A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

Using Return and Risk Model for Choosing Perfect Portfolio Applied Study in Cairo Stock Exchange

Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.

Fuzzy Inventory Model under Selling Price Dependent Demand and Variable Deterioration with Fully Backlogged Shortages

The objective is to develop a model considering demand dependent on selling price and deterioration occurs after a certain period of time, which follows two-parameter Weibull distribution. Shortages are allowed and fully backlogged. Fuzzy optimal solution is obtained by considering hexagonal fuzzy numbers and for defuzzification Graded Mean Integration Representation Method. A numerical example is provided for the illustration of crisp and fuzzy, both models. To observe the effect of changes in parameters, sensitivity analysis is carried out.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

An Efficient Approach for Transforming Unbalanced Transportation Problems into Balanced Problems in Order to Find Optimal Solutions

In operations research, the transportation problem (TP) is among the earliest and most effective applications of the linear programming problem. Unbalanced transportation problems reflect the reality of supply chain and logistics situations where the available supply of goods may not precisely match the demand at different locations. To deal with an unbalanced transportation problem (UTP), it is essential first to convert it into a balanced transportation problem (BTP) to find an initial basic feasible solution (IBFS) and hence the optimal solution. The present paper is concerned with introducing a new approach to convert an unbalanced transportation problem into a balanced one and as a consequence to obtain optimum total transportation cost. Numerical examples are provided to demonstrate the suggested method.

Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution System Along with ERP Back-End Integration: Ready-Made Garments as a Case Study

Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.

Who Benefits from Altruism in Supply Chain Management?

The significant effect of social preference on strategic behavior has been convinced by recent research. Along this stream of research, we study firms’ altruistic incentives in supply chains since the selfish rationality can’t deal with economic behaviors. We show that the performance of the supply chain in consideration of altruism is between those of scenarios under decentralization and under integration. We further shows that a manufacturer, as a leader, should find an egoistic retailer, while a retailer, as a follower, should find a manufacturer with altruistic liability, to form a good chain.

Altruism and Pricing Strategy in Dual-Channel Supply Chains

With the development of behavioral operational management, human behavior such as altruism, fairness and trust has received considerable attention. This paper studies the effect of altruism on retailer’s and manufacturer’s pricing strategy in two classic dual-channel supply chains by presenting Stackelberg game models. The analysis shows that the player’s altruism preference strongly affects their pricing strategies. The more altruistic one player is, the more profits the other player obtains. Moreover, the effect of manufacturer’s altruistic preference is larger than that of retailer’s. In addition, online price is always lower than offline price in dual-channel supply chain, which still holds true considering altruism. The results also reveal that the product web-fit has significant effect on the player’s optimal pricing strategies. The more compatible with online market the product is, the lower the retail price is set, and the more profit the manufacturer obtains whereas the less the retailer gets.

A Genetic Algorithm for the Split Delivery Vehicle Routing Problem

The Split Delivery Vehicle Routing Problem (SDVRP) allows customers to be assigned to multiple routes. Two hybrid genetic algorithms are developed for the SDVRP and computational results are given for thirty-two data sets from previous literature. With respect to the total travel distance and computer time, the genetic algorithm compares favorably versus a column generation method and a two-phase method.

Applications of Ultrasonic Techniques in Oil and Gas Pipeline Industries: A Review

The diversity of ultrasound techniques used in oil and gas pipeline plants provides us with a wealth of information on how to exploit this technology when combined with other techniques, in order to improve the quality of analysis. The fundamental theory of ultrasonic nondestructive evaluation (NDE) technology is offered, along with practical limitations as related to two factors (wave types and transducers). The focus is limited to the two main techniques used in pipe plants: First, straight beam evaluation and second, angle beam evaluation. The depth of defect (DD) is calculated using straight beam ultrasonic in six different materials according to their relative longitudinal wave (LW) velocities. The materials and respective velocities of LW are: rolled aluminum (6420 m/s), mild steel (5960 m/s), stainless steel-347 (5790 m/s), rolled copper (5010 m/s), annealed copper (4760 m/s), and brass (4700 m/s). In each material eight defects are modeled;the first represents l00% of the material thickness (D), 50.8 mm. The other seven cases represent the DD, as 87.5% of the material thickness, 75%, 62.5%, 50%, 37.5%, 25%, and 12.5%, respectively. Using angle beam evaluation, several parameters are calculated for six different reflection angles (βR) (45°, 50°, 55°, 60°, 65° and 70°). The surface distance (SD), ½skip distance (SKD), full SKD, and 1½SKD,½sound path (SP) length, full SP, and 1½SP are calculated for each βR. The relationship of SKD and SP to the βR is graphed. A chief limitation is noted that ultrasound testing is heavily dependent on the expertise of the operator, and because the reading of the outcome is subjective, precision may be hard to achieve. This review also clarifies and discusses the options used in solving the industrial engineering problem, with a comprehensive historical summary of the information available in the literature. Merging various NDE inspection techniques into the testing of objects is discussed. Eventually, it is hoped to find a suitable technique combined with ultrasonic inspection to deliver highly effective remote testing.

A Concise Model and Analysis for Heat-Induced Withdrawal Reflex Caused by Millimeter Wave Radiation

In this study, we consider the heat-induced withdrawal reflex caused by exposure to an electromagnetic beam. We propose a concise dose-response relation for predicting the occurrence of withdrawal reflex from a given spatial temperature profile. Our model is distilled from sub-step components in the ADT CHEETEH-E model developed at the Institute for Defense Analyses. Our model has only two parameters: the activation temperature of nociceptors and the critical threshold on the activated volume. When the spatial temperature profile is measurable, the two parameters can be determined from test data. We connect this dose-response relation to a temperature evolution model for electromagnetic heating. The resulting composite model governs the process from the electromagnetic beam deposited on the skin to the binary outcome of subject’s reflex response. We carry out non-dimensionalization in the time evolution model. The temperature solution of the non-dimensional system is the product of the applied power density and a parameter-free function. The effects of physical parameters are contained in non-dimensional time and depth. Scaling the physical temperature distribution into a parameter-free function greatly simplifies the analytical solution, and helps to pinpoint the effects of beam spot area and applied power density. With this formulation, we study the theoretical behaviors of the system, including the time of reflex, effect of heat conduction, biological latency in observed reflex, energy consumption by the time of reflex, and the strategy of selecting test conditions in experiments for the purpose of inferring model parameters from test data.

A Construction Heuristic for the Split Delivery Vehicle Routing Problem

The Split Delivery Vehicle Routing Problem (SDVRP) is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) where customers may be assigned to multiple routes. A new construction heuristic is developed for the SDVRP and computational results are given for thirty-two data sets from previous literature. With respect to the total travel distance, the construction heuristic compares favorably versus a column generation method and a two-phase method. In addition, the construction heuristic is computationally faster than both previous methods. This construction heuristic could be useful in developing initial solutions, very quickly, for a heuristic, algorithm, or exact procedure.

Tabu Search Implementation on Traveling Salesman Problem and Its Variations: A Literature Survey

The Traveling Salesman Problem (TSP) and its allied problems like Vehicle Routing Problem (VRP) are one of the most widely studied problems in combinatorial optimization. It has long been known to be NP-hard and hence research on developing algorithms for the TSP has focused on approximate methods in addition to exact methods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. In this paper, we review the tabu search literature on the TSP and its variations, point out trends in it, and bring out some interesting research gaps in this literature.

Genetic Algorithm for Scattered Storage Assignment in Kiva Mobile Fulfillment System

Scattered storage means an item can be stored in multiple inventory bins. The scattered storage assignment problem based on association rules in Kiva mobile fulfillment system is investigated, which aims to decide the pods for each item to put on so as to minimize the number of pods to be moved when picking a batch of orders. This problem is formulated into an integer programming model. A genetic algorithm is developed to solve the large-sized problems. Computational experiments and comparison between the scattered storage strategy and random storage strategy are conducted to evaluate the performance of the model and algorithm.

Development of an Efficient Genetic Algorithm for the Time Dependent Vehicle Routing Problem with Time Windows

This research considers the time-dependent vehicle routing problem (TDVRP). The time-dependent VRP does not assume constant speeds of the vehicles. The speeds of the vehicles vary during the various times of the day, based on the traffic conditions. During the periods of peak traffic hours, the vehicles travel at low speeds and during non-peak hours, the vehicles travel at higher speeds. A survey by TCI and IIM-C (2014) found that stoppage delay as percentage of journey time varied between five percent and 25 percent, and was very much dependent on the characteristics of routes. Costs of delay were also estimated and found not to affect margins by significant amounts. This study aims to overcome such problems arising out of traffic congestions that lead to unnecessary delays and hence, loss in customers and thereby valuable revenues to a company. This study suggests alternative routes to minimize travel times and travel distance, assuming a congestion in traffic situation. In this study, an efficient GA-based algorithm has been developed for the TDVRP, to minimize the total distance travelled, minimize the total number of vehicles utilized and also suggest alternative routes for congestion avoidance. This study will help to overcome and minimize the negative effects due to heavy traffic congestions and delays in customer service. The proposed algorithm has been shown to be superior to another existing algorithm in terms of the total distance travelled and also the number of vehicles utilized. Also the performance of the proposed algorithm is as good as the mathematical model for small size problems.

Scheduling Aircraft Landings at LSS Airport

This paper presents a short contribution in air transportation, specifically in scheduling aircraft (plane) landings at Léopol Sédar Senghor (LSS) airport of Dakar. The safety of air navigation of LSS is managed by ASECNA: Agency for Air Navigation Safety in Africa and Madagascar. Scheduling aircraft landing is the problem of deciding a landing time on an appropriate runway for each aircraft in a given set of aircraft such that each aircraft lands within a predetermined time window. The separation criteria between the landing of an aircraft, and the landing of all successive aircraft, are respected. Our objective is to minimize the cost of deviation from the target times. We present a mixed-integer 0 - 1 formulation for the single runway case. Numerical experiments and comparisons based on real datasets of LSS airport are presented.

The Effect of Reputation to the Moral Hazard in C2C E-Market with Game Theory

It is well known that the reputation is the basis of a seller to survive and gain trust from customers in a competitive business environment. But as the existence of information asymmetry between buyer and seller, the moral hazard problem is the key obstacle that impedes the benefits of related shareholders and reduces the efficiency of total market. It is crucial to design a control mechanism to avoid the negative impact of moral hazard. This paper studies the principal and agent relationship between buyer and seller in C2C e-market;because of the influence of information asymmetry, many customers suffered from being cheated by sellers with defective products in practice. These frequent cases will deteriorate long term relationship between sellers and buyers. Here we focus on the analysis of the causes of moral risks and the effect of reputation on oral risk with repeated game theory. The purpose of this paper is to help both firms and customers effectively avoid morality risk and realize a win-win situation.

Allocation of Repairable and Replaceable Components for a System Availability Using Selective Maintenance with Probabilistic Maintenance Time Constraints

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

Non-Dimensional Analysis of Thermal Effect on Skin Exposure to an Electromagnetic Beam

We consider the problem of inducing withdrawal reflex on a test subject by exposing the subject’s skin to an electromagnetic beam. Heat-sensitive nociceptors in the skin are activated wherever the temperature is above the activation temperature. Withdrawal reflex occurs when the activated volume reaches a threshold. We non-dimensionalize the problem to write the temperature as the product of a parameter-free function of non-dimensional variables and a function of beam parameters. This formulation allows studying beam parameters without knowing skin material parameters. We examine the effects of spot size, total power and distribution type of the electromagnetic beam on 3 quantities at reflex: 1) the time to reflex, 2) the maximum temperature increase, and 3) the total energy consumption. We find that the flat-top beam is the best, with the lowest energy consumption and the smallest maximum temperature increase. The Super-Gaussian beam is only slightly inferior to the flat-top. The Gaussian beam has by far the worst performance among these three.