The effect of material flow and workload on the performance of machine location heuristics

Depending on the problem structure and routing strategies a machine location problem plays an important role in controlling the material flow of work-in-process in discrete product manufacturing environment. In this paper we investigate the effect of material flow and workload on the performance of heuristics for solving an important design problem for job routing and material flow in a manufacturing system. In this research we first develop a model for workload or traffic intensity between machines in a shop floor and then identify different structures of the problems, especially the data. This measure is then used to evaluate the effect of workload on efficiency of the heuristics to solve machine location problems. Some concluding remarks are made on to the effect of the workload or the traffic intensity of materials within the machine cell on the performance of some known heuristics. Conclusions are also made on the performance measures such as makespan, transporter utilization and machine utilization, depending on the problem and data structures.     

Heuristics for multi-attribute vehicle routing problems: A survey and synthesis

The attributes of vehicle routing problems are additional characteristics or constraints that aim to better take into account the specificities of real applications. The variants thus formed are supported by a well-developed literature, including a large variety of heuristics. This article first reviews the main classes of attributes, providing a survey of heuristics and meta-heuristics for Multi-Attribute Vehicle Routing Problems (MAVRP). It then takes a closer look at the concepts of 64 remarkable meta-heuristics, selected objectively for their outstanding performance on 15 classic MAVRP with different attributes. This cross-analysis leads to the identification of “winning strategies” in designing effective heuristics for MAVRP. This is an important step in the development of general and efficient solution methods for dealing with the large range of vehicle routing variants.     

Mathematical programming based heuristics for the 0–1 MIP: a survey

The 0–1 mixed integer programming problem is used for modeling many combinatorial problems, ranging from logical design to scheduling and routing as well as encompassing graph theory models for resource allocation and financial planning. This paper provides a survey of heuristics based on mathematical programming for solving 0–1 mixed integer programs (MIP). More precisely, we focus on the stand-alone heuristics for 0–1 MIP as well as those heuristics that use linear programming techniques or solve a series of linear programming models or reduced problems, deduced from the initial one, in order to produce a high quality solution of a considered problem. Our emphasis will be on how mathematical programming techniques can be used for approximate problem solving, rather than on comparing performances of heuristics.     

有时间窗的车辆路径问题的局部下降搜索算法

介绍了一个求解有时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)的启发式算法——基于λ-交换的局部下降搜索算法(Local search descent method based on λ-interchange).VRPTW是指合理安排车辆行驶路线,为一组预先设定有时间限制的客户运送货物,在不违反时间要求和车辆容量限制的条件下使得成本最小.它是一个典型的NP-难题,可以通过启发式算法获得近优解来解决.通过两个实验验证,显示了局部下降搜索算法的优良性能,取得了很好的效果,可以作为进一步研究复杂算法的基础.     

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches.     

Modeling and solving profitable location and distribution problems

In this paper we present a general framework for tackling combined location and routing problems (LRPs), involving both costs and profits at the same time. Our framework is based on an extended model and a unified branch-and-cut-and-price method, using dynamic programming pricing routines, strengthening cuts, primal heuristics, stabilization and ad-hoc branching rules to exactly solve LRPs. First we describe our framework, discussing its algorithmic components. Then, we consider as a test case three problems from the literature, with increasing relative importance of the location decisions over the routing ones, and we analyze the performance of our framework for solving them. The first result of our investigation is to assess the tradeoff between modeling detail and computational effectiveness in tackling LRPs. At the same time, we also show that our integrated exact approach is effective for these problems.     

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

In this paper we propose and evaluate an evolutionary-based hyper-heuristic approach, called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper-heuristic is a high-level algorithm, which generates or chooses a set of low-level heuristics in a common framework, to solve the problem at hand. In our collaborative framework, we have included three different types of low-level heuristics: constructive, perturbative, and noise heuristics. Basically, the hyper-heuristic manages and evolves a sophisticated sequence of combinations of these low-level heuristics, which are sequentially applied in order to construct and improve partial solutions, i.e., partial routes. In presenting some design considerations, we have taken into account the allowance of a proper cooperation and communication among low-level heuristics, and as a result, find the most promising sequence to tackle partial states of the (dynamic) problem. Our approach has been evaluated using the Kilby’s benchmarks, which comprise a large number of instances with different topologies and degrees of dynamism, and we have compared it with some well-known methods proposed in the literature. The experimental results have shown that, due to the dynamic nature of the hyper-heuristic, our proposed approach is able to adapt to dynamic scenarios more naturally than low-level heuristics. Furthermore, the hyper-heuristic can obtain high-quality solutions when compared with other (meta) heuristic-based methods. Therefore, the findings of this contribution justify the employment of hyper-heuristic techniques in such changing environments, and we believe that further contributions could be successfully proposed in related dynamic problems.     

Lagrangian heuristics for instructor scheduling in executive development programmes

In this paper we present the problem of scheduling instructors in a university management development programme. Problems of similar structure arise in a number of scheduling applications like assigning officials to athletic competitions, inspectors to sites and maintenance crews to jobs. The problem is formulated as a zero-one linear integer programme but is difficult to solve in real life situations because of problem size. The bounds on total assignments for different nested time periods give sub-problems that can be solved as network flow problems. Four Lagrangian relaxation heuristics are developed using different relaxations of the problem. Computational results are reported on 1350 random problems. In over 85% of these problems, the heuristics find solutions within 1% of the optimal. Heuristic performance is also analyzed in terms of average percent deviation from optimal, percent of times optimal solution is found and the cpu time. Computational results on two significantly larger real problems indicate that the heuristics are capable of solving real sized problems with tolerable deviations of around 4% from the optimal. An integrated strategy utilizing the strengths of the optimal and heuristic approaches is described for schedule generation and updating.     

Using a TSP heuristic for routing order pickers in warehouses

In this paper, we deal with the sequencing and routing problem of order pickers in conventional multi-parallel-aisle warehouse systems. For this NP-hard Steiner travelling salesman problem (TSP), exact algorithms only exist for warehouses with at most three cross aisles, while for other warehouse types literature provides a selection of dedicated construction heuristics. We evaluate to what extent reformulating and solving the problem as a classical TSP leads to performance improvements compared to existing dedicated heuristics. We report average savings in route distance of up to 47% when using the LKH (Lin–Kernighan–Helsgaun) TSP heuristic. Additionally, we examine if combining problem-specific solution concepts from dedicated heuristics with high-quality local search features could be useful. Lastly, we verify whether the sophistication of ‘state-of-the-art’ local search heuristics is necessary for routing order pickers in warehouses, or whether a subset of features suffices to generate high-quality solutions.     

An artificial bee colony algorithm for the capacitated vehicle routing problem

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem.     

New assignment algorithms for the multi-depot vehicle routing problem

This paper considers the design and analysis of algorithms for the multi-depot vehicle routing problem with time windows (MDVRPTW). Given the intrinsic difficulty of this problem class, approximation methods of the type ‘cluster first, route second’ (two-step approaches) seem to offer the most promise for practical size problems. After describing six heuristics for the cluster part (assignment of customers to depots) an initial computational study of their performance is conducted. Finding, as expected, that the heuristics with the best results (in terms of the routing results) are those with the largest computational efforts.     

New heuristics for the Fleet Size and Mix Vehicle Routing Problem with Time Windows

In the Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW) customers need to be serviced in their time windows at minimal costs by a heterogeneous fleet. In this paper new heuristics for the FSMVRPTW are developed. The performance of the heuristics is shown to be significantly higher than that of any previous heuristic approach and therefore likely to achieve better solutions to practical routing problems.     

A route-neighborhood-based metaheuristic for vehicle routing problem with time windows

In this paper, a two-stage metaheuristic based on a new neighborhood structure is proposed to solve the vehicle routing problem with time windows (VRPTW). Our neighborhood construction focuses on the relationship between route(s) and node(s). Unlike the conventional methods for parallel route construction, we construct routes in a nested parallel manner to obtain higher solution quality. Valuable information extracted from the previous parallel construction runs is used to enhance the performance of parallel construction. In addition, when there are only a few unrouted customers left, we design a special procedure for handling them. Computational results for 60 benchmark problems are reported. The results indicate that our approach is highly competitive with all existing heuristics, and in particular very promising for solving problems of large size.     

Heuristics for scheduling in a flow shop with multiple processors

This study investigates the performance of scheduling heuristics in a flow shop with multiple processors. We investigated five better performing flow shop heuristics for their performances of makespan and mean flow time criteria in a flow shop with multiple processors. The study examined the effects of problem characteristics (number of jobs, number of machine stages and number of parallel processors at each stage) and the performance of heuristics using regression analysis. We found that although structural characteristics explain most of the variation in performance, heuristics also had an effect. The experimental results showed that flow shop heuristics developed by Nawaz, Enscore, and Ham and that of Ho were comparable in performance in a flow shop with multiple processors. However, the former was slightly more consistent in results for both criteria.     

District design for arc-routing applications

In this paper we address the problem of district design for the organisation of arc-routing activities. In particular, the focus is on operations like winter gritting and road maintenance. The problem involves how to allocate the road network edges to a set of depots with given locations. The collection of edges assigned to a facility forms a district in which routes have to be designed that start and end at the facility. Apart from the ability to support good arc routing, well-designed districts for road-maintenance operations should have the road network to be serviced connected and should define clear geographical boundaries. We present three districting heuristics and evaluate the quality of the partitions by solving capacitated arc routing problems in the districts, and by comparing the solution values with a multi-depot CARP cutting plane lower bound. Our experiments reveal that based on global information about the distribution system (ie the number of facilities or districts, the average edge demand and the vehicle capacity) and by using simple guidelines, an adequate districting policy may be selected.     

A comparison of heuristic algorithms for cost-oriented assembly line balancing

Two new heuristic algorithms for solving cost-oriented assembly line balancing problems -the Wage-Rate-Method (WR) and the Wage-Rate-Smoothing-Method (WRS) — are presented and compared with two known heuristics — the Positional-Weight-Method (PW) and the Positional-Weight-Wage-Rate-Difference-Method (PWWD) with respect to their solution qualities. Firstly, the heuristics are outlined and their computational effort is stated. Then, a theoretical worst-case bound for the solution quality is given and the results of an extensive performance study are reported. In the study the heuristics were investigated with respect to their solution quality by solving randomly generated line balancing problems and problems from literature. It can be concluded that PWWD and WRS are generally superior to PW and WR.Parts of this research have been supported by the Stiftung Industrieforschung.     

Heuristic method for a mixed capacitated arc routing problem: A refuse collection application

The capacitated arc routing problem (CARP) is known to be NP-hard. The aim of this paper is to present a new heuristic method to generate feasible solutions to an extended CARP on mixed graphs, inspired by the household refuse collection problem in Lisbon. Computational experience was done to compare the method with some well-known existing heuristics, generalised for a different extended CARP by Lacomme et al. [Fast algorithm for general arc routing problems, Presented at IFORS 2002 Conference, Edinburgh, UK], namely, the Path-Scanning, the Augment-Merge and the Ulusoy’s algorithms. The results reveal a good performance of the proposed heuristic method. Generally providing a good use of the vehicles capacity, the resulting sets of feasible trips may also be considered good. The test instances involve more than 300 randomly generated test problems with dimensions of up to 400 nodes and 1220 links.     

Heuristics for vehicle routing on tree-like networks

This paper presents two new heuristics for the vehicle routing problem on tree-like road networks. These networks occur, for example, in rural road systems where the supply (or delivery) nodes are located on rural roads leading off from a few highways which form the ‘trunks’ of a tree-like network. The heuristics have the conventional objective of minimising the total distance travelled by the vehicles. The development of the heuristics takes advantage of the tree-like structure of the network. These two new heuristics and two other heuristics from the published literature are applied to some test problems and computational results are presented. The computational experience indicates that one of the new heuristics provides superior solutions to the existing heuristics and in reasonable computing time. It therefore appears suitable for large-scale practical routing problems.     

现金押运路线问题——基于多类型的风险

以人民币现金押运为研究背景,考虑了一种基于多类型风险的现金押运路线问题,以在途风险成本、库存现金风险成本以及运输成本为优化目标,建立了混合整数线性规划模型,并提出了一种基于多样化策略和改进邻域搜索的混合遗传算法,其中遗传算法对押运路线进行选择,贪心算法用来求解各类风险指标。数值实验分别对问题特性和算法性能进行了分析。实验结果表明:1)混合遗传算法能求解更大规模的问题,得到较好的解,并很好地平衡了运行时间和求解质量;2)多类型风险影响了行驶路线;3)客户的期望需求影响了库存现金风险。     

Dynamic cruise ship revenue management

Recently, it has been recognized that revenue management of cruise ships is different from that of airlines or hotels. Among the main differences is the presence of multiple capacity constraints in cruise ships, i.e., the number of cabins in different categories and the number of lifeboat seats, versus a single constraint in airlines and hotels (i.e., number of seats or rooms). We develop a discrete-time dynamic capacity control model for a cruise ship characterized by multiple constraints on cabin and lifeboat capacities. Customers (families) arrive sequentially according to a stochastic process and request one cabin of a certain category and one or more lifeboat seats. The cruise ship revenue manager decides which requests to accept based on the remaining cabin and lifeboat capacities at the time of an arrival as well as the type of the arrival. We show that the opportunity cost of accepting a customer is not always monotone in the reservation levels or time. This non-monotone behavior implies that “conventional” booking limits or critical time periods capacity control policies are not optimal. We provide analysis and insights justifying the non-monotone behavior in our cruise ship context. In the absence of monotonicity, and with the optimal solution requiring heavy storage for “large” (industry-size) problems, we develop several heuristics and thoroughly test their performance, via simulation, against the optimal solution, well-crafted upper bounds, and a first-come first-served lower bound. Our heuristics are based on rolling-up the multi-dimensional state space into one or two dimensions and solving the resulting dynamic program (DP). This is a strength of our approach since our DP-based heuristics are easy to understand, solve and analyze. We find that single-dimensional heuristics based on decoupling the cabins and lifeboat problems perform quite well in most cases.