Extending the Inventory Routing Problem to Support Integrated Decision‐Making in an Urban Distribution Network

Analyzing the benefits of a city hub: An inventory and routing perspective

Greenhouse gases emission is a major concern globally since they are key players in global warming. Some countries have signed the Kyoto Protocol, and set up some regulations to reduce their CO2 emissions. Optimizing inventory and routing decisions can help in the reduction of CO2 emissions if these emissions are taken into account by the decision makers. In the formulation, CO2 emitted by transporting the product is modeled. The chapter investigates the effect of CO2 emissions on the inventory and routing decisions determined over a given time horizon. The model is coded and solved in GAMS. The test results are used to indicate that emission costs should be considered when deciding the routing and inventory decisions.

Urban consolidation centers and city toll schemes–Investigating the impact of city tolls on transshipment decisions

The inventory routing problem (IRP) and the production routing problem (PRP) are two difficult problems arising in the planning of integrated supply chains. These problems are solved in an attempt to jointly optimize production, inventory, distribution, and routing decisions. Although several studies have proposed exact algorithms to solve the single-vehicle problems, the multivehicle aspect is often neglected because of its complexity. We introduce multivehicle PRP and IRP formulations, with and without a vehicle index, to solve the problems under both the maximum level (ML) and order-up-to level (OU) inventory replenishment policies. The vehicle index formulations are further improved using symmetry breaking constraints; the nonvehicle index formulations are strengthened by several cuts. A heuristic based on an adaptive large neighborhood search technique is also developed to determine initial solutions, and branch-and-cut algorithms are proposed to solve the different formulations. The results show that the vehicle index formulations are superior in finding optimal solutions, whereas the nonvehicle index formulations are generally better at providing good lower bounds on larger instances. IRP and PRP instances with up to 35 customers, three periods, and three vehicles can be solved to optimality within two hours for the ML policy. By using parallel computing, the algorithms could solve the instances for the same policy with up to 45 and 50 customers, three periods, and three vehicles for the IRP and PRP, respectively. For the more difficult IRP (PRP) under the OU policy, the algorithms could handle instances with up to 30 customers, three (six) periods, and three vehicles on a single core machine, and up to 45 (35) customers, three (six) periods, and three vehicles on a multicore machine.

A heuristic method for the inventory routing problem with time windows

Incorporating location, routing and inventory decisions in supply chain network design

Critical factors for viable business models for urban consolidation centres

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework.

A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment

The exact solution of several classes of inventory-routing problems

A theoretical framework to evaluate the traffic impact of urban freight consolidation centres

Factors affecting public support for the development of urban consolidation center: The case of Hanoi city, Vietnam

Carbon credits and urban freight consolidation: An experiment using agent based simulation

Abstract. The increase in urbanization has led to a rise in of the number of vehicles in the city and the frequency of goods delivery in urban environments. The concepts of city logistics have been developed and implemented to cope with those problems in the urban freight transport system. One cooperative freight transport scheme is the urban consolidation scheme. Urban consolidation centers (UCC) have been used by some cities over the last two decades to minimize unnecessary vehicle movement, congestion, and pollution. For a company that sells fast-moving consumer goods (FMCG), business prospects in supermarkets or groceries are currently still quite promising. Yogyakarta City ranks as the 6th most populous city in Indonesia, making it one of the largest cities in the country. Since there are many citizens and students from other towns across Indonesia, many modern retail businesses operate in Yogyakarta. If each of these businesses distribute their goods from their distribution centers (DCs) to their respective retailers, the number of freight transports will be substantial, and the congestion level will increase. This study examines the benefit of a collaboration strategy in which the top four retailers in Yogyakarta use a UCC. The advantage is expressed in total transportation cost. We developed three different scenarios that will be compared. In this research, the gravity location model is used to determine the location of the UCC, and the demand allocation model is used to determine the optimum of demand allocation from the UCC. This study reveals that using a collaborative strategy using a UCC can decrease the total transportation cost of the retailers. Keywords: City logistics, urban consolidation center, location problem, gravity location model, demand allocation model

The inventory-routing problem with transshipment