A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty

This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem.     

Measuring housing and transportation affordability: A case study of Melbourne,Australia

Housing affordability is traditionally measured using the percentage of household income spent on housing. An important cost that is usually overlooked in measuring location affordability is the transportation or accessibility costs. In this paper, we present a modeling approach, driven by urban open data, to measure location affordability that incorporates both housing and transportation costs. We apply the developed model to assess housing affordability in Melbourne, Australia as a case study. Results suggest that neighbourhoods that appear to be affordable when only housing cost is considered are not necessarily affordable when transportation costs are taken into account. A negative correlation between housing affordability and transportation affordability is observed. We also identify the presence of a strong spatial clustering pattern in the affordability measure across the study area. A major methodological contribution of the paper is the inclusion of comprehensive private vehicle costs and public transportation expenses in the model that contributes to a more robust estimation and understanding of location affordability. The model also distinguishes between different trip purposes. Results suggest that plans and policies to improve housing affordability should be made in coordination with transportation infrastructure investment plans to ensure effective and equitable outcomes. Nevertheless, the focus of the paper is more on the measurement of affordability; rather than reviewing and recommending housing related policies.     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

Analysis of traffic congestion costs in a competitive supply chain

We study a competitive facility location problem with identical suppliers competing in multiple markets subject to distribution network congestion. We first analyze the solution of a symmetric competitive location game and discuss the firms’ facility location and equilibrium market supply decisions. Then, we utilize the model to provide analytical characterization of the effects of traffic congestion costs on equilibrium distribution flows. These analytical results permit qualitative characterization of how changes in congestion levels and costs affect equilibrium location and distribution decisions. The results of extensive numerical studies serve to further illustrate the effects of traffic congestion costs on location, market supply quantity, and distribution decisions.     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

A location-routing model for prepositioning and distributing emergency supplies

We propose a two-stage location-routing model with recourse for integrated preparedness and response planning under uncertainty. The model is used for risk management in disaster situations where there are uncertainties in demand and the state of the infrastructure. We solve the two-stage model by converting it into a single-stage counterpart. The latter is then implemented in an illustrative example. Comparative analyses are run to investigate the (1) value of planning location and routing in a single model, (2) value of transshipment, (3) differences when an expected-value objective is used, and (4) value of transshipment in the expected-value model.     

A carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement

We consider the carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement. Bidders (carriers) employ vehicle routing models to identify sets of lanes (origin-destination pairs) based on the actual routes that a fleet of trucks will follow in order to maximize profit. Routes are constructed by optimally trading off repositioning costs of vehicles and the rewards associated with servicing lanes. The carrier optimization represents simultaneous generation and selection of routes and can incorporate any existing commitment. We employ both column generation and Lagrangian based techniques for solving the carrier optimization model and present numerical results.     

Hazmat facility location and routing analysis with explicit consideration of equity using the Gini coefficient

This paper develops a model to analyze facility location and routing decisions in the context of hazardous materials transportation. A novel aspect of this model is the explicit consideration of equity using the Gini coefficient, an established computation to evaluate equity. To solve the model, we develop a method that combines Lagrangean relaxation with column generation and illustrate that method using a realistic case study. We present a case study of hazardous material railway transportation with consideration of social horizontal equity categorizing the population by household income.     

A vehicle routing cost evaluation algorithm for the strategic analysis of radial distribution networks

This article presents a new algorithm, based on bin packing algorithms, that determines vehicle routing costs on networks with certain geographic characteristics. Requiring very little data and computation time, the algorithm is used to test the impact of adding flexibility to a retailer’s Canadian distribution operations. It is shown that it is possible to save 5–10% of vehicle routing costs by modifying shipping schedules.     

The split delivery vehicle routing problem with minimum delivery amounts

In the vehicle routing problem, a fleet of vehicles must service the demands of customers in a least-cost way. By allowing multiple vehicles to service the same customer (i.e., splitting deliveries), substantial savings in travel costs are possible. However, split deliveries are often an inconvenience to the customer who would prefer to have demand serviced in a single visit. We consider the vehicle routing problem in which split deliveries are allowed only if a minimum fraction of a customer’s demand is serviced by a vehicle. We develop a heuristic method for solving this problem and report computational results on a wide range of problem sets.     

Planning towing processes at airports more efficiently

This paper addresses the towing process of airplanes as part of the turnaround process. We introduce a VRP based MIP model which assigns different types of towing tractors to jobs with specified service time windows. The objective function minimizes operating costs subject to operational restrictions such as technical compatibility of tractor types with plane types. Our modeling approach allows for multiple depots as well as multiple trips. To solve the model we develop a column generation heuristic. Computational results show the superior behavior of the proposed heuristic compared to the original MIP formulation solved with CPLEX. In a case study we derive insights which support schedulers in their daily work. For this, we identify cost drivers and evaluate the efficiency of manual schedules in retrospect.     

Designing a new supply chain for competition against an existing supply chain

We develop a bi-level model for designing an entrant supply chain (SC) in the presence of a pre-existing competing SC where demand is elastic with respect to price and distance. The model assumes dynamic competition between the new and pre-existing SCs in retailers’ level and probabilistic customers’ behavior. Strategic facility location and flow decisions are made while considering inventory carrying costs incurred on the operational level. We formulate the problem and propose exact and metaheuristic algorithms to solve it. The model is solved using data from a real-life case and also randomly generated test problems to extract managerial insights.     

Economic and environmental concerns in planning recyclable waste collection systems

This paper addresses the planning of recyclable waste collection systems while accounting for economic and environmental concerns. Service areas and vehicle routes are defined for logistics networks with multiple depots where different products are collected. The problem is modeled as a multi-product, multi-depot vehicle routing problem with two objective functions: distance and CO₂ emissions minimization. A decomposition solution method is developed and applied to a real case study. Six scenarios regarding different service areas and objective functions are studied. Savings of up to 22% in distance and 27% in CO₂ emissions are achieved, exceeding economic and environmental goals.     

Combining service frequency and vehicle routing for managing supplier shipments

This paper proposes a new approach to designing inbound material collection routes that considers pick-up frequency and spatial design as joint decisions to minimize total logistics (transportation plus inventory) cost. The clustering-based optimization uses an approximation to the actual cost of a routing solution without actual route construction. We show that the problem is analogous to a single-source fixed-charge facility location problem, and near-optimal solutions can be found using an efficient heuristic algorithm. Tests show the effectiveness of how this model is formulated and a case study demonstrates that substantial total cost savings can be achieved in realistic applications.     

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.     

A location-routing problem with disruption risk

This paper considers a location-routing problem in a supply-chain network with a set of producer–distributors that produce a single commodity and distribute it to a set of customers. The production capacity of each producer–distributor varies randomly due to a variety of possible disruptions, and the vehicles involved in the distribution system are disrupted randomly. The goal is to determine the location, allocation and routing decisions that minimize the annual cost of location, routing and disruption, under one of the moderate, cautious or pessimistic risk-measurement policies. Exact formulations and an efficient heuristic are presented for the problem.     

Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand

In this paper, we propose a simultaneous approach to incorporate inventory control decisions––such as economic order quantity and safety stock decisions––into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon. We present a non-linear-mixed-integer model and a heuristic solution approach, based on Lagrangian relaxation and the sub-gradient method. In a numerical application, we found that the potential cost reduction, compared to the traditional approach, increases when the holding costs and/or the variability of demand are higher.     

Individuals' social preferences in joint activity location choice: A negotiation model and empirical evidence

Out-of-home leisure activities are often conducted jointly by individuals implying that location and travel choices made for these activities are the result of a group interaction. Current utility-theoretic approaches assume an aggregated group utility function and hence ignore aspects of the group decision making process. In this study, an empirical model of joint-activity choice is developed that, in contrast, assumes a negotiation process. A social utility function describes how individuals deal with preference differences in the group. The model is estimated based on an experimental activity-travel choice task where group settings are mimicked. A sample (N = 315) from a national panel of individuals participated in the experiment. Estimation results based on a discrete mixture model show that individuals display a preference for locations in which losses are equally distributed in the group even when this comes at the costs of the total group outcome. Results further show that the social utility function is asymmetric: compromise solutions are favored more strongly when consequences relate to costs (travel costs) than when they concern rewards (attractiveness). Furthermore, there is considerable heterogeneity in how people make social trade-offs. It is concluded that the model offers new insights in location preferences for joint activities that should be taken into account in spatial choice models and accessibility analysis.     

Reliable emergency service facility location under facility disruption,en-route congestion and in-facility queuing

The planning of emergency service facility location, especially for those expecting high demand and severe conditions, requires consideration of victims’ en-route travel, in-facility service quality, and reliability of these service facilities themselves. This paper first presents a scenario-based stochastic mixed-integer non-linear program (MINLP) model that integrates facility disruption risks, en-route traffic congestion and in-facility queuing delay into an integrated facility location problem. We derive lower and upper bounds to this highly complex problem by approximating the expected total system costs across the normal and all probabilistic facility disruption scenarios. This allows us to develop a more tractable approximate MINLP formulation and a Lagrangian Relaxation (LR) based solution approach. The relaxed sub-problem for location and service allocation decisions is further reformulated into a second-order conic program. Numerical experiments show that the approximate model and LR solution approach are capable of overcoming the computational difficulties associated with the problem. Interesting findings and managerial insights are obtained from a series of sensitivity analyses, e.g., regarding the importance of considering in-facility queuing in location design, and the significance of resource pooling on the optimal facility deployment.