Showing papers in "International Journal of Industrial Engineering Computations in 2020"

Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems

Abstract: Article history: Received June 1 2019 Received in Revised Format June 4 2019 Accepted June 9 2019 Available online July 7 2019 Three simple metaphor-less optimization algorithms are developed in this paper for solving the unconstrained and constrained optimization problems. These algorithms are based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions. These algorithms require only the common control parameters like population size and number of iterations and do not require any algorithm-specific control parameters. The performance of the proposed algorithms is investigated by implementing these on 23 benchmark functions comprising 7 unimodal, 6 multimodal and 10 fixed-dimension multimodal functions. Additional computational experiments are conducted on 25 unconstrained and 2 constrained optimization problems. The proposed simple algorithms have shown good performance and are quite competitive. The research community may take advantage of these algorithms by adapting the same for solving different unconstrained and constrained optimization problems. © 2020 by the authors; licensee Growing Science, Canada

Multi-objective optimization of production scheduling with evolutionary computation: A review

Abstract: Article history: Received August 22 2019 Received in Revised Format November 2

An efficient improvement of ant colony system algorithm for handling capacity vehicle routing problem

Abstract: Article history: Received November 1

The electric vehicle routing problem with backhauls

Abstract: Article history: Received May 14 2019 Received in Revised Format May 14 2019 Accepted June 5 2019 Available online June 5 2019 In the classical vehicle routing problem with backhauls (VRPB) the customers are divided into two sets; the linehaul and backhaul customers, so that the distribution and collection services of goods are separated into different routes. This is justified by the need to avoid the reorganization of the loads inside the vehicles, to reduce the return of the vehicles with empty load and to give greater priority to the customers of the linehaul. Many logistics companies have special responsibility to make their operations greener, and electric vehicles (EVs) can be an efficient solution. Thus, when the fleet consists of electric vehicles (EVs), the driving range is limited due to their battery capacities and, therefore, it is necessary to visit recharging stations along their route. In this paper the electric vehicle routing problem with backhauls (EVRPB) is introduced and formulated as a mixed integer linear programming model. This formulation is based on the generalization of the open vehicle routing problem considering a set of new constraints focussed on maintaining the arborescence condition of the linehaul and backhaul paths. Different charging points for the EVs are considered in order to recharge the battery at the end of the linehaul route or during the course of the backhaul route. Finally, a heuristic initialization methodology is proposed, in which an auxiliary graph is used for the efficient coding of feasible solutions to the problem. The operation and effectiveness of the proposed formulation is tested on two VRPB instance datasets of literature which have been adapted to the EVRPB. © 2020 by the authors; licensee Growing Science, Canada

A robust approach for solving a vehicle routing problem with time windows with uncertain service and travel times

Abstract: Article history: Received March 23 2019 Received in Revised Format June 26 2019 Accepted July 6 2019 Available online July 9 2019 The main purpose of this paper is to study the vehicle routing problem with hard time windows where the main challenges is to include both sources of uncertainties, namely the travel and the service time that can arise due to multiple causes. We propose a new approach for the robust problem based on the implementation of an adaptive large neighborhood search algorithm and the use of efficient mechanisms to derive the best robust solution that responds to all uncertainties with reduced running times. The computational experiments are performed and improve the objective function of a set of instances with different levels of the uncertainty polytope to obtain the best robust solutions that protect from the violation of time windows for different scenarios. © 2020 by the authors; licensee Growing Science, Canada

A modified tabu search algorithm for the single-machine scheduling problem using additive manufacturing technology

Abstract: Article history: Received October 8 2019 Received in Revised Format December 28 2019 Accepted December 31 2019 Available online January 2 2020 The Additive Manufacturing (AM) scheduling problem is becoming a very felt issue not only by the scholars but also by the practitioners who are looking to this new technology as a new integrated part of their traditional production systems. They need new scheduling models to adapt the traditional scheduling rules to the changed ones of the additive manufacturing. This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms. © 2020 by the authors; licensee Growing Science, Canada

The use of labour flexibility for output control in workload controlled flow shops: A simulation analysis

Abstract: Article history: Received August 8 2019 Received in Revised Format November 28 2019 Accepted November 28 2019 Available online November 28 2019 Workload control theory seeks to align capacity and demand to improve delivery performance. However, workload control researchers mainly focused on input control, which regulates the input of work to the production system, thereby neglecting output control, which uses capacity adjustments to regulate the outflow of the work. Moreover, few existing studies on output control investigate a temporarily increase in capacity. This paper introduces a new search direction for output control which does not require an increase in capacity – labour flexibility. Idle operators can move from their workstation to another, thus temporarily increasing the output of that workstation without extra capacity. Using simulation of a five workstations flow shop line, we highlight the positive performance effect of labour flexibility. However, this comes at the cost of high labour movement. Introducing a load-based constraint on when workers are allowed to move significantly reduces labour movement, while realizing most of the performance improvement observed for unconstrained labour movement. This has important implications for future research and practice. © 2020 by the authors; licensee Growing Science, Canada

Price and profit decisions in manufacturer-led dual-channel supply chain configurations

Abstract: Article history: Received September 25 2019 Received in Revised Format December 28 2019 Accepted December 31 2019 Available online January 2 2020 In the world of digitization, e-commerce practices has become more popular and attracts manufacturers to combine their traditional retail channel with an e-channel. To add some salient features in the existing study, this study develops an optimal pricing and profit decision model for manufacturer-led dual-channel supply chain configurations; namely Vertically Integrated Dual-Supply Chain (VID-SC), Decentralized Dual-channel Supply Chain (DD-SC), Partially Integrate Dual-Supply Chain (PID-SC) and Horizontally Integrated Dual-Supply Chain (HIDSC). The aim of this study is to examine the effect of selected decision parameters namely cooperative advertisement, delivery lead time and free-riding on price and profit of manufacturer-led dual supply chain configurations. A linear programming for profit maximization is developed and backward induction method is used to find the optimum values of price and profit. A numerical analysis is performed to evaluate the effect of selected decision parameters on price and profit. To check the robustness of the outcomes an interaction plot is made to indicate the relationship between the selected decision parameters on optimum price. The best fit values of these decision parameters lead to the optimum price and the profit. The study helps to find the best fit value of the selected decision parameters for their specified dualchannel configuration. As a result, the model contributes as a guideline moreover it is proficient to guide manufacturers and channel members as a decision making practices without actual implementation of any strategy or policy. © 2020 by the authors; licensee Growing Science, Canada

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Abstract: Article history: Received April 1 2019 Received in Revised Format June 19 2019 Accepted June 19 2019 Available online June 19 2019 The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other methods. © 2020 by the authors; licensee Growing Science, Canada

Memetic algorithm for multi-tours dynamic vehicle routing problem with overtime (MDVRPOT)

Abstract: Article history: Received February 5 2020 Received in Revised Format February 28 2020 Accepted March 5 2020 Available online March 5 2020 After three decades of its introduction, the dynamic vehicle routing problem (DVRP) remains a fertile field for new studies. The technological evolution, which continues to progress day by day, has allowed better communication between different actors of this model and a more encouraging execution time. This encouraged researchers to investigate new variants of the DVRP and use more complicated algorithms for the resolution. Among these variants is the multi-tour DVRP (MTDVRP) with overtime (MTDVRPOT), which is the subject of this article. This paper proposes an approach with a memetic algorithm (MA). The results obtained in this study are better than those of the ant colony system (ACS) applied to the same problem and published in an earlier paper. © 2020 by the authors; licensee Growing Science, Canada

A postponement model for multi-item replenishment decision considering overtime, commonality, and quality reassurance

Abstract: Article history: Received May 23 2019 Received in Revised Format May 23 2020 Accepted June 4 2020 Available online June, 4 24 2020 This study develops a postponement model for the multi-item replenishment decision featuring commonality, an overtime strategy, and product quality reassurance. A single machine is used to meet the steady demand for multiple products wherein product commonality exists among these end products. The proposed postponement model assumes that all pertinent common parts are fabricated in Stage 1 and the finished products are sequentially fabricated in Stage 2. Random nonconformance rates are associated with both fabrication stages, the repairable nonconforming common parts are separated from scrap, and reworking in each cycle helps ensure product quality for each completed batch. An overtime strategy is used to reduce the lengthy fabrication and rework times for common parts. Mathematical analyses and derivation allow us to obtain the total system costs. The optimization method helps find the optimal replenishment decision. We provide a numerical illustration to show (1) how our model works; (2) the individual impact of the system features (e.g., the overtime factor, commonality in terms of the common part completion rate and its relative value, and the issues pertaining to scrap/rework) on the optimal decision, utilization, and the total system cost; and (3) the collective influence of system features on the highlighted problem. This proposed decision-support model helps production managers achieve the operating goals of lowering total system expenses and cutting the length of the production cycle. © 2020 by the authors; licensee Growing Science, Canada

Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding pattern

Abstract: Cross-docking is a logistics strategy that consolidates the products of different inbound trucks according to their destinations in order to reduce the inventory, order picking, and transportation costs. It requires a high level of collaboration between inbound trucks, internal operations, and outbound trucks. This article addresses the truck-to-door sequencing problem. Truck-to-door sequencing has been studied by some researchers in different titles such as scheduling and sequencing of inbound and outbound trucks of the cross-dock center. However, previous studies have not considered repeat truck holding pattern. Therefore, it is important to determine the doors and the sequence of the inbound and outbound trucks that should be assigned in a cross-dock center. This paper focuses on optimizing truck-to-door sequencing with consideration of repeat truck holding pattern in inbound trucks in order to minimize makespan. Two methods are considered to solve this problem, including mathematical modeling and a heuristic algorithm. In the first method, a mixed integer-programming model is developed to minimize the makespan. Then, GAMS software is used to solve small-scale problems. In the second approach, a heuristic algorithm is developed to find near-optimal solutions within the shortest time possible and the algorithm is used to solve large-scale problems. The results of the mathematical model and the heuristic algorithm are slightly different and show the good quality of the presented heuristic algorithm.

A new distribution-free generally weighted moving average monitoring scheme for detecting unknown shifts in the process location

Abstract: Article history: Received July 15 2019 Received in Revised Format September 1 2019 Accepted September 1 2019 Available online September 2 2019 Distribution-free (or nonparametric) monitoring schemes are needed in industrial, chemical and biochemical processes or any other analytical non-industrial process when the assumption of normality fails to hold. The Mann-Whitney (MW) test is one of the most powerful tests used in the design of these types of monitoring schemes. This test is equivalent to the Wilcoxon ranksum (WRS) test. In this paper, we propose a new distribution-free generally weighted moving average (GWMA) monitoring scheme based on the WRS statistic. The performance of the proposed scheme is investigated using the average run-length, the standard deviation of the runlength, percentile of the run-length and some characteristics of the quality loss function through extensive simulation. The proposed scheme is compared with the existing parametric and nonparametric GWMA monitoring schemes and other well-known control schemes. The effect of the estimated design parameters as well as the effect of the Phase I sample size on the Phase II performance of the new monitoring scheme are also investigated. The results show that the proposed scheme presents better and attractive mean shifts detection properties, and therefore outperforms the existing monitoring schemes in many situations. Moreover, it requires a reasonable number of Phase I observations to guarantee stability and accuracy in the Phase II performance. © 2020 by the authors; licensee Growing Science, Canada

A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

Abstract: Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. “when” and “where” rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a ‘couple’ made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to “when” and “where” rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.

Multiobjective optimization in delivering pharmaceutical products with disrupted vehicle routing problem

Abstract: Article history: Received April 19 2019 Received in Revised Format June 27 2019 Accepted July 26 2019 Available online July 26 2019 This paper is interested in pharmaceuticals distribution which is one of the most important activities and ensures the availability of drug products to a set of customers (pharmacies). The study introduces the Disrupted Vehicle Routing problem with Soft Time Windows since pharmaceutical distributors should respond to increased demands for products to ensure timely and efficient delivery to dynamic demands. We also propose an improved multiobjective local search (IMOLS), which uses methods of neighborhood search such as large neighborhood search (LNS) and variable neighborhood search (VNS) based on a hybrid approach in the optimization of vehicle routes. The algorithm is expected to achieve competitive results compared with previously published studies. © 2020 by the authors; licensee Growing Science, Canada

A heuristic approach for scheduling patient treatment in an emergency department based on bed blocking

Abstract: Maximising the patient flows throughout the emergency care patient pathway is one of the most important objectives in the healthcare system The emergency department (ED) is the critical point of this pathway in most hospitals, as the potential delays reduce the number of patients seen in the recommended time One of the key delays in the ED is the waiting time of a patient prior to treatment, which can be reduced by optimising the patient treatment schedules with priorities In this paper, a novel blocking patient flow (BPF) algorithm is developed and tested using the real data from a hospital in Brisbane, Australia Initially, a simulation model of real-life ED operations is developed by characterising patient interarrival and treatment times according to different disease categories Subsequently, a BPF heuristic algorithm is designed and benchmarked via computational experiments using two dominance rules: first come first served (FCFS) and shortest processing time (SPT) The computational results show that the proposed approach leads to a reduction of the total waiting time by more than 8 % in comparison to the current hospital practice, which implies that more patients will be served in a specified time window

A new variable sampling size and interval synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors

Abstract: Article history: Received October 21 2019 Received in Revised Format April 8 2020 Accepted April 17 2020 Available online April 17 2020 Autocorrelation and measurement errors have a negative effect on the performance of any monitoring scheme; therefore, more efficient monitoring schemes are required to monitor such special processes. Hence, in this paper, the use of improved synthetic and runs-rules X ̅schemes with an embedded variable sample size and sampling interval (VSSI) approach to efficiently monitor the mean of a process under the combined effect of autocorrelation and measurement errors is proposed. These new monitoring schemes incorporate a linearly covariate error model with a constant standard deviation and a first-order autoregressive model to the variability of this special process in order to account for measurement errors and autocorrelation, respectively. Moreover, in order to evaluate the zeroand steady-state run-length properties of the proposed monitoring schemes, a dedicated Markov chain matrix that takes into account the following is constructed: (i) VSSI approach, (ii) improved charting regions design of the synthetic and runsrules X ̅schemes, and (iii) the combined effect of autocorrelation and measurement errors. Also, the probability elements of the Markov chain matrix incorporate two special sampling methods that aid in the reduction of the negative effect of autocorrelation and measurement errors. A real life example is given to illustrate the implementation of the proposed monitoring schemes.

Mixed integer linear programming approaches for solving the raw material allocation, routing and scheduling problems in the forest industry

Abstract: Fil: Bordon, Maximiliano Ramon Consejo Nacional de Investigaciones Cientificas y Tecnicas Centro Cientifico Tecnologico Conicet - Santa Fe Instituto de Desarrollo y Diseno Universidad Tecnologica Nacional Facultad Regional Santa Fe Instituto de Desarrollo y Diseno; Argentina

Dynamic and reactive optimization of physical and financial flows in the supply chain

Abstract: Article history: Received May 13 2019 Received in Revised Format June 4 2019 Accepted June 1

Using a hybrid heuristic to solve the balanced vehicle routing problem with loading constraints

Abstract: Article history: Received July 1 2019 Received in Revised Format August 1

Relief operations as a multi-project: Colombian case

Abstract: Article history: Received November 11 2018 Received in Revised Format May 24 2019 Accepted May 24 2019 Available online May 24 2019 The purpose of this paper is to present the relief operations (RO), responding to a sudden, natural, national disaster (SNND) as a multi-mode resource-constrain multi-project scheduling problem (MRCMPSP). A conceptual framework at a strategic level is constructed and the Colombian RO for an earthquake response is shown as an illustrative case. We concluded that RO can be addressed as a MRCMPSP and that for Colombian case, it is a convenient way to board it. Addressing RO as a MRCMPSP allows managers to implement different project scheduling tools successful in other contexts. © 2020 by the authors; licensee Growing Science, Canada

Critical paths of non-permutation and permutation flow shop scheduling problems

Abstract: Article history: Received July 1 2019 Received in Revised Format August 1

Applying heuristics in supply chain planning in the process industry

Abstract: Article history: Received January 4 2020 Received in Revised Format February 2 2020 Accepted April 17 2020 Available online April 17 2020 In this paper a mixed-integer linear programming (MILP) model is developed to be used as a decision support tool for the chemical company Perstorp Oxo AB. The intention with the mathematical model is to maximize the profit and the model can be used in the process of planning the supply chain for the company. Perstorp Oxo is classified as a global company in the process industry and is has production sites in Gent, Castellanza, Stenungsund and Perstorp. The site in Stenungsund is in focus in this paper. The company produces chemicals that later are used for example in textiles, plastic and glass production. Perstorp Oxo also uses inventories in other countries for enabling the selling abroad. It has two larger inventories in Antwerp and in Tees and two smaller in Philadelphia and in Aveiro. The larger facilities store five different products and the smaller take care of one type each. To be able to find feasible and profitable production plans for the company we have developed and implemented rolling horizon techniques for a time horizon of one year and used real sales data. The outcomes from the model show the transportation of products between different production sites, the different production rates, the levels of inventory, setups and purchases from external suppliers. The numerical results are promising and we conclude that a decision support tool based on an optimization model could improve the situation for the planners at Perstorp Oxo AB. © 2020 by the authors; licensee Growing Science, Canada

Edge covering with continuous location along the network

Abstract: Article history: Received February 5 2020 Received in Revised Format February 28 2020 Accepted March 5 2020 Available online March 5 2020 The set covering problem is to find the minimum cardinality set of locations to site the facilities which cover all of the demand points in the network. In this classical problem, it is assumed that the potential facility locations and the demand points are limited to the set of vertices. Although this problem has some applications, there are some covering problems in which the facilities can be located along the edges and the demand exists on the edges, too. For instance, in the public service environment the demand (population) is distributed along the streets. In addition, in many applications (like bus stops), the facilities are not limited to be located at the vertices (intersections), rather they are allowed to be located along the edges (streets). For the first time, this paper develops a novel integer programming formulation for the set covering problem wherein the demand and facility locations lie continuously along the edges. In order to find good solutions in a reasonable time, a matheuristic algorithm is developed which iteratively adds dummy vertices along the edges and solves a simpler problem which does not allow non-nodal facility locations. Finally, a Benders decomposition reformulation of the problem is developed and the lower bounds generated by the Benders algorithm are used to evaluate the quality of the heuristic solutions. Numerical results show that the Benders lower bounds are tight and the matheuristic algorithm generates good quality solutions in short time. © 2020 by the authors; licensee Growing Science, Canada

Upstream logistic transport planning in the oil-industry: a case study

Abstract: Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca. Instituto de Matematica Bahia Blanca. Universidad Nacional del Sur. Departamento de Matematica. Instituto de Matematica Bahia Blanca; Argentina

Solving the permutation flow shop problem with blocking and setup time constraints

Abstract: Article history: Received October 5 2019 Received in Revised Format November 6 2019 Accepted November 6 2019 Available online November 6 2019 In this paper, the flow shop with blocking and sequence and machine dependent setup time problem aiming to minimize the makespan is studied. Two mixed-integer programming models are proposed (TNZBS1 and TNZBS2) and two other mixed-integer programming models, originally proposed for the no setup problem, are adapted to the problem. Furthermore, an Iterated Greedy algorithm is proposed for the problem. The permutation flow shop with blocking and sequence and machine dependent setup time is an underexplored problem and the authors did not find the use of mixed-integer programming models for the problem in any other work. To compare the models, a database of 80 problems was generated, which vary in number of machines and jobs. For the small sized problems, the adapted MILP model obtained the best results. However, for bigger problems, both proposed MILP models obtained significantly better results compared to the adapted models, proving the efficiency of the new models. When comparing the Iterated Greedy algorithm with the MILP models, the former outperformed the latter. © 2020 by the authors; licensee Growing Science, Canada