This paper investigates different factors that affect the performance of containerised transportation in parcel delivery networks. Motivated by challenges confronted by a postal delivery company in Australia, we study how container utilisation rate, sortation decisions, and changes in cost parameters can affect the overall performance of a parcel delivery network. Leveraging a combination of mixed-integer programming and machine learning, we model a realistic parcel delivery network and evaluate its performance using data from a major postal service provider. The insights obtained from our analysis offer valuable guidance to parcel delivery companies, empowering them to make informed parcel sortation and containerisation decisions.

Complete and accurate data is an important enabler of effective supply chain decision making. Despite the increasing efforts to fully automate data collection processes using advanced sensors and scanners, human operators are still in charge of data entry tasks in most industries. Unfortunately, operators do not often comply with the standard operating procedures (SOPs) and do not always exhibit the consistency and commitment required to collect high-quality data. In fact, data collection is often perceived as a non-value-adding activity that increases workloads and lowers productivity. We aim to empirically study the extent to which compliance with SOPs for data collection is affected by some of the key factors. Using a large dataset obtained from a leading postal service provider in Australia, we find that an operator's workload, fatigue, and related work experience directly impact the compliance levels. We also find that a company's compliance reinforcement intervention to improve compliance behavior can moderate these impacts.

Carriers and postal companies are under increasing pressure to reduce their operating costs and increase efficiency. One way to reduce costs is to improve the utilisation of drivers' working hours by employing more efficient rest break policies. A rest break policy is a restrictive set of rules consistent with national regulations for hours of service. We develop and validate a novel framework to model and analyse a class of these policies that concern the location of the rest breaks. In particular, we compare two representative rest break policies using data from a major Australian postal carrier. The first policy imposes no restriction on the location of a rest break. The second policy requires the driver to return to a depot for rest taking allowing time for socialising and making use of full amenities. Using postal transport data from Sydney metropolitan area, we find that the difference between the two policies in terms of tour length is only over 1%. We further apply the proposed framework to assess the impact of increasing the minimum break time on the two representative policies.

We study a capacity alignment planning problem for a coal chain. Given a set of train operators, a set of train paths and a terminal comprising of a dump station and a set of routes from the dump station to the stockyard, we seek a feasible assignment of train operators to train paths, to time slots at the dump station, and to routes. The assignment must maximize the number of system paths in the resulting schedule and the schedule should perform well with respect to various performance criteria. We model the problem as a mixed-integer conic program (MICP) with multiple objectives which we solve using a hierarchical optimization procedure. In each stage of this procedure, we solve a single objective MICP. Depending upon whether we evaluate the associated performance criteria under a 2- or 1-norm, we reformulate the MICP as either a mixed-integer second-order cone program or as a mixed-integer linear program, respectively, and can streamline the hierarchical optimization procedure by exploiting properties of the model or observed behaviour on practical instances. We compare the performance of the procedure under the different norms on a real instance of the problem and find that the quality of the solutions found by the faster 1-norm procedure compares well to the solution found under the 2-norm.

Abstract. We investigate a novel scheduling problem which is motivated by an application in the Australian railway industry. Given a set of maintenance jobs and a set of train paths over a railway corridor with bidirectional traffic, we seek a schedule of jobs such that a minimum number of train paths are cancelled due to conflict with the job schedule. We show that the problem is NP-complete in general. In a special case of the problem when every job under any schedule just affects one train path, and the speed of trains is bounded from above and below, we show that the problem can be solved in polynomial time. Moreover, in another special case of the problem where the traffic is unidirectional, we show that the problem can be solved in time O(n).

We investigate a novel scheduling problem which is motivated by an application in the Australian railway industry. Given a set of maintenance jobs and a set of train paths over a railway corridor with bidirectional traffic, we seek a schedule of jobs such that a minimum number of train paths are cancelled due to conflict with the job schedule. We show that the problem is NP-complete in general. In a special case of the problem when every job under any schedule just affects one train path, and the speed of trains is bounded from above and below, we show that the problem can be solved in polynomial time. Moreover, in another special case of the problem where the traffic is unidirectional, we show that the problem can be solved in time $O(n^4)$.

Australia has a large operational heavy railway network which is approximately 33,355 routekilometres.This network accounted for approximately 55 percent of all freight transport activity in Australia in the financial year 2013-14, almost 367 billion tonne-kilometres which was up 50 percent from 2011-12 (BITRE ( 2016)).

In this paper, we address a basic production planning problem with price dependent demand and stochastic yield of production. We use price and target quantity as decision variables to control the risk of low production yield. The value of risk control becomes more important especially for products with short life cycle where high losses are unbearable in the short run. In this cases, optimization of a solely scalar function of profit is not sufficient to control the risk. We apply Conditional Value at Risk (CVaR) measure to model the risk preferences of the producer. The producer is interested in shaping the risk by bounding from below the means of alpha-tail distributions of profit for different values of alpha. The resulting model is nonconvex. We propose an efficient solution algorithm and present a sufficient optimality condition. (C) 2016 Elsevier B.V. All rights reserved.

In this paper, we address a basic production planning problem with price dependent demand and stochastic yield of production. We use price and target quantity as decision variables to lower the risk of low yield. The value of risk control becomes more important especially for products with short life cycle. This is because, the profit implications of low yield might be unbearable in the short run. We apply Conditional Value at Risk (CVaR) to model the risk. CVaR measure is a coherent risk measure and thereby having nice conceptual and mathematical underpinnings. It is also widely used in practice. We consider the problem under general demand function and general distribution function of yield and find sufficient conditions under which the problem has a unique local maximum. We also both analytically and numerically analyze the impact of parameter change on the optimal solution. Among our results, we analytically show that with increasing risk aversion, the optimal price increases. This relation is opposite to that of in Newsvendor problem where the uncertainty lies in demand side.

Risk aversion is a prevalent phenomenon when sufficiently large amounts are at risk. In this paper, we introduce a new prescriptive approach for coping with risk in sequential decision problems with discrete scenario space. We use Conditional Value-at-Risk (CVaR) risk measure as optimization criterion and prove that there is an explicit linear representation of the proposed model for the problem. (C) 2013 Elsevier B.V. All rights reserved.

In this paper, we develop a demand model for influenza vaccine. When the vaccination is not compulsory, exploration of demand mechanism and total demand anticipation, help the governments better organize their efforts and adopt more effective measures to contain influenza outbreak in advance of influenza season. In this research, the population is divided into two classes. Pre 65 years old class and over 65-year-old class. Current influenza vaccines have different effectiveness on each age class. We build a game theoretic demand model for each age class based on utilities of individuals. Utilities of individuals is a function of the vaccine effectiveness, price, chance of getting infected, estimated cost of infection and a random element specific to each individual. Since getting vaccinated for an individual exerts positive externalities on the others, the decision of individuals is not independent of each other.

In this paper, we propose an exact method for solving a special integer program associated with the classical capacitated arc routing problems (CARPs) called split demand arc routing problems (SDARP). This method is developed in the context of monotropic programming theory and bases a promising foundation for developing specialized algorithms in order to solve general integer programming problems. In particular, the proposed algorithm generalizes the relaxation algorithm developed by Tseng and Bertsekas (Math. Oper. Res. 12(4):569–596, 1987) for solving linear programming problems. This method can also be viewed as an alternative for the subgradient method for solving Lagrangian relaxed problems. Computational experiments show its high potential in terms of efficiency and goodness of solutions on standard test problems.