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Course timetabling is the process of allocating, subject to constraints, limited rooms and timeslots for a set of courses to take place. Usually, in addition to constructing a feasible timetable (all constraints satisfied), there are desirable goals like minimising the number of undesirable allocations (e.g. courses timetabled in the last timeslot of the(More)
In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers' locations, security guards performing rounds at different(More)
The numerical peculiarities which inhabit the numerical instance of a MOCO problem may seriously decrease the effectiveness of an approximation method. To deal with this problem we propose a flexible two-phase method for MOCO. Phase 1 produces a good approximation of the efficient frontier. However it may not be of good enough quality in terms of density.(More)
We propose an approach based on mixed integer programming (MIP) with decomposition to solve a workforce scheduling and routing problem, in which a set of workers should be assigned to tasks that are distributed across different geographical locations. We present a mixed integer programming model that incorporates important realworld features of the problem(More)
This paper describes a hybrid heuristic approach to construct transportation plans for a singlecustomer multi-carrier scenario that arises at 3T Logistics Ltd, a UK company that provides outsourced transportation planning and management services. The problem consists on planning the delivery, using a set of carrier companies, of a set of shipments from a(More)
Universities strive to manage space as effectively as possible. A major part of the management process is the allocation of teaching activities to rooms which have appropriate size, layout and resource availability. Matching the needs of the overall teaching requirement with the space provision within an institution is difficult to achieve and is currently(More)
This paper proposes a dynamic lexicographic approach to tackle multi-objective optimization problems. In this method, the ordering of objectives, which reflects their relative preferences, is changed in a dynamic fashion during the search. This approach eliminates the need for the decision-maker to establish fixed preferences among the competing objectives,(More)
The utilisation of University teaching space is notoriously low: rooms are often unused, or only half full. We expect that one of the reasons for this is overall mismatch the sizes of rooms and the sizes events. For example, there might be an excess of large rooms. Good space planning should match the set of rooms to the set of events whilst taking account(More)
This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of reinforcement(More)
This extended abstract outlines four hybrid heuristics to generate initial solutions to the University course timetabling problem. These hybrid approaches combine graph colouring heuristics and local search in different ways. Results of experiments using two benchmark datasets from the literature are presented. All the four hybrid initialisation heuristics(More)