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Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates mathematical programming approaches for addressing the maintenance scheduling problem. Gives examples to demonstrate the utility of these approaches. Proposes expansion of the state‐of‐the‐art maintenance management information system to utilize the mathematical programming approaches and to have better control over the maintenance scheduling problem.

The purpose of this paper is to discuss and to help address the need for quantitative models to support and improve procurement in the context of humanitarian relief efforts.

This research presents a two‐stage stochastic decision model with recourse for procurement in humanitarian relief supply chains, and compares its effectiveness on an illustrative example with respect to a standard solution approach.

Results show the ability of the new model to capture and model both the procurement process and the uncertainty inherent in a disaster relief situation, in support of more efficient and effective procurement plans.

The research focus is on sudden onset disasters and it does not differentiate between local and international suppliers. A number of extensions of the base model could be implemented, however, so as to address the specific needs of a given organization and their procurement process.

Despite the prevalence of procurement expenditures in humanitarian efforts, procurement in humanitarian contexts is a topic that previously has only been discussed in a qualitative manner in the literature. This work provides practitioners with a new approach to quantitatively assess and improve their procurement decision processes.

This study adds to the existing literature by demonstrating the applicability and effectiveness of an analytic modeling technique based on uncertainty, such as stochastic programming with recourse, in the context of humanitarian relief procurement activities.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

Maintenance control plays a key role in achieving the stated objective of effectiveness and efficiency of the maintenance system. In a recent paper, Gits proposed a reference framework that guides in the design and structuring of maintenance control. The framework is conceptual in nature and its use in practice is limited. Poses Gits’ framework as a set of mathematical programming models. Extends some of Gits’ procedure for maintenance control, then outlines the required expansion in the maintenance management information system (MMIS) in order to provide the needed data to execute the models. The models provide operational plans and schedules ready for implementation.

Passage of the 1996 Farm Bill marked a dramatic departure in federal farm policy as the longstanding deficiency payment program was replaced with non‐risk responsive transition payments. In light of the departure, subsidized savings has been proposed as a mechanism to provide risk protection to agricultural producers. Using Canada’s National Income Stabilization Account (NISA) program as an example of a subsidized savings program, a stochastic programming model of income stabilization is developed. The model is then used to investigate the optimizing behavior of a typical Midwestern crop producer. The results suggest a fair amount of program design flexibility exists, and that the government can use this flexibility to stimulate initial and continual participation while minimizing capital outlays.

The liquefied natural gas (LNG) business comprises a number of economic activities with inherent risks. The purpose of this paper is to propose an integrated modelling approach, as part of the investment decision‐making process, for optimising economic returns from LNG whilst taking into account uncertainty in various key input parameters.

Inter‐linked cash flow and pricing models of the LNG chain were constructed. Net present value was maximised based on selection of netback pricing variables and level of investment shareholding. Constraints were placed on the minimum acceptable returns. The risk affinity of the decision maker was captured in the form of a chance‐constrained optimisation problem. A genetic algorithm was applied for numerical optimisation, in combination with Monte Carlo simulations to account for the stochastic nature of the problem.

Based on the results of a case study, the deterministic solution, having no consideration to uncertainty, was found to be both sub‐optimal and provided an unsatisfactory risk outcome. The stochastic approach yielded an optimal solution with due consideration to risk. Various scenarios show that the choice of the decision variables significantly impacts the trade‐off between risk and returns along the LNG chain to government and investor.

The suitability of the methodology to the operational phase of the LNG business which incorporates different elements of risk, such as market dynamics and logistics, is as yet untested.

This framework may be useful in the formulation of optimal commercial structure of firms, investment portfolio and gas/LNG pricing arrangements for host governments involved in the LNG business.

The purpose of this paper is to study the problem of a routing problem with uncertain try-on service time (VRPUS) for apparel distribution, and to devise solution strategies coping with the uncertainty by an evolutionary algorithm. VRPUS belongs to the category of practical routing models integrated with uncertain service times. However, in the background of apparel distribution, it has distinct features. The try-on service will improve the customer satisfaction by providing experiences to customers; the return cost is saved; the customer loyalty is improved for experiencing face-to-face try-on services. However, the uncertainty of try-on service time makes the apparel distribution process uncertain and incurs additional risk management cost, such that the logistics companies should optimally make decisions on the choice of the service and the service processes.

This paper devised a mixed-integer programming (MIP) model for the base vehicle routing problem (VRP) and then it is extended to support the solution strategies for uncertain try-on times. A try-on time estimation parameter and a time reservation parameter are used to cope with the uncertain try-on time, and the try-on rejection strategy is applied when the uncertain try-on time is realized at customer and no surplus time can be used for try-on service besides distributing to remainder customers. Due to the computational complexity of VRPUS, an evolutionary algorithm is designed for solving it. These parameters and strategy options are designed for the operational decisions by logistics companies. Finally, a decision support system (DSS) is designed.

Five experimental scenarios are performed to reveal the impacts of parameters and solution strategies coping with uncertain try-on time on the distribution cost, return cost, and the try-on service failure. The tuning methods are designed to assist the decisions by logistics companies.

A new routing problem is addressed for apparel distribution in fashion industry especially in the context of booming apparel e-commerce, which is a VRP with uncertain try-on service time for apparel distribution; three strategies are developed to cope with the try-on time uncertainty. The proposed method is also a theoretical base for designing a practical DSS for logistics companies to provide try-on service to customers.

Strategic planning of the supply chain is an important decision problem determining the long‐term survival and prosperity of companies in the manufacturing, retail, and other industrial sectors. In general such companies rely on their information systems to acquire the essential data that are used in their planning models. The interaction of information systems and decision modelling, and the progressive transformation of data, into information, and knowledge is a key process underlying any decision support system (DSS) for strategic, tactical or operational planning. In this paper we consider a DSS for supply chain planning (SCP) decisions. The SCP system has an embedded decision engine that uses a two‐stage stochastic program as a paradigm for optimisation under uncertainty. The system has been used for decision making in diverse domains, including automotive manufacturing and consumer products.