Effective Neighborhood Search Research Articles

Effective neighborhood search with optimal splitting and adaptive memory for the team orienteering problem with time windows

The Team Orienteering Problem with Time Windows (TOPTW) is an extension of the well-known Orienteering Problem. Given a set of locations, each one associated with a profit, a service time and a time window, the objective of the TOPTW is to plan a set of routes, over a subset of locations, that maximizes the total collected profit while satisfying travel time limitations and time window constraints. Within this paper, we present an effective neighborhood search for the TOPTW based on (1) the alternation between two different search spaces, a giant tour search space and a route search space, using a powerful splitting algorithm, and (2) the use of a long term memory mechanism to keep high quality routes encountered in elite solutions. We conduct extensive computational experiments to investigate the contribution of these components, and measure the performance of our method on literature benchmarks. Our approach outperforms state-of-the-art algorithms in terms of overall solution quality and computational time. It finds the current best known solutions, or better ones, for 89% of the literature instances within reasonable runtimes. Moreover, it is able to achieve better average deviation than state-of-the-art algorithms within shorter computation times. Moreover, new improvements for 57 benchmark instances were found.

Towards delivery-as-a-service: Effective neighborhood search strategies for integrated delivery optimization of E-commerce and static O2O parcels

In this paper, we investigate a new variant of last-mile delivery that integrates the scheduling of static E-commerce parcels and Online-to-Offline(O2O) parcels. The O2O parcels, such as flowers and cakes, are often delivered intra city with a time window constraint. It is driven by the concept of delivery-as-a-service, which targets at building consolidated infrastructure and using the same fleet of vehicles to provide standardized delivery services for different types of merchants. We formulate it as an integration of Multi-Depot Multi-Trip Vehicle Routing Problem (MDMTVRP) and Paired Pickup and Delivery Problem with Time Window (PPDPTW). To solve the mixed problem of MDMTVRP and PPDPTW, we present its Mixed-Integer Programming (MIP) model to obtain the optimal solution for small-scale instances. To solve large-scale problems, we propose a hybrid neighborhood search strategy to effectively combine the merits of ALNS and tabu search. We also present a two-level pruning strategy that can significantly accelerate the local search procedure. We conduct extensive numeric experiments on multiple datasets, and results showed that our hybrid approach achieved near-optimal performance and established clear superiority over ALNS and tabu search.

Improved Integer Programming-Based Neighborhood Search for Less-Than-Truckload Load Plan Design

Less-than-truckload (LTL) carriers operate networks of consolidation terminals, and route each customer shipment through a sequence of transfer terminals. At each terminal stop, a shipment is unloaded from an inbound trailer and reloaded onto an outbound trailer. A load plan determines the specific path of terminals to be used to transfer freight moving between each origin and destination. The design of the load plan determines the line-haul transportation and handling costs required to serve customers. This paper develops an effective neighborhood search heuristic for solving a natural integer programming model for LTL load plan design. During each iteration of the heuristic, a modified version of the integer programming model is solved to find improving changes freight transfer paths for a subset of the origin-destination terminal pairs. Unlike existing approaches in the literature, the modified model solved here allows the simultaneous rerouting of freight destined for multiple terminals during each neighborhood search iteration, yet remains computationally practical. Computational experiments using data from a large U.S. carrier show substantial cost improvements generated by increased freight consolidation, in the order of 6%–7%, accounting for over $300,000 per week.

An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective

In this study, scheduling problem in dual-resource constrained (DRC) job shop with interval processing time and heterogeneous resources is investigated. A lexicographical method is applied to minimize interval carbon footprint and makespan. A dynamical neighborhood search (DNS) is proposed, which is composed of two phases. Two-string representation is used and its chromosome consists of the operation-based string and the resource string. Several initial solutions are produced and improved in the first phase and only one solution is applied in the second phase. Four neighborhood structures and their dynamical transition mechanism are utilized to produce new solutions. DNS is tested on a number of instances and compared with other algorithms. Computational experiments show DNS can provide the promising results for the problem.

An effective neighborhood search algorithm for scheduling a flow shop of batch processing machines

This paper considers scheduling problem of flow shop with many batch processing machines and objective of maximum lateness. An effective neighborhood search algorithm (NSA) is proposed for the problem, in which a job permutation and a batch permutation are used to indicate the solution of two sub-problems, respectively. Each job permutation consists of several family-permutations for the representation of jobs from the same family. Two swaps are applied to two permutations to produce new solutions. NSA is applied to a number of instances and compared with some methods, and computational results validate the good performance of NSA.

A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.

Crane scheduling with non-crossing constraint

In this paper, we examine crane scheduling for ports. This important component of port operations management is studied when the non-crossing spatial constraint, which is common to crane operations, is considered. We assume that ships can be divided into holds and that cranes can move from hold to hold but jobs are not pre-emptive, so that only one crane can work on one hold or job to complete it. Our objective is to minimize the latest completion time for all jobs. We formulate this problem as an integer programming problem. We provide the proof that this problem is NP-complete and design a branch-and-bound algorithm to obtain optimal solutions. A simulated annealing meta-heuristic with effective neighbourhood search is designed to find good solutions in larger size instances. The elaborate experimental results show that the branch-and-bound algorithm runs much faster than CPLEX and the simulated annealing approach can obtain near optimal solutions for instances of various sizes.

A simple filter-and-fan approach to the facility location problem

The design of effective neighborhood search procedures is a primary issue for the performance of local search and advanced metaheuristic algorithms. Several recent studies have focused on the development of variable depth neighborhoods that generate sequences of interrelated elementary moves to create more complex compound moves. These methods are chiefly conceived to produce an adaptive search as the number of elementary moves in a compound move may vary from one iteration to another depending on the state of the search. The objective is to achieve this goal with modest computational effort. Although ejection chain methods are currently the most advanced methods in this domain, they usually require more complex implementations. The filter-and-fan (F&F) method appears as an alternative to ejection chain methods allowing for the creation of compound moves based on an effective tree search design. This paper reports the first implementation of the F&F approach to the uncapacitated facility location problem (UFLP). We examine a simple version of the F&F method in this study, and explore two search strategies under this framework. The results obtained on a set of 105 standard benchmark problems from the literature demonstrate that this simple but well-structured approach is highly effective for providing optimal and near-optimal solutions for the UFLP in a very short computation time, and indeed can be advantageously compared with the most advanced metaheuristic procedures in solving these problems.