The Global Food Supply Chain (GFSC) often encounters challenges in maintaining the continuous flow of essential food products such as rice and wheat. In this paper, we consider possible disruptions in supply and trans...
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The Global Food Supply Chain (GFSC) often encounters challenges in maintaining the continuous flow of essential food products such as rice and wheat. In this paper, we consider possible disruptions in supply and transportation in GFSC. Disruptions may cause severe food shortages in some parts of the world. Moreover, a disruption is not necessarily a single independent event, as multiple disruptions may occur sequentially or simultaneously. After any interruption, it is essential to reoptimize the remaining flow activities in a short period under the changed conditions. Although both the initial flow plan and post-disruption plan can be generated by formulating them as Mixed Integer Linear Programming (MILP) models, such a mitigation plan is challenging because of time constraints and when multiple disruptions are considered. To address this issue, we proposed a novel heuristic algorithm that revises the ideal plan in the event of a disruption. The developed heuristic can deal with different types of disruptions, as well as a series of disruptions. The performance of the heuristic was judged by solving 300 problem instances and comparing the results with those obtained from the exact method. To demonstrate the applicability of the proposed algorithm in practice, we have solved three real-world disruption Scenarios. The analysis of results uncovered crucial managerial insights, recommending strategies for disruption mitigation, and proved to be an effective method for post-disruption planning.
The capacitated vehicle routing problem (CVRP) has been extensively investigated in the past years due to their applications in a variety of real-world scenarios. However, it is still very challenging for most existin...
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The capacitated vehicle routing problem (CVRP) has been extensively investigated in the past years due to their applications in a variety of real-world scenarios. However, it is still very challenging for most existing algorithms to tackle large-scale CVRPs (LSCVRPs), namely, CVRPs with hundreds or thousands of customers. In this article, we propose a heuristic algorithm, called EMRG-HA, to address LSCVRPs based on the framework of divide and conquer, where an evolutionary multiobjective route grouping (EMRG) method is suggested to decompose an LSCVRP into small subcomponents. The suggested EMRG adopts a multiobjective evolutionary algorithm for route grouping by simultaneously optimizing three well-defined objectives, intragroup distance, intergroup distance, and intergroup balance in size, which can obtain a set of promising decompositions of LSCVRPs. Based on the EMRG, a local search method is suggested to improve the quality of routes in groups, where only routes in one group instead of in all groups are improved which is determined according to the average serving cost of routes in each group. The performance of the proposed EMRG-HA is verified on 42 test instances from three popular benchmark suites. The experimental results show that the EMRG-HA is superior over eight existing algorithms on most test instances of LSCVRPs, in terms of both computational efficiency and solution quality.
We examine a parallel machine scheduling problem with a job splitting property, sequence-dependent setup times, and limited setup operators, for minimizing makespan. Jobs are split into arbitrary (job) sections that c...
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We examine a parallel machine scheduling problem with a job splitting property, sequence-dependent setup times, and limited setup operators, for minimizing makespan. Jobs are split into arbitrary (job) sections that can be processed on different machines simultaneously. When a job starts to be processed on a machine, a setup that requires an operator is performed, and the setup time is sequence-dependent. The number of setup operators is limited, and hence not all of the machines can be set up at the same time. For this problem, we propose a mathematical programming model and analyze a lower bound. We then develop a simple but efficient heuristic algorithm so that it can be used in practice, and analytically derive a worst-case bound of the algorithm. We finally evaluate the performance of the proposed algorithm numerically with various scenarios.
Molecular signatures of cancer, e.g., genes or microRNAs (miRNAs), have been recognized very important in predicting the occurrence of cancer. From gene-expression and miRNA-expression data, the challenge of identifyi...
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Molecular signatures of cancer, e.g., genes or microRNAs (miRNAs), have been recognized very important in predicting the occurrence of cancer. From gene-expression and miRNA-expression data, the challenge of identifying molecular signatures lies in the huge number of molecules compared to the small number of samples. To address this issue, in this paper, we propose a heuristic algorithm to identify molecular signatures, termed HAMS, for cancer diagnosis by modeling it as a multi-objective optimization problem. In the proposed HAMS, an elitist-guided individual update strategy is proposed to obtain a small number of molecular signatures, which are closely related with cancer and contain less redundant signatures. Experimental results demonstrate that the proposed HAMS achieves superior performance over seven state-of-the-art algorithms on both gene-expression and miRNA-expression datasets. We also validate the biological significance of the molecular signatures obtained by the proposed HAMS through biological analysis.
Aircraft conflict resolution is an important part of air traffic control operations. This study presents a mixed integer linear programming model (MILP) using a space discretisation technique to deal with aircraft con...
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Aircraft conflict resolution is an important part of air traffic control operations. This study presents a mixed integer linear programming model (MILP) using a space discretisation technique to deal with aircraft conflict resolutions in en-route flight operations. The purpose of space discretisation is to concentrate on only the significant points of the airspace. The model integrates the multi entry point approach with an airspeed adjustment technique in the horizontal plane. The model aims to generate conflict-free trajectories while minimising the total changes in entry points and airspeed values. A new heuristic algorithm was developed due to the complexity of the problem. The computational results demonstrated that the proposed approach resolved aircraft conflicts for 450 different traffic scenarios in less than a minute. Considerable fuel savings were achieved with no significant increase in delay or flight time compared to conventional vectoring techniques in a fixed entry point airspace structure.
This study considers the problem of health examination scheduling. Depending on their gender, age, and special requirements, health examinees select one of the health examination packages offered by a health examinati...
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This study considers the problem of health examination scheduling. Depending on their gender, age, and special requirements, health examinees select one of the health examination packages offered by a health examination center. The health examination center must schedule all the examinees, working to minimize examinee/doctor waiting time and respect time and resource constraints, while also taking other limitations, such as the sequence and continuity of the examination procedures, into consideration. The Binary integer programming (BIP) model is one popular way to solve this health examination scheduling problem. However, as the number of examinees and health examination procedures increase, solving BIP models becomes more and more difficult, if not impossible. This study proposes health examination scheduling algorithm (HESA), a heuristic algorithm designed to solve the health examination scheduling problem efficiently and effectively. HESA has two primary objectives: minimizing examinee waiting time and minimizing doctor waiting time. To minimize examinee waiting time, HESA schedules the various parts of each examinee's checkup for times when the examinee is available, taking the sequence of the examination procedures and the availability of the resources required into account. To minimize doctor waiting time, HESA focuses on doctors instead of examinees, assigning waiting examinees to a doctor as soon as one becomes available. Both complexity analysis and computational analyses have shown that HESA is very efficient in solving the health examination scheduling problem. In addition to the theoretical results, the results of HESA's application to the concrete health examination scheduling problems of two large hospitals in Taiwan are also reported. (c) 2007 Elsevier B.V. All rights reserved.
This paper proposes a heuristic algorithm in order to reduce the total number of wavelengths required to accommodate light-paths in a WDM networks with static traffic loading. Proposed algorithm is compared with Dijks...
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This paper proposes a heuristic algorithm in order to reduce the total number of wavelengths required to accommodate light-paths in a WDM networks with static traffic loading. Proposed algorithm is compared with Dijkstra's algorithm for average light-path length and wavelength number of network. To see the efficiency of this 01 algorithm new parameter. reduced wavelength cost (RCX) has been defined. (C) 2011 Elsevier GmbH. All rights reserved.
In this correspondence, we describe a heuristic method for the construction of linear codes with given parameters n, k, q, and a prescribed minimum distance of at least d. Our approach is based on a function estimatin...
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In this correspondence, we describe a heuristic method for the construction of linear codes with given parameters n, k, q, and a prescribed minimum distance of at least d. Our approach is based on a function estimating the probability that a code of dimension k and blocklength n' < n over G F (q) is extendable to a code with the given properties. Combining this evaluation function with a search algorithm, we were able to improve 40 entries in the international tables for the best known minimum distance in the cases q = 2 5 71 9 and found at least two new optimal linear codes.
In this note we consider the problem of scheduling a set of jobs on, m identical parallel machines. For each job, a setup has to be done by a single server. The objective is to minimize the sum of the completion times...
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In this note we consider the problem of scheduling a set of jobs on, m identical parallel machines. For each job, a setup has to be done by a single server. The objective is to minimize the sum of the completion times in the case of unit setup times and arbitrary processing times. For this strongly NP-hard problem, we give a heuristic algorithm with an absolute error bounded by the product of the number of short jobs (with processing times less than m - 1) and m - 2. (C) 2001 Elsevier Science B.V. All rights reserved.
The two-dimensional strip packing problem is to pack a given set of rectangles into a strip with a given width and infinite height so as to minimize the required height of the packing. From the computational point of ...
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The two-dimensional strip packing problem is to pack a given set of rectangles into a strip with a given width and infinite height so as to minimize the required height of the packing. From the computational point of view, the strip packing problem is an NP-hard problem. With the B*-tree representation, this paper first presents a heuristic packing strategy which evaluates the positions used by the rectangles. Then an effective local search method is introduced to improve the results and a heuristic algorithm (HA) is further developed to find a desirable solution. Computational results on randomly generated instances and popular test instances show that the proposed method is efficient for the strip packing problem.
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