Quality decisions are one of the major decisions in inventory management. It affects customer’s demand, loyalty and customer satisfaction and also inventory costs. Every manufacturing process is inherent to have some chance causes of variation which may lead to some defectives in the lot. So, in order to cater the customers with faultless products, an inspection process is inevitable, which may also be prone to errors. Thus for an operations manager, maintaining the quality of the lot and the screening process becomes a challenging task, when his objective is to determine the optimal order quantity for the inventory system. Besides these operational tasks, the goal is also to increase the customer base which eventually leads to higher profits. So, as a promotional tool, trade credit is being offered by both the retailer and supplier to their respective customers to encourage more frequent and higher volume purchases. Thus taking into account of these facts, a strategic production model is formulated here to study the combined effects of imperfect quality items, faulty inspection process, rework process, sales return under two level trade credit. The present study is a general framework for many articles and classical EPQ model. An analytical method is employed which jointly optimizes the retailer’s credit period and order quantity, so as to maximize the expected total profit per unit time. To study the behavior and application of the model, a numerical example has been cited and a comprehensive sensitivity analysis has been performed. The model can be widely applicable in manufacturing industries like textile, footwear, plastics, electronics, furniture etc.
The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client’s order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.
This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.
A dynamic system differs from a static system in that it contains signal factor and the target value depends on the level of the signal factor set by the system operator. The aim of optimizing a multi-response dynamic system is to find a setting combination of input controllable factors that would result in optimum values of all response variables at all signal levels. The most commonly used performance metric for optimizing a multi-response dynamic system is the composite desirability function (CDF). The advantage of using CDF is that it is a simple unit less measure and it has a good foundation in statistical practice. However, the problem with the CDF is that it does not consider the variability of the individual response variables. Moreover, if the specification limits for the response variables are not provided the CDF cannot be computed. In this paper, a new performance metric for multi-response dynamic system, called multiple regression-based weighted signal-to-noise ratio (MRWSN) is proposed, which overcome the limitations of CDF. Two sets of experimental data on multi-response dynamic systems, taken from literature, are analysed using both CDF-based and the proposed MRWSN-based approaches for optimization. The results show that the MRWSN-based approach also results in substantially better optimization performance than the CDF-based approach.
We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains better robust solutions with less simulation runs.
In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.
A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1 buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.
Scheduling and layout planning are two important areas of operations research, which are used in the areas of production planning, logistics and supply chain management. In many industries locations of machines are not specified, previously, therefore, it is necessary to consider both location and scheduling, simultaneously. This paper presents a mathematical model to consider both scheduling and layout planning for parallel machines in discrete and continuous spaces, concurrently. The preliminary results have indicated that the integrated model is capable of handling problems more efficiently.
We present here a hybrid algorithm for the Flexible Job-Shop Scheduling Problem (FJSSP). This problem involves the optimal use of resources in a flexible production environment in which each operation can be carried out by more than a single machine. Our algorithm allocates, in a first step, the machines to operations and in a second stage it sequences them by integrating a Multi-Objective Evolutionary Algorithm (MOEA) and a path-dependent search algorithm (Multi-Objective Simulated Annealing), which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the FJSSP. An important step in the development of the algorithm was the selection of the right MOEA. Candidates were tested on problems of low, medium and high complexity. Further analyses showed the relevance of the search algorithm in the hybrid structure. Finally, comparisons with other algorithms in the literature indicate that the performance of our alternative is good.
This paper tackles an extension to the Multi-activity Combined Timetabling and Crew Scheduling Problem (MCTCSP). The goal of the original problem is to schedule the minimum number of homogenous workers required, in order to visit a set of customers characterized by services needed against schedule availability. However, since in home services it is common to have specialized workers, a mathematical model considering a heterogeneous workforce is proposed. As a solution, a GRASP-based algorithm is designed. In order to test the metaheuristic performance, 110 instances from the literature are adapted to include categorical skills. In addition, another 10 instances are randomly generated to consider large problems. The results show that the proposed GRASP finds optimal solutions in 46% of the cases and saves 40–96% computational time.