Considerable growth in container transportation has been witnessed over the past two decades with an indispensable need for optimization. Several studies tackling problems related to container operations have been reported by the academic world since then. These studies mainly focus on operations performed either onboard container ships or at the port side. Container operations onboard ships aim to optimize the proper assignment of containers to the corresponding storage areas on container ships to gain improvement in cost, time, and stability. In literature, this problem is defined as Container Stowage Planning (CSP) problem. This problem is studied either by being decomposed into sub-problems or by being approached holistically. This paper provides a detailed survey of studies that focus on addressing the CSP problem. Specifically, we cover the two main sub-problems of CSP, namely the bay planning problem and the slot planning problem. Additionally, we discuss the current state-of-the-art solution approaches that aim to achieve efficient and effective container placement.

Abstract Multi-objective optimization problems and their solution algorithms are of great importance as single-objective optimization problems are not usually a true representation of many real-world problems. In general, multi-objective optimization problems result in a large set of Pareto optimal solutions. Each solution in this set is optimal with some trade-offs. Therefore, it is difficult for the decision-maker to select a solution, especially in the absence of subjective or judgmental information. Moreover, an analysis of all the solutions is computationally expensive and, hence, not practical. Thus, researchers have proposed several techniques such as clustering and ranking of Pareto optimal solutions to reduce the number of solutions. The ranking methods are often used to obtain a single solution, which is not a good representation of the entire Pareto set. This paper deviates from the common approach and proposes a novel graph-theoretical clustering method. The quality of the clustering based on the Silhouette score is used to determine the number of clusters. The connectivity in the objective space is used to find representative solutions for clusters. One step forward, we identify ‘extreme solutions’. Hence, the reduced set contains both extreme solutions and representative solutions. We demonstrate the performance of the proposed method by using different 3D and 8D benchmark Pareto fronts as well as Pareto fronts from a case study in Royal Australian Navy. Results revealed that the reduced set obtained from the proposed method outperforms that from theK-means clustering, which is the most popular traditional clustering approach in Pareto pruning.

Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.32% and 51.43% better than those obtained by other two comparative algorithms.

Increases in demand for a greater variety of products help companies gain more shares of growing competitive markets but, in contrast, lead to an increase in production processes and, therefore, higher costs and longer lead times. Although several techniques for platform formations and assembly lines have been introduced to enable more varieties of goods to be produced, this also makes a system more complex and less cost-efficient. This paper proposes a differential evolution (DE) approach that incorporates a new heuristic method, improved solution representation and enhanced crossover and mutation operators for solving the modular-based product family design problem in a reconfigurable manufacturing system. The heuristic is applied to repair some solutions in the initial population by replacing eligible components with packages to provide near-optimal solutions in the initial stage and enable DE to find the optimal solution quickly. The proposed crossover is designed to further use the repaired solutions to produce new individuals with better qualities. Finally, a case study of a kettle family is conducted to validate this heuristic method, with the experimental results showing that it saves 57.5% of the purchasing costs of components and, on average, 41.35% of setup costs compared with those of median-joining phylogenetic network- and non-platform-based heuristics. Moreover, the proposed DE achieves improved performances with average errors of 63.34% and 38.52% from those of the standard versions of DE and a genetic algorithm, respectively, in terms of the total production costs of producing the same variants.

In this paper, we model and solve a strategic problem of fleet renewal to meet future operational needs under uncertain conditions. The fleet renewal problem focuses on mainly strategic decisions involving from fleet size, fleet mix and timing of replacement, yet it is essential to consider a significant amount of detail regarding short-term decisions to prevent inferior or infeasible strategies. In this direction, we develop a hybrid simulation model by combining system dynamics (SD) and discrete event simulation (DES) approaches. The standalone use of this model enables the decision maker to analyze the effects of both short- and long-term decisions on availability by simulating the processes that the fleet undertakes through its life-cycle from asset acquisition to retirement. Nevertheless, the simulation neither suggests nor seeks the best renewal strategy(ies). To alleviate this difficulty, we propose a simulation-based optimization that uses a genetic algorithm (GA) to effectively search a very large set of feasible fleet renewal strategies and uses the developed hybrid simulation model to evaluate candidate strategies found by GA. To provide a decision context where the approach has been developed and applied, we use a naval fleet renewal application. The extensive numerical experiments show that the proposed approach not only finds good and robust renewal strategies but also identify critical resources that influence the fleet’s availability. Finally, the robustness of optimized strategies under uncertainty is tested by sensitivity analysis, and mappings between implemented strategies and the fleet performance are constructed by scenario discovery analysis to provide insights for decision makers.

We discuss the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. The repair shop consists of several parallel multi-skilled servers, and storage facilities for the repaired items. The effectiveness of repair shops and the total cost of a spare part supply system depend highly on the design of repair facility and the management of inventory levels of the spare parts. In this paper, we concentrate on a design scheme known as pooling. A repair shop can be considered as a pooled structure if the spare parts can be divided into clusters such that each part type is unambiguously assigned to a single cluster (cell). Nonetheless, it is both an important and tough combinatorial optimization question to determine which type of spares to pool together. We propose a sequential solution heuristic to find the best pooled design by considering inventory allocation and capacity level designation of the repair shop. The numerical experiments show that the suggested solution approach has a reasonable algorithm run time and yields considerable cost reductions. Copyright 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.