Operational transportation planning of freight forwarding companies in horizontal coalitions

In order to improve profitability, freight forwarding companies try to organize their operational transportation planning systematically, considering not only their own fleet but also external resources. Such external resources include vehicles from closely related subcontractors in vertical cooperations, autonomous common carriers on the transportation market, and cooperating partners in horizontal coalitions. In this paper, the transportation planning process of forwarders is studied and the benefit of including external resources is analyzed. By introducing subcontracting, the conventional routing of own vehicles is extended to an integrated operational transportation planning, which simultaneously constructs fulfillment plans with overall lowest costs using the own fleet and subcontractors’ vehicles. This is then combined with planning strategies, which intend to increase the profitability by exchanging requests among members in horizontal coalitions. Computational results show considerable cost reductions using the proposed planning approach.     

Branch-and-price and constraint programming for solving a real-life technician dispatching problem

We consider a real problem faced by a large company providing repair services of office machines in Santiago, Chile. In a typical day about twenty technicians visit seventy customers in a predefined service area in Santiago. We design optimal routes for technicians by considering travel times, soft time windows for technician arrival times at client locations, and fixed repair times. A branch-and-price algorithm was developed, using a constraint branching strategy proposed by Ryan and Foster along with constraint programming in the column generation phase. The column generation takes advantage of the fact that each technician can satisfy no more than five to six service requests per day. Different instances of the problem were solved to optimality in a reasonable computational time, and the results obtained compare favorably with the current practice.     

An exact algorithm for team orienteering problems

Optimising routing of vehicles constitutes a major logistic stake in many industrial contexts. We are interested here in the optimal resolution of special cases of vehicle routing problems, known as team orienteering problems. In these problems, vehicles are guided by a reward that can be collected from customers, while the length of routes is limited. The main difference with classical vehicle routing problems is that not all customers have to be visited. The solution method we propose here is based on a Branch & Price algorithm. It is, as far as we know, the first exact method proposed for such problems, except for a preliminary work from Gueguen (Methodes de résolution exacte pour problémes de tournées de véhicules. Thése de doctorat, école Centrale Paris 1999) and a work from Butt and Ryan (Comput Oper Res 26(4):427–441 1999). It permits to solve instances with up to 100 customers.      

Path relinking for the vehicle routing problem

This paper describes a tabu search heuristic with path relinking for the vehicle routing problem. Tabu search is a local search method that explores the solution space more thoroughly than other local search based methods by overcoming local optima. Path relinking is a method to integrate intensification and diversification in the search. It explores paths that connect previously found elite solutions. Computational results show that tabu search with path relinking is superior to pure tabu search on the vehicle routing problem.     

Cooperative Parallel Variable Neighborhood Search for the p-Median

We propose a cooperative multi-search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p-median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.     

A Tabu Search Heuristic for Resource Management in Naval Warfare

Effective utilization of scarce resources, in particular weapon resources, is a prominent issue in naval anti-air warfare. In this paper, defence plans are constructed to guide the allocation and scheduling of different types of defence weapons against anti-ship missiles, subject to various physical and operational constraints. To reduce the frequency of replanning, decision trees are considered to explicitly account, in a probabilistic manner, for all possible outcomes of a particular action. A construction heuristic is first developed to generate an initial tree. A tabu search heuristic then improves this tree through the removal or addition of defence actions, followed by update operations aimed at maintaining the consistency. Numerical results obtained on scenarios with an increasing number of threats show that substantial improvements, in terms of survivability of the ship, can be obtained in reasonable computation times using tabu search.     

Metaheuristics in Combinatorial Optimization

The emergence of metaheuristics for solving difficult combinatorial optimization problems is one of the most notable achievements of the last two decades in operations research. This paper provides an account of the most recent developments in the field and identifies some common issues and trends. Examples of applications are also reported for vehicle routing and scheduling problems.     

Freight railway operator timetabling and engine scheduling

In this paper we consider timetable design at a European freight railway operator. The timetable is designed by choosing the time of service for customer unit train demands among a set of discrete points. These discrete points are all found within the a time-window. The objective of the model is to minimize cost while adhering to constraints regarding infrastructure usage, demand coverage, and engine availability. The model is solved by a column generation scheme where feasible engine schedules are designed in a label setting algorithm with time-dependent cost and service times.     

A Constraint Programming Framework for Local Search Methods

We propose in this paper a novel integration of local search algorithms within a constraint programming framework for combinatorial optimization problems, in an attempt to gain both the efficiency of local search methods and the flexibility of constraint programming while maintaining a clear separation between the constraints of the problem and the actual search procedure. Each neighborhood exploration is performed by branch-and-bound search, whose potential pruning capabilities open the door to more elaborate local moves, which could lead to even better approximate results. Two illustrations of this framework are provided, including computational results for the traveling salesman problem with time windows. These results indicate that it is one order of magnitude faster than the customary constraint programming approach to local search and that it is competitive with a specialized local search algorithm.     

Hyper-heuristics: a survey of the state of the art

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.