Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

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A survey on metaheuristics for stochastic combinatorial optimization

Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case

Supply chain network design (SCND) determines the structure of a chain and affects its costs and performance. SCND deals with a variety of decisions such as determining number, size and location of facilities in a supply chain (SC) and may include tactical decisions (such as distribution, transportation and inventory management policies) as well as operational decisions (such as fulfilling customers demand). SCND has a voluminous literature. Most of the literature deals with a single SC and ignores the existing competitor SCs and future emerging ones. However, SCs compete together to capture more market shares. Even if there is not any competitor at the moment, SCs should be prepared for possible future competitive situation at the SCND stage. On the other hand, many competitive models assume that the supply chain network (SCN) and its structure already exist. Few research papers consider both aspects of design and competition. In this paper, we provide a review of SCND literature and highlight the effects of competitive environment on SCND. We review, classify, and introduce the major features of the proposed models in both SCND and competition literature. After investigating proposed competitive SCND models we develop a general framework for modeling the competitive SCND problems considering managerial insight and propose potential areas for future research.

Competitive supply chain network design: An overview of classifications, models, solution techniques and applications

It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers' attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research.

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Review article: A review of soft computing applications in supply chain management

Decision support for integrated refinery supply chains: Part 1. Dynamic simulation

A simulation‐based genetic algorithm for inventory optimization in a serial supply chain

Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives

This paper presents simulation-based heuristic methodologies to compute installation base-stock levels in a serial supply chain so as to minimise the total supply chain cost. A simulated annealing heuristic is proposed to compute the installation base-stock levels for different installations in a serial supply chain. In addition, a simple heuristic to determine the installation base-stock levels is also presented. Simulation is used to evaluate the installation base-stock levels yielded by the proposed simulated annealing heuristic and the simple heuristic. A best-move local search is also used as a benchmark procedure. The solution constructed by the simple heuristic is initialised as the seed solution to both simulated annealing heuristic and the best-move local search, and their performances are studied. All the solution methodologies are extensively evaluated with different supply chain test problems. The solutions yielded by the proposed solution methodologies is compared against the optimal installation base-stock level which is obtained through a complete enumeration of the solution space for each of the supply chain test problems. A comparison of the performance of all the solution methodologies shows that the proposed simulated annealing heuristic initialised with the heuristic solution as the initial seed solution performs the best by consistently producing good-quality solutions with little computational effort.

J. Sudhir Ryan Daniel

Papers

A simulation‐based genetic algorithm for inventory optimization in a serial supply chain

Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives

Determination of base-stock levels in a serial supply chain: a simulation-based simulated annealing heuristic