Simon Dunstall

Heuristic methods for the identical parallel machine flowtime problem with set-up times

We consider the scheduling of N jobs divided into G families for processing on M identical parallel machines. No set-up is necessary between jobs belonging to the same family. A set-up must be scheduled when switching from the processing of family i jobs to those of another family j, i ≠ j, the duration of this set-up being the sequence-independent set-up time sj for family j. We propose heuristics for this problem and computationally evaluate the performance of the heuristics relative to lower bounds and solutions obtained using an exact algorithm.

Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution

In a globalised world, land use change outlooks are influenced by both locally heterogeneous land attributes and world markets. We demonstrate the importance of high resolution land heterogeneity representation in understanding local impacts of future global scenarios with carbon markets and land competition influencing food prices. A methodologically unique Australian continental model is presented with bottom-up parcel scale granularity in land use change, food, carbon, water, and biodiversity ecosystem service supply determination, and partial equilibrium food price impacts of land competition. We show that food price feedbacks produce modest aggregate national land use and ecosystem service supply changes. However, high resolution results show amplified land use change and ecosystem service impact in some places and muted impacts in other areas relative to national averages. We conclude that fine granularity modelling of geographic diversity produces local land use change and ecosystem service impact insights not discernible with other approaches. We modeled Australian land use change and ecosystem service responses to global scenarios.The model features a novel approach to very high resolution land heterogeneity representation.To demonstrate, we model how food price feedbacks of land competition differ spatially.Modest land use change and ecosystem service impacts are observed in aggregate for Australia.High resolution impacts vary from large to minuscule depending on local land heterogeneity.

A comparison of branch-and-bound algorithms for a family scheduling problem with identical parallel machines

This paper investigates branch-and-bound algorithms for the problem of scheduling jobs with family setups on identical parallel machines to minimize the weighted sum of completion times. In particular, we propose a new branching scheme that appears to substantially outperform current procedures in terms of computation time and search tree size.

An automated itinerary planning system for holiday travel

This article describes a prototype travel recommender system called the Electronic Travel Planner (ETP), which prepares travel itineraries for tourists. The system is driven by models of a traveler’s preferences and requirements, and makes reference to databases containing information pertaining to tourism and travel products. Its main tasks are to select destinations for the traveler to visit, to decide which tours or attractions are to be taken, and to compose a detailed itinerary linking up the chosen components. These tasks entail difficult optimization problems, which the prototype addresses by means of an heuristic problem-solving framework. Computational tests confirm the effectiveness of the methods used, and suggest that an automated approach will be feasible in full-scale travel planning applications.

Lower bounds and algorithms for flowtime minimization on a single machine with set-up times

SUMMARY A recent trend in the analysis of scheduling models integrates batching decisions with sequencing decisions. The interplay between batching and sequencing reects the realities of the small-volume, high-variety manufacturing environment and adds a new feature to traditional scheduling problems. Practical interest in this topic has given rise to new research eorts, and there has been a series of articles in the research literature surveying the rapidly developing state of knowledge. Exam- ples include Ghosh (1), Liaee and Emmons (2), Potts and Van Wassenhove (3), and Webster and Baker (4). This paper deals with an important theoretical and practical problem in this area. We examine the single-machine model with family (or group) set-up times and a criterion of minimizing total weighted job completion time (weighted owtime). We propose new lower bounds for this problem, and then turn our attention to renement of a previously proposed branch-and-bound algorithm. The benets of our renements are illustrated by computational experiments.

Towards Supporting Collaborative Scheduling in Adaptive Supply Networks with Negotiation Agents

This paper presents an approach to support collaborative scheduling in adaptive supply networks. Intelligent decision-making agents represent the interests of different suppliers and customers that independently participate in the network. The agents interact to dynamically select suppliers and build supply schedules. This interaction involves coordinated negotiation and the exchange of schedule proposals and supporting information. The final supply schedule can be composed of complementary supply schedules offered by several agents.

Using agents for solving a multi-commodity-flow problem

We investigate a commodity trading problem in a flow network with arbitrary topology where sinks combine commodities into bundles in order to generate profits. Our focus is the profit maximization problem for the trading network under both central and distributed control. We compute solutions for the central control problem using an integer linear program while we compute solutions for the distributed case by implementing the nodes in the network as software-agents that exchange messages in order to establish profitable trades. We report on computational results using both methods and demonstrate that there is a connection between agent profits and a centrality measure developed for the problem. We also demonstrate that with our current agent strategy, there is a trade-off between the agents acting too quickly before enough information is available and waiting too long and thus giving each agent too much information and thus too much power over the outcome.

Robustness of cyclic schedules for the charging of batteries

In 2002 the Mathematics in Industry Study Group (MISG) investigated the question of optimally scheduling cyclic production in a battery charging and finishing facility. The facility produces various types of battery and the scheduling objective is tomaximize battery throughout subject to achieving a pre-specified product-mix. In this paperwe investigate the robustness of such schedules using simulation experiments that span multiple production cycles. We simulate random variations (delays) in battery charging time and find that an optimal off-line schedule yields higher throughput in comparison to a common on-line dispatching rule. This result has been found to hold for a range of expected chargingtime delays and has significant practical implications for scheduling battery charging and finishing facilities.

The Value of Flexible Road Designs Through Ecologically Sensitive Areas

Mining haul road traffic can have significant impacts on nearby animal populations that can threaten an entity’s licence to operate. As operators have the ability to control heavy vehicle traffic flow through mining road networks, opportunities exist to reroute traffic away from more damaging roads in response to uncertain animal population dynamics. The presence of this flexibility in turn has a positive effect on the future value of proposed road designs. In this paper, we present an approach for evaluating the flexibility of controlling traffic flow in proposed road designs between two locations separated by an intervening species habitat. We do this by treating the design problem as a Real Options Valuation (Stochastic Optimal Control) problem solved using Least-Squares Monte Carlo. Here, the different control actions are the discrete traffic flow rates, and the uncertain-state variables are the animal populations at each location in the region of interest. Because the control chosen has a direct impact on the path of the uncertain variables, we use the technique of control randomisation in generating the Monte Carlo paths and computing the costs-to-go in the Real Options Valuation. In addition, we use a state-reduction parameter called Animals at Risk to reduce the dimensionality of the problem to improve tractability. In an example scenario, the addition of routing flexibility resulted in an increase in project value over the case without flexibility while maintaining the animal population above a critical threshold.

Combined Aggregation and Column Generation for Land-Use Trade-Off Optimisation

In this paper we developed a combination of aggregation-disaggregation technique with the concept of column generation to solve a large scale LP problem originating from land use management in the Australian agricultural sector. The problem is to optimally allocate the most profitable land use activities including agriculture, carbon sequestration, environmental planting, bio-fuel, bio-energy, etc., and is constrained to satisfy some food demand considerations and expansion policies for each year from 2013 to 2050. In this research we produce a higher resolution solution by dividing Australia’s agricultural areas into square kilometer cells, which leads to more than thirteen million cells to be assigned, totally or partially, to different activities. By accepting a scenario on agricultural products’ return, carbon related activities, future energy prices, water availability, global climate change, etc. a linear programming problem is composed for each year. However, even by using a state of the art commercial LP solver it takes a long time to find an optimal solution for one year. Therefore, it is almost impossible to think about simultaneous scenarios to be incorporated, as the corresponding model will become even larger. Based on the properties of the problem, such as similar economical and geographical properties of nearby land parcels, the combination of clustering ideas with column generation to decompose the large problem into smaller sub-problems yields a computationally efficient algorithm for the large scale problem.