Design and optimization of healthcare location-inventory problem in the relief supply chains

Given the increase of natural disasters, the community's need for health services has increased dramatically. Firstly, the condition of the injured people should be determined and then the treatment should be start, and in the event of increasing in the severity of the injured, these people should be transfer to the hospitals in the shortest possible time. On the other hand, another essential measure at the time of the accident is to send medicines and medical items at the appropriate time for treatment of injured. In this regard, we follow to design an emergency relief supply chain network including suppliers of medical items, treatment center, warehouses, and disaster points. Therefore, a location and inventory mathematical modeling is proposed to provide better services with the goal of minimizing the costs associated with locating and inventory of health systems. Thenو, to analyze and optimize the problem, proposed some numerical example and genetic algorithm

A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies

A Continuous Review inventory Control Model within Batch Arrival Queuing Framework: A Parameter-Tuned Imperialist Competitive Algorithm

In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems

Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed-integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including non-dominated sorting genetic algorithm (NSGA II), multi-objective simulated annealing (MOSA), multi-objective particle swarm optimization (MOPSO), and multi-objective hybrid vibration-damping optimization (MOHVDO), are used to find a near-optimal solution for this NP-hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and medium-scale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon-constraint method. In particular, the error percentage of MOHVDO’s objective functions is less than 2% compared to the epsilon-constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned meta-heuristic algorithms have been largely introduced in multi-objective environments, a simultaneous implementation of these algorithms as well as a compared study of their performance when solving flow shop scheduling problems has been so far overlooked

Developing a Method for Increasing Accuracy and Precision in Measurement System Analysis: A Fuzzy Approach

Measurement systems analysis (MSA) has been applied in different aspect of industrial assessments to evaluate various types of quantitative and qualitative measures. Qualification of a measurement system depends on two important features: accuracy and precision. Since the capability of each quality system is severely related to the capability of its measurement system, the weakness of the two mentioned features can reduce the reliance on the qualitative decisions. Consequently, since in the literature fuzzy MSA is not considered as an independent study, in this paper, a fuzzy method is developed for increasing method accuracy and precision by encountering the impreciseness of some measures of MSA. To do so, bias, capability, and gauge repeatability and reproducibility (GR&R) indices are considered as triangular fuzzy numbers. The application of the proposed method is illustrated through a case study taken from an automotive parts industry. All rights reserved

An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness

Supplier selection and order allocation constitute vital strategic decisions that must be made by managers within supply chain management environments. In this paper, we propose a multi-objective fuzzy model for supplier selection and order allocation in a two-level supply chain with multi-period, multi-source, and multi-product characteristics. The supplier evaluation objectives considered in this model include cost, delay, and electronic-waste (e-waste) minimization, as well as coverage and weight maximization. A signal function is used to model the price discount offered by the suppliers. Triangular fuzzy numbers are used to deal with the uncertainty of delay and e-waste parameters while the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain the weights of the suppliers. The resulting NP-hard problem, a Pareto-based meta-heuristic algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA), is developed. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to validate the applicability of the CENSGA algorithm and the Taguchi technique to tune the parameters of the algorithms. The results are analysed using graphical and statistical comparisons illustrating how the proposed CENSGA dominates NSGA-II and MOPSO in terms of mean ideal solution distance (MID) and spacing metrics

A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been extensively applied in the literature. This problem generally falls within the class of NP-hard problems, which are difficult to solve. Therefore, choosing a proper solution method to optimize the problem is a key factor. Even though CFLPs have been consistently solved and investigated, an important question that keeps being neglected is how to choose an appropriate solution technique. Since there are no specific criteria for choosing a solution method, the reasons behind the selection approach are mostly unclear. These models are generally solved using several optimization techniques. As harder-to-solve problems are usually solved using meta-heuristics, we apply different meta-heuristic techniques to optimize a new version of the CFLP that incorporates reliability and congestion. We divide the algorithms into four categories based on the nature of the meta-heuristics: evolution-based, swarm intelligence-based, physics-based, and human-based. GAMS software is also applied to solve smaller-size CFLPs. The genetic algorithm and differential evolution of the first category, particle swarm optimization and artificial bee colony optimization of the second, Tabu search and harmony search of the third, and simulated annealing and vibration damping optimization of the fourth are applied to solve our CFLP model. Statistical analyses are implemented to evaluate and compare their relative performances. The results show the algorithms of the first and third categories perform better than the others

The Redundancy Queuing-Location-Allocation Problem: A Novel Approach

Redundancy queuing-location-allocation problems (RQLAPs) involve the economical allocation of facilities, each with a number of servers, to suitable locations with appropriate levels of redundancy or reliability. The goal in RQLAPs is to find the facilities which are both inexpensive and reliable. We take into consideration the congestion of the system by modeling each facility as an M/M/m queuing system and formulate the problem as a multiobjective nonlinear mixed integer programming problem. However, these problems are nondeterministic polynomial-time hard (NP-hard) problems where an exact solution cannot be produced in polynomial time. We propose a novel soft-computing approach based on the vibration theory called vibration damping optimization (VDO) to solve the RQLAP. We develop a multiobjective version of the VDO called multiobjective VDO (MOVDO) based on the fast nondominated sorting and crowding distance concepts in the literature. The performance of the proposed MOVDO is statistically compared with two other commonly used metaheuristic algorithms called the nondominated sorting genetic algorithm and multiobjective simulated annealing. A comparison of the results based on different problem sizes favors the MOVDO method proposed in this study