a real‐life routing problem for supply vessels serving offshore installations at Haltenbanken off the northwest coast of Norway from its onshore supply base. The purpose of the paper is to explore how the offshore installation's limited storage capacity affects the routing of the supply vessels aiming towards creating efficient routes.

A simplified version of the real‐life routing problem for one supply vessel is formulated as a mixed integer linear programming model that contains constraints reflecting the storage requirements problem. These constraints ensure that there is enough capacity at the platform decks and that it is possible to perform both pickup and delivery services.

The model has been tested on real‐life‐sized instances based on data provided by the Norwegian oil company Statoil ASA. The tests show that in order to obtain optimal solutions to the pickup and delivery problem with limited free storage capacities at installations, one has to include in the formulation the new sets of constraints, the storage feasibility and the service feasibility requirements. In addition, two visits to some platforms are necessary to obtain optimality.

The main limitation is the present inability to solve large cases.

The contribution of this paper is to provide a better insight into a real‐life routing problem which has a unique feature arising from the limited deck capacity at the offshore installations that complicates the performance of service. This feature has neither been discussed nor modeled in the vehicle routing literature before, hence the formulation of the problem is original and reveals some interesting results.

The purpose of this paper is to deal with the transportation of a high number of injured people after a disaster in a highly populated large area. Each patient should be delivered to the hospital before the specific deadline to survive. The objective of the study is to maximize the survival rate of patients by proper assignment of existing emergency vehicles to hospitals and efficient generation of vehicle routes.

The concepts of non-fixed multiple depot pickup and delivery vehicle routing problem (MDPDVRP) is utilized to capture an image of the problem encountered in real life. Due to NP-hardness of the problem, a hybrid genetic algorithm (GA) is proposed as the solution method. The performance of the developed algorithm is investigated through a case study.

The proposed hybrid model outperforms the traditional GA and also is significantly superior compared to the nearest neighbor assignment. The required time for running the algorithm on a large-scale problem fits well into emergency distribution and the promptness required for humanitarian relief systems.

This paper investigates the efficient assignment of emergency vehicles to patients and their routing in a way that is most appropriate for the problem at hand.

The purpose of this paper is to manage the demand uncertainty considered as lower and upper levels for a medium-scale industrial distribution planning problem in a biobjective inventory routing problem (IRP). In order to achieve this, the grey system theory is applied since no statistical distribution from the past data and incomplete information.

This study is investigated with optimizing the distribution plan, which involves 30 customers of 12 periods in a manufacturing company under demand uncertainty that is considered as lower and upper levels for a biobjective IRP with using grey demand parameters as a grey integer programming model. In the data set, there are also some missing demand values for the customers. So, the seven different grey models are developed to eliminat the effects on demand uncertainties in computational analysis using a piece of developed software considering the logistical performance indicators such as total deliveries, total cost, the total number of tours, distribution capacity, average remaining capacity and solution time.

Results show that comparing the grey models, the cost per unit and the maximum number of vehicle parameters are also calculated as the new key performance indicator, and then results were ranked and evaluated in detail. Another important finding is the demand uncertainties can be managed with a new approach via logistics performance indicators using alternative solutions.

The results enable logistics managers to understand the importance of demand uncertainties if more reliable decisions are wanted to make with obtaining a proper distribution plan for effective use of their expectations about the success factors in logistics management.

The study is the first in terms of the application of grey models in a biobjective IRP with using interval grey demand data. Successful implementation of the grey approaches allows obtaining a more reliable distribution plan. In addition, this paper also offers a new key performance indicator for the final decision.

This paper aims to propose two solution approaches to determine the number of ground transport vehicles that are required to ensure the on-time delivery of military equipment between origin and destination node pairs in some geographic region, which is an important logistics problem at the US Transportation Command.

The author uses a mathematical program and a traditional heuristic to provide optimal and near-optimal solutions, respectively. The author also compares the approaches for random, small-scale problems to assess the quality and computational efficiency of the heuristic solution, and also uses the heuristic to solve a notional, large-scale problem typical of real problems.

This work helps analysts identify how many ground transport vehicles are needed to meet cargo delivery requirements in any military theater of operation.

This research assumes all problem data is deterministic, so it does not capture variations in requirements or transit times between nodes.

This work provides prescriptive details to military analysts and decision-makers in a timely manner. Prior to this work, insights for this type of problem were generated using time-consuming simulation taking about a week and often involving trial-and-error.

This research provides new methods to solve an important logistics problem. The heuristic presented in this paper was recently used to provide operational insights about ground vehicle requirements to support a geographic combatant command and to inform decisions for railcar recapitalization within the US Army.

This paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.

This study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.

The result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.

There are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.

Existing research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.

This paper presents a case study of Renault Samsung Motors (RSM) that recently encounters dynamic changes unveiling various opportunities and challenges due to increasing complexity of the supply network with growing sales volume, diversifying models, and intensifying global competition. Such competitive environment puts constant pressure on the logistics operations to reduce supply costs and lead time, but the RSM has not been paying much attention to aligning interests of supply chain partners. In 2007, RSM’s effort to build partnership with new 3PLs turned abortive due to their unexpected default on the contract throwing RSM into confusion and disruptions. In this study, the problem was investigated by examining route planning process and incentive scheme of 3PL, and an optimization model was constructed to evaluate the performance of existing 3PL operation. The results indicate that transportation cost can be reduced by relocating consolidation centers, utilizing regional terminal and redesigning routing sequence. However, the research found that the key to successful implementation of the optimized solutions is in designing effective incentive system that induces partners to participate in continuous improvement initiatives.