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The relevance of multiple criteria decision-making/aiding is reinforced by the prominence of these methods in a wide range of applications. Whether by solving problems with a single decision-maker (DM) or a group of DMs, additive modelling, based on value or utility functions, is the most traditional. However, applying this kind of method raises a critical issue: the difficulty in eliciting DM's preferences and recommending a decision. Actually, it is a hard task for the DM to provide complete information regarding their preferences, because the DM may not be able to provide such information in the detailed way required, or even they may not be willing to do so. From this perspective, the emergence and growth of partial (incomplete or imprecise) information-based methods is indicative that these are a useful way of guiding decision-making as they require less cognitive input from a DM. Thus, this paper systematically reviews the literature on multicriteria decision methods that deal with partial information, focusing on the Multi-Attribute Value/Utility Theory context. Strategic research questions guide a bibliometric and content analysis of 105 peer-reviewed papers selected from the Web of Science (Main Collection). An integrated analysis of the results provides scholars, researchers and other professionals with a deeper comprehension of methodological advances and respective contributions, and of the main challenges and trends in this field of knowledge. Our analysis aims to show that when these methods are applied more reliable decision-making can be achieved. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective mathematical formulation for supplier-material selection in cardboard box manufacturing. The aim is to minimize three different scaled objectives: wastage of raw material, raw material cost and product surplus. To model industry reality more closely, the formulation is extended to an environment that includes uncertain costs and demands. In this sense, the model is reformulated as a robust optimization problem. To solve this problem, a weighted global criterion approach is developed and applied to find Pareto optimal solutions. A case study from a cardboard box manufacturer is used to test the efficiency of the proposed robust formulation and the proposed solution approach. The sensitivity of the Pareto optimal solutions to different levels of uncertainty in the parameters and different weights of the objective functions is also studied. [ABSTRACT FROM AUTHOR]

The clustered and generalized vehicle routing problem (CGVRP) extends the well-known vehicle routing problem by grouping the demand points into multiple distinct zones, and within each zone, further separation is made by forming clusters. The objective of the CGVRP is to determine the optimal routes for a fleet of vehicles dispatched from a depot, visiting all zones within each cluster. This requires making two simultaneous optimization decisions. Firstly, each zone must be visited by exactly one node, and secondly, all zones within a cluster must be visited by the same vehicle. In this paper, we introduce two mixed-integer linear programming formulations for the CGVRP, aimed at solving a joint order batching and picker routing problem with alternative locations in a warehouse environment featuring mixed-shelves configuration. Both formulations are tested on three scenarios of randomly generated small- and medium-sized instances. Additionally, we propose a general rule approach for calculating a cost matrix in a rectangular environment. The results demonstrate the effectiveness of the proposed mathematical formulations in efficiently solving problems with up to 180 nodes. [ABSTRACT FROM AUTHOR]

The inventory routing problem (IRP) deals with the study of the inter-relationship between two important activities in the supply chain: transportation of commodities and inventory management. In this paper, we study a variant of the multi-product IRP and we propose a general variable neighbourhood search metaheuristic for solving it. We present several neighbourhood structures based on the movement of quantities between routes and periods. Computational experiments show the efficiency of the proposed metaheuristic when compared with the state-of-the-art based on variable neighbourhood search. [ABSTRACT FROM AUTHOR]

This article evaluates 186 papers, published between 1978 and 2013 in 16 representative scientific journals, related to maintenance and reliability problems that were tackled from a multi-criteria (MC) perspective. An overview and insights are presented. This study may be useful to researchers and others concerned with maintenance and reliability who seek not only to understand the potential of MC and multi-objective models but also to develop and apply an MC decision model to help solve a real problem in these areas. There are some discussions on some principles for the application of MC in maintenance and reliability and some guidance on how to choose a suitable MC method is given, based on previous applications. [ABSTRACT FROM AUTHOR]

The complexity of dependence between different types of components results in many challenges to estimate system reliability and to optimize maintenance plans. In this paper, we develop a reliability model to study the failure dependence of a system with a main component and several protective auxiliary components. Damage to the main component caused by random environmental shocks depends on the number of auxiliary components in operation. When the execution of inspection and maintenance actions during system operation is difficult, failures of the main component provide opportunities to inspect and replace the auxiliary components. We derive the system reliability using Laplace transforms and the matrix method. The optimization problem is solved by an enumeration method. A numerical example and sensitivity studies of cost parameters show how the evolution of the parameters influences the optimal maintenance strategy. The results show that a high replacement threshold of the auxiliary components is required when the replacement cost of the main component is high. Conversely, the threshold could be adjusted to a lower level when the replacement cost of the auxiliary components and the downtime cost increase. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for the quantitative analysis of collaborative service platforms. Services built around platforms require the coordinated collaboration of several independent agents, each from a different field or area of expertise and each providing one or more components to the overall service. One of these agents takes the further role of platform coordinator, whose role consists in assembling the services from components provided by other agents and sharing the revenue between the participants. The agents decide which services to make a contribution to by selecting service portfolios that maximize their expected profit under constraints on risk and capacity. The coordinator selects the revenue-sharing scheme that balances the offers from other agents and ensures the functioning of the platform. We develop two stochastic optimization models with bilevel structure for the analysis of the collaborative service platform provision, analyse the properties of the solutions and provide numerical experiments that show the qualitative difference of the profit/risk trade-off in the multiagent case compared with the classical single agent case. [ABSTRACT FROM AUTHOR]

In this paper, we consider a recently introduced problem specifically designed for Smart Cities and City Logistics applications: the multi-path travelling salesman problem with stochastic travel costs (mpTSPs). The mpTSPs is a variant of the TSP problem where a set of paths exists between any two nodes and each path is characterized by a random travel time. We propose a two-stage stochastic programming formulation where tour design makes up the first stage, while recourse decisions related to the choice of the path to follow are made in the second stage. To solve this formulation, we propose a heuristic method inspired by the Progressive Hedging (PH) algorithm of Rockafellar & Wets (1991, Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res., 16, 119-147.) We then benchmark the solution method by solving model instances derived from the traffic speed sensor network of the city of Turin. Furthermore, the impact of the stochastic travel time costs on the problem solution is examined, showing the benefits of the proposed methodology in both solution quality and computational effort when compared to solving the deterministic equivalent formulation using a commercial solver. [ABSTRACT FROM AUTHOR]

As the frequency and severity of disasters around the world increase, there is an urgent need for more efficient and effective allocation of response resources to impact jurisdictions. In a disaster situation, time wasted may define the line between life and death. In this paper, we develop an integer programming model for the capability-based multi-resource multi-location allocation problem. The model is intended to take advantage of a nationwide resource inventory listing in order to allocate response personnel, facilities, supplies and equipment to impact jurisdictions. [ABSTRACT FROM PUBLISHER]

Thangam & Uthayakumar (2010, Optimal pricing and lot-sizing policy for a two warehouse supply chain system with perishable items under partial trade credit financing. Oper. Res. Int. J., 10, 133–161) investigated perishable items with limited storage capacity under a partial trade credit policy. For simplification, they used Taylor series approximations to derive their solution procedures. In our paper, we develop an exact solution procedure that avoids the problem of assuming that the deterioration rate is small, thereby improving the accuracy of the solution but more fundamentally increasing the practical relevance of the solution. [ABSTRACT FROM PUBLISHER]

Recently, Konstantaras et al. (2007, Economic ordering policy for an item with imperfect quality subject to the in-house inspection. Int. J. Syst. Sci., 38, 473–482) presented an economic order quantity model with imperfect quality subject to in-house inspection. In their study, two options for handling defectives were proposed. The first option considered the defective items to be sold at a lower price and the second considered the defective items to be reworked to an acceptable quality standard to meet demand. Their inventory model is interesting and valuable, but the process for evaluating the expected profit per unit time for the first option can be improved. The main purpose of this paper is to offer an alternative approach for the model, where we apply a renewal reward theorem to calculate the expected profit per unit time and solve the model without derivatives. Numerical examples are provided to show the solution procedure. [ABSTRACT FROM PUBLISHER]

The deterministic traveling purchaser problem (TPP) aims to select a subset of suppliers, offering products at different prices and quantities, to satisfy demand while minimizing travelling and purchasing costs. In this paper, we study a variant of the TPP in which both the available quantities and the purchasing prices are uncertain. This more challenging version of the problem, TPP under uncertainty, allows a purchaser to protect himself against risks of insufficient demand fulfilment and to exploit the benefits of procurement at lower prices. We introduce a two-stage stochastic programming formulation of the problem and we present a tailored solution approach based on a Branch-and-Cut method and on a heuristic approach to find good initial solutions. Extensive computational experiments show the efficiency of the proposed approach in finding the optimal solution of the deterministic equivalent problem for instances with up to 75 suppliers, 50 products and 200 scenarios in less than 2 h. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new heuristic for the covering design problem based on a large neighbourhood search (LNS) metaheuristic that can be seen as a special case of a variable neighbourhood search. As the initial solution, we use a well-known greedy heuristic as well as a new tie-braking rule within greedy algorithms for choosing blocks in the covering. Some theoretical aspects of the greedy heuristic are discussed. The proposed LNS-based heuristic called level reduction can be applied to any covering design and different pre-defined orders, such as lex, colex, etc. With our simple approach, we establish 21 new best known upper bounds on the covering number. [ABSTRACT FROM AUTHOR]

Variable neighbourhood search (VNS) and all its variants have been successfully proved in hard combinatorial optimization problems. However, there are only few works concerning parallel VNS algorithms, compared with the amount of works devoted to sequential VNS design. In this paper, we propose different parallel designs for the VNS schema. We illustrate the performance of these general strategies by parallelizing a new VNS variant called variable formulation search (VFS). Specifically, we propose six different variants which differ in the VNS stages to be parallelized as well as in the communication mechanisms among processes. We group these variants into three different templates. The first one is oriented to parallelize the whole VNS method. The second one parallelizes the shake and the local search procedures. Finally, the third one explores in parallel the set of predefined neighbourhoods. We test the resulting designs on the cutwidth minimization problem (CMP). Experimental results show that the parallel implementation of the VFS outperforms previous methods in the state of the art for the CMP. This fact is also confirmed by non-parametric statistical tests. [ABSTRACT FROM AUTHOR]

Several variants of variable neighbourhood search (VNS) for solving unconstrained and constrained continuous optimization problems have been proposed in the literature. In this paper, we suggest two new variants, one of which uses the recent modified Nelder-Mead (MNM) direct search method as a local search and the other an extension of the MNM method obtained by increasing the size of the simplex each time the search cannot be continued. For these new and some previous VNS variants, extensive computational experiments are performed on standard and large non-differentiable test instances. Some interesting observations regarding comparison of some VNS variants with NM based local search are made. [ABSTRACT FROM AUTHOR]

In this paper, we develop a possibilistic linear programming model for supply chain network design with imprecise inputs: market demands, supplied quantities, transportation costs, opening costs, treatment and storage costs are modelled as fuzzy numbers. An efficient possibilistic linear programme is constructed with fuzzy objective function that minimizes the sum of investment costs and operating costs of the supply chain. A method for solving the programming problem with fuzzy parameters is proposed. Application to a supply chain problem at a European textile company illustrates our methodology. Numerical results show that the performance of the proposed model in handling data uncertainty is better when compared to a classical deterministic model. [ABSTRACT FROM PUBLISHER]

This paper addresses the capital budgeting problem under uncertainty. In particular, we propose a multistage stochastic programming model aimed at selecting and managing a project portfolio. The dynamic uncertain evolution of each project value is modelled by a scenario tree over the planning horizon. The model allows the decision maker to revise decisions by decommitting from a given project if it shows a negative performance. Risk is explicitly assessed by defining a mean-risk objective function, where the conditional value at risk is used. A customized branch-and-bound method is also introduced for solving the proposed model. Extensive computational experiments have been carried out to validate the model effectiveness, also in comparison with other possible benchmark policies. The numerical results collected by solving randomly generated instances with the proposed branch-and-bound approach seems to be encouraging. [ABSTRACT FROM PUBLISHER]

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Up till now, neighbourhood search has been the most efficient optimization framework for solving the problem. The discovery and innovative application of neighbourhood properties is a key issue in the design of such algorithms. Effective neighbourhood properties help one to exclude unpromising solutions in the neighbourhood, and thus accelerate the convergence of the whole searching process. In this paper, we aim at solving the JSSP with the objective of minimizing total weighted tardiness, which includes most other regular objective functions as special cases. A neighbourhood property of the problem is discovered based on the duality of linear programming models. Then, the neighbourhood property is utilized by a tree search procedure, which is finally embedded into the particle swarm optimization framework. According to extensive computational experiments and the real-world application to a large-scale manufacturing firm in China, the proposed approach is effective for solving the JSSP. [ABSTRACT FROM PUBLISHER]

A number of problems in economics, finance and insurance rely on determining the sign of the covariance of two transformations of a random variable. The classical Chebyshev's inequality offers a powerful tool for solving the problem, but it assumes that the transformations are monotonic, which is not always the case in applications. For this reason, in the present paper, we establish new results for determining the covariance sign and provide further insights into the area. Unlike many previous works, our method of analysis, which is probabilistic in its nature, does not rely on the classical Höffding's representation of the covariance or on any of its numerous extensions and generalizations. We motivate our research with several problems arising in economics, finance and insurance. [ABSTRACT FROM PUBLISHER]

In this paper we deal with the problem of the evaluation of a set of alternatives when they are measured by values in different rank positions of an ordinal scale. We propose a class of indices to evaluate the alternatives where we give each alternative the opportunity to rank as high as possible assuming only certain conditions on the weights that represent the relative importance of the ordinal categories. We derive explicit formulas for the indices under different assumptions about the appropriate weights and analyse their behaviour in relation to the collective rationality of a group decision making procedure. An application to new staff members selection is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

We investigate a class of specially structured multistage robust optimization problems under uncertainty for which efficient computation of exact optimal robust strategies is possible. As an essential feature of this class of problems, we introduce the concept of a state-space representable uncertainty set, based on an underlying graph-theoretic structure. This concept is shown to extend several previously proposed types of uncertainty sets, and naturally lends itself to compact representations of scenario sets of huge cardinality. It is also convenient to represent uncertainty sets featuring dependence among the realizations of the uncertain parameters in successive time periods. Computational results are reported on a series of test problems featuring up to 50 time periods, in connection with an application to a multiperiod energy production planning problem. These results are shown to provide an experimental basis for comparing optimal robust strategies against optimal solutions derived from a related robust 2-stage model. [ABSTRACT FROM AUTHOR]

An improved Weiszfeld algorithm, termed the fortified Weiszfeld algorithm, for the solution of the single facility Weber problem with Euclidean distances is proposed. The new approach is based on a parabolic approximation of the objective function and testing demand points for optimality. Computational experiments show the superiority of the fortified approach to the original Weiszfeld algorithm. [ABSTRACT FROM AUTHOR]

Operating theatre scheduling is a critical task that directly impacts the efficient delivery of surgical care. In this context, we propose a comprehensive stochastic programming modelling framework which handles the inherent uncertainty characterizing the arrival of emergency patients and the duration of surgery. In particular, three recourse strategies are presented with the aim of modelling different reactive scheduling policies actually adopted by hospital managers. In order to solve realistic-sized instances in a reasonable amount of time, we develop tailored heuristic solution strategies that exploit the problem structure. Computational results obtained on a set of randomly generated problems show the effective impact of the stochastic programming approach and the efficiency of the proposed heuristics. [ABSTRACT FROM AUTHOR]

We present a family of knapsack problems (KPs) while highlighting their particular applications. Though most of the problems are derived from the classical KP, the differences arise in the addition or modification of the constraints or in the way the objective function is defined. Appropriate techniques that were found to be successful in solving these problems are briefly reviewed. Hybrid methods that combine the strengths of different methods such as exact and heuristics are also briefly discussed. Some research avenues that we believe to be useful and challenging are also pointed out. [ABSTRACT FROM AUTHOR]