78 research outputs found

    Disjunctive Programming for Multiobjective Discrete Optimisation

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    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 show that it is particularly effective, and superior to the state-of-the-art, when solving constrained multiobjective discrete optimisation problem instances

    Benders decomposition for the mixed no-idle permutation flowshop scheduling problem

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    [EN] The mixed no-idle flowshop scheduling problem arises in modern industries including integrated circuits, ceramic frit and steel production, among others, and where some machines are not allowed to remain idle between jobs. This paper describes an exact algorithm that uses Benders decomposition with a simple yet effective enhancement mechanism that entails the generation of additional cuts by using a referenced local search to help speed up convergence. Using only a single additional optimality cut at each iteration, and combined with combinatorial cuts, the algorithm can optimally solve instances with up to 500 jobs and 15 machines that are otherwise not within the reach of off-the-shelf optimization software, and can easily surpass ad-hoc existing metaheuristics. To the best of the authors' knowledge, the algorithm described here is the only exact method for solving the mixed no-idle permutation flowshop scheduling problem.This research project was partially supported by the Scientific and Technological Research Council of Turkey (TuBITAK) under Grant 1059B191600107. While writing this paper, Dr Hamzaday was a visiting researcher at the Southampton Business School at the University of Southampton. Ruben Ruiz is supported by the Spanish Ministry of Science, Innovation and Universities, under the Project 'OPTEP-Port Terminal Operations Optimization' (No. RTI2018-094940-B-I00) financed with FEDER funds. Thanks are due to two anonymous reviewers for their careful reading of the paper and helpful suggestions.Bektas, T.; Hamzadayi, A.; Ruiz García, R. (2020). Benders decomposition for the mixed no-idle permutation flowshop scheduling problem. Journal of Scheduling. 23(4):513-523. https://doi.org/10.1007/s10951-020-00637-8S513523234Adiri, I., & Pohoryles, D. (1982). Flowshop no-idle or no-wait scheduling to minimize the sum of completion times. Naval Research Logistics, 29(3), 495–504.Baker, K. R. (1974). Introduction to sequencing and scheduling. New York: Wiley.Baptiste, P., & Hguny, L. K. (1997). A branch and bound algorithm for the FF/no-idle/CmaxC_{max}. In Proceedings of the international conference on industrial engineering and production management, IEPM’97, Lyon, France (Vol. 1, pp. 429–438).Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238–252.Cordeau, J. F., Pasin, F., & Solomon, M. (2006). An integrated model for logistics network design. Annals of Operations Research, 144(1), 59–82.Costa, A. M., Cordeau, J. F., Gendron, B., & Laporte, G. (2012). Accelerating benders decomposition with heuristic master problem solutions. Pesquisa Operacional, 32(1), 3–20.Deng, G., & Gu, X. (2012). A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion. Computers & Operations Research, 39(9), 2152–2160.Goncharov, Y., & Sevastyanov, S. (2009). The flow shop problem with no-idle constraints: A review and approximation. European Journal of Operational Research, 196(2), 450–456.Kalczynski, P. J., & Kamburowski, J. (2005). A heuristic for minimizing the makespan in no-idle permutation flow shops. Computers & Industrial Engineering, 49(1), 146–154.Magnanti, T. L., & Wong, R. T. (1981). Accelerating benders decomposition: Algorithmic enhancement and model selection criteria. Operations Research, 29(3), 464–484.Pan, Q. K., & Ruiz, R. (2014). An effective iterated greedy algorithm for the mixed no-idle flowshop scheduling problem. Omega, 44(1), 41–50.Pan, Q. K., Tasgetiren, M. F., & Liang, Y. C. (2008). A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Computers & Industrial Engineering, 55(4), 795–816.Pan, Q. K., & Wang, L. (2008a). No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. International Journal of Advanced Manufacturing Technology, 39(7–8), 796–807.Pan, Q. K., & Wang, L. (2008b). A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems. European Journal of Industrial Engineering, 2(3), 279–297.Papadakos, N. (2008). Practical enhancements to the Magnanti–Wong method. Operations Research Letters, 36(4), 444–449.Röck, H. (1984). The three-machine no-wait flow shop is NP-complete. Journal of the Association for Computing Machinery, 31(2), 336–345.Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479–494.Ruiz, R., Vallada, E., & Fernández-Martínez, C. (2009). Scheduling in flowshops with no-idle machines. In U. Chakraborty (Ed.), Computational intelligence in flow shop and job shop scheduling, chap 2 (pp. 21–51). New York: Springer.Saadani, N. E. H., Guinet, A., & Moalla, M. (2003). Three stage no-idle flow-shops. Computers & Industrial Engineering, 44(3), 425–434.Saharidis, G., & Ierapetritou, M. (2013). Speed-up Benders decomposition using maximum density cut (MDC) generation. Annals of Operations Research, 210, 101–123.Shao, W., Pi, D., & Shao, Z. (2017). Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion. Applied Soft Computing, 54, 164–182.Tasgetiren, M. F., Buyukdagli, O., Pan, Q. K., & Suganthan, P. N. (2013). A general variable neighborhood search algorithm for the no-idle permutation flowshop scheduling problem. In B. K. Panigrahi, P. N. Suganthan, S. Das, & S. S. Dash (Eds.), Swarm, evolutionary, and memetic computing (pp. 24–34). Cham: Springer.Vachajitpan, P. (1982). Job sequencing with continuous machine operation. Computers & Industrial Engineering, 6(3), 255–259

    Using â„“p-norms for fairness in combinatorial optimisation

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    The issue of fairness has received attention from researchers in many fields, including combinatorial optimisation. One way to drive the solution toward fairness is to use a modified objective function that involves so-called â„“p-norms. If done in a naive way, this approach leads to large and symmetric mixed-integer nonlinear programs (MINLPs), that may be difficult to solve. We show that, for some problems, one can obtain alternative MINLP formulations that are much smaller, do not suffer from symmetry, and have a reasonably tight continuous relaxation. We give encouraging computational results for certain vehicle routing, facility location and network design problems

    Compact Formulations for Multi-depot Routing Problems: Theoretical and Computational Comparisons

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    Multi-depot routing problems mainly arise in distribution logistics where a fleet of vehicles are used to serve clients from a number of (potential) depots. The problem concerns deciding on the routes of each vehicle and the depots from which the vehicles depart, so as to minimize the total cost of travel. This paper reviews a number of existing compact formulations, and proposes new ones, for two types of multi-depot routing problems, one that includes the depot selection decisions, and the other where depots are pre-selected. The formulations are compared theoretically in terms of the strength of their linear programming relaxation, and computationally in terms of the running time needed to solve the instances to optimality

    The green location-routing problem

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    This paper introduces the Green Location-Routing Problem (GLRP), a combination of the classical Location-Routing Problem (LRP) and the Pollution-Routing Problem (PRP). The GLRP consists of (i) locating depots on a subset of a discrete set of points, from where vehicles of limited capacity will be dispatched to serve a number of customers with service requirements, (ii) routing the vehicles by determining the order of customers served by each vehicle and (iii) setting the speed on each leg of the journey such that customers are served within their respective time windows. The objective of the GLRP is to minimize a cost function comprising the fixed cost of operating depots, as well as the costs of the fuel and CO2 emissions. The amount of fuel consumption and emissions is measured by a widely used comprehensive modal emission model. The paper presents a mixed integer programming formulation and a set of preprocessing rules and valid inequalities to strengthen the formulation. Two solution approaches; an integer programming based algorithm and an iterated local search algorithm are also presented. Computational analyses are carried out using adaptations of literature instances to the GLRP in order to analyze the effects of a number parameters on location and routing decisions in terms of cost, fuel consumption and emission. The performance of the heuristic algorithms are also evaluated

    Route and speed optimization for autonomous trucks

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    Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed

    Combined maritime fleet deployment and inventory management with port visit flexibility in roll-on roll-off shipping

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    We consider a combined fleet deployment and inventory management problem in Roll-on Roll-off shipping. Along given trade routes there are ports with inventories that should be kept within their limits. Current planning practice is to visit all ports every time a trade route is serviced. We instead aim at determining the sailing routes of each voyage along the trade route, where some ports can be skipped on certain voyages. A novel mixed integer programming model is proposed and tested on realistic instances. The results indicate that substantial gains can be achieved from this more flexible way of planning

    ICT for Sustainable Last-Mile Logistics:Data, People and Parcels

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    In this paper we present a vision of how ICT can be leveraged to help combat the impact on pollution, congestion and carbon emissions contributed by the parcel delivery sector. This is timely given annual growth in parcel deliveries, especially same-day deliveries, and the need to inform initiatives to clean up our cities such as the sales ban on new petrol and diesel vehicles in the UK by 2040. Our insights are informed by research on parcel logistics in Central London, leveraging a data set of parcel manifests spanning 6 months. To understand the impact of growing e-commerce trends on parcel deliveries we provide a mixed methods case study leveraging data-driven analysis and qualitative fieldwork to demonstrate how ICT can uncover the impact of parcel deliveries on delivery drivers and their delivery rounds during seasonal deliveries (or “the silly season”). We finish by discussing key opportunities for intervention and further research in ICT4S and co-created Smart Cities, connecting our findings with existing research and data as a call to the ICT4S community to help tackle the growth in carbon emissions, pollution and congestion linked to parcel deliveries
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