An adaptive memory programming framework for the resource-constrained project scheduling problem

The Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most intractable combinatorial optimisation problems that combines a set of constraints and objectives met in a vast variety of applications and industries. Its solution raises major theoretical challenges due to its complexity, yet presenting numerous practical dimensions. Adaptive memory programming (AMP) is one of the most successful frameworks for solving hard combinatorial optimisation problems (e.g. vehicle routing and scheduling). Its success stems from the use of learning mechanisms that capture favourable solution elements found in high-quality solutions. This paper challenges the efficiency of AMP for solving the RCPSP, to our knowledge, for the first time in the literature. Computational experiments on well-known benchmark RCPSP instances show that the proposed AMP consistently produces high-quality solutions in reasonable computational times

Production Scheduling with Complex Precedence Constraints in Parallel Machines

Heuristic search is a core area of artificial intelligence and the employment of an efficient search algorithm is critical to the performance of an intelligent system. This paper addresses a production scheduling problem with complex precedence constraints in an identical parallel machines environment. Although this particular problem can be found in several production and other scheduling applications; it is considered to be NP-hard due to its high computational complexity. The solution approach we adopt is based on a comparison among several dispatching rules combined with a diagram analysis methodology. Computational results on large instances provide relatively high quality practical solutions in very short computational times, indicating the applicability of the methodology in real life production scheduling applications

A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints

We present a metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L-CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two-dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a metaheuristic algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neighbourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were conducted to calibrate the algorithmic parameters. The effectiveness of the proposed metaheuristic algorithm was tested on benchmark instances and led to several new best solutions.Vehicle routing Loading constraints Tabu Search Guided Local Search

An adaptive memory methodology for the vehicle routing problem with simultaneous pick-ups and deliveries

This paper deals with a routing problem variant which considers customers to simultaneously require delivery and pick-up services. The examined problem is referred to as the Vehicle Routing Problem with Simultaneous Pick-ups and Deliveries (VRPSPD). VRPSPD is an NP-hard combinatorial optimization problem, practical large-scale instances of which cannot be solved by exact solution methodologies within acceptable computational times. Our interest was therefore focused on metaheuristic solution approaches. In specific, we introduce an Adaptive Memory (AM) algorithmic framework which collects and combines promising solution features to generate high-quality solutions. The proposed strategy employs an innovative memory mechanism to systematically maximize the amount of routing information extracted from the AM, in order to drive the search towards diverse regions of the solution space. Our metaheuristic development was tested on numerous VRPSPD instances involving from 50 to 400 customers. It proved to be rather effective and efficient, as it produced high-quality solutions, requiring limited computational effort. Furthermore, it managed to produce several new best solutions.Vehicle routing Simultaneous pick-ups and deliveries Adaptive memory

The military band of the 5th Australian Infantry Brigade, passing through the ruins of the town Bapuame, France, 19 March 1917 [picture] /

Title devised by cataloguer based on caption.; In: World War 1914-1918 campaigns in France and Belgium.; "The band of the 5th Australian Infantry Brigade, led by Sergeant Pheagan of the 19th Battalion, passing through the Grande Palace, at Bapaume, in France, on 19th March, 1917, playing the 'Victoria March'. The ruins of the town were still smouldering, and a few miles away, on the Lagnicourt-Noreuil line, the fighting continued. Of the Australian official photographs none gained a wider publicity than this. It was generally regarded as characteristic of the fine fighting spirit which animated the Australian troops in the dramatic events of that period."--Printed caption.; Also available online at: http://nla.gov.au/nla.pic-an10885587-s2

A well-scalable metaheuristic for the fleet size and mix vehicle routing problem with time windows

This paper presents an efficient and well-scalable metaheuristic for fleet size and mix vehicle routing with time windows. The suggested solution method combines the strengths of well-known threshold accepting and guided local search metaheuristics to guide a set of four local search heuristics. The computational tests were done using the benchmarks of [Liu, F.-H., & Shen, S.-Y. (1999). The fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research Society, 50(7), 721-732] and 600 new benchmark problems suggested in this paper. The results indicate that the suggested method is competitive and scales almost linearly up to instances with 1000 customers