Batch dispersion problem (BDP) restricts batch traceability in large-scale discrete production and negatively impacts batch recall costs. However, previous research has ignored the complexity of the BDP in their analyses. This paper investigates the BDP under the composed bill of materials (BOM) and develops a mathematical model for the BDP with the goal of minimizing the total batch dispersion by utilizing the batch dispersion as a measure of the degree of dispersed usage of part batches. BDP-GAVNS, a hybrid genetic algorithm with variable neighborhood search, is devised for the BDP based on the demonstration that the BDP is an NPC problem. In BDP-GAVNS, memory banks were introduced to increase the diversity of individuals performing crossover operations. Additionally, the encoding method and infeasible solution repair program are designed according to the characteristics of BDP. Numerical experiments validate the viability and effectiveness of BDP-GAVNS in solving BDP. They demonstrate that (1) the optimal combination occurs when the ratio of individuals produced by the three types of population initialization methods, namely global selection (GS), local selection (LS), and random selection (RS), to the population takes values of 0.30, 0.10, and 0.60, respectively; (2) The memory bank enriches the source of individuals required for crossover operations and improves the performance of crossover operations; and (3) The BDP-GAVNS is more effective than the other five heuristic algorithms including genetic algorithms in seeking the optimal solution of BDP.

Safety is a top concern for the civil aviation industry, and the risk of safety accidents will increase due to pilot fatigue. To ensure the safety of civil aviation, this paper proposes a method to solve the crew scheduling problem considering pilot fatigue. In order to reflect individual differences and fatigue levels of pilots, an improved three-stage alertness calculation model is first proposed based on subjective and objective perspectives to represent pilots’ alertness levels and fatigue working duration quantitatively. Then, for the crew scheduling problem considering pilot fatigue, a mixed integer programming model is constructed to simultaneously achieve the optimization objectives of reducing the overall scheduling cost and crew fatigue working duration. Next, since the actual crew scheduling problem is large-scale, a solution algorithm based on a column generation framework is developed to improve the quality and efficiency of solving the large-scale crew scheduling problem. Furthermore, in the case study, we collected actual data from an airline company to validate the effectiveness of our proposed method. Finally, through multiple experimental comparisons and analyses, to balance the two optimization objectives mentioned above, it is more reasonable to handle pilot fatigue working duration with soft constraints. Sensitivity analysis reveals the variation rules of the crew cost and fatigue, providing some valuable managerial insights for the crew scheduling problem considering pilot fatigue.

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.

In this paper, an efficient rolling horizon-based heuristic is presented to solve the integrated berth allocation and crane assignment problem in bulk ports. We were guided by a real case study of a multi-terminal port, owned by our Moroccan industrial partner, under several restrictions as high tides and installation’s availability. First, we proposed a mixed integer programming model for the problem. Then, we investigated a strategy to dissipate the congestion within the presented rolling horizon. A variety of experiments were conducted, and the obtained results show that the proposed methods were efficient from a practical point of view.

The Orienteering Problem (OP) is an optimization problem that finds the locations and routes that will return the highest profit/benefit, starting from the initial location of the traveler/vehicle, visiting these locations, and ending with the starting location of the tour within a given time or distance limit. There is no obligation to visit all locations in the problem structure. OP has many real-life applications, such as staff routing and disaster relief routing. In this study, OP with Time Windows (OPTW), an extension of OP, is discussed with hotel selection and mandatory visits. Although the main objective of OPTW is profit maximization, it is also essential to minimize the total travel time to complete the tour efficiently. For this reason, we consider the OPTW as a multi-objective problem. In the problem considered here, it is assumed that the profit/benefit, travel time between locations, service period, and time interval that each location can be visited are determined to be known. Within the scope of the study, first, a Mixed Integer Programming (MIP) model is prepared for the problem. Since the proposed mathematical model does not provide solutions in a reasonable time for large networks, the problem is solved by a Constraint Programming (CP) approach. Attractive tourist points of interest for Izmir, one of Turkey's major tourist cities, are determined, and the proposed method is applied to the real-life problem. The problem is modeled as Multi-Objective OPTW with MIP and CP and solved. Also, sensitivity analysis is performed by considering two different scenarios.

As an attractive feature for modern transportation systems, the potential of the transfers capability (the load/passenger transfer between the two vehicles in its route) in reducing costs, increasing customer satisfaction and increasing the flexibility of the system, has been approved. But how profitable it could be under different circumstances? In other words, to which factors its influence depends on? what are its benefits versus its costs? The present research aimed to give a relatively comprehensive answer to these questions using a mathematical model of the pickup and delivery system with transfers. According to the model results under different situations, many factors such as modeling assumptions, system goals, transportation network scheme, vehicle fleet in terms of capacity, cost rate, and time window of activity and requests in terms of the length (direct distance between the pickup and delivery points), time windows and the volume to vehicle capacity ratio, affect the transfers benefits. As the small-scale numerical results indicate, we have an average of 5.7% reduction in the trip cost under normal conditions, which increases with the heterogeneity of vehicles, shorter time windows, and an increase in the length of the request. On the other hand, it is expected that profitability increases by problem size.

One of the primary concerns to market a product is to find appropriate channel to target customers. The recent advances on information technology have created new products with tremendous opportunities. This paper presents a mixed integer programming technique based on McCarthy & apos; s 4PS to locate suitable billboards for marketing newly introduced IPHONE product. The paper considers two types of information including age and income and tries to find the best places such that potential consumers aged 25-35 with high income visit the billboards and the cost of advertisement is minimized. The model is formulated in terms of mixed integer programming and it has been applied for potential customers who live in city of Tabriz, Iran. Using a typical software package, the model detects appropriate places in various parts of the city.

Job shop scheduling (JSS) problem has been one of the most interesting research issues
in the literature during the recent years. JSS problem has been studied in different forms
of deterministic, fuzzy, and stochastic at different depths. The idea of robust
optimization (ROP), on the other hand, has earned a particular value to become a
popular subject of the breakthrough for problem solving affairs amongst the researchers.
Based on the emerged opportunity for illustrating a new area of search, a robust JSS
problem is proposed as a challenge to this boundary of knowledge. The proposed
method is capable of handling the perturbation which exists amongst the processing
times. In fact, in many real world job scheduling problems, a small change in the
processing times, not only causes a non-optimal solution, but also the infeasibility of the
final solution may also occur. The proposed robust method could guarantee that, a small
deviation of the processing times does not affect the feasibility. The implementation of
the proposed method is illustrated using some numerical examples and the outcomes of
the investigation are discussed

Railroad industry has received tremendous challenges in the world in terms of handling cost and efficiency. For many years, the railroad business lost money in many countries such as Japan until many governments decided to privatize the industry in an attempt to reduce the cost components and to increase the efficiency of various units, significantly. In this paper, we propose a new goal programming technique to handle two objectives of operating cost and the number of passengers travel by train. We consider different types of trains for public transportation of passengers in order to make the proposed model of this paper more realistic. The implementation of the proposed model is demonstrated using some numerical examples to show the effectiveness of the method.

Railroad transportation planning is strategically a long term and an important decision making problem especially in the area of travelling passengers. There have been literally various methods to use in order to provide optimum traveling schedule such as direct or indirect methods. Direct solutions involve the implementation of mixed integer programming, which is often hard to solve for real-world applications. The proposed model of this paper uses a column generation method to decompose a large-scale railroad passenger-scheduling problem into some smaller scale problems, which are easier to solve. The primary concern with the resulted problem is that final solutions of the method need to be integer and this is in contrast with convexity assumption of column generation techniques. We propose heuristic method to handle this problem and apply the proposed model for some examples. The preliminary results indicate that the proposed model of this paper could provide optimal solutions for small-scale problems and it can reach some reasonable solutions for larger problems when direct implementation fails to do in reasonable amount of time.