Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

https://ira.lib.polyu.edu.hk/bitstream/10397/1269/1/SetupSurvey-2006%28June-19%29.pdf

A survey of scheduling problems with setup times or costs

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

Survey of Green Vehicle Routing Problem: Past and future trends

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2011-61.pdf

A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows

This paper presents a scheduling problem for unrelated parallel machines with sequence-dependent setup times, using simulated annealing (SA). The problem accounts for allotting work parts of L jobs into M parallel unrelated machines, where a job refers to a lot composed of N items. Some jobs may have different items while every item within each job has an identical processing time with a common due date. Each machine has its own processing times according to the characteristics of the machine as well as job types. Setup times are machine independent but job sequence dependent. SA, a meta-heuristic, is employed in this study to determine a scheduling policy so as to minimize total tardiness. The suggested SA method utilizes six job or item rearranging techniques to generate neighborhood solutions. The experimental analysis shows that the proposed SA method significantly outperforms a neighborhood search method in terms of total tardiness.

Unrelated parallel machine scheduling with setup times using simulated annealing

This paper addresses the batch scheduling problem of unrelated parallel machines attempting to minimize the total weighted tardiness. Identical or similar jobs are typically processed in batches to decrease setup and/or processing times. Local dispatching rules such as the earliest weighted due date, the shortest weighted processing time, and the earliest weighted due date with a process utilization spread are tailored to the batch scheduling requirements. Based on the features of batch scheduling, a two-level batch scheduling framework is suggested. Existing heuristics, which show excellent performance in terms of total weighted tardiness for the single machine scheduling, such as the modified earliest due date rule and the modified cost over time rule, are extended for the problem. The simulated annealing algorithm as a meta-heuristic is also presented to obtain near optimal solutions. The proposed heuristics are compared through computational experiments with data from the dicing process of a compound semiconductor manufacturing facility

Scheduling unrelated parallel machines to minimize total weighted tardiness

Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models

Small agricultural production enterprises have been under immense economic pressures for many years. It is our belief that a favorable impact can be made on small farm enterprises through the development and implementation of models that address their basic needs and open new markets and production initiatives. In this paper, we develop models for supply chain issues facing small farmers, solve them, and suggest their uses and future considerations. In particular, we consider B2C and B2B aspects of an agricultural supply chain model. The B2C aspect is considered in direct farmer to consumer sales in retail markets, while the B2B aspect is represented by transactions through agricultural cooperatives. We evaluate key strategic decisions such as whether to form cooperative agreements with other farmers and if so, how large to make the cooperative, and the production quantities at which farmers would like to sell directly to customers with or without using the cooperative.

Wooseung Jang

Papers

Unrelated parallel machine scheduling with setup times using simulated annealing

Scheduling unrelated parallel machines to minimize total weighted tardiness

Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models

Supply chain models for small agricultural enterprises

A new rule for minimizing the number of tardy jobs in dynamic flow shops