Abstract This article introduces the cumulative school bus routing problem, which concerns the transport of students from a school using a fleet of identical buses. The objective of the problem is to select a drop‐off point for each student among potential locations within a certain walking distance and to generate routes such that the sum of arrival times of all students from their school to their homes is minimized. The article describes six polynomial‐size mixed integer linear programming formulations based on original and auxiliary graphs, and the formulations are numerically compared on real instances. The article reports the results of computational experiments performed to evaluate the performance of the proposed models.

Road congestion, air pollution and sustainability are increasingly important in major cities. We look to understand how last-mile deliveries in the parcel sector are impacting our roads. Using formative field work and quantitative analysis of consignment manifests and location data, we identify how the effectiveness of life-style couriers is contributing to both environmental and non-environmental externalities. This paper presents an analysis of delivery performances and practices in last-mile logistics in central London, quantifying the impacts differing levels of experience have on overall round efficiency. We identify eleven key opportunities for technological support for last-mile parcel deliveries that could contribute to both driver effectiveness and sustainability. We finish by examining how HCI can lead to improved environmental and social justice by re-considering and realizing future collaborative visions in last-mile logistics.

In this paper, I view and present the multiobjective discrete optimisation problem as a particular case of disjunctive programming where one seeks to identify efficient solutions from within a disjunction formed by a set of systems. The proposed approach lends itself to a simple yet effective iterative algorithm that is able to yield the set of all nondominated points, both supported and nonsupported, for a multiobjective discrete optimisation problem. Each iteration of the algorithm is a series of feasibility checks and requires only one formulation to be solved to optimality that has the same number of integer variables as that of the single objective formulation of the problem. The application of the algorithm shows that it is particularly effective when solving constrained multiobjective discrete optimisation problem instances. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0804 .

Multi‐depot routing problems arise in distribution logistics where a set of vehicles based at several depots are used to serve a number of clients. Most variants of this problem have the basic requirement that the route of each vehicle starts and ends at the same depot. This article describes new inequalities, namely multi‐cut constraints (MCC), which enforce this requirement in mathematical programming formulations of multi‐depot routing problems. The MCCs are exponential in size, and are equivalent to a compact three‐index formulation for the problem in terms of the associated linear programming relaxations. The article describes how a generalization of the MCCs can be obtained, in a similar manner, by using a stronger version of the three‐index formulation. The connection between the compact and the exponential formulations implies a separation procedure based on max‐flow/min‐cut computations, which has reduced complexity in comparison with a previously known set of constraints described for the same purpose. The new inequalities are used in a branch‐and‐cut algorithm. Computational results are presented for instances with up to 300 clients and 60 depots. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(3), 246–261 2017

This article is concerned with the problem of finding optimal vehicle routes to minimize the overall travel time, with constraints on the minimum and maximum amount of time spent on each route. The problem extends previous work on the distance‐constrained vehicle routing problem by introducing lower bounds on route durations to ensure that the resulting routes are balanced. The article also explicitly addresses the situation where a solution is artificially balanced as a result of inoptimal orders of visits. The article describes alternative ways in which the restrictions on route connectivity, duration, and artificial balancing can be formulated, and introduces an exact algorithm based on cutting planes and mixed‐integer linear programming. To the best of our knowledge, this is the first exact algorithm proposed for such a problem that explicitly addresses artificially balanced routes. Computational results are presented for three versions of the exact algorithm using TSPLIB instances. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 65(2), 166–179 2015

Abstract This article discusses problems in the context of multicommodity network design where additional constraints (such as capacity), rather than being imposed in a strict manner, are allowed to be violated at the expense of additional penalty costs. Such penalized cost structures allow these constraints to be treated as utilization targets and provide a better modelling framework in terms of strategic or tactical level planning of network design, especially in freight transportation systems. However, due to the penalized costs, these problems are generally in the form of a nonlinear integer multicommodity network design problem. This article presents two algorithms based on Lagrangean relaxation and decomposition for the solution of such problems. The first relies upon dualizing the capacity constraints that results in a flow decomposition, and the second is through relaxing flow constraints that results in an arc decomposition. It is shown that nonlinearities in the decomposed substructures can be handled in a very efficient manner. Arc decomposition is shown, through computational experiments, to have better convergence properties. Through the proposed algorithms, reasonably good solutions can be obtained for these problems where publicly available state‐of‐the‐art nonlinear optimization codes fail to identify feasible solutions. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010