Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flow-shop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems.

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 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.

The Medical System of Transportation deals with two online real-time decisions: ambulance dispatching and relocation. Dispatching consists of selecting which ambulance to send to an emergency call; relocation consists of determining how to modify the location of available ambulances in response to changes in the system’s state. Although the literature regarding this problem is extensive, only a limited number of online real-time approaches for ambulance management have been proposed, much less one taking into consideration different types of emergencies and vehicles. This paper proposes an online real-time matheuristic algorithm that combines: i) a new preparedness index defined as the availability probability of a multi-server queue model which is used as an optimization objective and as a control variable for relocation strategies, ii) two mathematical models to solve the relocation problem, one oriented to the maximization of coverage and other to the minimization of the maximum relocation time, and iii) two heuristic algorithms oriented to the maximization of the preparedness level, one to solve the dispatch problem and other to solve the location problem of one ambulance. The computational experiments, based on discrete event simulation and historical data of Bogotá, Colombia, have shown their capability to adequately respond to the necessities of real-time operation.

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.

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.

This paper introduces a covering model for collaborative bidirectional multi-period vehicle routing problems under profit-sharing agreements (CB-VRPPA) in bulk transportation (BT) networks involving one control tower and multiple shippers and carriers. The objective is to maximize the total profits of all parties subject to profit allocation constraints among carriers, terminal capability limitations, transport capability limitations and time-window constraints. The proposed method includes three stages: (a) generate all feasible routes of each carrier, (b) eliminate unattractive feasible routes via a proposed screening technique to reduce the initial problem size, and (c) solve the reduced problem using a branch-and-bound algorithm. Computational experiments are performed for real-life, medium- and large-scale instances. The proposed method provides satisfactory results when applied to solve the CB-VRPPA. We also conduct a sensitivity analysis on a critical parameter of the profit-sharing agreement to confirm the effectiveness of the proposed method.

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.

Nowadays, oil companies have to deal with an increasingly competitive environment. In this sense, the optimization of operational processes to enhance efficiency is crucial. This article addresses the design of a decision support tool for the inland upstream transport logistics in the oil industry based on a case of study in Argentina. This problem is traditionally difficult to solve for managers due to the large number of demand facilities scattered on a large geographic area that have to be served and the consideration of several operational requirements, such as maximum allowable travel times for vehicles, availability of a limited fleet size with a small number of drivers, plus the usual demand constraints as well as those arising from security risks derived from the incompatibility of chemical products. A novel mathematical formulation and a constructive heuristic are proposed in order to address this problem. The results allow to reduce the time that the company spends for obtaining a feasible distribution plan that minimizes the time horizon of the distribution schedule provided to the clients and enhances customer satisfaction.

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 rank-sum (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 run-length, 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.

The Vehicle Routing Problem with Loading Constraints (VRPLC) is strongly related to real life applications in distribution logistics. It addresses the simultaneous loading and routing of vehicles, which are two crucial activities in transportation. Since treating these operations separately may result in impractical solutions, the development of applications for VRPLCs has gained the attention of researchers in recent years. Several heuristic methods have been proposed, but they consider only a limited group of practical characteristics that arise in real world situations. This study proposes a hybrid heuristic method based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and the Clarke and Wright Savings algorithm, to solve a VRPLC with several loading and routing constraints that have not been considered simultaneously before. Experimental results show that the proposed procedure produces competitive solutions in short processing times. Lastly, the impact of the added operational constraints is also analyzed.