Asia-Pacific Journal of Operational Research

A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. In this work subjectivity of evaluation criteria has been tackled by exploring fuzzy set theory. A dominance based approach has been conceptualized which is basically a simplified version of TODIM. Application potential of the proposed dominance based fuzzy decision making approach has been compared to that of fuzzy-TOPSIS, fuzzy-VIKOR and also fuzzy-TODIM. The concept of a unique performance index, i.e. “g-resilient” index has been introduced here to help in assessing suppliers’ performance and thereby selecting the best candidate. The work has also been extended to identify the areas in which suppliers are lagging; these seek further improvement towards g-resilient suppliers’ performance to be boosted up to the desired level.

This paper deals with the single machine scheduling problem with deteriorating jobs in which there are two distinct families of jobs (i.e., two-agent) pursuing different objectives. In this model the processing time of a job is defined as a function that is proportional to a linear function of its stating time. For the following three scheduling criteria: minimizing the makespan, minimizing the total weighted completion time, and minimizing the maximum lateness, we show that some basic versions of the problem are polynomially solvable. We also establish the conditions under which the problem is computationally hard.

Hesitant fuzzy linguistic term sets (HFLTSs) have attracted lots of attention recently due to their distinguished power and efficiency for dealing with multi-criteria decision making. To extend the applicability of HFLTSs, this paper first presents a more concise comparison formula of hesitant fuzzy linguistic term sets based on probability criterion of uniform distribution and develops novel distance measures considering the hesitance degree. Next, an aggregation scheme is designed to integrate OWD measure into the TOPSIS analysis procedure, in which we calculate different criterion weights according to the distance between criterion values and positive and negative ideal solutions. The weighted distances between the alternatives and positive and negative ideal solutions are calculated. Then the relative closeness degree to the positive ideal solution is calculated to rank all alternatives. Finally, an example is given to illustrate the capabilities and validation of the proposed algorithm.

In this paper, we consider customers’ equilibrium and socially optimal behavior in a single-server Markovian queue with multiple vacations and sequential abandonments. Upon arrival customers decide for themselves whether to join or balk, based on the level of information available to them. During the server’s vacation, present customers become impatient and decide sequentially whether they will abandon the system or not upon the availability of a secondary transport facility. Assuming the linear reward-cost structure, we analyze the equilibrium balking strategies of customers under four cases: fully and almost observable as well as fully and almost unobservable. In all the above cases, the individual and social optimal strategies are derived. Finally, the dependence of performance measures on system parameters are demonstrated via numerical experiments.

In this paper, we consider a two-agent scheduling problem in an $m$-machine ordered flow shop where each agent is responsible for his own set of jobs and wishes to minimize the makespan. Since the problem is NP-hard, we develop a pseudo-polynomial time approach for the case with a fixed number of machines and investigate the conditions that make the problem polynomially solvable. Finally, we consider a three-machine problem with a special processing time structure, and demonstrate its polynomiality.

Mathematical programming representations (MPRs) are discrete-event simulation models represented using math programming. In this paper, we first introduce an MPR formulation for single-server queueing systems based on idle times. We use this formulation to conduct a perturbation analysis to study the effect of imposing a constraint on idle times. We apply the formulation to obtain a linear programming-based gradient estimator for idle times. We demonstrate the integration of optimization into MRP and identify properties of the optimal solution to facilitate finding the optimal solution.

This paper addresses a scheduling problem in a flexible supply chain where the jobs can be either processed in house, or outsourced to a third-party supplier with the goal of minimizing the sum of holding and delivery costs subject to an upper bound on the outsourcing cost. The problem with identical job processing times has been proved as binary ${𝒩}{𝒫}$-hard one and a fully polynomial time approximation scheme (FPTAS) that runs in $O(\frac{n^8}{{\epsilon }^2})$ time has also been given. The aim of this paper is to derive a more effective FPTAS running in $O(\frac{n^{4}lognlogmax\{n,1/\epsilon \}}{{\epsilon }^2})$ time for this problem.

In this paper, we develop improved rearrangement algorithms to find the dependence structure that minimizes a convex function of the sum of dependent variables with given margins. We propose a new multivariate dependence measure, which can assess the convergence of the rearrangement algorithms and can be used as a stopping rule. We show how to apply these algorithms for example to finding the dependence among variables for which the marginal distributions and the distribution of the sum or the difference are known. As an example, we can find the dependence between two uniformly distributed variables that makes the distribution of the sum of two uniform variables indistinguishable from a normal distribution. Using MCMC techniques, we design an algorithm that converges to the global optimum.

In home health care (our motivating application), consistency is representative of the general health care principle of continuity of care, which is often correlated with quality of care. Much of the existing research involving consistency in routing uses planning horizons that are a week or shorter. Yet in many settings the relationship between an organization and its customers lasts much longer. Hence, this paper looks at how one should plan when seeking consistency in routes extends the impact of caregiver-patient assignments. Specifically, the paper examines appropriate planning horizon length and, with an extensive computational study, demonstrates that a long planning horizon can have significant potential for savings in terms of transportation costs and staffing levels. Initially, a deterministic setting is considered, with all patient requests during the planning horizon known a priori, and the routing cost of planning for two to three months is compared with the cost when planning is done on a weekly basis. With uncertainty inherent in planning for such a long time horizon, a methodology is presented that anticipates future patient requests that are unknown at the time of planning. Computational evidence shows that its use is superior to planning on a weekly, rolling horizon basis.

In this paper, we address the problem of finding the $K$ best paths connecting a given pair of nodes in a directed graph with arbitrary lengths. We introduce an algorithm to determine the $K$ best paths in order of length when repeat nodes in the paths are allowed. We obtain an O$(m+nlogn+K(n+logK))$ time and O$(K+m)$ space algorithm to implicitly determine the $K$ shortest paths in a directed graph with $n$ nodes and $m$ arcs. Empirical results where the algorithm was used to compute one hundred million paths in USA road networks are reported. A non-trivial modification of the previous algorithm is performed obtaining an O$(k_1(nm+log{k}_1))$ time method to compute paths without repeat nodes and to answer the next question: how many paths $k_1$ in practice are needed to identify $K$ simple paths using the previous algorithm? We find that the response is usually O$(K)$ based on an experiment computing one million paths in USA road networks. The determination of a theoretical tight bound on $k_1$ remains as an open problem.