We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

The interest in using drones in various applications has grown significantly in recent years. The reasons are related to the continuous advances in technology, especially the advent of fast microprocessors, which support intelligent autonomous control of several systems. Photography, construction, and monitoring and surveillance are only some of the areas in which the use of drones is becoming common. Among these, last-mile delivery is one of the most promising areas. In this work we focus on routing problems with drones, mostly in the context of parcel delivery. We survey and classify the existing works and we provide perspectives for future research.

Drone-aided routing: A literature review

In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

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Last-mile delivery concepts: a survey from an operational research perspective

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

Unmanned aerial vehicles/drones in vehicle routing problems: a literature review

/pdf/the-storage-location-assignment-problem-a-literature-review-36pqm929zz.pdf

The storage location assignment problem: A literature review

Graphical abstractDisplay Omitted HighlightsA robust version of the vehicle routing with uncertain travel times is studied.Uncertainties are given as a set of discrete scenarios.A mixed integer model (MIP), a local search and four meta-heuristics are developed.The meta-heuristics retrieve the optimal solutions of the MIP on small problems.Results are reported up to 100 customers for the meta-heuristics. The Capacitated Vehicle Routing Problem (CVRP) is extended here to handle uncertain arc costs without resorting to probability distributions, giving the Robust VRP (RVRP). The unique set of arc costs in the CVRP is replaced by a set of discrete scenarios. A scenario is for instance the travel time observed on each arc at a given traffic hour. The goal is to build a set of routes using the lexicographic min-max criterion: the worst cost over all scenarios is minimized but ties are broken using the other scenarios, from the worst to the best. This version of robust CVRP has never been studied before. A Mixed Integer Linear Program (MILP), two greedy heuristics, a local search and four metaheuristics are proposed: a Greedy Randomized Adaptive Search Procedure, an Iterated Local Search (ILS), a Multi-Start ILS (MS-ILS), and an MS-ILS based on Giant Tours (MS-ILS-GT) converted into feasible routes via a lexicographic splitting procedure. The greedy heuristics provide the other algorithms with good initial solutions. Tests on small instances (10-20 customers, 2-3 vehicles, 10-30 scenarios) show that the four metaheuristics retrieve all optima found by the MILP. On larger cases with 50-100 customers, 5-20 vehicles and 10-20 scenarios, MS-ILS-GT dominates the other approaches. As our algorithms share the same components (initial heuristic, local search), the positive contribution of using the giant tour approach is confirmed on the RVRP.

Local search based metaheuristics for the robust vehicle routing problem with discrete scenarios

We consider the problem of scheduling jobs in a hybrid flowshop with two stages. Our objective is to minimize both the makespan and the total completion time of jobs. This problem has been little studied in the literature. To solve the problem, we propose an ant colony optimization procedure. Computational experiments are conducted using random-generated instances from the literature. In comparison against other well-known heuristics from the literature, experimental results show that our algorithm outperforms such heuristics.

Elyn L. Solano-Charris

Papers

Unmanned aerial vehicles/drones in vehicle routing problems: a literature review

The storage location assignment problem: A literature review

Local search based metaheuristics for the robust vehicle routing problem with discrete scenarios

Ant colony optimization algorithm for a Bi-criteria 2-stage hybrid flowshop scheduling problem

Control and monitoring for sustainable manufacturing in the Industry 4.0: A literature review